A method and system for optimizing cabin layout of a blended wing body passenger aircraft

Abstract

This invention relates to a method and system for optimizing the cabin layout of a blended wing-body passenger aircraft. The method includes: constructing a cellular automaton model using the open-source cellular automaton architecture Mesa; building multiple evacuation scenarios with different cabin layouts and generating multiple agents with different behavioral parameters; simulating the smoke diffusion process using the open-source pollutant diffusion simulation architecture CONTAM and coupling it with the cellular automaton model; evaluating the simulation results to determine at least one evacuation scheme that meets preset conditions; and optimizing the cabin layout of the blended wing-body passenger aircraft based on the evacuation scheme. The solution of this application can meet the emergency evacuation simulation requirements in various scenarios, especially in the frequently occurring scenario of smoke entering the cabin, and establishes a joint optimization method for cabin layout elements and emergency evacuation schemes to propose an overall cabin structure that meets airworthiness requirements.

Description

Technical Field

[0001] This invention relates to the field of intelligent simulation, specifically to a method and system for optimizing the cabin layout of a blended wing-body passenger aircraft. Background Technology

[0002] As a popular option for next-generation commercial airliners, blended wing-body aircraft are expected to meet the growing demands of the civil aviation industry for good fuel economy, low noise, and low emissions. In recent years, domestic and international research institutions and aircraft manufacturers have conducted extensive research and proposed various conceptual designs. Compared to traditional tubular-wing aircraft, blended wing-body aircraft are expected to have a significantly different shape, thus requiring a completely new cabin design.

[0003] Emergency evacuation of civil aircraft is crucial for ensuring passenger safety after an accident and is an important aspect of airworthiness certification. Organizing emergency evacuations on civil aircraft is particularly challenging due to limited cabin space and high passenger density. The current international standard requires all personnel to evacuate within 90 seconds when only half of the emergency exits are available. Existing simulations show that blended-wing-body aircraft cannot fully meet the 90-second airworthiness requirement for emergency evacuation, which is a significant obstacle to their practical deployment. Modern emergency evacuation organization methods for civil aircraft are primarily designed for traditional tubular-wing aircraft. With the development of blended-wing-body aircraft concept design, significant differences between their wide-body cabin structure and traditional aircraft have gradually become apparent, including a large lateral cabin dimension, multiple internal compartments and partitions, a limited number of side emergency exits, and a longer distance between the front and rear exits. These characteristics make it difficult to directly apply traditional emergency evacuation organization methods.

[0004] Smoke is a common obstacle during emergency evacuations of passenger aircraft. In recent real-world emergency evacuation cases, the negative effects of smoke have been significant. For example, in the Haneda Airport collision on January 2, 2024, the damaged commercial aircraft was already filled with smoke before the emergency evacuation began, severely impairing passenger visibility. However, coupling smoke with the evacuation process has always been challenging. High-precision fire simulation software, such as FDS, requires considerable time to solve the three-dimensional Navier-Stokes equations, making real-time coupling impossible and difficult to analyze multiple different cabin layouts.

[0005] Because current research on blended wing-body aircraft is still mainly based on conceptual design and scaled-down models, there is still considerable uncertainty regarding its cabin layout. Existing studies largely assume that cabin layout elements such as aisle width, inter-cabin connecting doors, and emergency exit distribution will follow the design of existing commercial aircraft. This approach fails to effectively utilize the advantages of the larger lateral dimensions of blended wing-body aircraft and lacks consideration for the unavoidable disadvantages during evacuation, such as cabin partitions and cabin smoke. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a cabin layout optimization method and system for blended wing-body passenger aircraft, which can meet the emergency evacuation simulation requirements of various scenarios, especially the scenario of smoke entering the cabin, which often occurs in emergency evacuation, and establish a joint optimization method for cabin layout elements and emergency evacuation scheme to propose an overall cabin structure that meets airworthiness requirements.

[0007] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for optimizing the cabin layout of a blended wing-body passenger aircraft, comprising: A cellular automaton model was constructed using the open-source cellular automaton architecture Mesa. Build multiple evacuation scenarios with different cabin layouts and generate multiple intelligent agents with different behavioral parameters; Conduct multi-scenario emergency evacuation simulations; The simulation results are evaluated to determine at least one evacuation plan that meets the preset conditions; The cabin layout of the blended wing-body aircraft is optimized based on the aforementioned evacuation plan.

