Recursive closed-loop method for overall design of eVTOL aircraft

Abstract

The invention discloses a recursive closed-loop method for the overall design of an eVTOL aircraft. The method comprises the following steps: firstly, determining a quantized system-level design index according to a top-level task demand specification; respectively generating design parameter sets of the aerodynamic configuration and the propulsion subsystem based on the key parameters, and setting an initial value range and a current value of the key parameters; then cooperatively operating a multidisciplinary integration model to perform coupling iterative calculation, and outputting a performance prediction parameter set; comparing and verifying with system indexes, generating a key parameter range adjusting instruction according to a result, updating a design space in a recursive manner, and iterating again until all indexes are met. According to the method, by constructing a recursive feedback closed loop and system-level coupling analysis, the design efficiency and systematicness are improved, and the problem that subject splitting and optimization are uncontrollable is solved.

Description

Technical Field

[0001] This invention relates to the field of aircraft overall design and systems engineering, and in particular to a recursive closed-loop method for the overall design of eVTOL aircraft. Background Technology

[0002] Currently, the development of electric vertical takeoff and landing (eVTOL) aircraft generally follows the traditional technical path from concept to prototype, with the overall design often exhibiting a serial, segmented "design-analysis-modification" cycle. This model has inherent limitations: various professional fields (such as aerodynamics, power, structure, and control) typically operate in relatively isolated stages, relying on the fixed output of the previous stage as the input of the current stage, and often failing to fully consider or even ignore the coupling effects between disciplines. When systemic conflicts, such as the rotor downwash severely degrading tail efficiency, are discovered only during the detailed design or development phase, it often leads to significant design iterations, thereby significantly increasing the development cycle and costs. The fundamental reason is that existing methods lack an integrated design framework that can start from the top level of the system, run through all design levels, and allow for real-time multidisciplinary trade-offs and feedback.

[0003] Specifically, existing technical solutions typically employ a linear design process based on empirical formulas and isolated disciplinary assessments. Typical steps include: first, determining preliminary performance indicators based on the mission specification; then, referring to similar aircraft models and selecting the configuration and key parameters (such as wing area and power) based on experience; followed by aerodynamic calculations, power selection, and weight estimation; then, mission performance verification; if the verification does not meet the indicators, individual parameters are manually adjusted and the calculation is repeated. While this approach has a certain flow, it is essentially a "drop-through" serial operation, meaning the process is unidirectional and lacks coordination. The next step only begins after the previous one is completed, resulting in fragmented interface information between steps and a lack of a system-level integration and feedback hub capable of effectively coordinating multidisciplinary conflicts and supporting design iterations. The design information flow is essentially unidirectional, making it difficult to effectively identify the mutual influences and deep constraints between subsystems in the early stages.

[0004] In summary, existing technical solutions suffer from the following systemic defects: First, the process does not strictly reflect the top-down decomposition and integrated verification principles emphasized in systems engineering. Design activities tend to be fragmented, lacking the proactive optimization capability for overall system performance (such as key trade-offs like range-weight-cost). Second, problem feedback typically occurs in the later stages of detailed design, resulting in lengthy feedback paths that traverse multiple isolated stages, leading to significant adjustment costs and a lack of early, rapid, and accurate multidisciplinary cross-verification and feedback mechanisms. Third, aerodynamic, power, and structural models operate independently in the process, with insufficient data exchange, making it impossible to assess the optimal trade-offs under the combined influence of multiple disciplines in real time. For example, it is difficult to quantify typical interdisciplinary contradictions such as "increasing the aspect ratio improves cruise efficiency but also increases structural weight and affects rotor arrangement." Finally, the design iteration process heavily relies on the designer's personal experience and trial and error, resulting in a highly random convergence process that cannot guarantee finding a feasible or optimal solution within a limited number of iterations, let alone systematically exploring the vast potential design space. Therefore, there is an urgent need for a rapid overall design method for eVTOL aircraft that can overcome the above-mentioned defects and strictly follow the principles of systems engineering. Summary of the Invention

[0005] The purpose of this invention is to provide a recursive closed-loop method for the overall design of eVTOL aircraft. By constructing a recursive feedback closed loop and a system-level coupled analysis mechanism, the efficiency and systematicity of design iteration are improved, and the problems of disciplinary fragmentation, feedback lag, and uncontrollable optimization in traditional serial design are solved.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a recursive closed-loop method for the overall design of an eVTOL aircraft, comprising the following steps: S100 determines quantified system-level design indicators based on top-level task requirements specifications; S200, based on the system-level design indicators, generate subsystem parameter sets for the aerodynamic shape subsystem and the propulsion subsystem respectively, set initial value ranges for several key parameters in each subsystem parameter set and determine the key parameter values ​​for the current iteration round; S300, based on the subsystem parameter set and key parameter values ​​of the aerodynamic shape subsystem and propulsion subsystem, collaboratively runs the aerodynamic analysis model, propulsion subsystem model, weight estimation model and rotor arrangement model, and performs system-level coupled calculations through data interaction and iteration between models, and outputs a performance prediction parameter set; S400, quantitatively compare the performance prediction parameter set with the system-level design indicators to generate verification results; S500: If the verification results show that all indicators are met, the final design parameters are output; if there are unmet indicators, a key parameter range adjustment instruction is generated based on the verification results, the initial value range of the corresponding key parameter set in S200 is modified according to the key parameter range adjustment instruction, and S300, S400 and this step are re-executed until the verification results show that all indicators are met.

[0007] Furthermore, the system-level coupled calculation through data interaction and iteration between models, outputting a set of performance prediction parameters, includes: Based on the key parameter values ​​of the aerodynamic shape subsystem and the propulsion subsystem, the aerodynamic analysis model, propulsion subsystem model, weight estimation model and rotor arrangement model are operated in a coordinated manner. By using a multidisciplinary integrated model, data exchange is performed between the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model, and cross-model iterative calculations are executed to obtain the system state that satisfies the coupling relationship. After the iterative calculation of the multidisciplinary integrated model satisfies the convergence condition, the output includes the set of performance prediction parameters, which includes range, cruise speed, maximum takeoff weight, and service ceiling.

[0008] Furthermore, the step of collaboratively operating the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model based on the key parameter values ​​of the aerodynamic shape subsystem and the propulsion subsystem includes: The key parameter values ​​of the aerodynamic shape subsystem are input into the aerodynamic analysis model to calculate the lift coefficient, drag coefficient, and the interference coefficient of the rotor downwash on the fixed wing surface. The drag value in flight state is calculated based on the drag coefficient. The key parameter values ​​of the propulsion subsystem are input into the propulsion subsystem model, and combined with the drag value output by the aerodynamic analysis model, the power requirements and energy consumption that meet the flight profile are calculated. The geometric parameters of the aerodynamic shape subsystem, the mass properties of the propulsion subsystem, and the payload are input into the weight estimation model to calculate the total weight and center of gravity of the aircraft. Input the geometric installation parameters of the rotor arrangement model, the total weight and center of gravity of the aircraft, and the power requirements into the rotor arrangement model to calculate the minimum control power and available control power required to maintain attitude stability during takeoff, landing, and transition flight phases.

[0009] Furthermore, the process involves using a multidisciplinary integrated model to schedule data exchange between the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model, and to perform cross-model iterative calculations, including... The multidisciplinary integrated model establishes a data interface between the aerodynamic analysis model, the propulsion subsystem model, the weight estimation model, and the rotor arrangement model, and controls each model to execute sequentially according to a preset iterative logic. In each cross-model iteration calculation, the multidisciplinary integrated model transmits the updated total weight and center of gravity position data from the weight estimation model to the propulsion subsystem model and the rotor arrangement model, and re-inputs the updated aerodynamic and propulsion data into the weight estimation model to start the next round of calculation; The multidisciplinary integrated model continuously performs the cross-model iterative calculation until the change in the total weight and the power requirement between two consecutive iterations is less than a preset convergence threshold, at which point the system state that satisfies the coupling relationship is determined.

[0010] Furthermore, the multidisciplinary integrated model exchanges data with the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model through a central parameter database. The central parameter database stores key parameter values ​​shared by the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model. The multidisciplinary integrated model reads input data from the central parameter database according to a preset iterative logic and drives the corresponding model to run, and then sends the output results of the model to the central parameter database.

[0011] Furthermore, the preset iteration logic includes: The weight estimation model and the aerodynamic analysis model are invoked to perform an initial estimation; The propulsion subsystem model and the rotor arrangement model are alternately and iteratively called. Each call to the propulsion subsystem model is based on the aerodynamic drag data calculated by the aerodynamic analysis model in the current iteration, and each call to the rotor arrangement model is based on the total weight and center of gravity data updated by the weight estimation model in the current iteration, as well as the power requirements updated by the propulsion subsystem model.

