9865 results about "Aviation" patented technology

Disclosed in the present invention are an aviation equipment reliability evaluation method and system based on a knowledge graph and model inference. The method comprises: acquiring data of human factors, equipment systems, and a working environment of aviation equipment; carrying out preprocessing and text labeling on the acquired data; inputting the labeled text information into a constructed entity relationship joint extraction model to form a high-quality structured triple of the knowledge graph; constructing an elastic knowledge graph for the aviation equipment, wherein the elastic knowledge graph comprises an online knowledge graph and an offline knowledge graph which has aviation equipment reliability; and extracting semantic features, and analyzing the similarity between the extracted features to realize indirect inference of the aviation equipment reliability. The present invention fully fuses expert experience and knowledge data, and exerts respective advantages of a human brain and machine intelligence, so as to achieve accurate analysis and prediction of aviation equipment reliability, thereby providing intelligent risk analysis, early warning and optimization suggestions for command and control personnel, and reducing a fault occurrence rate.

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention relates to an aviation big data intelligent analysis method based on trajectory anomaly detection, and belongs to the technical field of aviation data processing. The method comprises the following steps: acquiring operation state data and flight parameters of an aircraft, and extracting aerodynamic parameters of an aircraft type and deviation data corresponding to horizontal navigation and vertical navigation; carrying out stream batch integrated processing on the data through a real-time processing engine, and carrying out space-time reference unification to obtain standardized data; performing data cleaning, abnormal point correction and missing value interpolation on the standardized data; performing pneumatic-performance correlation analysis after data processing, and quantifying the influence of parameter deviation on the climbing performance through a dynamic response model to obtain a strong correlation coefficient; performing coupling analysis through the climbing rate attenuation prediction model to obtain an aircraft climbing performance anomaly prediction result; and generating a correction instruction according to the persistent climb rate anomaly of the performance anomaly prediction result. The efficient and accurate analysis of the aviation big data is realized, and the safety and reliability of the operation of the aircraft are effectively improved.

The invention relates to the technical field of aviation, in particular to a low-altitude flight micrometeorological intelligent decision-making system based on multi-source sensing and AI fusion, and aims at solving the problems of difficulty in local disturbance identification, insufficient meteorological data granularity, weak flight task adaptation and the like in urban and complex terrain low-altitude flight scenes. According to the system, multi-dimensional meteorological data such as wind speed, wind direction, temperature and humidity are acquired through cooperation of fixed nodes and an air-based unmanned aerial vehicle, a risk area is dynamically identified in combination with a disturbance index model, and meteorological perception with minute-level and hectometer-level resolution is realized. The system supports intelligent perception scheduling, edge AI processing and multi-link communication transmission based on task characteristics, has task adaptability evaluation, flight path optimization, risk early warning and closed-loop learning capabilities, is suitable for scenes such as low-altitude airspace management, flight examination and approval, urban air traffic and the like, and remarkably improves the safety and intelligent level of low-altitude operation.

The invention belongs to the technical field of data processing, relates to an aircraft multi-mode instruction conflict resolution method and system, and aims to solve the problems of high conflict detection time delay and priority strategy staticization of a traditional method. The method comprises the steps that after a multi-mode instruction is received, semantic feature extraction and time sequence alignment are completed through a three-layer attention mechanism and a dynamic time warping algorithm, and a structured instruction set is generated; evaluating the priority of each instruction by combining a four-dimensional dynamic weight system with reinforcement learning; inputting the instruction set with the priority into a six-tuple model to identify a conflict type and an instruction set; and a three-level progressive arbitration method is adopted to resolve conflicts, an optimal execution sequence is generated, and the four-dimensional dynamic weight system is fed back and optimized according to an execution result. According to the method, the real-time performance and the dynamic adaptability of multi-mode instruction conflict resolution are remarkably improved, and reliable guarantee is provided for aviation instruction interaction safety.

