15159 results about "Flight vehicle" patented technology

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

The invention belongs to the technical field of three-dimensional airspace management, and provides a three-dimensional airspace rasterizing management method and system based on multistage dynamic division, and the method comprises the steps: dividing height layers for a three-dimensional airspace, carrying out the rasterizing division of the three-dimensional airspace in each height layer, and carrying out the rasterizing division of the three-dimensional airspace; a live-action three-dimensional model of a three-dimensional airspace of a target area is constructed through multi-source data fusion, passable grids are marked, selection priority values of the passable grids are calculated through multi-source data fusion, when an aircraft is about to enter the three-dimensional airspace, route dynamic priority values of the grids are initialized, and a basic planning route of the aircraft is generated. Detecting the collision risk of the aircraft on the grids in the basic planning route, dynamically adjusting the route dynamic priority value, updating to obtain the actual planning route of the aircraft, monitoring the space between the aircrafts in the three-dimensional airspace in real time, and judging whether the collision risk exists between the aircrafts; and if there is a collision risk signal, triggering an aircraft obstacle avoidance and path re-planning strategy, and dynamically adjusting and updating the grid side length.

The invention discloses an aircraft flow field prediction method and system of a multi-region physical drive neural network, and the method comprises the steps: constructing a continuous region mask and high-dimensional physical parameter sampling system, carrying out the global sampling of high-dimensional physical parameters through employing a Latin hypercube sampling method, and carrying out the space division through combining with a KMeans clustering algorithm; inputting the space coordinates, the continuous area mask, the wall surface distance and the physical condition parameters into an AMPD model, and generating a boundary layer mask, an eddy current mask and a physical residual error; inputting the boundary layer mask and the eddy current mask into a physical constraint driven loss function system, and establishing a multi-target residual minimization loss function for training an AMPD model; based on the multi-target residual error minimization loss function and the physical residual error, training an AMPD model by adopting a course learning training strategy; wing surface flow field reconstruction is carried out through the trained AMPD model, aircraft flow field prediction is completed, and high-precision and high-efficiency intelligent prediction of wing streaming is achieved.

The invention relates to the technical field of adaptive control, in particular to an unmanned aerial vehicle adaptive flight control system and method based on multi-modal fusion, and the system comprises an airspace analysis module, a signal screening control module, a state recognition module, a parameter matching module and a feedback adjustment module. According to the method, through a multi-source task data analysis and trend signal screening mechanism, the accuracy and real-time performance of path planning and adjustment are improved, through combination of channel fluctuation trend identification and abnormal state locking, the stability of sensing data input is enhanced, and the identification accuracy of an unbalance state is analyzed and optimized by using attitude change direction consistency. The attitude error trend and control parameter segment similarity comparison mode is adopted, parameter matching has dynamic responsiveness, control weight configuration is adjusted through collaborative analysis of the path direction component and the attitude adjustment trend, rhythm self-adaptive adjustment of aircraft control output is achieved, and the control precision of the aircraft is improved. And the dynamic adjustment capability and the safety redundancy response capability of flight control are enhanced.

The invention relates to the technical field of low-altitude aircraft navigation, and discloses a low-altitude aircraft track real-time planning method and system. The system comprises a flight situation awareness module, a track constraint calculation module, a real-time track planning module, a conflict prediction module and a track dynamic correction module. The flight situation sensing module generates a flight situation matrix through multi-source data fusion; a track constraint calculation module extracts static obstacle contours and dynamic obstacle tracks according to the static obstacle contours and the dynamic obstacle tracks, and generates a multi-dimensional track constraint set in combination with aircraft performance parameters; the real-time flight path planning module builds a three-dimensional flight path search domain by using an adaptive space division technology, and iteratively solves an optimal flight path sequence by using an intelligent search algorithm; the conflict prediction module combines the real-time dynamic obstacle trajectory to calculate the space-time proximity, and generates a conflict early warning map; and the track dynamic correction module re-draws an obstacle avoidance constraint area according to the map, and triggers local track correction. The system improves the comprehensiveness, real-time performance and safety of flight path planning, and guarantees the stable operation of the low-altitude aircraft.