[0012] Furthermore, the top-level mission requirements specification includes mission profile, payload, core performance requirements, operating environment conditions, airworthiness constraints, and safety objectives; The quantitative system-level design metrics determined based on the top-level task requirements specifications include: The numerical requirements for range, speed, and service ceiling are extracted from the mission profile and directly defined as the range, cruise speed, and service ceiling in the system-level design specifications. The weight and spatial dimensions are determined based on the payload, and the weight is defined as the maximum payload index in the system-level design specifications. Based on the core performance requirements, the operating environment conditions, and the airworthiness constraints, the ultimate load factor and safety margin required to meet structural strength, the front and rear limit positions of the center of gravity required to meet stability, and the minimum available power ratio required to meet power redundancy are calculated. Based on the ultimate load factor, the safety margin, the front and rear limits of the center of gravity, and the minimum available power ratio, calculate the maximum takeoff weight index in the system-level design parameters respectively; The system-level design specifications are constituted based on the range index, the cruise speed index, the service ceiling index, the maximum payload index, and the maximum takeoff weight index.

[0013] Further, the step of generating a key parameter range adjustment instruction based on the verification result, and modifying the initial value range of the corresponding key parameter set in S200 according to the key parameter range adjustment instruction, includes: Identify at least one system performance metric that has not met the system-level design metric from the verification results; Based on a predefined system performance index-subsystem responsibility mapping relationship, a target subsystem corresponding to the at least one unmet system performance index is determined, wherein the target subsystem is the aerodynamic shape subsystem or the propulsion subsystem; Based on the specific numerical deviations of the system performance indicators in the verification results, and in conjunction with the predefined target subsystem key parameter-performance impact relationship, determine one or more key parameters in the target subsystem that cause the deviations. For the one or more key parameters, generate a key parameter range adjustment instruction including adjustment direction and adjustment magnitude, and update the initial value range of the one or more key parameters set for the target subsystem in S200; Based on the updated initial value range, the key parameter values ​​for the current iteration are redefined, and S300, S400, and S500 are triggered.

[0014] Furthermore, based on the specific numerical deviations of the system performance indicators in the verification results, and in conjunction with the predefined target subsystem key parameter-performance impact relationship, one or more key parameters in the target subsystem that cause the deviation are determined, including: Based on the sensitivity coefficient in the key parameter-performance impact relationship, the key parameters of the target subsystem are sorted, and one or more key parameters with sensitivity coefficients higher than a preset threshold are selected.

[0015] Furthermore, the key parameters of the aerodynamic shape subsystem include: aspect ratio, wing loading, and stability margin; The key parameters of the propulsion subsystem include: rotor diameter, rotor disk load, and number of propulsion units.

[0016] The above-described technical solutions of the embodiments of the present invention have the following beneficial technical effects: 1. By constructing a five-level design process that strictly maps to the V-shaped model of systems engineering, the vague top-level requirements are decomposed from top to bottom into quantifiable system-level and subsystem-level indicators and parameter spaces. At the same time, a closed loop is formed through bottom-up integration, verification and confirmation, ensuring that design activities are always carried out within a unified system framework. This fundamentally solves the problems of lack of system thinking and fragmented design goals in traditional serial design, and significantly improves the integrity of the design and the ability to trace the goals. 2. By introducing a multidisciplinary coupled analysis hub composed of a multidisciplinary analysis model and a system-level integrated analysis module, and performing iterative calculations involving strong coupling relationships during the scheme demonstration stage, this method significantly advances the identification and trade-off of interdisciplinary contradictions (such as aerodynamic-structural-dynamic interference) that are traditionally only exposed in the later stages of detailed design to the early stages of design, achieving "early verification and frequent verification," thereby greatly reducing the risk of cycle delays and cost surges caused by repeated design in the later stages; 3. By creating a recursive feedback mechanism that directly operates on the "parameter design space" (i.e., the boundary of the value range of key parameters), this method does not randomly adjust specific parameter values ​​when verification fails, but rather adjusts the boundary of the parameter space based on the diagnostic results. This strategy provides macro-level guidance and constraints at the system level, while retaining detailed optimization flexibility at the subsystem level, thereby achieving efficient and targeted exploration and optimization of a broad design space, effectively avoiding getting trapped in local optima, and improving global optimization capabilities. 4. By combining recursive feedback loops with multidisciplinary coupled analysis loops, an automated iterative process of "design-analysis-verification-feedback-redesign" is formed, transforming the entire design convergence process from trial and error relying on personal experience to controllable optimization driven by data and rules. The triggering, execution, and termination of each iteration have clear logic and judgment criteria, making the evolution path of the design solution transparent and traceable, and ultimately enabling the design solution to reliably and efficiently converge to a solution that satisfies all complex constraints. 5. By standardizing and structuring the entire design process, subject model interfaces, key parameter sets, and performance-parameter mapping relationships, an organizational-level design knowledge carrying and reuse framework is formed. Engineering experience, trade-off criteria, and optimization strategies can be solidified into models, rules, and databases, thereby transforming individual, tacit design knowledge into organizational, explicit assets, and continuously improving the team's and even the entire organization's ability and efficiency in handling complex system design tasks. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall logic of the recursive closed-loop method for the overall design of the eVTOL aircraft provided in this embodiment of the invention; Figure 2 This is a flowchart of the recursive closed-loop method for the overall design of an eVTOL aircraft provided in this embodiment of the invention; Figure 3 This is a schematic diagram of the V-shaped model of the eVTOL aircraft overall rapid design system engineering provided in this embodiment of the invention; Figure 4a This is a schematic diagram of the system requirements and indicator definitions for the first level of the V-shaped model provided in this embodiment of the invention; Figure 4b This is a schematic diagram of the subsystem concept scheme of the second level of the V-shaped model provided in the embodiments of the present invention; Figure 4c This is a schematic diagram of multidisciplinary engineering modeling at the third level of the V-shaped model provided in this embodiment of the invention; Figure 4d This is a schematic diagram of the multidisciplinary integration and verification of the fourth level of the V-shaped model provided in this embodiment of the invention; Figure 4e This is a schematic diagram of the system-level decision-making and confirmation at the fifth level of the V-shaped model provided in this embodiment of the invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0019] like Figure 1 As shown, this invention follows the "V-shaped" development model of systems engineering, constructing a hierarchical, recursive closed-loop design framework. Starting from the top-level mission requirements of the eVTOL spacecraft, it decomposes and quantifies these requirements layer by layer in a top-down manner into clear system-level and subsystem-level design goals and parameter constraints. Based on this, a multidisciplinary integrated model is used for system-level coupled calculations and performance predictions. Then, through bottom-up integrated verification, the performance prediction results are compared with the original requirement indicators. When deviations occur, the verification conclusions are transformed into directional adjustment instructions for the design parameters of upstream subsystems, thereby triggering a new round of more focused design iterations. This process embodies a "design spiral," through repeated cycles of "decomposition-design-analysis-verification-feedback," gradually converging the design from a macroscopic concept to a refined solution that satisfies all constraints and performance indicators, achieving efficient and systematic optimization under complex multidisciplinary coupling.

[0020] Please refer to Figure 2 and Figure 3 This invention provides a recursive closed-loop method for the overall design of an eVTOL aircraft, comprising the following steps: S100 determines quantifiable system-level design metrics based on top-level task requirements specifications.

[0021] The top-level mission requirements specification fully defines the aircraft's mission. For example, for urban air traffic scenarios, the specification clearly states that the aircraft must perform point-to-point passenger transport between city centers and transportation hubs, and specifically stipulates requirements such as a range of no less than 200 kilometers, a cruising speed of no less than 200 kilometers per hour, carrying one pilot and four passengers, and complying with noise limits stipulated by specific airworthiness regulations. This step involves requirements analysis and engineering transformation. By systematically decomposing, weighing, and deriving the above specifications, a set of unambiguous system-level design indicators is output. This set of indicators serves as the common benchmark and final verification basis for all subsequent design activities. For example, it includes specific values ​​such as a maximum takeoff weight of 3200 kg, a service ceiling of 3000 meters, and a range of 220 kilometers (including reserves), ensuring that the design process begins with clear and measurable objectives.

[0022] S200, based on system-level design indicators, generates subsystem parameter sets for the aerodynamic shape subsystem and the propulsion subsystem respectively, sets initial value ranges for several key parameters in each subsystem parameter set and determines the key parameter values ​​for the current iteration.

[0023] Based on the system-level design metrics output by the S100, the conceptual design of the two major subsystems—aerodynamic shape and propulsion—is carried out in parallel. Its core output is a set of subsystem parameters, a structured data object rather than a traditional scheme document. Specifically, firstly, multiple feasible technical paths (such as tiltrotor and compound airfoil configurations) are evaluated based on the metrics, and a dominant scheme is selected. Then, for this dominant scheme, the initial value ranges that each key parameter must meet to satisfy the system metrics are derived through engineering empirical formulas or preliminary analysis. For example, to achieve a given range and speed, the initial range of the aerodynamic subsystem's aspect ratio can be determined to be 8 to 12, and the initial range of the propulsion subsystem's rotor diameter to be 2.0 to 2.8 meters. This set of value ranges defines the exploration space for the current design iteration. Finally, within this space, a set of specific values ​​(such as an aspect ratio of 10 and a rotor diameter of 2.5 meters) is selected as the key parameter values ​​for the current iteration to initiate detailed analysis.