The invention relates to the technical field of intelligent early warning, and discloses an aviation case electrical safety intelligent early warning method, device and equipment and a storage medium. The method comprises the following steps: carrying out voltage, current, temperature and vibration parameter acquisition on a wiring row position, an input wiring terminal position and a zero sequence current transformer installation position of the aviation intelligent distribution box to obtain an electrical parameter data set; executing dynamic adjustment of an electrical safety detection threshold to obtain an aviation environment adaptability threshold parameter; performing feature extraction on the electrical parameter data set to obtain electrical parameter abnormal features; performing electrical fault mode matching and positioning analysis on the electrical parameter abnormal characteristics to obtain fault type, position and severity information; and generating a target early warning response strategy of the aviation intelligent distribution box in combination with the flight stage characteristics of the aircraft. According to the invention, a complete electrical safety protection system is constructed, the false alarm rate and the missing report rate are significantly reduced, and the safety protection capability of the aviation intelligent distribution box electrical system is improved.

The invention provides an aeronautical part multi-process parameter cooperative surface treatment standardization optimization method, and relates to the technical field of aeronautical manufacturing surface engineering, and the method comprises the following steps: S1, constructing a process parameter hierarchical coding system and a correlation mapping model; s2, establishing a parameter solving method based on multi-objective collaborative optimization; s3, constructing a modular process chain; s4, establishing real-time monitoring and closed-loop control of multi-source data fusion; s5, establishing a digital simulation body and verifying the digital simulation body; and S6, constructing a whole-process quality tracing database. According to the method, dynamic collaborative optimization of multiple process parameters is realized through parameter hierarchical coding, association mapping and a cross-process-chain intelligent decision model, the parameter conflict rate is reduced, and the process decision efficiency is improved. Meanwhile, a self-adaptive optimization engine based on reinforcement learning can correct process parameters in real time, the batch consistency of key performance indexes is improved, and the process stability and the product quality are remarkably improved.

The invention relates to a large-model-driven adaptive closed-loop optimization method for machining process parameters of an aviation complex thin-wall component. The method comprises the following steps: acquiring multi-source heterogeneous machining data and quality detection data of the aviation complex component; constructing and training an aviation complex component processing quality prediction model and a multi-target process parameter optimization model; packaging the trained model into a micro service and deploying the micro service to a DeepSeek large model container platform; a DeepSeek large model is used as a controller, and closed-loop calling of the micro-service is achieved; an online self-adaptive optimization process is formed; an analysis report is automatically generated based on an optimization result; and optimizing the processing quality prediction model by using the new data increment and designing an automatic rollback strategy. According to the method, deep learning and a large model technology are fused, accurate prediction of the machining quality and efficient regulation and control of the technological parameters are achieved, and meanwhile the manufacturing efficiency and quality of the aviation complex component are effectively improved.

The invention provides a flexible preparation system and method for sustainable aviation fuel, and the system comprises a green power supply module which is used for outputting fluctuating green electric energy; the process execution device is connected with the green power supply module and comprises five process modules, namely an air carbon capture module, a co-electrolysis module, a Fischer-Tropsch synthesis module, a hydrogen production module and a hydrogenation refining module; the data acquisition module is used for acquiring the temperature, the pressure, the liquid level, the raw material input flow and / or the product output flow of each process module in the process execution device; and the decision control module is used for predicting the future power supply condition of the green electricity based on the historical weather data of the green electricity location, adjusting the distribution of the power in each process module according to the future power supply condition, and adjusting the temperature, pressure and / or raw material input flow in each process module. According to the invention, the cost, the reliability, the operation and maintenance performance, the product quality and the green power utilization rate can be substantially improved at the same time, and the flexible and stable operation of the system under the green power condition is realized.

The invention discloses an electromagnetic detection data efficient three-dimensional inversion method based on operator learning, and belongs to the technical field of geophysical inversion, and the method comprises the steps: constructing an operator learning data set which comprises a plurality of electromagnetic forward modeling models and forward modeling responses of the electromagnetic forward modeling models; performing aviation electromagnetic frequency domain forward calculation training based on the operator learning data set by using a DeepONet network to obtain a trained aviation electromagnetic three-dimensional forward operator; inputting the initial uniform half-space model, replacing a generator with an aviation electromagnetic three-dimensional positive operator, and constructing a WGAN network by taking a KAN network as a discriminator to carry out aviation electromagnetic three-dimensional inversion; training is carried out based on the loss function and the data fitting difference convergence condition to update the KAN network and the uniform half-space model, and finally the updated uniform half-space model is obtained to serve as an inversion result. According to the method, more efficient and accurate three-dimensional aviation electromagnetic inversion can be realized, and the method is more suitable for geophysical inversion application under complex data distribution.