The invention discloses an aircraft surface flow field intelligent reconstruction method and system based on multi-source data fusion, and belongs to the technical field of flow field intelligent prediction. Acquiring multi-source sensor data on the surface of the aircraft, and constructing a multi-source data set; a flow field reconstruction model based on Transform is constructed; a multi-source data set is adopted to train a flow field reconstruction model; and outputting an aircraft surface flow field reconstruction result based on the trained flow field reconstruction model, and visually displaying the aircraft surface flow field reconstruction result. Based on the strong nonlinear mapping capability of the deep learning model, through training of a large amount of sensor data, high-order correlation characteristics among pressure, heat flow and friction resistance stress can be automatically extracted, through fusion of sparse discrete data, a continuous physical field of the whole surface of the aircraft can be effectively reconstructed, and the method is suitable for the aircraft. The limitation of a traditional analytical model in dealing with a strong nonlinear problem is broken through, and the precision and reliability of aircraft flow field prediction are improved.

A docking system is disclosed for an unmanned aerial vehicle (UAV). The docking system includes a base station that is configured to receive the UAV and a pedestal that is configured to support the base station in an elevated position. The pedestal defines an interior space that is configured in correspondence with an outer contour of the base station such that the base station is positionable within the pedestal to protect the base station during nonuse.

A base station may include a base body, a landing pad, and a base station opening and closing structure. The base station opening and closing structure may include an open state and a closed state. In the open state, the base station opening and closing structure may provide a landing space for an aerial vehicle to land on the landing pad, and in the closed state, the base station opening and closing structure may provide an accommodating space to accommodate the aerial vehicle. During a process of the base station opening and closing structure transitioning from the open state to the closed state, the base station opening and closing structure may be configured to propel some blades of the aerial vehicle partially protruding out of the accommodating space to fold or retract so as to accommodate the aerial vehicle within the accommodating space.

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 discloses a multi-channel meteorological risk early warning information self-adaptive targeted publishing system and method based on low-altitude flight, and solves the problems that existing low-altitude meteorological early warning is low in temporal-spatial resolution, single in risk identification and insufficient in information pertinence. The system integrates air-based, space-based, foundation, social and topographic data, and generates a low-altitude meteorological dynamic database through fusion; micro-scale risks such as wind shear are extracted and graded through an AI model, a digital twin simulation response is constructed in combination with the state of the aircraft, and a risk grid model and coordinates are output; calculating a comprehensive risk index, generating evasion guidance, converting into multi-role early warning information, generating a three-dimensional thermodynamic diagram, and finally adaptively selecting a publishing channel according to user roles, terminals and network states. According to the invention, the accuracy and timeliness of low-altitude flight meteorological risk early warning are improved, and the safety of low-altitude flight is guaranteed.

The invention relates to an aircraft surface damage area segmentation method based on adaptive multi-modal fusion, belongs to the technical field of nondestructive testing, and solves the technical problem of low segmentation precision of an aircraft skin curved surface falling damage area. Obtaining a grey-scale map, a depth map and a point cloud map of the aircraft surface damage skin; synthesizing the grey-scale map, the depth map and the point cloud map, and performing preprocessing, feature extraction, feature conversion, image display and projection and weighted fusion on the synthesized grey-scale map, the synthesized depth map and the synthesized point cloud map to obtain a total edge probability map of skin damage; and performing targeted screening, continuity detection, connectivity detection, convex hull construction, marking and skin falling damage area division on the total edge probability graph. The method is used for aircraft skin health monitoring, maintenance guarantee and operation and maintenance.

The invention relates to an autonomous energy-saving soaring flight path planning method for a small-sized low-cost aircraft, belongs to the technical field of aircraft trajectory planning, solves the problem of low-cost wind field energy acquisition of the small-sized low-cost aircraft in the prior art, and comprises the following steps: S1, configuring a sensor for the aircraft, and measuring through the sensor to obtain observation parameters; s2, establishing a state vector of the aircraft; s3, establishing an aerodynamic force model, introducing a dynamic equation and a state transition equation, and performing accurate modeling on aerodynamic force; s4, performing multi-source data fusion by adopting extended Kalman filtering, establishing an extended Kalman filter of a nonlinear system, and executing real-time wind vector high-precision sensing; and S5, performing global wind field modeling, estimating a wind field environment, and performing energy-obtaining flight path planning to obtain an optimal energy-obtaining soaring flight path planning scheme.

