Application of the variational steepest- descent method to high performance aircraft trajectory optimization
Application of steepest descent method to trajectory optimization and aircraft performance problems
Hague, D. S.↗
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Turbulence modeling of free shear layers for high-performance aircraft
The High Performance Aircraft (HPA) Grand Challenge of the High Performance Computing and Communications (HPCC) program involves the computation of the flow over a high performance aircraft. A variety of free shear layers, including mixing layers over cavities, impinging jets, blown flaps, and exhaust plumes, may be encountered in such flowfields. Since these free shear layers are usually turbulent, appropriate turbulence models must be utilized in computations in order to accurately simulate these flow features. The HPCC program is relying heavily on parallel computers. A Navier-Stokes solver (POVERFLOW) utilizing the Baldwin-Lomax algebraic turbulence model was developed and tested on a 128-node Intel iPSC/860. Algebraic turbulence models run very fast, and give good results for many flowfields. For complex flowfields such as those mentioned above, however, they are often inadequate. It was therefore deemed that a two-equation turbulence model will be required for the HPA computations. The k-epsilon two-equation turbulence model was implemented on the Intel iPSC/860. Both the Chien low-Reynolds-number model and a generalized wall-function formulation were included.
Sondak, Douglas L.↗
Optimization of aircraft performance and mission completion through research on the pilot and aircraft as an overall system.
Optimization of aircraft performance, pilot performance and mission completion
AIRCRAFT PERFORMANCE↗
Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction
The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.
Aida Sharif Rohani↗
Overview of high performance aircraft propulsion research
The overall scope of the NASA Lewis High Performance Aircraft Propulsion Research Program is presented. High performance fighter aircraft of interest include supersonic flights with such capabilities as short take off and vertical landing (STOVL) and/or high maneuverability. The NASA Lewis effort involving STOVL propulsion systems is focused primarily on component-level experimental and analytical research. The high-maneuverability portion of this effort, called the High Alpha Technology Program (HATP), is part of a cooperative program among NASA's Lewis, Langley, Ames, and Dryden facilities. The overall objective of the NASA Inlet Experiments portion of the HATP, which NASA Lewis leads, is to develop and enhance inlet technology that will ensure high performance and stability of the propulsion system during aircraft maneuvers at high angles of attack. To accomplish this objective, both wind-tunnel and flight experiments are used to obtain steady-state and dynamic data, and computational fluid dynamics (CFD) codes are used for analyses. This overview of the High Performance Aircraft Propulsion Research Program includes a sampling of the results obtained thus far and plans for the future.
Biesiadny, Thomas J.↗
Overview of high performance aircraft propulsion research
The overall scope of the NASA Lewis High Performance Aircraft Propulsion Research Program is presented. High performance fighter aircraft of interest include supersonic flights with such capabilities as short take off and vertical landing (STOVL) and/or high maneuverability. The NASA Lewis effort involving STOVL propulsion systems is focused primarily on component-level experimental and analytical research. The high-maneuverability portion of this effort, called the High Alpha Technology Program (HATP), is part of a cooperative program among NASA's Lewis, Langley, Ames, and Dryden facilities. The overall objective of the NASA Inlet Experiments portion of the HATP, which NASA Lewis leads, is to develop and enhance inlet technology that will ensure high performance and stability of the propulsion system during aircraft maneuvers at high angles of attack. To accomplish this objective, both wind-tunnel and flight experiments are used to obtain steady-state and dynamic data, and computational fluid dynamics (CFD) codes are used for analyses. This overview of the High Performance Aircraft Propulsion Research Program includes a sampling of the results obtained thus far and plans for the future.
Biesiadny, Thomas J.↗
An ODE-Fitting Approach to Estimate Critical Aircraft Performance Parameters for Trajectory Prediction
Ground-based decision support tools (DST) in air traffic management (ATM) typically perform trajectory prediction based on aircraft performance model (APM) parameters, but some or all of these parameters might not be readily available. In particular, the three critical parameters required for trajectory prediction are the thrust setting, drag coefficients, and takeoff weight of the aircraft. Unfortunately, these parameters are coupled and appear together in physics-based kinetic models. Past approaches utilize data from a specific phase of flight (climb, level flight or descent), where one or more of the parameters are assumed to be known and estimate the remaining unknown parameters. This approach introduces bias/errors and also does not extend to scenarios where all of the above parameters are not known with sufficient accuracy. This paper is the first of its kind to propose a generalized framework for simultaneous estimation of all three critical APM parameters (thrust, drag, and mass). The proposed approach utilizes data from both the climb and descent phases and fits the ordinary differential equation for altitude in each phase using historical trajectory data available from radar tracks or ADS-B. The approach yields a set of optimized APM parameters that are best suited to fit each historical flight record. The methodology is applied on on sample flights from three different aircraft types, and the results demonstrate low fit error and consequently will yield a high level of prediction accuracy.