An aircraft gas turbine engine includes a combustor, with a combustion chamber and fuel spray nozzles to inject fuel into the chamber. The nozzles include first and second subsets. The first subset of nozzles is supplied with fuel at a greater rate than each of the second subset. A ratio of nozzles in the first subset to the second subset is 1:3 to 1:6. A lean cruise nvPM emissions index ratio isEIcruise⁡(lean),SAFEIcruise⁡(lean),FF,where EIcruise(lean),SAF isEImaxTO,SAF+EIclimb,SAF2and EIcruise(lean),FF isEImaxTO,FF+EIclimb,FF2.EImaxTO,SAF is nvPM emissions index operating at 100% available thrust and EIclimb,SAF is the nvPM emissions index operating at 85% available thrust if fuel includes sustainable aviation fuel. EImaxTO,FF is the nvPM emissions index operating at 100% available thrust and EIclimb,FF is the nvPM emissions index at 85% available thrust if fuel is a fossil-based hydrocarbon. The lean cruise nvPM emissions index ratio is less than 1.

A gas turbine engine for an aircraft is disclosed. The gas turbine engine comprises: a combustor, comprising a combustion chamber and a plurality of fuel spray nozzles configured to inject fuel into the combustion chamber, wherein the plurality of fuel spray nozzles comprises a first subset of fuel spray nozzles and a second subset of fuel spray nozzles, wherein the combustor is operable in a condition in which each of the fuel spray nozzles of the first subset of fuel spray nozzles is supplied with fuel at a greater fuel flow rate than each of the fuel spray nozzles of the second subset of fuel spray nozzles, wherein a ratio of the number of fuel spray nozzles in the first subset of fuel spray nozzles to the number of fuel spray nozzles in the second subset of fuel spray nozzles is in the range of 1:2 to 1:5. A thrust nvPM emissions index ratio is defined as:EImaxTO / FmaxTOEIidle / Fidlewhere: EIidle is the system loss corrected nvPM emissions index in mg / kg of the gas turbine engine if operating at around 7% available thrust for given operating conditions; EImaxTO is the system loss corrected nvPM emissions index in mg / kg of the gas turbine engine if operating at around 100% available thrust for the given operating conditions; FmaxTO is the thrust of the gas turbine engine at around 100% available thrust in kN for the given operating conditions; and Fidle is the thrust of the gas turbine engine at around 7% available thrust in kN for the given operating conditions. The thrust nvPM emissions index ratio is greater than 0.001. The gas turbine engine is configured to provide fuel comprising a sustainable aviation fuel (SAF) to the plurality of fuel spray nozzles. Also disclosed is a method of operating the gas turbine engine.

The invention relates to the technical field of air traffic management, and particularly provides an airport scene aircraft taxiing conflict real-time prediction and dynamic scheduling method, which comprises the following steps: collecting the motion state of an aircraft and airport road network data, and generating the original trajectory of the aircraft by adopting a hidden Markov model; predicting a future taxiing trajectory of the aircraft to obtain a predicted trajectory; screening potential conflict aircrafts by adopting a dynamic R-Tree spatial index technology, judging whether tracks between the potential conflict aircrafts are intersected or not, and generating a dynamic risk value; executing a dynamic scheduling decision based on the dynamic risk value, and generating an aircraft bypass path by adopting a path generation algorithm; rolling time domain control is adopted, and a local path is re-planned based on data obtained in real time. According to the method, prediction of the taxiing trajectory of the aircraft is fully considered, the risk value of the taxiing conflict of the aircraft is pre-judged in advance, and dynamic path adjustment is performed, so that the labor cost is reduced, and the efficiency and safety of airport ground operation are improved.