[0008] Based on the above technical solution, the present invention can be further improved as follows.

[0009] Furthermore, the construction of the cellular automaton model using the open-source cellular automaton architecture Mesa includes: Refer to the concept design data of blended wing-body passenger aircraft to obtain configuration files for multiple cabin layouts; Construct a cellular automaton model based on the configuration file.

[0010] Furthermore, the layout elements in the configuration file include at least the aisle width, emergency exit width, emergency exit location, available emergency exit, unavailable emergency exit, whether there are obstacles next to the emergency exit, and the location and opening / closing status of the inter-cabin connecting door.

[0011] Furthermore, the method also includes: A smoke diffusion model was constructed using the open-source pollutant diffusion simulation architecture CONTAM. Multiple smoke diffusion scenarios with different cabin layouts were constructed, and multiple intelligent agents with different behavioral parameters were generated. Based on the smoke diffusion model, a coupled simulation of smoke diffusion and emergency evacuation in multiple scenarios was performed; The simulation results are evaluated to determine at least one evacuation plan that meets the preset conditions; The cabin layout of the blended wing-body aircraft is optimized based on the aforementioned evacuation plan.

[0012] Furthermore, the construction of the smoke diffusion model includes: Building a fire growth model ; Building an air quality model ; Constructing a regional smoke model ; Constructing a flue gas optical density model ; in, The rate of heat release during a fire. This represents the fire growth coefficient. The time after the fire For the quality of the incoming air, For flow coefficient, The area of ​​the breach. air density, The internal and external pressure difference Regional air quality (kg) For regional pollutant concentrations, The mass flow rate between the two regions. For pollutant source items, For pollutant removal items, The extinction coefficient of the smoke is denoted as .

[0013] Furthermore, the construction of multiple smoke diffusion scenarios with different cabin layouts also includes: Set virtual room dividing lines to mark connected areas surrounded by physical doors and virtual dividing lines as independent areas; By setting different opening and closing states of the physical door, multiple smoke diffusion scenarios can be built in different independent areas.

[0014] This application also provides a cabin layout optimization system for a blended wing-body passenger aircraft, including a model building module, a scene building module, a simulation module, and an evaluation and optimization module; The model building module is used to build cellular automata models using the open-source cellular automata architecture Mesa. The scenario building module is used to build multiple evacuation scenarios with different cabin layouts and generate multiple intelligent agents with different behavioral parameters. The simulation module is used to perform multi-scenario emergency evacuation simulations using the cellular automata model. The evaluation and optimization module is used to evaluate the simulation results to determine at least one evacuation plan that meets preset conditions, and to optimize the cabin layout of the blended wing-body aircraft based on the evacuation plan.

[0015] Furthermore, the model building module is also used to construct a smoke diffusion model using the open-source pollutant diffusion simulation architecture CONTAM; The scenario building module is also used to build multiple smoke diffusion scenarios with different cabin layouts and generate multiple intelligent agents with different behavioral parameters. The simulation module is also used to perform coupled simulations of smoke diffusion and emergency evacuation in multiple scenarios based on the smoke diffusion model.

[0016] Furthermore, the model building module is also used for: Building a fire growth model ; Building an air quality model ; Constructing a regional smoke model ; Constructing a flue gas optical density model ; in, The rate of heat release during a fire. This represents the fire growth coefficient. The time after the fire For the quality of the incoming air, For flow coefficient, The area of ​​the breach. air density, The internal and external pressure difference Regional air quality (kg) For regional pollutant concentrations, The mass flow rate between the two regions. For pollutant source items, For pollutant removal items, The extinction coefficient of the smoke is denoted as .

[0017] Furthermore, the scene building module is also used to: set virtual room dividing lines, and mark the connected areas surrounded by physical doors and virtual dividing lines as independent areas; By setting different opening and closing states of the physical door, multiple smoke diffusion scenarios can be built in different independent areas.