[0024] The S300, based on the subsystem parameter sets and key parameter values ​​of the aerodynamic shape subsystem and the propulsion subsystem, collaboratively runs the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model. Through data interaction and iteration between models, it performs system-level coupled calculations and outputs a set of performance prediction parameters.

[0025] The input consists of the subsystem parameter set defined by S200 and its current parameter values. A multidisciplinary integrated model schedules and manages the collaborative work of four core discipline models: the aerodynamic analysis model calculates aerodynamic coefficients based on the input geometric parameters and estimates the interference of rotor wake on the fixed airfoil; the propulsion subsystem model calculates the required power and energy consumption based on aerodynamic drag and flight profile; the weight estimation model estimates the overall aircraft weight and center of gravity by integrating geometry, materials, and system mass; and the rotor layout model assesses attitude control capabilities during takeoff, landing, and transition phases based on weight, center of gravity, and power requirements. These models are connected through a shared data interface and are driven by the multidisciplinary integrated model to exchange data and perform coupled calculations according to a pre-defined logic (e.g., an iterative loop of aerodynamics-weight-propulsion). This iteration continues until the change in key output parameters (such as overall aircraft weight and total required power) is less than a convergence threshold. Finally, a performance prediction parameter set is output, containing system-level performance predictions based on the current design, such as range, speed, and takeoff weight, representing the first quantitative assessment of compliance with S100 specifications.

[0026] S400 quantitatively compares the set of performance prediction parameters with system-level design metrics to generate verification results.

[0027] Based on the performance prediction parameter set calculated by S300 and the original system-level design indicators established by S100, an objective quantitative comparison is performed. For example, the predicted range value is directly compared with the minimum range value required by the indicator to determine whether the former is greater than or equal to the latter, and the specific margin or difference is calculated. This process is performed one by one for each item in the indicator set (such as range, speed, weight, service ceiling, etc.). After the comparison is completed, a structured verification result is generated. The verification result clearly lists the predicted value, required value, whether the standard is met, and the specific deviation when the standard is not met for each indicator.

[0028] S500: If the verification results show that all indicators are met, the final design parameters are output. If there are unmet indicators, a key parameter range adjustment instruction is generated based on the verification results. The initial value range of the corresponding key parameter set in S200 is modified according to the key parameter range adjustment instruction, and S300, S400 and this step are re-executed until the verification results show that all indicators are met.

[0029] This step is the core of system-level decision-making and recursive feedback control, based on the judgment of the verification results of the S400 output. If the verification results show that all indicators have been met, the current design is considered converged, the process terminates, and the final overall design parameters are output. If any non-compliance exists, the recursive feedback mechanism is activated. At this time, root cause diagnosis is first performed based on the verification results: First, the non-compliant system performance indicators (such as insufficient range) are identified; then, based on the predefined system performance indicator-subsystem responsibility mapping relationship, the target subsystem with primary responsibility is determined (for example, insufficient range may be related to the aerodynamic shape subsystem or propulsion subsystem); furthermore, combined with the predefined target subsystem key parameter-performance impact relationship, one or more key parameters causing the performance deviation are located (for example, the aspect ratio or propulsion efficiency affecting the range). After the diagnosis is completed, a key parameter range adjustment instruction is generated, which clearly guides how to adjust the initial value range boundary of the corresponding parameter in the S200 (for example, increasing the upper limit of the aspect ratio). The instruction is fed back to S200, the value range of the corresponding parameter is updated, and then the process automatically re-triggers a new round of iterations from S200 (determining parameter values ​​based on the new range) to S300, S400, and this step. This closed-loop process repeats itself, forming a design spiral, allowing the design solution to continuously approach and ultimately meet all system requirements in successive iterations. Through the repeated triggering and execution of the recursive feedback mechanism described in step S500, the design process of this invention is macroscopically represented as a design spiral. In this spiral, the design solution is not completed all at once, but rather starts from the initial requirements and goes through multiple cycles of requirement decomposition, solution design, multidisciplinary analysis, verification, and decision feedback. Each cycle focuses on and corrects the design space based on the results of the previous round, allowing the system configuration and parameter values ​​to be gradually deepened and optimized through successive iterations. This spiraling process simulates the cognitive laws of solving complex engineering problems, enabling the design to gradually converge from a macroscopic concept to a refined and feasible solution that meets all constraints and performance indicators.

[0030] like Figure 4a , Figure 4b , Figure 4c , Figure 4d and Figure 4eAs shown, the above specific implementation constructs a five-level design system with distinct layers and deep integration with the classic V-shaped development model of systems engineering. These five levels strictly correspond to and extend each stage of the V-shaped model: Step S100 (System Requirements and Indicator Definition) corresponds to the stakeholder requirements definition and system requirements analysis at the left apex of the V-shaped model; Step S200 (Subsystem Scheme and Parameter Space Definition) corresponds to the system architecture design and subsystem concept definition on the left; Step S300 (Multidisciplinary Modeling and Coupling Analysis) corresponds to the detailed design and modeling of the subsystem at the bottom of the V, which is the core for realizing multidisciplinary integration and data synthesis; Step S400 (Multidisciplinary Integration and Verification) corresponds to the subsystem integration verification and system confirmation preparation on the right; Step S500 (System Decision and Recursive Feedback) corresponds to the system confirmation at the right apex of the V-shaped model, and innovatively incorporates a dynamic feedback link. This structured mapping relationship ensures that the entire design method combines the rigor of the systems engineering process with the dynamism of recursive optimization.

[0031] This invention systematically overcomes the inherent defects of traditional serial design, such as disciplinary isolation, feedback lag, and uncontrollable optimization processes, by constructing a hierarchical process deeply integrated with the V-model and a recursive feedback mechanism based on the parametric design space. This method enables significantly earlier and more frequent execution of multidisciplinary coupled analysis, allowing for the early detection and resolution of interdisciplinary contradictions in the design phase, and significantly reducing the risk and technical cost of major rework in later stages. Its data-driven, rule-oriented feedback control logic transforms the design iteration process from relying on experience-based trial and error to transparent and traceable system optimization, thereby ensuring the reliability and efficiency of design convergence.

[0032] Furthermore, in step S300, system-level coupled calculations are performed through data interaction and iteration between models to output a set of performance prediction parameters, including: Step S310: Based on the key parameter values ​​of the aerodynamic shape subsystem and the propulsion subsystem, the aerodynamic analysis model, the propulsion subsystem model, the weight estimation model, and the rotor arrangement model are run collaboratively.

[0033] This step inputs the key parameter values ​​of the aerodynamic shape subsystem and propulsion subsystem determined in step S200. These specific values ​​(e.g., aspect ratio of 10, wing loading of 180 kg / m², rotor diameter of 2.5 meters) provide clear input conditions for the models of each discipline. In a specific design example, such as designing a tiltrotor eVTOL for intercity commuting, four core engineering analysis models are activated simultaneously: the aerodynamic analysis model, based on the input geometric parameters, begins to calculate the lift and drag characteristics of the entire aircraft and prepares to analyze the rotor wake field; the propulsion subsystem model, based on the input rotor, motor, and other parameters, prepares to calculate the power requirements for different flight phases (hovering, transition, cruise); the weight estimation model, based on geometric dimensions, material density, and system configuration list, begins to estimate the weight and distribution of the structure, system, and payload; and the rotor layout model, based on the rotor installation position, number, and other geometric parameters, prepares to evaluate its impact on the overall aircraft balance and control characteristics. Collaborative operation indicates that these models are ready under the overall coordination of the multidisciplinary integrated model. Their internal computational logic has been initialized according to the current design parameters, laying the foundation for subsequent data exchange and iterative solution, and realizing the transformation from a static parameter set to a dynamic executable analysis task.

[0034] In step S320, through a multidisciplinary integrated model, data exchange is scheduled between the aerodynamic analysis model, the propulsion subsystem model, the weight estimation model, and the rotor arrangement model, and cross-model iterative calculations are performed to obtain the system state that satisfies the coupling relationship.

[0035] The multidisciplinary integrated model acts as a data bus and process controller, scheduling the execution of each model sequentially according to a predefined iterative logic. For example, a typical logic is as follows: first, the aerodynamic analysis model and weight estimation model are executed to obtain preliminary flight drag and aircraft weight; this drag data is then passed to the propulsion subsystem model to calculate the power required to meet the full flight profile; this power requirement, along with updated weight and center of gravity data, is then passed to the rotor layout model to assess flight controllability and stability. Subsequently, data on possible weight changes (such as increased actuation system weight) or aerodynamic disturbances caused by the rotor layout are fed back to the weight estimation model and aerodynamic analysis model, initiating a new round of calculations. The key to cross-model iterative calculations lies in handling strong coupling relationships. For example, rotor downwash data is output from the aerodynamic analysis model (or a dedicated downwash analysis submodule) and used in real time to correct the aerodynamic calculations of the wing and horizontal stabilizer, forming coupling within the aerodynamic discipline; simultaneously, the updated overall aircraft weight leads to changes in required power, and these power changes may affect the power system weight, constituting a coupled iterative process between weight and dynamics. The multidisciplinary integrated model continues to drive this cycle until the changes in key system state parameters such as total weight and total power demand between two consecutive iterations are less than the preset convergence threshold. At this point, it is determined that the calculation results of each discipline model have been coordinated with each other, and the entire system has reached a self-consistent and stable system state that satisfies the coupling relationship.