The invention relates to an aviation equipment manufacturing digital main line engine system supporting multi-source heterogeneous data access, and belongs to the technical field of aviation manufacturing data management. The system comprises a multi-source heterogeneous data access adaptation module, a data fusion processing module and a full-process data link construction module. The multi-source heterogeneous data access adaptation module passes through a data interface protocol and format conversion component; the data standardization and fusion processing module completes association fusion through an aviation heterogeneous data collaborative computing framework by means of an aviation manufacturing field data element standard library, a unified heterogeneous data model is generated, and the framework comprises a data fragmentation layer, a parallel node layer and a result aggregation layer; and the full-process data link construction module constructs a data link covering the full life cycle of the product based on a time sequence association algorithm and a product unique identifier mapping mechanism. The system can effectively solve the integration and management problems of multi-source heterogeneous data in aviation equipment manufacturing, and improves the data processing efficiency and the whole-process data tracing capability.

The invention relates to a turbine blade global temperature field reconstruction method based on a multi-modal physical constraint network, and belongs to the technical field of aero-engine thermal management. Aiming at the problems of insufficient global heat conduction path modeling, lack of physical rules and blank region differentiation perception, carrying out normalization and position coding on sparse temperature measurement points to form a Transform encoder input sequence; extracting features through an encoder and compressing the features into global feature vectors through self-attention pooling; decoding the image into a temperature field image by adopting a U-Net generator fused with jump connection; a multi-scale discriminator is constructed, a semantic weighting mechanism is introduced, and differentiated weights are distributed to cooling holes, tenons and pressure surface areas; a physical constraint loss and local energy conservation loss are constructed based on a heat conduction equation, and a weighted training model is combined with confrontation loss and temperature error loss. The method is used for providing high-precision temperature distribution data support for turbine blade structure optimization design, cooling system efficiency evaluation and thermal fatigue prevention.

Disclosed in the present invention are a multi-source aviation remote sensing data acquisition apparatus, system and control method, which are applied to the technical field of aerial survey remote sensing. The apparatus comprises: a flight platform and an airborne device disposed on the flight platform. The airborne device comprises: a multi-sensor ultra-compact integrated device, an airborne integrated synchronization control device, and a GNSS receiving antenna. The multi-sensor ultra-compact integrated device and the airborne integrated synchronization control device are connected by means of cables, and the GNSS receiving antenna is disposed on an upper end face of the flight platform. The multi-sensor ultra-compact integrated device is used for acquiring multi-source remote sensing data; and the airborne integrated synchronization control device is used for controlling the operation of the multi-sensor ultra-compact integrated device. The present invention ultra-compactly integrates a LiDAR, a hyperspectral camera, a thermal infrared camera, and a visible-light camera on the basis of a high-precision inertial navigation system, and achieves efficient synchronous acquisition of texture, spectral, temperature, and geometric information by means of using an integrated control subsystem.

A gas turbine engine for an aircraft is disclosed. The gas turbine engine comprises: a combustor, comprising a combustion chamber and a plurality of fuel spray nozzles configured to inject fuel into the combustion chamber, wherein the plurality of fuel spray nozzles comprises a first subset of fuel spray nozzles and a second subset of fuel spray nozzles, wherein the combustor is operable in a condition in which each of the fuel spray nozzles of the first subset of fuel spray nozzles is supplied with fuel at a greater fuel flow rate than each of the fuel spray nozzles of the second subset of fuel spray nozzles, wherein a ratio of the number of fuel spray nozzles in the first subset of fuel spray nozzles to the number of fuel spray nozzles in the second subset of fuel spray nozzles is in the range of 1:3 to 1:6. A MTO nvPM emissions index ratio is defined as:EImaxTO,SAFEImaxTO,FFwhere: EImaxTO,SAF is the system loss corrected nvPM emissions index in mg / kg of the gas turbine engine when operating at around 100% available thrust for given operating conditions if a fuel provided to the plurality of fuel spray nozzles comprises a sustainable aviation fuel (SAF); and EImaxTO,FF is the system loss corrected nvPM emissions index in mg / kg of the gas turbine engine when operating at around 100% available thrust for the given operating conditions if a fuel provided to the plurality of fuel spray nozzles is a fossil-based hydrocarbon fuel. The MTO nvPM emissions index ratio of the gas turbine engine is less than 1. The gas turbine engine is configured to provide fuel comprising a SAF to the plurality of fuel spray nozzles. Also disclosed are methods of operating the gas turbine engine.