[0018] The advantages of adopting the above scheme are: jointly optimizing the cabin layout elements of the blended wing-body aircraft with the emergency evacuation scheme can make the overall emergency evacuation scheme more reliable; the modular design can meet the emergency evacuation simulation requirements of various scenarios, especially the scenario of smoke entering the cabin, which often occurs in emergency evacuation, thus providing diversified simulation schemes for emergency evacuation research of blended wing-body aircraft. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the cabin layout optimization method for a blended wing-body passenger aircraft according to the present invention.

[0020] Figure 2This is an example diagram of the cabin layout for a blended wing-body passenger aircraft.

[0021] Figure 3 This is a diagram illustrating two possible emergency evacuation plans.

[0022] Figure 4 This is a schematic diagram of the results of an emergency evacuation simulation.

[0023] Figure 5 This is a schematic diagram of the cabin layout optimization system for the blended wing-body passenger aircraft of the present invention.

[0024] Figure 6 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0025] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0026] Example 1 Figure 1 This is a flowchart illustrating the cabin layout optimization method for a blended wing-body passenger aircraft according to the present invention. Figure 1 As shown, this application provides a method for optimizing the cabin layout of a blended wing-body passenger aircraft, including: S1. Construct a cellular automaton model using the open-source cellular automaton architecture Mesa.

[0027] Based on Example 1, a cellular automaton model is constructed using the open-source cellular automaton architecture Mesa, including: Refer to the concept design data of blended wing-body passenger aircraft to obtain configuration files for multiple cabin layouts; Build a cellular automaton model based on the configuration file.

[0028] The model is built on the open-source architecture Mesa. Compared to existing commercial simulation software, the open-source architecture allows for adjustments to the underlying code based on actual conditions, and also makes it easier to introduce new features and couple different architectures.

[0029] In this embodiment, the cabin geometry is first inferred based on the simulated blended-wing-body aircraft concept design and other relevant data. The design primarily references NASA's N2A-HWB blended-wing-body aircraft concept design, but can also be adapted to other single-deck blended-wing-body aircraft models. Since the cabin design of blended-wing-body aircraft still has considerable uncertainty, adjustments to specific cabin layout elements, such as overall width and emergency exit locations, are practically possible. This embodiment provides an example cabin layout diagram of a blended-wing-body aircraft, such as... Figure 2It includes 22 zones (zones 1-22), 8 emergency exits (two at the head, one on each side of the neck, and four at the tail), 10 doors (D1-10), 22 bulkheads (DIV1-22), and a large number of seats.

[0030] The cellular automata model has a pre-reserved interface for recognizing CSV files. By using specific numbers to correspond to different layout elements, the model can quickly import cabin layout elements such as walls, seats, aisles, obstacles, available emergency exits, and unavailable emergency exits into the model using easily editable CSV files.

[0031] Specifically, the cabin layout elements in the configuration file that have a significant impact on emergency evacuation include aisle width, emergency exit width, emergency exit location, available emergency exits, unavailable emergency exits, the presence of obstacles next to emergency exits, and the location and opening / closing status of inter-cabin connecting doors. The model can conveniently conduct simulation tests on the above cabin layout elements.

[0032] S2. Build multiple evacuation scenarios with different cabin layouts and generate multiple intelligent agents with different behavioral parameters.

[0033] The overall cabin layout is transformed into a 0.4 m × 0.4 m cellular mesh. All passenger agents are generated based on the cabin seating distribution. Each passenger agent is generated according to the simulation requirements and assigned various behavioral parameters, including movement speed based on a real emergency evacuation experiment, the selected emergency exit at the start of evacuation, and the intention to change to a different emergency exit when facing congestion. By default, passengers are randomly assigned a movement speed according to the conditions given in Appendix J of CCAR-25, meaning that 35% of passengers are over 50 years old and move slower. There will also be some random differences in the movement speeds of different passengers. Since the cellular automaton is a discrete model, if a passenger moves an additional distance after moving, this distance is recorded in the agent's own accumulator. Once the distance recorded in the accumulator reaches 0.4 m, the agent will move an additional distance.

[0034] During an emergency evacuation, due to unavoidable panic, it's unrealistic to "intelligently" select a globally optimal emergency exit for each passenger. The model assumes passengers will choose the emergency exit with the shortest physical distance, and the route is provided by the A* pathfinding algorithm. To avoid unnecessary performance overhead from repeatedly calling the A* algorithm, each passenger's path is stored in the agent's own cache. Passengers will only lose patience and attempt to change their destination if they remain immobile for an extended period and are a certain distance from their current target emergency exit, at which point the A* algorithm will be called again. The specific threshold can be adjusted to reflect different emergency evacuation scenarios.