[0036] Step S330: After the iterative calculation of the multidisciplinary integrated model meets the convergence condition, output a set of performance prediction parameters including range, cruise speed, maximum takeoff weight, and service ceiling.

[0037] Once the iterative calculation in step S320 meets the convergence condition, it indicates that the current aircraft system model based on the input parameters (from S200) has reached a balanced and achievable physical state. At this point, the multidisciplinary integrated model, from this balanced state, integrates the final output data of various disciplinary models to perform comprehensive calculations of high-level performance indicators. For example, based on the converged total aircraft weight, aerodynamic drag characteristics, propulsion system efficiency, and energy capacity (such as battery charge), the mission range is calculated through integration; based on aerodynamic performance and power limitations, the maximum level flight speed and cruise speed are determined; the final maximum takeoff weight is directly extracted; and the service ceiling is determined based on climb performance. The resulting performance prediction parameter set is a collection containing all the above key performance indicator data. For example, the output might be: predicted range 215 km, predicted cruise speed 210 km / h, predicted maximum takeoff weight 3050 kg, and predicted service ceiling 3200 m. This dataset is a quantitative prediction of the system-level capabilities that the current specific design scheme (defined by a set of key parameter values) submitted in step S200 can achieve. It will be passed to step S400 for rigorous comparison with the initial design performance requirements. It is the key data link connecting the design and verification stages in the entire recursive closed loop.

[0038] Furthermore, step S310, based on the key parameter values ​​of the aerodynamic shape subsystem and the propulsion subsystem, involves the coordinated operation of the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model, including: Step S311: Input the key parameter values ​​of the aerodynamic shape subsystem into the aerodynamic analysis model, calculate the lift coefficient, drag coefficient and the interference coefficient of the rotor downwash on the fixed wing surface, and calculate the drag value of the flight state based on the drag coefficient.

[0039] This sub-step performs basic aerodynamic characteristic calculations, inputting key parameter values ​​from the aerodynamic shape subsystem of step S200. These values ​​specifically define the geometric and shape characteristics of the aircraft in the current design iteration, such as wing aspect ratio, wing area (reflecting wing loading), and wing sweep angle. Taking a tiltrotor eVTOL design for urban air traffic as an example, these specific parameter values ​​are input into the aerodynamic analysis model. This model typically runs based on engineering estimation methods (such as the vortex lattice method) or a pre-set numerical calculation program. The model first calculates the basic aerodynamic coefficients of the aircraft under target cruise conditions, outputting lift and drag coefficients, which are core to evaluating cruise efficiency and required thrust. Simultaneously, the model performs a crucial calculation: based on the rotor's geometric position, rotational speed, thrust, and other parameters, it predicts the local airflow velocity and direction changes caused by the resulting wake (downwash) when it reaches fixed surfaces such as the wing and horizontal tail, and quantifies this impact as the rotor downwash interference coefficient on the fixed surfaces. This coefficient is used to correct the local effective angle of attack of the fixed surfaces, thereby affecting their lift and drag. Finally, based on the calculated drag coefficient and pre-set flight state parameters (such as cruise speed and air density), the model calculates the drag value experienced by the aircraft under this flight state using the aerodynamic formula (drag = drag coefficient × dynamic pressure × reference area). This drag value is a key input for subsequent propulsion system power calculations.

[0040] Step S312: Input the key parameter values ​​of the propulsion subsystem into the propulsion subsystem model, and combine them with the drag values ​​output by the aerodynamic analysis model to calculate the power requirements and energy consumption that meet the flight profile.

[0041] This sub-step performs a power and energy budget analysis of the propulsion system. The inputs include two parts: first, key parameter values ​​of the propulsion subsystem from step S200, such as rotor diameter, number of blades, motor characteristics, and transmission efficiency; second, drag values ​​output by the aerodynamic analysis model from step S311. In a specific design scenario, the propulsion subsystem model performs calculations based on these inputs according to a predefined flight profile. The flight profile describes the phases of the entire mission (e.g., vertical takeoff, transition acceleration, cruise, transition deceleration, and vertical landing) and the speed, altitude, and time requirements for each phase. The core task of the model is to calculate the power requirements needed to meet the entire flight profile. For example, during the cruise phase, the main challenge is overcoming aerodynamic drag, and the required power is calculated based on the cruise speed and the drag value provided in step S311; during the hovering phase, the main challenge is overcoming gravity, and the power is calculated based on the takeoff weight and rotor efficiency. The model performs integration or piecewise calculations for each phase, ultimately summing up the total power requirement curve and total energy consumption to complete the entire mission profile. This calculation process fully considers the coupling between aerodynamics and propulsion, because cruise drag directly determines cruise power, while the efficiency of the propulsion system itself (determined by key parameter values) affects the effectiveness of converting electrical energy into propulsion. The output power demand and energy consumption data are the basis for evaluating the matching of the power system, determining energy (such as battery) capacity requirements, and performing weight iterations.

[0042] Step S313: Input the geometric parameters of the aerodynamic shape subsystem, the mass attributes of the propulsion subsystem, and the payload into the weight estimation model to calculate the total weight and center of gravity of the aircraft.

[0043] This sub-step performs a basic estimation of the overall aircraft weight and center of gravity. Its inputs are multi-dimensional: geometric parameters of the aerodynamic subsystem (such as wing area, fuselage length, tail size, etc.) are used to estimate the surface area and volume of the airframe structure, and then the structural weight is estimated based on material density and manufacturing parameters; the mass attributes of the propulsion subsystem refer to the weights of each component of the propulsion system obtained through statistical formulas or component databases based on its key parameters (such as motor power density, rotor material, battery energy density); and the payload is the given mission payload weight (such as the standard weight of passengers and baggage). In the design of an eVTOL passenger aircraft, the weight estimation model receives this input data. This model typically employs a statistical weight component method or parametric formulas. It decomposes the aircraft into several large groups, including structure, propulsion system, avionics system, electromechanical system, interior, and payload, assigns a weight estimate to each group according to the input parameters, and sums them to finally calculate the overall aircraft weight. Simultaneously, the model estimates the center of gravity of the entire aircraft based on the three-dimensional geometric position assumptions or installation location information of each component. Accurate weight and center of gravity data are prerequisites for evaluating an aircraft's balance, stability, and maneuverability. Their output will directly affect subsequent rotor layout analysis and power requirement calculations in the next iteration (because weight changes will alter the required lift and power).

[0044] Step S314: Input the geometric installation parameters, total weight and center of gravity position, and power requirements of the rotor arrangement model into the rotor arrangement model, and calculate the minimum control power and available control power required to maintain attitude stability during takeoff, landing and transition flight phases.

[0045] This sub-step performs a specific assessment of the rotor layout's impact on flight control capabilities. The inputs include three parts: the geometric installation parameters of the rotor layout model (referring to the three-dimensional coordinate positions and installation angles of each rotor on the fuselage); the total aircraft weight and center of gravity position output in step S313; and the power requirements output in step S312 (specifically referring to the power available for control, or the power that the rotor system can allocate within the total power). Taking a distributed electric propulsion eVTOL as an example, based on these inputs, the rotor layout model first analyzes the required attitude control torque (such as offsetting pitch and roll torques caused by center of gravity shift or wind disturbance) during vertical takeoff and landing and low-speed transition flight phases. The model calculates the maximum control torque that each rotor can generate based on its installation position (related to available power and rotor thrust coefficient). By comparing the required control torque with the maximum torque that each rotor can provide, the minimum control power required to maintain attitude stability during takeoff, landing, and transition flight phases is calculated, i.e., the minimum power threshold required to meet basic stability control. Simultaneously, the model also calculates the available control power, which is the actual power margin available for generating control torque for each rotor after fulfilling its primary lift or thrust tasks under the current propulsion system configuration. By comparing the available control power with the minimum control power, the control redundancy and safety of the current configuration during critical flight phases can be assessed.

[0046] Furthermore, in step S320, data exchange between the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model is scheduled through a multidisciplinary integrated model, and cross-model iterative calculations are performed, including... Step S321: Through a multidisciplinary integrated model, establish a data interface between the aerodynamic analysis model, the propulsion subsystem model, the weight estimation model, and the rotor arrangement model, and control each model to execute sequentially according to the preset iterative logic.