The invention discloses an intelligent aero-engine bearing fault migration diagnosis method based on multi-source data and an attention mechanism, and the method comprises the steps: automatically learning the importance difference of different modes in different fault states through an attention weight, and achieving the dynamic optimization combination of mode features; meanwhile, an attention mechanism is utilized to capture deep correlation characteristics among multi-modal data of the aero-engine bearing part, space-time coupling relations among heterogeneous signals such as vibration, temperature and current are excavated, and an attention weight is utilized to automatically suppress modal contribution polluted by noise, so that noise robustness is enhanced; according to the method, the weight difference under different modes and different fault states can be automatically learned, the deep correlation of the modes is captured, the noise pollution is inhibited, and the robustness of the model is enhanced.

A gas turbine engine for an aircraft. The gas turbine engine comprising: a combustor, comprising a combustion chamber and a plurality of fuel spray nozzles configured to inject fuel into the combustion chamber, wherein the plurality of fuel spray nozzles comprises a first subset of fuel spray nozzles and a second subset of fuel spray nozzles, wherein the combustor is operable in a condition in which each of the fuel spray nozzles of the first subset of fuel spray nozzles is supplied with fuel at a greater fuel flow rate than each of the fuel spray nozzles of the second subset of fuel spray nozzles, wherein a ratio of the number of fuel spray nozzles in the first subset of fuel spray nozzles to the number of fuel spray nozzles in the second subset of fuel spray nozzles is in the range of 1:2 to 1:5. An MTO nvPM emissions index ratio-modified fuel flow is defined as:EImaxTO,SAFEImaxTO,FF×Wf,maxTOwhere: EImaxTO,SAF is the system loss corrected nvPM emissions index in mg / kg of the gas turbine engine when operating at around 100% available thrust for given operating conditions if a fuel provided to the plurality of fuel spray nozzles comprises a sustainable aviation fuel (SAF); EImaxTO,FF is the system loss corrected nvPM emissions index in mg / kg of the gas turbine engine when operating at around 100% available thrust for the given operating conditions if a fuel provided to the plurality of fuel spray nozzles is a fossil-based hydrocarbon fuel; and Wf,maxTO is the mass flow rate of fuel provided to the plurality of fuel spray nozzles in kg / s when the gas turbine engine is operating at around 100% available thrust for the given operating conditions. The MTO nvPM emissions index ratio-modified fuel flow of the gas turbine engine in kg / s is less than 2. The gas turbine engine is configured to provide fuel comprising a SAF to the plurality of fuel spray nozzles. Also disclosed is a method of operating the gas turbine engine.

The invention provides a composite material performance prediction and process optimization method based on a neural network, and the method comprises the steps: firstly collecting multi-source data in the preparation and test process of a composite material, carrying out the preprocessing of the data, screening key feature variables as input variables, constructing a feedforward artificial neural network model, and predicting and outputting the performance indexes of the composite material. And training the model, performing iterative optimization on model parameters, and optimizing composite material process parameters by using the optimized model based on a reverse optimization strategy of a genetic algorithm to obtain an optimal process parameter combination. The invention provides a scientific, efficient and reliable tool for design and optimization of composite materials, and particularly has wide application prospects in high-requirement industries such as aerospace and the like.

An aircraft engine, has: a turbine; a scroll case having an inlet connected to a source of combustion gases and an outlet connected to the turbine, and a conduit extending from the inlet to the outlet; a bearing housing including a support flange; an exhaust case downstream of the turbine; and a turbine support case secured to the bearing housing and to the exhaust case, the turbine support case having spokes extending along a direction having an axial component, the spokes extending through the scroll case and radially supported by the bearing housing, a spoke having a distal end secured to the support flange via: one or more fasteners, and a retaining member at the distal end, the retaining member defining an abutment face facing an axial direction and circumferentially overlapping the support flange, a portion of the support flange located axially between the distal end and the abutment face.