[0035] S3. Conduct multi-scenario emergency evacuation simulations.

[0036] The model automatically simulates multiple scenarios of emergency evacuation, covering different corridor widths, emergency exit widths, and the opening and closing of connecting doors.

[0037] Considering the diversity of actual evacuation scenarios, multiple different emergency evacuation plans can be enabled for the model. For example, the model can be set to have only four emergency exits at the front of the cabin available, or it can be set to a scenario that more closely resembles the situation in modern commercial airliners where emergency evacuation tests would randomly select one of the emergency exits on the left or right side. Figure 3 The diagram illustrates two possible emergency evacuation plans, as follows: Figure 3 In the left image, only the four front emergency exits are available; in the right image, only the four side emergency exits are available. One approach is to consider either not interfering with passengers' choice of emergency exits, or to bypass the passengers' own selection mechanism and assign an emergency exit to each passenger in each compartment. Given the complexity of emergency evacuation, forcibly assigning a mandatory emergency exit to each passenger is impractical. Therefore, the process of selecting an emergency exit essentially involves closing the connecting doors between compartments to guide passenger movement.

[0038] S4. Evaluate the simulation results to determine at least one evacuation plan that meets the preset conditions.

[0039] The simulation results should include at least the overall evacuation time, the evacuation time per passenger, the number of people using different emergency exits, and the number of casualties at different emergency exits.

[0040] Based on different cabin layouts and emergency evacuation plans, the system automatically simulates all relevant emergency evacuation scenarios, evaluates the results, filters out simulation scenarios that meet the evacuation conditions, and determines the corresponding evacuation plan and cabin layout plan for each simulation scenario. Evacuation conditions can be: the total evaluation score of the simulation scenario is greater than a first preset threshold; the overall evacuation time is less than a first time; the individual evacuation time is less than a second time; or the number of casualties is less than a preset value. Optionally, the evacuation condition is that all personnel on the aircraft should complete the evacuation within 90 seconds.

[0041] S5. Optimize the cabin layout of the blended wing-body aircraft based on the evacuation plan.

[0042] Optionally, based on the individual simulation scenario obtained in step S4 and its corresponding evacuation plan and cabin layout plan, the layout elements of the simulation scenario are adjusted, such as adjusting the aisle width, adjusting the emergency exit width, adjusting the emergency exit position, setting the number of available and unavailable emergency exits, and adjusting the position and opening / closing status of the inter-cabin connecting doors, to obtain an updated cabin layout plan. The evacuation plan of the scenario is updated based on the updated cabin layout plan, and multiple rounds of simulation optimization are performed on the updated cabin layout plan to obtain a cabin layout plan in a single simulation scenario that tends to meet the evacuation conditions.

[0043] Furthermore, based on the multiple simulation scenarios obtained in step S4 and their corresponding different evacuation plans and cabin layout plans, a layout plan can be jointly adjusted, updated, and output. The updated cabin layout plan can be optimized through multiple rounds of simulation to obtain a cabin layout plan that tends to meet the evacuation conditions in multi-scenario combined simulation.

[0044] Based on Example 1, the method further includes: A smoke diffusion model was constructed using the open-source pollutant diffusion simulation architecture CONTAM. Multiple smoke diffusion scenarios with different cabin layouts were constructed, and multiple intelligent agents with different behavioral parameters were generated. Coupled simulations of smoke diffusion and emergency evacuation in multiple scenarios were conducted based on a smoke diffusion model. The simulation results are evaluated to determine at least one evacuation plan that meets the preset conditions; The cabin layout of the blended wing-body aircraft will be optimized based on the evacuation plan.