[0047] The multidisciplinary integrated model, acting as the central hub, first performs initialization operations, establishing data interfaces between the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor layout model. In a specific eVTOL overall design software implementation, the multidisciplinary integrated model defines the names, data types, and physical units of the input / output variables for each discipline model and establishes mapping relationships between these variables. For example, it associates the drag value variable output by the aerodynamic analysis model with the cruise drag input variable required by the propulsion subsystem model. These interfaces can be implemented through shared memory, database tables, or standardized file formats. Next, the multidisciplinary integrated model loads or follows a pre-defined iterative logic. This logic is a clearly defined execution sequence rule; for example, it specifies that the starting point of each complete iteration is the weight estimation model and the aerodynamic analysis model, using initial parameters for the first round of estimation. Then, their outputs (weight, drag) are passed to the propulsion subsystem model to calculate power; then, the weight, center of gravity, and power data are passed to the rotor layout model for control evaluation; finally, any structural weight increase requirements proposed by the rotor model or disturbance data updated by the aerodynamic model are fed back to the weight estimation model, thereby controlling the sequential execution of each model.

[0048] In step S322, during each cross-model iteration calculation, the multidisciplinary integrated model transmits the updated total weight and center of gravity position data from the weight estimation model to the propulsion subsystem model and the rotor layout model, and re-inputs the updated aerodynamic and propulsion data into the weight estimation model to start the next round of calculation.

[0049] In a cross-model iterative calculation, after each model completes a round of calculations according to the logical sequence of S321, the multidisciplinary integrated model is responsible for managing the transfer of key data. The multidisciplinary integrated model first transfers the updated overall aircraft weight and center of gravity position data from the weight estimation model to the propulsion subsystem model and the rotor layout model. For example, after one iteration, the weight estimation model might revise the overall aircraft weight from 3000 kg to 3050 kg due to consideration of more detailed structural design. This new weight value is immediately transferred to the propulsion subsystem model to recalculate the power required to meet flight performance (because the increased weight leads to increased hovering and climb power); simultaneously, the new weight and center of gravity position are also transferred to the rotor layout model to reassess the aircraft's trim status and handling effectiveness. On the other hand, the multidisciplinary integrated model re-inputs updated aerodynamic and propulsion data into the weight estimation model. For example, the propulsion subsystem model might select different types of motors or battery packs due to changes in power requirements, and its mass properties might be updated accordingly; the aerodynamic analysis model might propose reinforcement requirements for local wing structures due to consideration of more precise rotor disturbances. New data generated by aerodynamics and propulsion that may affect weight (i.e., updated aerodynamic and propulsion data) is collected by the multidisciplinary integrated model and fed back to the weight estimation model. The weight estimation model then runs again based on this new data, initiating a new round of calculations, thus forming a feedback loop that includes the coupling relationship between weight, aerodynamics, propulsion, and layout.

[0050] Step S323: The multidisciplinary integrated model continues to perform cross-model iterative calculations until the change in the total weight and power requirements between two consecutive iterations is less than the preset convergence threshold. Then, the system state that satisfies the coupling relationship is determined.

[0051] During the repeated execution of the iterative loop described in step S322, the multidisciplinary integrated model continuously monitors key parameters characterizing whether the system has reached equilibrium. These parameters are typically global variables that are significantly affected by multidisciplinary coupling and are crucial to the design, such as total weight and power requirements. At the end of each iteration, the multidisciplinary integrated model records the values ​​of these parameters and calculates the changes between them and the corresponding values ​​of the previous iteration. This process continues until the multidisciplinary integrated model detects that these changes are less than a preset convergence threshold. The threshold is set based on engineering accuracy requirements; for example, it may be stipulated that the change in total weight between two consecutive iterations is less than 5 kg and the change in total power requirements is less than 2 kW. When this condition is met, the multidisciplinary integrated model determines that the system state satisfies the coupling relationship. The results calculated by each discipline model based on the current input parameters no longer change significantly after multiple rounds of mutual adjustment. For example, a small increase in weight no longer causes a significant increase in power, and a small change in power no longer leads to a significant adjustment in the weight of the power system; the effects of aerodynamics and layout have also stabilized. At this point, the entire aircraft system has reached a solution of equilibrium in a mathematical and physical sense. This equilibrium state is the system state corresponding to the current design scheme.

[0052] Specifically, the multidisciplinary integrated model exchanges data with the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model through a central parameter database. The central parameter database stores key parameter values ​​shared by the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model. The multidisciplinary integrated model reads input data from the central parameter database according to a preset iterative logic and drives the corresponding model to run, and then sends the model output results back to the central parameter database.

[0053] In practice, the multidisciplinary integrated model and the central parameter database together constitute the data hub and scheduling center of the entire coupled analysis process. The central parameter database is a structured data storage system that stores key parameter values ​​shared by the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model. These parameter values ​​constitute a complete state description of the current design iteration, including wing geometry parameters, rotor parameters, material properties, current weight estimates, calculated aerodynamic coefficients, and power requirements. The multidisciplinary integrated model does not communicate directly with each analysis model point-to-point; instead, it exchanges data with each model separately through interaction with this central database. The multidisciplinary integrated model first establishes data interfaces with each model, which typically means defining the database field locations for each model's required input parameters and the database fields where its output parameters should be written. Simultaneously, the multidisciplinary integrated model loads pre-defined iteration logic, which explicitly specifies the model execution order, data dependencies, and triggering conditions in scripts or configuration files. In an eVTOL design scenario, the logic might be as follows: First, initial geometric and weight assumptions are read from a central database, driving the aerodynamic analysis model and weight estimation model to perform the first round of calculations, and the resulting drag coefficient, disturbance coefficient, initial weight, and other results are written back to the database. Then, the multidisciplinary integrated model reads these new results, driving the propulsion subsystem model to calculate power requirements and update the database. Next, the rotor arrangement model is driven to perform calculations. The multidisciplinary integrated model drives the model by reading input data from the central database and sends the results after the model runs to the central parameter database, thereby ensuring that all models work based on the same set of authoritative and consistent data, achieving centralized data management and version synchronization.

[0054] Furthermore, the aforementioned preset iteration logic includes: Step S301: Call the weight estimation model and the aerodynamic analysis model to perform initial estimation.

[0055] In a specific eVTOL overall design process, before the multidisciplinary integrated model begins multiple iterations, it first requires a set of basic data as the starting point for calculations. To this end, the multidisciplinary integrated model simultaneously drives the execution of two models based on key geometric and initial configuration parameters read from the central parameter database and derived from step S200. The weight estimation model, based on the input geometric dimensions, preset material system, and given payload, uses parametric or statistical formulas to calculate the initial total weight and initial center of gravity position of the aircraft. For example, for a six-seat tiltrotor configuration, the model might output an initial weight estimate of 3100 kg. Simultaneously, the aerodynamic analysis model, based on the same key geometric parameters (such as wing area and aspect ratio), runs its preset calculation program to output the basic aerodynamic drag coefficient and other aerodynamic characteristics of the aircraft under target cruise conditions. The initial estimation results (weight, center of gravity, basic aerodynamic coefficients) in this step are preliminary values ​​that have not yet undergone multidisciplinary coupling correction, but they provide initial input conditions for subsequent propulsion system power calculations and rotor layout analysis, serving as the data foundation for the entire iterative cycle.

[0056] Step S302: Alternately iterate and call the propulsion subsystem model and the rotor arrangement model. Each call to the propulsion subsystem model is based on the aerodynamic drag data calculated by the aerodynamic analysis model in the current iteration. Each call to the rotor arrangement model is based on the total weight and center of gravity data updated by the weight estimation model in the current iteration, as well as the power requirements updated by the propulsion subsystem model.

[0057] After providing initial estimates in step S301, the multidisciplinary integrated model enters an alternating iterative call loop, primarily between two models: the propulsion subsystem model and the rotor arrangement model. The data transfer mechanism is clearly defined: each time the propulsion subsystem model is called, the aerodynamic drag data in its input must be based on the latest result calculated by the aerodynamic analysis model in the current iteration. Even if the aerodynamic analysis model updates the drag coefficient in subsequent iterations due to factors such as rotor interference, the propulsion model will immediately use this latest data to recalculate the power, ensuring that the real-time impact of aerodynamic characteristics changes on propulsion requirements is incorporated into the calculation. Similarly, each time the rotor arrangement model is called, its input total aircraft weight and center of gravity data must be based on the latest values ​​updated by the weight estimation model in the current iteration, and its power requirements must also be based on the latest values ​​updated by the propulsion subsystem model in the current iteration. For example, in one iteration, the weight estimation model might update the weight to 3150 kg due to structural refinement, while the propulsion subsystem model updates the cruise power to a certain value based on the latest drag calculations. The rotor layout model then performs its current control capability assessment based on this new set of weight, center of gravity, and power data. After the assessment, the model can propose modifications to certain structures (such as stiffeners) or actuation systems. These suggestions are then fed back to the weight estimation model, driving it to update the weight in the next cycle. This alternating iterative process forms a closed loop: weight / aerodynamic update → propulsion power update → rotor layout and control assessment → feedback to weight / aerodynamics. This allows the multidisciplinary coupling effects to be gradually transmitted, balanced, and ultimately converged during the iteration.