The invention relates to the technical field of risk prediction, in particular to a collision risk prediction method of a low-altitude aircraft in high-density flight in a complex environment, which comprises the following steps: acquiring a point cloud through a three-dimensional laser radar, clustering to generate an obstacle trajectory, matching the trajectory to identify a disturbance characteristic segment, calculating a deviation angle by combining a path vector to generate a disturbance frequency graph, and calculating the collision risk of the low-altitude aircraft. And extracting a parameter modeling dynamic safety interval, and fusing multiple factors to evaluate a collision risk level. According to the method, obstacle trajectory topology is constructed through combination of three-dimensional laser radar point cloud time window division and density clustering, sudden change features are identified through trajectory similarity matching, a Gaussian kernel dynamic safety envelope is generated through combination of course offset statistics and included angle operation, and a self-matching threshold value is established through normalization parameters and radial basis weighting. The method improves the high-density flight collision prediction precision, enhances the weather and obstacle heterogeneity matching capability, reduces the misjudgment early warning delay, and achieves the multi-variable flight situation collaborative analysis.

The invention discloses an aircraft cabin environment personalized adjustment method based on sentiment analysis, and the method comprises the steps: collecting the facial expression, voice waveform and environment parameters of a passenger in real time through a cabin multi-source sensor, and generating a standardized physiological signal matrix and anonymized voice features through noise reduction and feature extraction; inputting the physiological signal matrix and the voice features into a pre-trained deep learning model, outputting an emotion index and a classification label, and updating model parameters through a federal learning framework; dynamically generating temperature, humidity and oxygen concentration adjusting instructions and dynamic weights by adopting a fuzzy reasoning system in combination with the emotion indexes and passenger preset preferences; environment adjustment is executed through a closed-loop control system, and environment parameter errors are fed back; synchronously updating a fuzzy inference system rule base and deep learning model parameters by utilizing reinforcement learning in combination with environmental parameter errors and emotion index changes, and completing optimization of a closed loop; according to the invention, real-time dynamic regulation and control and continuous adaptation optimization of the personalized cabin environment can be realized.

The invention relates to a low-altitude flight dynamic safety guarantee method and system based on airspace gridding, and relates to the technical field of low-altitude airspace management, and the method comprises the steps: constructing a gridding airspace; on the basis of the gridding airspace, labeling network attributes for each grid unit in real time and evaluating a low-altitude safety state; according to network attributes, extracting a grid center point of an airspace grid which is evaluated to be in a low-risk range as a safe flight point, and dividing the gridding airspace into a plurality of safe triangular grids; and according to the predicted trajectory of the aircraft, selecting a candidate point with the lowest risk cost from adjacent triangles of the safety triangular mesh, and introducing a risk cost function to dynamically adjust the distribution of flight path points. According to the invention, the flight path of the aircraft group can be dynamically adjusted according to the environment change, and the stability and safety of cluster task execution are improved.

The invention discloses a curved surface quadrilateral dominant grid generation method, device and equipment for an aircraft and a storage medium, and relates to the technical field of computational fluid mechanics, and the method comprises the steps: creating a surface unstructured grid based on surface grid generation parameters of a CAD file of the aircraft, carrying out the conformal mapping of the surface unstructured grid to a parameter domain plane, and calculating the vertex compression ratio of each grid vertex; selecting the current starting point with the minimum compression ratio and the maximum to-be-compared vertex in the adjacency list, and determining the current to-be-processed vertex with the minimum compression ratio in the adjacent vertexes and the advancing direction; determining to-be-processed discrete points according to the current compression ratio and the coordinate information to generate a current isoline, if the current compression ratio does not exceed a threshold value, iteratively updating the current starting point, and skipping to the step of determining the current to-be-processed vertex again; the discrete points are projected to the outer-layer isolines according to the isoline sequence, and the curved-surface quadrilateral dominant grid is generated based on the distance proportion between each discrete point and the projection point, so that the generation efficiency of the curved-surface quadrilateral grid is improved.