[0045] Smoke, a common obstacle during emergency evacuation of passenger aircraft, was incorporated into the model as an optional module, considering its diffusion process and its obstructive effect on passenger agents. After an emergency landing, the composite materials used in the fuselage of modern commercial aircraft typically do not immediately ignite, and the flames themselves pose a relatively small threat to passengers during emergency evacuation. However, external ignition sources such as engines and leaking fuel still generate large amounts of smoke that seep into the cabin. Even if pilots can shut off the venting system during standard emergency evacuation procedures to prevent the air conditioning system from directly introducing smoke into the cabin, smoke can still enter the cabin through fuselage breaches.

[0046] In this embodiment, a smoke diffusion model is constructed to simulate emergency evacuation in a smoke scenario.

[0047] Based on Example 1, multiple smoke diffusion scenarios with different cabin layouts were constructed, including: Set virtual room dividing lines to mark connected areas surrounded by physical doors and virtual dividing lines as independent areas; Set different opening and closing states for physical doors and build multiple smoke diffusion scenarios in different independent areas.

[0048] In the steps of constructing the smoke diffusion model, it is assumed that the external fire conforms to... Growth Model: Constructing a Fire Growth Model ,in, The rate of heat release during a fire. This represents the fire growth coefficient. This refers to the time following the fire.

[0049] Smoke from a fire enters the cabin through the breach according to a pressure differential-driven mass flow formula; the specific location can be adjusted to construct an air quality model. ,in, For the quality of the incoming air, For flow coefficient, The area of ​​the breach. air density, This represents the pressure difference between the inside and outside of the cabin. Since exhaust valves need to be opened during emergency evacuation to balance the air pressure inside and outside the cabin, the model assumes that there is an exhaust valve at the rear of the cabin that connects to the outside.

[0050] After setting the parameters of the above model, input the smoke diffusion model into CONTAM for smoke diffusion simulation. CONTAM is an open-source multi-zone airflow and pollutant transport model. To avoid solving the extremely time-consuming three-dimensional Navier-Stokes equations in fire simulation software such as FDS, CONTAM treats each zone as a separate zone, where pollutants are instantaneously and uniformly mixed within the zone.

[0051] Therefore, CONTAM only needs to solve the mass conservation equation: ,in, Regional air quality (kg) For regional pollutant concentrations, The mass flow rate between the two regions. For pollutant source items, This refers to the removal of pollutants.

[0052] CONTAM operates at extremely high speed, enabling real-time updates of smoke concentration in each cabin and achieving real-time coupling with passenger movement within the cellular automata model. Considering the relatively short evacuation time, the smoke primarily serves to obstruct passenger vision and reduce movement speed; situations where the smoke becomes severe enough to threaten passenger safety are not considered. The smoke optical density OD is estimated using an empirical formula: , The extinction coefficient of the smoke is denoted as .

[0053] When smoke diffusion is activated, the model automatically reads the cabin layout and performs a comprehensive smoke diffusion simulation of the cabin, completely synchronized with the evacuation simulation. Simultaneously, the smoke diffusion process interacts with the cabin layout. For example, open inter-cabin connecting doors allow smoke to pass directly through, while closed inter-cabin connecting doors significantly hinder smoke diffusion. The smoke diffusion simulation can also be deactivated to compare emergency evacuation procedures with and without smoke effects.

[0054] Figure 4 This is a schematic diagram illustrating the results of an emergency evacuation simulation, such as... Figure 4The four graphs represent the evacuation progress curve (top left), the remaining number of people versus time curve (top right), the individual evacuation time distribution graph (bottom left), and the speed decay curve under smoke (bottom right). Based on the evacuation progress curve and the remaining number of people versus time curve, it can be concluded that the overall evacuation progress increases with time, but the increase is rapid before 50 seconds, and the evacuation progress curve flattens out after 50 seconds. This is presumably because passengers left their seats for emergency exits before 50 seconds, and congestion occurred at the emergency exits after 50 seconds. Based on the individual evacuation time distribution graph, it can be concluded that most passengers can complete the evacuation within 100 seconds, while the remaining passengers complete the evacuation within 300 seconds due to congestion and smoke. Since the smoke source in this simulation is in area 1, the evacuation speed of passengers in area 2 and area 1 decreases the most between 100-150 seconds. As time increases, the smoke spreads to other areas, and the evacuation speed of passengers in other areas decreases due to the smoke.