[0058] Specifically, the top-level mission requirements specification includes the mission profile, payload, core performance requirements, operating environment conditions, airworthiness constraints, and safety objectives. The top-level mission requirements specification is a comprehensive input document that initiates the overall design process of an eVTOL aircraft. It systematically defines the aircraft's intended mission and external boundaries, including: the mission profile, which is the sequential requirements for altitude, speed, and time at each stage of a typical flight mission (such as vertical takeoff, transition, cruise, and landing); the payload, which is the specific quantity and weight of personnel or cargo that the aircraft must carry; the core performance requirements, which are key performance indicators such as target range, cruise speed, and service ceiling; the operating environment conditions, which cover the external physical conditions such as temperature, humidity, wind speed, precipitation, and airspace environment that the aircraft must adapt to; the airworthiness constraints, which are the aviation regulations and certification standards of specific countries or regions that must be met; and the safety objectives, which typically specify the safety level that the system must achieve through quantitative failure probabilities or qualitative safety levels. These elements together constitute a complete design input framework, serving as the fundamental basis for all subsequent design decisions and verification activities.

[0059] Accordingly, step S100, which determines quantified system-level design metrics based on the top-level task requirement specifications, includes: Step S110: Extract the numerical requirements for range, speed, and service ceiling from the mission profile and directly define them as the range index, cruise speed index, and service ceiling index in the system-level design specifications.

[0060] This step directly quantifies and extracts the mission profile. The input mission profile describes the various stages and parameters experienced by the aircraft during a typical mission. For example, a mission profile for an urban air traffic (UAM) scenario might include: vertical takeoff from a vertical takeoff and landing field to a safe altitude, gradual acceleration to cruise altitude at a specific climb rate, flying a specified distance at a cruise speed at a designated altitude, and then gradual deceleration and vertical landing. Specifically, the range requirement is usually derived directly from the horizontal distance to be covered during the cruise phase of the mission profile, taking into account the legally required margin for alternate landings; speed typically refers to the cruise speed to be maintained during the cruise phase of the profile; and the service ceiling corresponds to the maximum cruise flight altitude or safe obstacle clearance altitude required in the profile. After extraction, these specific values ​​are directly defined as corresponding items in the system-level design specifications. For example, the range specification is defined as no less than 200 kilometers, the cruise speed specification as no less than 250 kilometers per hour, and the service ceiling specification as no less than 3000 meters.

[0061] Step S120: Determine the weight and spatial dimensions based on the payload, and define the weight as the maximum payload index in the system-level design specifications.

[0062] In eVTOL passenger transport scenarios, the payload is typically defined as the weight of one pilot and N passengers, or an equivalent commercial payload. This step involves two parts: First, determining the weight and dimensions. The weight is usually calculated by summing the standard passenger weight (e.g., 85 kg per person, including carry-on baggage) and cargo weight as specified in airworthiness regulations or industry standards. Dimensions are determined based on the geometric requirements of passenger seating layout, aisle space, and baggage compartments, primarily affecting subsequent fuselage volume design. Second, the calculated total weight is defined as the maximum payload specification in the system-level design parameters. For example, for a configuration with 4 passengers and 1 pilot, the maximum payload specification might be defined as 5 × 85 kg = 425 kg. This specification is one of the fundamental inputs for subsequent weight estimation and center of gravity analysis, directly contributing to the aircraft's takeoff weight, and is an immutable lower limit for payload.

[0063] Step S130: Based on core performance requirements, operating environment conditions and airworthiness constraints, calculate the ultimate load factor and safety margin required to meet structural strength, the front and rear limit positions of the center of gravity required to meet stability, and the minimum available power ratio required to meet dynamic redundancy.

[0064] This step derives supporting engineering parameters based on comprehensive constraints, inputting three types of constraints: core performance requirements, operating environment conditions, and airworthiness constraints. Calculations are then performed based on engineering standards and physical laws. The calculation of the ultimate load factor and safety margin required to meet structural strength is primarily based on airworthiness constraints. For example, CCAR-23 or FAR-23 specifies the ultimate load factors (e.g., +2.5g, -1.0g) that an aircraft must withstand under different flight conditions (e.g., gusts, maneuvers). The safety margin is the structural design allowance calculated based on the allowable stress of the material and the aforementioned loads. The calculation of the forward and aft limits of the center of gravity required to meet stability requirements integrates core performance requirements (e.g., requirements for handling qualities) and operating environment conditions (e.g., handling and stability characteristics under extreme center of gravity conditions). Static stability analysis determines the range of longitudinal positions that the center of gravity must be in to ensure the aircraft's safety and controllability. The calculation of the minimum available power ratio required to meet power redundancy is based on safety objectives and airworthiness requirements for power system redundancy. For example, in order to maintain safe flight or landing after the failure of one propulsion unit, it is stipulated that the remaining normal operating units must be able to provide thrust of no less than a certain proportion (such as 50%) of the total required power.

[0065] Step S140: Calculate the maximum takeoff weight index in the system-level design parameters based on the ultimate load factor, safety margin, center of gravity front and rear limit positions, and minimum available power ratio.

[0066] Based on the ultimate load factor, safety margin, center of gravity aft and apex position, and minimum available power ratio, these constraining parameters are comprehensively transformed into the maximum takeoff weight index in the system-level design specifications. The ultimate load factor and safety margin directly determine the weight coefficients of major structures such as the wings and fuselage; the center of gravity aft and apex position constrains the weight distribution of each component, affecting structural layout and trim weight; and the minimum available power ratio imposes minimum requirements on the power rating and weight of the propulsion system. A maximum takeoff weight needs to be found that, at this weight, the aircraft structure can meet all load conditions, the center of gravity is within acceptable limits, and the propulsion system can still provide sufficient power after considering redundancy.

[0067] Step S150: Based on range indicators, cruise speed indicators, service ceiling indicators, maximum payload indicators, and maximum takeoff weight indicators, system-level design indicators are constructed.

[0068] Input the sub-indicators defined in the preceding steps S110, S120, and S140: range, cruise speed, service ceiling, maximum payload, and maximum takeoff weight. Collect and confirm these sub-indicators to form a set of system-level design indicators. For example, the maximum takeoff weight must be greater than the maximum payload plus the estimated empty weight; given range and speed indicators must be compatible with the maximum takeoff weight under estimated energy and propulsion efficiency. The final set of indicators provides a clear, quantifiable, and unified ultimate goal for subsequent design and optimization, and all subsequent steps (S200-S500) will be based on this.

[0069] Further, in step S500, a key parameter range adjustment instruction is generated based on the verification results. The initial value range of the corresponding key parameter set in S200 is modified according to the key parameter range adjustment instruction, including: Step S510: Identify at least one system performance metric from the verification results that has not met the system-level design metric.

[0070] The verification results are a structured dataset that clearly lists the predicted values, required values, and compliance status (compliant or non-compliant) for each system-level design metric (such as range, cruise speed, and maximum takeoff weight). This step identifies at least one system performance metric that fails to meet the system-level design metric. In a specific design iteration scenario, for example, after calculation by S300, the current design scheme shows a predicted range of 180 kilometers, while the range requirement defined by S100 is no less than 200 kilometers. Through quantitative comparison by S400, the verification results will record the range metric as non-compliant and calculate a specific numerical deviation of 20 kilometers. All non-compliant metric items and their deviation data are automatically scanned and extracted from the verification results, providing clear problem input for subsequent root cause diagnosis.

[0071] Step S520: Based on the predefined system performance index-subsystem responsibility mapping relationship, determine the target subsystem corresponding to at least one unmet system performance index. The target subsystem is either the aerodynamic shape subsystem or the propulsion subsystem.

[0072] Based on a predefined system performance index-subsystem responsibility mapping relationship, system-level performance problems are associated with specific responsible subsystems. This mapping relationship is a set of rules stored in a knowledge base, essentially a solidification of engineering experience, clarifying which (or which) subsystem characteristics primarily dominate different performance indices. For example, rules can be defined as follows: range is strongly correlated with the lift-to-drag ratio of the aerodynamic shape subsystem and the efficiency of the propulsion subsystem; cruise speed is mainly related to the available power of the propulsion subsystem and the drag characteristics of the aerodynamic shape subsystem; maximum takeoff weight is comprehensively related to multiple subsystems such as structure and power. Inputting the specific non-compliant index (e.g., insufficient range) identified in step S510, this mapping relationship is queried to determine the target subsystem primarily responsible for resolving this issue. If insufficient range is related to both aerodynamics and propulsion, but the aerodynamic shape has been optimized in the current design cycle, the mapping rule may determine the propulsion subsystem as the target subsystem for this adjustment based on deviation characteristics (e.g., energy insufficiency).

[0073] Step S530: Based on the specific numerical deviations of the system performance indicators in the verification results, and in conjunction with the predefined target subsystem key parameter-performance impact relationship, determine one or more key parameters in the target subsystem that cause the deviation.