A gas turbine engine for an aircraft. The gas turbine engine comprising: a rich burn, quick quench, lean burn (RQL) combustor having a number of fuel spray nozzles in the range of 14-22 or a number of fuel spray nozzles per unit engine core size in the range 2 to 6. An MTO nvPM emissions index ratio is defined as:EImaxTO,SAFEImaxTO,FFwhere: EImaxTO,SAF is the system loss corrected nvPM emissions index in mg / kg of the gas turbine engine when operating at around 100% available thrust for given operating conditions if a fuel provided to the fuel spray nozzles comprises a sustainable aviation fuel (SAF); and EImaxTO,FF is the system loss corrected nvPM emissions index in mg / kg of the gas turbine engine when operating at around 100% available thrust for the given operating conditions if a fuel provided to the fuel spray nozzles is a fossil-based hydrocarbon fuel. The MTO nvPM emissions index ratio of the gas turbine engine is less than 1. The gas turbine engine is configured to provide fuel comprising a SAF to the fuel spray nozzles. Also disclosed is a method of operating a gas turbine engine.

A gas turbine engine for an aircraft is disclosed. The gas turbine engine comprises: a combustor, comprising a combustion chamber and a plurality of fuel spray nozzles configured to inject fuel into the combustion chamber, wherein the plurality of fuel spray nozzles comprises a first subset of fuel spray nozzles and a second subset of fuel spray nozzles, wherein the combustor is operable in a condition in which each of the fuel spray nozzles of the first subset of fuel spray nozzles is supplied with fuel at a greater fuel flow rate than each of the fuel spray nozzles of the second subset of fuel spray nozzles, wherein a ratio of the number of fuel spray nozzles in the first subset of fuel spray nozzles to the number of fuel spray nozzles in the second subset of fuel spray nozzles is in the range of 1:3 to 1:6. A MTO nvPM emissions index ratio is defined as:EImaxTO,SAFEImaxTO,FFwhere: EImaxTO,SAF is the nvPM emissions index in mg / kg of the gas turbine engine when operating at around 100% available thrust for given operating conditions if a fuel provided to the plurality of fuel spray nozzles comprises a sustainable aviation fuel (SAF); and EImaxTO,FF is the nvPM emissions index in mg / kg of the gas turbine engine when operating at around 100% available thrust for the given operating conditions if a fuel provided to the plurality of fuel spray nozzles is a fossil-based hydrocarbon fuel. The MTO nvPM emissions index ratio of the gas turbine engine is less than 1. The gas turbine engine is configured to provide fuel comprising a SAF to the plurality of fuel spray nozzles. Also disclosed are methods of operating the gas turbine engine.

The invention belongs to the technical field of aero-engine state monitoring and intelligent fault diagnosis, and discloses a multi-modal information fusion bearing fault diagnosis method based on self-supervised learning. The method comprises the following steps: firstly, through mask reconstruction self-supervision pre-training, extracting stable feature representation insensitive to mask disturbance from an unlabeled multi-modal signal, and dynamically updating each modal feature reference point by using an index moving average algorithm; in a downstream fault diagnosis task, a multi-modal joint decision model comprising a pre-training encoder, a single-modal classifier and a fusion classifier is constructed, and adaptive weighted fusion of multi-modal decision is realized through contribution degree calculation based on a cooperative game Shapley value in combination with a deviation degree of modal features and a reference point. According to the method, the dependence of the deep neural network on fault labeling data is effectively reduced, the accuracy and robustness of the diagnosis system in a multi-modal signal diagnosis scene are improved through a dynamic fusion mechanism, and the method is suitable for industrial scenes with limited sample label resources.

The invention discloses a multi-field coupled microwave deicing intelligent system and an energy efficiency dynamic regulation and control method, and relates to the field of material surface ice layer desorption behavior research and deicing technology experimental testing. In order to solve the technical defects in the prior art that the existing microwave deicing technology still has obvious defects in the aspects of power adjustability, field distribution uniformity, interface depth temperature measurement, real-time mechanical test and the like, the technical scheme provided by the invention is as follows: the microwave deicing device comprises a microwave box body which is a metal closed cavity and is provided with a waveguide interface; a stirrer is arranged inside; the refrigerating system is connected with the back surface of the bearing plate of the microwave box body; a pressure head at the tail end of the mechanical loading module applies thrust to the ice layer; the device and the method are used for realizing construction, microwave heating and real-time mechanical testing of a low-temperature icing environment on the same platform. The method can be applied to dynamic monitoring and research work in multi-physics coupling deicing scenes such as aviation, traffic and new energy.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on sensing fusion and reinforcement learning, and belongs to the technical field of aviation flight control, and the method comprises the following steps: S1, sensor fusion time-space synchronization; s2, sensing fusion obstacle recognition and positioning; s3, obstacle avoidance route planning based on reinforcement learning; the low-resolution global map and a local occupation grid map with the unmanned aerial vehicle as the center are superposed to serve as multi-channel input, and the current state and the historical action sequence of the unmanned aerial vehicle are combined to be input into an intelligent agent model fused by a trained convolutional neural network and a long-short-term memory network; and an obstacle avoidance decision is output through a reward function of the agent model, and the airborne computer executes the obstacle avoidance decision to control the unmanned aerial vehicle to complete an obstacle avoidance action. Through sensor fusion, the obstacle sensing capability under different weather conditions and different flight environments is enhanced, the sensing distance is also increased, and the flight safety under extreme conditions can be ensured.