[0055] comprehensive Figure 4 The four images show that although the smoke only began to significantly affect visibility in some cabin rooms after 100 seconds, the severe congestion meant that the smoke still had a significant negative impact on the evacuation process. The overall evacuation time was 300 seconds, with each passenger taking an average of 93.7 seconds to evacuate.

[0056] The solution of the present invention has the following beneficial effects: (1) A highly flexible, low-investment, and highly feasible method is provided for conducting airworthiness analysis of emergency evacuation for the concept design of blended wing-body passenger aircraft, providing reliable support for the future related design of blended wing-body passenger aircraft.

[0057] (2) Establish a dedicated emergency evacuation model for blended wing-body aircraft. The model can be flexibly adjusted to adapt to the cabin layout of different blended wing-body aircraft, and can also be fine-tuned for a specific aircraft type.

[0058] (3) By combining the cabin layout elements of the blended wing-body aircraft with the emergency evacuation plan, the overall emergency evacuation plan can be made more reliable.

[0059] (4) The emergency evacuation simulation model is modularly designed and can meet the emergency evacuation simulation requirements of various scenarios, especially the scenario where smoke enters the cabin, which is frequently encountered in emergency evacuation. This provides a variety of simulation schemes for the emergency evacuation research of blended wing body aircraft.

[0060] Example 2 Figure 5 This is a structural schematic diagram of the cabin layout optimization system for the blended wing-body passenger aircraft of the present invention, as shown below. Figure 5This application also provides a cabin layout optimization system for a blended wing-body passenger aircraft, including a model building module, a scene building module, a simulation module, and an evaluation and optimization module.

[0061] The model building module is used to build cellular automata models using the open-source cellular automata architecture Mesa; The scenario building module is used to build multiple evacuation scenarios with different cabin layouts and generate multiple intelligent agents with different behavioral parameters. The simulation module is used to perform emergency evacuation simulations in multiple scenarios; The evaluation and optimization module is used to evaluate the simulation results to determine at least one evacuation plan that meets the preset conditions, and to optimize the cabin layout of the blended wing-body aircraft based on the evacuation plan.

[0062] Optionally, the simulation module can be a hybrid model of cellular automata and multi-agent system (CA-MAS), or simulation software such as Pathfinder and AnyLogic.

[0063] Based on Example 2, the model building module is also used to build a smoke diffusion model using the open-source pollutant diffusion simulation architecture CONTAM; The scene building module is also used to build multiple smoke diffusion scenarios with different cabin layouts and generate multiple intelligent agents with different behavioral parameters. The simulation module is also used to perform coupled simulations of smoke diffusion and emergency evacuation in multiple scenarios based on the smoke diffusion model.

[0064] Based on Example 2, the model building module is also used for: Building a fire growth model ; Building an air quality model ; Constructing a regional smoke model ; Constructing a flue gas optical density model .

[0065] Based on Example 2, the scene building module is also used to: set virtual room dividing lines and mark the connected areas surrounded by physical doors and virtual dividing lines as independent areas; Set different opening and closing states for physical doors and build multiple smoke diffusion scenarios in different independent areas.

[0066] Example 3 In some embodiments, the cabin layout optimization system for the blended wing-body aircraft of the present invention can be implemented using a combination of hardware and software. As an example, the cabin layout optimization system for the blended wing-body aircraft of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the cabin layout optimization method for the blended wing-body aircraft of the present invention. For example, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0067] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0068] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned cabin layout optimization methods for blended wing-body passenger aircraft. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the cabin layout optimization method for blended wing-body passenger aircraft shown in any embodiment of the present invention by calling the computer program.

[0069] In one alternative embodiment, an electronic device is provided, such as Figure 6 As shown, Figure 6 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0070] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0071] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0072] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0073] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0074] Among them, electronic devices can also be terminal devices, which can be any device that can install applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0075] It should be noted that, Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0076] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-described methods for optimizing the cabin layout of a blended wing-body passenger aircraft.

[0077] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0078] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the aforementioned cabin layout optimization method for a blended wing-body passenger aircraft.

[0079] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0080] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0081] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EEPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0082] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0083] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0084] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0085] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0086] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing the cabin layout of a blended wing-body passenger aircraft, characterized in that, include: A cellular automaton model was constructed using the open-source cellular automaton architecture Mesa. Build multiple evacuation scenarios with different cabin layouts and generate multiple intelligent agents with different behavioral parameters; Conduct multi-scenario emergency evacuation simulations; The simulation results are evaluated to determine at least one evacuation plan that meets the preset conditions; The cabin layout of the blended wing-body aircraft is optimized based on the aforementioned evacuation plan.