[0074] Step S530, within the identified target subsystem, precisely locates the specific design variables primarily responsible for performance deviations. Its input comprises two parts: first, the performance numerical deviation with a specific value identified in step S510 (e.g., a 20 km range shortfall); and second, predefined key parameters of the target subsystem and their performance impact relationships. This relationship constitutes an engineering knowledge base, commonly represented as a sensitivity coefficient matrix or parameterized influence function. Its content quantitatively or qualitatively describes the magnitude and direction of the effect of changes in each key parameter within the target subsystem on various system-level performance indicators.

[0075] Taking the propulsion subsystem as an example, the knowledge base might explicitly state that increasing the rotor diameter, a key parameter, typically has a positive impact on range by improving aerodynamic efficiency, but it also brings certain negative effects due to increased weight and drag. Changes in the disk load, another key parameter, will affect propulsion efficiency and noise levels through different functional relationships. Based on this, by combining specific performance deviations (such as a 20km range shortfall) with the aforementioned influence model, and through calculation and analysis (e.g., evaluating and ranking the partial derivatives or sensitivity coefficients of each parameter at the current design point), one or a few key parameters that contribute most significantly to compensating for the current specific performance deviation can be selected from all the key parameters of the subsystem. For example, the analysis might determine that, under the current design scheme, the rotor diameter parameter has the highest sensitivity to range, meaning that increasing this parameter is the most effective way to improve range; therefore, it is identified as the key parameter that needs to be adjusted in this iteration.

[0076] Step S540: For one or more key parameters, generate a key parameter range adjustment instruction including adjustment direction and adjustment magnitude, and update the initial value range of one or more key parameters set for the target subsystem in S200.

[0077] This step generates adjustment instructions for key parameter ranges, including the direction and magnitude of the adjustment. The direction of adjustment is directly determined by the parameter-performance impact relationship: if increasing the parameter has a positive effect on improving performance deviation, the instruction direction is to increase it; otherwise, it is to decrease it. The magnitude of adjustment is usually quantitatively calculated based on the numerical value of the performance deviation, the sensitivity coefficient of the parameter, and engineering experience rules. For example, to compensate for a 20-kilometer range shortfall, based on the sensitivity of rotor diameter to range, it is calculated that the upper limit of its current value range needs to be increased by 0.2 meters. Subsequently, the system updates the initial value range of one or more key parameters set for the target subsystem in S200 according to this instruction. That is, in the data structure storing the design space, the rotor diameter parameter of the propulsion subsystem is found, and its initial value range is modified from the original [2.0m, 2.5m] to the new [2.0m, 2.7m]. This operation does not specify a new specific value, but rather relaxes or tightens the optimization search space boundary of the parameter, reflecting a strategy of providing macro-level guidance at the system level while retaining detailed optimization flexibility at the subsystem level.

[0078] Furthermore, during implementation, the initial value ranges for key parameters and subsequent adjustments are based on engineering experience and physical constraints. For example, the typical initial value range for the key parameter aspect ratio of the aerodynamic shape subsystem can be set to 8 to 20, the wing loading range to 100 to 250 kg / m², and the stability margin range to 5% to 40%. For the propulsion subsystem, the key parameters rotor diameter can range from 0.1 to 4.0 meters, the rotor disk loading range to 20 to 70 kg / m², and the number of propulsion units can be balanced within a range of 4 to 8 based on redundancy requirements. When step S540 generates adjustment instructions, it involves targeted modifications to the boundaries of these parameter ranges, such as adjusting the upper limit of the aspect ratio from 15 to 18, or increasing the lower limit of the rotor diameter from 2.0 meters to 2.2 meters, thereby guiding the design optimization direction at the system level.

[0079] Step S550: Based on the updated initial value range, redetermine the key parameter values ​​for the current iteration round and trigger the execution of S300, S400 and S500.

[0080] First, based on the updated initial value range, the key parameter values ​​for the current iteration are redefined. This typically means selecting a new set of specific values ​​from the new, adjusted parameter range, using a strategy (such as median selection or sampling based on optimization algorithms), as the design input for the new iteration. For example, 2.5 meters is selected as the new value from the updated rotor diameter range [2.0m, 2.7m]. Subsequently, the system triggers executions S300, S400, and S500. That is, using these new parameter values, multidisciplinary coupled analysis and performance prediction are performed again (S300), then the new prediction results are verified and compared with the original indicators (S400), and the decision-making step is entered again (S500). If the new solution meets all indicators, the process ends; if there are still unmet requirements, a new round of identification-mapping-positioning-adjustment feedback loop is started. This step implements the effect of the directional adjustment command and closes the loop of the entire recursive iteration process.

[0081] Furthermore, in step S530, based on the specific numerical deviations of the system performance indicators in the verification results, and in conjunction with the predefined target subsystem key parameter-performance impact relationship, one or more key parameters in the target subsystem that cause the deviation are determined. This is specifically implemented in the following ways: Based on the sensitivity coefficient in the key parameter-performance impact relationship, the key parameters of the target subsystem are sorted, and one or more key parameters with sensitivity coefficients higher than a preset threshold are selected.

[0082] First, a predefined target subsystem key parameter-performance impact relationship is invoked. This relationship uses a quantified sensitivity coefficient to characterize the unit impact of each key parameter change on the relevant system performance index. Then, based on the specific performance index that is currently not met (such as range), the sensitivity coefficients of each key parameter corresponding to that index are extracted, and all key parameters are sorted in descending order according to the absolute value of their sensitivity coefficients. Finally, based on a preset screening threshold from engineering experience, one or more key parameters with sensitivity coefficients higher than the threshold are selected from the sorted list as the variables to be optimized that cause the current performance deviation and have the greatest adjustment potential. For example, regarding the deviation of insufficient range, if the analysis shows that the sensitivity coefficients of rotor diameter and rotor disk load on range are significantly higher than other parameters, then they will be simultaneously identified as key parameters for this adjustment. Through data-driven sorting and threshold screening, an objective comparison of the degree of influence of multiple parameters and the accurate identification of key influencing factors are achieved.

[0083] In a specific embodiment of the present invention, the key parameters of the aerodynamic shape subsystem include: aspect ratio, wing loading, and stability margin. The key parameters of the propulsion subsystem include: rotor diameter, rotor disk loading, and number of propulsion units.

[0084] In specific embodiments of this invention, the key parameters of the aerodynamic shape subsystem are explicitly defined as aspect ratio, wing loading, and stability margin; the key parameters of the propulsion subsystem are explicitly defined as rotor diameter, disk loading, and number of propulsion units. The selection of these parameters stems from their core influence in the overall eVTOL design: aspect ratio directly dominates the lift-to-drag ratio and structural spanwise dimensions during the cruise phase; wing loading is related to wing area, stall characteristics, and structural load distribution; and stability margin specifies the quantitative requirements for the aircraft's static stability. In the propulsion subsystem, rotor diameter is a key geometric variable determining rotor efficiency, noise, and compactness; disk loading affects hovering efficiency, downwash velocity, and power system response characteristics; while the number of propulsion units directly relates to power redundancy schemes, system layout complexity, and control allocation strategies.

[0085] As can be seen from the above, the design method proposed in this invention possesses good scalability and applicability. On the one hand, the V-shaped model process can be expanded into a W-shaped or double V-shaped model covering a larger cycle of design-manufacturing-testing, but each design stage still contains the recursive feedback core. On the other hand, to accelerate the optimization process, a proxy model based on artificial intelligence or machine learning can be introduced into the multidisciplinary coupled analysis engine to approximate high-fidelity simulation, enabling rapid exploration of ultra-large-scale design spaces. Furthermore, this V-shaped model + recursive feedback + design space adjustment systems engineering method is not limited to eVTOL aircraft, but is also applicable to the scheme demonstration and overall rapid design stages of other complex equipment systems such as missiles, robots, and electric vehicles.

[0086] The embodiments of this invention aim to protect a recursive closed-loop method for the overall design of an eVTOL aircraft, which has the following effects: 1. By constructing a five-level design process that strictly maps to the V-shaped model of systems engineering, the vague top-level requirements are decomposed from top to bottom into quantifiable system-level and subsystem-level indicators and parameter spaces. At the same time, a closed loop is formed through bottom-up integration, verification and confirmation, ensuring that design activities are always carried out within a unified system framework. This fundamentally solves the problems of lack of system thinking and fragmented design goals in traditional serial design, and significantly improves the integrity of the design and the ability to trace the goals. 2. By introducing a multidisciplinary coupled analysis hub composed of a multidisciplinary analysis model and a system-level integrated analysis module, and performing iterative calculations involving strong coupling relationships during the scheme demonstration stage, this method significantly advances the identification and trade-off of interdisciplinary contradictions (such as aerodynamic-structural-dynamic interference) that are traditionally only exposed in the later stages of detailed design to the early stages of design, achieving "early verification and frequent verification," thereby greatly reducing the risk of cycle delays and cost surges caused by repeated design in the later stages; 3. By creating a recursive feedback mechanism that directly operates on the "parameter design space" (i.e., the boundary of the value range of key parameters), this method does not randomly adjust specific parameter values ​​when verification fails, but rather adjusts the boundary of the parameter space based on the diagnostic results. This strategy provides macro-level guidance and constraints at the system level, while retaining detailed optimization flexibility at the subsystem level, thereby achieving efficient and targeted exploration and optimization of a broad design space, effectively avoiding getting trapped in local optima, and improving global optimization capabilities. 4. By combining recursive feedback loops with multidisciplinary coupled analysis loops, an automated iterative process of "design-analysis-verification-feedback-redesign" is formed, transforming the entire design convergence process from trial and error relying on personal experience to controllable optimization driven by data and rules. The triggering, execution, and termination of each iteration have clear logic and judgment criteria, making the evolution path of the design solution transparent and traceable, and ultimately enabling the design solution to reliably and efficiently converge to a solution that satisfies all complex constraints. 5. By standardizing and structuring the entire design process, subject model interfaces, key parameter sets, and performance-parameter mapping relationships, an organizational-level design knowledge carrying and reuse framework is formed. Engineering experience, trade-off criteria, and optimization strategies can be solidified into models, rules, and databases, thereby transforming individual, tacit design knowledge into organizational, explicit assets, and continuously improving the team's and even the entire organization's ability and efficiency in handling complex system design tasks.