A thermal management system defines a thermal management system flowpath to provide a flow of a fluid to an electric machine and a power electronics assembly electrically connected to the electric machine. The thermal management system includes a first heat exchanger thermally connected to the thermal management system flowpath and to the electric machine, and a second heat exchanger thermally connected to the thermal management system flowpath downstream of the first heat exchanger. The second heat exchanger is thermally connected to the power electronics assembly.

The invention belongs to the technical field of flight simulator maintenance, particularly relates to a flight simulator predictive maintenance method based on machine learning, and solves the problems that existing maintenance depends on regular inspection and passive maintenance, fault early warning lags behind, and the false and missing report rate is high. The method comprises the following steps: acquiring historical operation data, sensor time sequence data, fault records and environmental parameters of a flight simulator, and carrying out cleaning, labeling and feature fusion preprocessing on the historical operation data, the sensor time sequence data, the fault records and the environmental parameters; constructing a composite health feature set containing statistical features, dynamic health state values and aerial material reliability parameters; a mixed prediction model (random forest feature screening + LSTM time sequence prediction + adaptive correction reliability evaluation) is adopted to train a model, prediction result fusion analysis and multistage decision rule post-processing are combined, and a maintenance work order and a spare part demand plan are output. According to the method, the accuracy and timeliness of fault prediction are improved, the maintenance conversion from passive response to active pre-judgment is realized, and the maintenance cost and the non-planned shutdown risk are greatly reduced.

The invention relates to the technical field of aero-engines, in particular to a sealing and cooling structure and method for adjusting the axial force of an aero-engine rotor, which comprises a turbine disc provided with a rear baffle, and further comprises a low-pressure turbine guide vane arranged opposite to the turbine disc, a low guide front inner supporting ring and a low guide rear inner supporting ring are arranged in the middle of the low-pressure turbine guide vane; labyrinth teeth correspondingly matched with the low-guide front inner supporting plate are formed on the rear baffle, radial labyrinth tooth gaps are formed, and when airflow in an air collection cavity enters a rotor cavity from a low-pressure pre-rotation nozzle, the airflow is embedded into an air supply channel from a front pre-rotation hole at the same time; one part of airflow entering the air supply channel enters the disc rear cavity, and the other part of airflow enters the rotor cavity. By adjusting the sealing labyrinth structure, it is ensured that the axial force of the rotor does not exceed the allowable maximum value in the large-state working process and is not subjected to light load in the small-state working process, meanwhile, sealing gas used behind a turbine disc is ensured, turbine hot gas is prevented from flowing into an inner disc cavity from a main channel, and it is ensured that an engine works reliably.

The invention relates to the technical field of hydrogen energy aviation, and discloses a hydrogen energy aviation fuel storage-supply system and method based on solid hydrogen storage, and the hydrogen energy aviation fuel storage-supply system and method based on solid hydrogen storage are characterized in that the hydrogen release amount adjusting range of a hydrogen storage material is matched with the hydrogen fuel demand adjusting range of a power device by adjusting the hydrogen release catalytic reaction environment temperature of the solid hydrogen storage material; by adopting the pressure stabilizing device, the problem of flow and pressure fluctuation caused by hysteresis of hydrogen release adjustment is solved, and hydrogen release flow adjustment and engine throttling control can be carried out through the thrust rod.