The invention provides a vortex aircraft detection signal processing method and system based on optical fiber sensing. The method comprises the following steps: firstly, acquiring an original micro-vibration signal; secondly, performing environment self-adaptive cooperative processing on the original micro-vibration signal to generate a self-adaptive micro-vibration signal; converting the generated self-adaptive micro-vibration signal into a spatio-temporal evolution sequence based on eddy current physical characteristics; then analyzing the spatio-temporal evolution sequence to output a type discrimination result of the vortex aircraft; and finally, generating a customized detection report based on the type discrimination result. According to the technical scheme provided by the invention, the detection and identification capability of a low-altitude low-speed small target in a complex environment is improved, and reliable detection and identification of the vortex aircraft in the complex environment are also realized.

The invention discloses an unmanned aerial vehicle wind resistance control method and system based on wind field sensing, and relates to the technical field of aircraft wind resistance control and wind field sensing. The method comprises the following steps: acquiring local wind field information around an aircraft in real time through wind speed and direction sensors arranged in a distributed manner, and performing fusion processing to form three-dimensional wind field distribution; generating a feedforward control quantity based on the wind field disturbance identification and short-term prediction model; feedforward control and a feedback control law of a flight control system are fused to form a composite control strategy, and flight attitude stabilization and control response optimization of the unmanned aerial vehicle under complex wind disturbance are realized. Correspondingly, the system comprises a flow field sensing and data processing module, a disturbance identification and feedforward control module and a flight control execution module, and has the functions of wind field real-time sensing, disturbance prediction, control preposition deployment and the like. According to the invention, the wind resistance and autonomous flight safety of the unmanned aerial vehicle in a low-altitude complex wind field environment can be remarkably improved.

The invention belongs to the field of flight path prediction, and discloses a flight path stability optimization method and system, a storage medium and equipment, and the method comprises the steps: responding to aircraft kinetic parameters, pilot physiological state parameters and three-dimensional space environment parameters which are synchronously collected from multiple sources, and constructing a dynamic fusion data set through timestamp alignment; based on a pre-constructed dual-channel feature extraction architecture, dual-channel features of the dynamic fusion data set are extracted, a first channel analyzes a dynamic coupling relationship in a time dimension through a sparse attention mechanism, and a second channel models a spatial correlation topology of environmental parameters through a graph neural network; a deep reinforcement learning decision engine is constructed by fusing dual-channel features, an environment state-control action-flight path stability mapping strategy is established, and control strategy optimization is driven through a reward mechanism; and generating a multi-parameter collaborative optimization instruction of the flight control plane based on the real-time decision engine. According to the method, the efficiency and precision during flight path prediction can be improved, and then the flight path is optimized.

The invention discloses an unmanned aerial vehicle collision risk early warning method and system based on a dynamic space grid, and relates to the technical field of aircraft control, and the method comprises the steps: obtaining the historical operation data of a target region based on a preset space-time collection constraint, and extracting a space and unmanned aerial vehicle reference index set; constructing a dynamic space grid comprising an analysis grid and a shared grid; taking the shared grid as a data acquisition constraint, traversing the analysis grid to preliminarily screen the analysis object, and obtaining an associated object set containing the subject machine and the object machine; and performing collision assessment based on a preset discrimination condition, and outputting a collision risk early warning result. Therefore, the technical effects of improving early warning accuracy and early warning efficiency and facilitating efficient parallel early warning are achieved.

The invention relates to flight path planning of aircrafts, in particular to a collaborative flight path planning method and system for a low-altitude multi-aircraft mixed take-off and landing field, and the method comprises the steps: carrying out the three-dimensional subdivision of an airspace based on a Beidou grid code, and carrying out the real-time mapping of an airspace state of a three-dimensional grid unit; dividing the three-dimensional grid units according to the grid state; constructing a geometric track generation rule, and eliminating a track crossing risk; introducing a time dimension on the basis of a three-dimensional space, carrying out four-dimensional space-time conflict detection on all tracks, and carrying out conflict resolution according to a four-dimensional space-time conflict detection result; a double-queue dynamic scheduling mechanism is constructed, and the aircraft is guided to land safely through queue separation and polling distribution; according to the technical scheme provided by the invention, the defects that the avoidance path of the low-priority aircraft is difficult to reasonably plan and the flight safety of the aircraft in the take-off and landing stage cannot be effectively guaranteed in the prior art can be effectively overcome.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

A control system for controlling a second unmanned aerial vehicle that flies while holding a first cable connected to a first unmanned aerial vehicle that performs work and a second cable extending from a cable reeling machine, includes a sensor to sense a surrounding environment and output sensor data, and a controller configured or programmed to control operation of the unmanned aerial vehicle and, during flight of the second unmanned aerial vehicle, detect the first cable and the second cable based on the sensor data, and upon predicting that at least one of the first cable and the second cable will contact the ground or an obstacle on the ground, change a trajectory of the second unmanned aerial vehicle to avoid the contact.