[0087] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A recursive closed-loop method for the overall design of an eVTOL aircraft, characterized in that, Includes the following steps: S100 determines quantified system-level design indicators based on top-level task requirements specifications; S200, based on the system-level design indicators, generate subsystem parameter sets for the aerodynamic shape subsystem and the propulsion subsystem respectively, set initial value ranges for several key parameters in each subsystem parameter set and determine the key parameter values ​​for the current iteration round; S300, based on the subsystem parameter sets and key parameter values ​​of the aerodynamic shape subsystem and propulsion subsystem, collaboratively runs the aerodynamic analysis model, propulsion subsystem model, weight estimation model and rotor arrangement model, and performs system-level coupled calculations through data interaction and iteration between models, outputting a performance prediction parameter set; S400, quantitatively compare the performance prediction parameter set with the system-level design indicators to generate verification results; S500, if the verification results show that all indicators are met, then output the final design parameters; If any indicators are not met, a key parameter range adjustment instruction is generated based on the verification results. The initial value range of the corresponding key parameter set in S200 is modified according to the key parameter range adjustment instruction, and S300, S400 and this step are re-executed until the verification results show that all indicators are met.

2. The recursive closed-loop method for the overall design of an eVTOL aircraft according to claim 1, characterized in that, The system-level coupled computation, achieved through data interaction and iteration between models, outputs a set of performance prediction parameters, including: Based on the key parameter values ​​of the aerodynamic shape subsystem and the propulsion subsystem, the aerodynamic analysis model, propulsion subsystem model, weight estimation model and rotor arrangement model are operated in a coordinated manner. By using a multidisciplinary integrated model, data exchange is performed between the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model, and cross-model iterative calculations are executed to obtain the system state that satisfies the coupling relationship. After the iterative calculation of the multidisciplinary integrated model satisfies the convergence condition, the output includes the set of performance prediction parameters, which includes range, cruise speed, maximum takeoff weight, and service ceiling.

3. The recursive closed-loop method for the overall design of an eVTOL aircraft according to claim 2, characterized in that, The process of collaboratively operating the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model based on the key parameter values ​​of the aerodynamic shape subsystem and the propulsion subsystem includes: The key parameter values ​​of the aerodynamic shape subsystem are input into the aerodynamic analysis model to calculate the lift coefficient, drag coefficient, and the interference coefficient of the rotor downwash on the fixed wing surface. The drag value in flight state is calculated based on the drag coefficient. The key parameter values ​​of the propulsion subsystem are input into the propulsion subsystem model, and combined with the drag value output by the aerodynamic analysis model, the power requirements and energy consumption that meet the flight profile are calculated. The geometric parameters of the aerodynamic shape subsystem, the mass properties of the propulsion subsystem, and the payload are input into the weight estimation model to calculate the total weight and center of gravity of the aircraft. Input the geometric installation parameters of the rotor arrangement model, the total weight and center of gravity of the aircraft, and the power requirements into the rotor arrangement model to calculate the minimum control power and available control power required to maintain attitude stability during takeoff, landing, and transition flight phases.

4. The recursive closed-loop method for the overall design of an eVTOL aircraft according to claim 3, characterized in that, The process involves using a multidisciplinary integrated model to schedule data exchange between the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model, and to perform cross-model iterative calculations, including... The multidisciplinary integrated model establishes a data interface between the aerodynamic analysis model, the propulsion subsystem model, the weight estimation model, and the rotor arrangement model, and controls each model to execute sequentially according to a preset iterative logic. In each cross-model iteration calculation, the multidisciplinary integrated model transmits the updated total weight and center of gravity position data from the weight estimation model to the propulsion subsystem model and the rotor arrangement model, and re-inputs the updated aerodynamic and propulsion data into the weight estimation model to start the next round of calculation; The multidisciplinary integrated model continuously performs the cross-model iterative calculation until the change in the total weight and the power requirement between two consecutive iterations is less than a preset convergence threshold, at which point the system state that satisfies the coupling relationship is determined.

5. The recursive closed-loop method for the overall design of an eVTOL aircraft according to claim 2, characterized in that, The multidisciplinary integrated model exchanges data with the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model through a central parameter database. The central parameter database stores key parameter values ​​shared by the aerodynamic analysis model, propulsion subsystem model, weight estimation model, and rotor arrangement model. The multidisciplinary integrated model reads input data from the central parameter database according to a preset iterative logic and drives the corresponding model to run, and then sends the output results of the model to the central parameter database.

6. The recursive closed-loop method for the overall design of an eVTOL aircraft according to claim 5, characterized in that, The preset iteration logic includes: The weight estimation model and the aerodynamic analysis model are invoked to perform an initial estimation; The propulsion subsystem model and the rotor arrangement model are alternately and iteratively called. Each call to the propulsion subsystem model is based on the aerodynamic drag data calculated by the aerodynamic analysis model in the current iteration, and each call to the rotor arrangement model is based on the total weight and center of gravity data updated by the weight estimation model in the current iteration, as well as the power requirements updated by the propulsion subsystem model.

7. The recursive closed-loop method for the overall design of an eVTOL aircraft according to claim 1, characterized in that, The top-level mission requirements specification includes mission profile, payload, core performance requirements, operating environment conditions, airworthiness constraints, and safety objectives. The quantitative system-level design metrics determined based on the top-level task requirements specifications include: The numerical requirements for range, speed, and service ceiling are extracted from the mission profile and directly defined as the range, cruise speed, and service ceiling in the system-level design specifications. The weight and spatial dimensions are determined based on the payload, and the weight is defined as the maximum payload index in the system-level design specifications. Based on the core performance requirements, the operating environment conditions, and the airworthiness constraints, the ultimate load factor and safety margin required to meet structural strength, the front and rear limit positions of the center of gravity required to meet stability, and the minimum available power ratio required to meet power redundancy are calculated. Based on the ultimate load factor, the safety margin, the front and rear limits of the center of gravity, and the minimum available power ratio, calculate the maximum takeoff weight index in the system-level design parameters respectively; The system-level design specifications are constituted based on the range index, the cruise speed index, the service ceiling index, the maximum payload index, and the maximum takeoff weight index.

8. The recursive closed-loop method for the overall design of an eVTOL aircraft according to claim 1, characterized in that, The step of generating a key parameter range adjustment instruction based on the verification result, and modifying the initial value range of the corresponding key parameter set in S200 according to the key parameter range adjustment instruction, includes: Identify at least one system performance metric that has not met the system-level design metric from the verification results; Based on a predefined system performance index-subsystem responsibility mapping relationship, a target subsystem corresponding to the at least one unmet system performance index is determined, wherein the target subsystem is the aerodynamic shape subsystem or the propulsion subsystem; Based on the specific numerical deviations of the system performance indicators in the verification results, and in conjunction with the predefined target subsystem key parameter-performance impact relationship, determine one or more key parameters in the target subsystem that cause the deviations. For the one or more key parameters, generate a key parameter range adjustment instruction including adjustment direction and adjustment magnitude, and update the initial value range of the one or more key parameters set for the target subsystem in S200; Based on the updated initial value range, the key parameter values ​​for the current iteration are redefined, and S300, S400, and S500 are triggered.

9. The recursive closed-loop method for the overall design of an eVTOL aircraft according to claim 8, characterized in that, Based on the specific numerical deviations of the system performance indicators in the verification results, and in conjunction with the predefined target subsystem key parameter-performance impact relationship, one or more key parameters in the target subsystem that cause the deviation are determined, including: Based on the sensitivity coefficient in the key parameter-performance impact relationship, the key parameters of the target subsystem are sorted, and one or more key parameters with sensitivity coefficients higher than a preset threshold are selected.

10. The recursive closed-loop method for the overall design of an eVTOL aircraft according to any one of claims 1-9, characterized in that, Key parameters of the aerodynamic shape subsystem include: aspect ratio, wing loading, and stability margin; The key parameters of the propulsion subsystem include: rotor diameter, rotor disk load, and number of propulsion units.

Images