2. The method according to claim 1, characterized in that, The construction of the cellular automaton model using the open-source cellular automaton architecture Mesa includes: Refer to the concept design data of blended wing-body passenger aircraft to obtain configuration files for multiple cabin layouts; Construct a cellular automaton model based on the configuration file.

3. The method according to claim 2, characterized in that, The layout elements in the configuration file include at least the aisle width, emergency exit width, emergency exit location, available emergency exits, unavailable emergency exits, whether there are obstacles next to the emergency exits, and the location and opening / closing status of the inter-cabin connecting doors.

4. The method according to claim 1, characterized in that, The method further includes: A smoke diffusion model was constructed using the open-source pollutant diffusion simulation architecture CONTAM. Multiple smoke diffusion scenarios with different cabin layouts were constructed, and multiple intelligent agents with different behavioral parameters were generated. Based on the smoke diffusion model, a coupled simulation of smoke diffusion and emergency evacuation in multiple scenarios was performed; The simulation results are evaluated to determine at least one evacuation plan that meets the preset conditions; The cabin layout of the blended wing-body aircraft is optimized based on the aforementioned evacuation plan.

5. The method according to claim 4, characterized in that, The construction of the smoke diffusion model includes: Building a fire growth model ; Building an air quality model ; Constructing a regional smoke model ; Constructing a flue gas optical density model ; in, The rate of heat release during a fire. This represents the fire growth coefficient. The time after the fire For the quality of the incoming air, For flow coefficient, The area of ​​the breach. air density, The internal and external pressure difference Regional air quality (kg) For regional pollutant concentrations, The mass flow rate between the two regions. For pollutant source items, For pollutant removal items, The extinction coefficient of the smoke is denoted as .

6. The method according to claim 4, characterized in that, The construction of multiple smoke diffusion scenarios with different cabin layouts also includes: Set virtual room dividing lines to mark connected areas surrounded by physical doors and virtual dividing lines as independent areas; By setting different opening and closing states of the physical door, multiple smoke diffusion scenarios can be built in different independent areas.

7. A cabin layout optimization system for a blended wing-body passenger aircraft, characterized in that, It includes a model building module, a scene building module, a simulation module, and an evaluation and optimization module; The model building module is used to build cellular automata models using the open-source cellular automata architecture Mesa. The scenario building module is used to build multiple evacuation scenarios with different cabin layouts and generate multiple intelligent agents with different behavioral parameters. The simulation module is used to perform emergency evacuation simulations in multiple scenarios. The evaluation and optimization module is used to evaluate the simulation results to determine at least one evacuation plan that meets preset conditions, and to optimize the cabin layout of the blended wing-body aircraft based on the evacuation plan.

8. The system according to claim 7, characterized in that, The model building module is also used to build a smoke diffusion model using the open-source pollutant diffusion simulation architecture CONTAM. The scenario building module is also used to build multiple smoke diffusion scenarios with different cabin layouts and generate multiple intelligent agents with different behavioral parameters. The simulation module is also used to perform coupled simulations of smoke diffusion and emergency evacuation in multiple scenarios based on the smoke diffusion model.

9. The system according to claim 8, characterized in that, The model building module is also used for: Building a fire growth model ; Building an air quality model ; Constructing a regional smoke model ; Constructing a flue gas optical density model ； in, The rate of heat release during a fire. This represents the fire growth coefficient. The time after the fire For the quality of the incoming air, For flow coefficient, The area of ​​the breach. air density, The internal and external pressure difference Regional air quality (kg) For regional pollutant concentrations, The mass flow rate between the two regions. For pollutant source items, For pollutant removal items, The extinction coefficient of the smoke is denoted as .

10. The system according to claim 9, characterized in that, The scene building module is also used to: set virtual room dividing lines and mark the connected areas surrounded by physical doors and virtual dividing lines as independent areas; By setting different opening and closing states of the physical door, multiple smoke diffusion scenarios can be built in different independent areas.

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