The invention discloses a multi-mode communication module oriented to deep integration of a low-altitude aircraft and a ground network and a networking and dynamic switching method thereof, relates to the technical field of low-altitude aircraft communication, and discloses a networking and dynamic switching method of the multi-mode communication module oriented to deep integration of the low-altitude aircraft and the ground network and the multi-mode communication module. Through dynamic threshold calculation, a pre-switching prediction model and an FPGA hard trigger mechanism, intelligent communication resource allocation based on the real-time state and environment change of the aircraft is realized, and dual-mode switching is quickly triggered when the link quality is reduced or a blind area is predicted to be entered. The problems that a traditional communication mode is poor in stability and low in switching efficiency in a complex low-altitude environment are effectively solved, the communication reliability can be improved, and the data transmission continuity can be guaranteed.

The invention discloses an aircraft intelligent trajectory planning and cooperative control method based on offline optimization-intelligent learning-online guidance, and the method comprises the steps: firstly building an aircraft motion model and an aircraft-target relative motion model, and constructing a full-airspace and full-feature-point optimal trajectory data set; secondly, parameterized representation is carried out on the three-dimensional optimal trajectory, and a proportional guidance coefficient data set is constructed; then neural network training fitting is carried out on the proportional guidance coefficient data set, and a multi-constraint guidance method based on a neural network is designed; and finally, designing a three-dimensional collaborative guidance law by combining a multi-constraint guidance method and a residual time accurate estimation technology. According to the invention, the problems of insufficient real-time performance, incapability of meeting index optimality, incapability of coping with multiple constraints, difficulty in realizing multiple flight modes and the like of the existing trajectory planning and collaborative guidance technology are solved; the multi-constraint and optimality of the trajectory are realized through offline optimization, the global adaptability and small calculation amount of the trajectory are realized through intelligent learning, and a multi-cooperative flight mode is realized through online cooperative guidance.

The invention relates to the technical field of building models, in particular to a building model processing method and system and a medium. The method comprises the following steps: acquiring initial image data of a high-rise structure through an aircraft, and synchronously recording motion state data; calculating a jitter characteristic parameter according to the initial image data and the motion state data; establishing a jitter compensation mechanism of the long-distance image based on the jitter characteristic parameters; processing the initial image data by using a jitter compensation mechanism to obtain stable image data; extracting structural feature point information in the stable image data; establishing a high-rise structure reference coordinate system according to the structural feature point information; and performing high-precision registration on pre-acquired multi-angle image data of the high-rise structure and the reference coordinate system of the high-rise structure to obtain registered image data. According to the invention, by analyzing the jitter characteristics of different frequency bands, a multi-level jitter compensation mechanism is established, and the stability problem during long-distance image acquisition is effectively solved.

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.

The invention discloses an intelligent obstacle avoidance system and method for a low-altitude aircraft. Efficient obstacle avoidance is realized by fusing an improved attention mechanism and a binocular vision sensing multi-dimensional analysis model. In the system, a binocular vision perception unit collects double-path images and establishes motion association, a feature enhancement attention unit performs dynamic weighting according to image features, and a multi-dimensional analysis processing unit constructs a three-dimensional feature space to accurately recognize obstacles. The path planning decision-making unit combines the aircraft state and a safety threshold value, an optimal obstacle avoidance path is screened through heuristic search and cost evaluation, and the motion control instruction generation unit converts the path into a posture and speed control instruction to drive obstacle avoidance. According to the invention, the defects of the traditional technology in obstacle identification and path planning are overcome, dynamic or hidden obstacles can be accurately identified in a complex environment, the obstacle avoidance path is efficiently planned, and the operation safety and reliability of the low-altitude aircraft are improved.

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.