trajectory prediction, aircraft performance model,↗
An ODE-Fitting Approach to Estimate Critical Aircraft Performance Parameters for Trajectory Prediction
Ground-based decision support tools (DST) in air traffic management (ATM) typically perform trajectory prediction based on aircraft performance model (APM) parameters, but some or all of these parameters might not be readily available. In particular, the three critical parameters required for trajectory prediction are the thrust setting, drag coefficients, and takeoff weight of the aircraft. Unfortunately, these parameters are coupled and appear together in physics-based kinetic models. Past approaches utilize data from a specific phase of flight (climb, level flight or descent), where one or more of the parameters are assumed to be known and estimate the remaining unknown parameters. This approach introduces bias/errors and also does not extend to scenarios where all of the above parameters are not known with sufficient accuracy. This paper is the first of its kind to propose a generalized framework for simultaneous estimation of all three critical APM parameters (thrust, drag, and mass). The proposed approach utilizes data from both the climb and descent phases and fits the ordinary differential equation for altitude in each phase using historical trajectory data available from radar tracks or ADS-B. The approach yields a set of optimized APM parameters that are best suited to fit each historical flight record. The methodology is applied on on sample flights from three different aircraft types, and the results demonstrate low fit error and consequently will yield a high level of prediction accuracy.
trajectory prediction↗
Aircraft performance on slippery runways in crosswinds
Aircraft performance on slippery runways in cross winds
AIRCRAFT CONTROL↗
Optimisation of aircraft performance and mission completion through research on the pilot and aircraft as an overall system.
Optimization of aircraft performance, pilot performance and mission completion
Beeler, De E.↗
Loiter performance of turbojet-powered aircraft performance determined by off-design cycle analysis
Loitering performance of airplanes equipped with various turbojet engines was analytically investigated. The engines used in this analysis were selected to show the effects of various engine design variables. Engine performance was determined by means of an off-design cycle analysismaking use of experimentally determined component characteristics. The method of off-design cycle analysis is presented herein and is verified by a check with experimental data.
Koutz, Stanley L↗
Human and behavioral factors contributing to spine-based neurological cockpit injuries in pilots of high-performance aircraft: recommendations for management and prevention
In high-performance aircraft, the need for total environmental awareness coupled with high-g loading (often with abrupt onset) creates a predilection for cervical spine injury while the pilot is performing routine movements within the cockpit. In this study, the prevalence and severity of cervical spine injury are assessed via a modified cross-sectional survey of pilots of multiple aircraft types (T-38 and F-14, F-16, and F/A-18 fighters). Ninety-five surveys were administered, with 58 full responses. Fifty percent of all pilots reported in-flight or immediate post-flight spine-based pain, and 90% of fighter pilots reported at least one event, most commonly (> 90%) occurring during high-g (> 5 g) turns of the aircraft with the head deviated from the anatomical neutral position. Pre-flight stretching was not associated with a statistically significant reduction in neck pain episodes in this evaluation, whereas a regular weight training program in the F/A-18 group approached a significant reduction (mean = 2.492; p < 0.064). Different cockpit ergonomics may vary the predisposition to cervical injury from airframe to airframe. Several strategies for prevention are possible from both an aircraft design and a preventive medicine standpoint. Countermeasure strategies against spine injury in pilots of high-performance aircraft require additional research, so that future aircraft will not be limited by the human in control.
Case Reports↗
Aircraft performance and control in downburst wind shear
The methods developed for analyses of the winds and of aircraft performance during an investigation of a downburst wind-shear-induced accident have been utilized in a more general study of aircraft performance in such encounters. The computed responses of a generic, large transport aircraft to take-off and approach encounters with a downburst wind field were used in examining the effects of performance factors and control procedures on the ability of the aircraft to survive. Obvious benefits are seen for higher initial encounter speeds, maximum thrust-weight values typical of two-engined aircraft, and immediacy of pilot response. The results of controlling to a constant, predetermined, pitch attitude are demonstrated. Control algorithms that sacrifice altitude for speed appear to provide a higher level of survivability, but guidance displays more explicitly defining flightpath than those commonly in use might be required.
Bray, Richard S.↗
Advances in Experiment Design for High Performance Aircraft
A general overview and summary of recent advances in experiment design for high performance aircraft is presented, along with results from flight tests. General theoretical background is included, with some discussion of various approaches to maneuver design. Flight test examples from the F-18 High Alpha Research Vehicle (HARV) are used to illustrate applications of the theory. Input forms are compared using Cramer-Rao bounds for the standard errors of estimated model parameters. Directions for future research in experiment design for high performance aircraft are identified.
Morelli, Engene A.↗
Advances in Experiment Design for High Performance Aircraft
A general overview and summary of recent advances in experiment design for high performance aircraft is presented, along with results from flight tests. General theoretical background is included, with some discussion of various approaches to maneuver design. Flight test examples from the F-18 High Alpha Research Vehicle (HARV) are used to illustrate applications of the theory. Input forms are compared using Cramer-Rao bounds for the standard errors of estimated model parameters. Directions for future research in experiment design for high performance aircraft are identified.
Morelli, Eugene A.↗
Aircraft Analysis Using the Layered and Extensible Aircraft Performance System (LEAPS)
The Layered and Extensible Aircraft Performance System (LEAPS) is a new air- craft analysis tool being developed by members of the Aeronautics Systems Analysis Branch (ASAB) and the Vehicle Analysis Branch (VAB) at NASA Langley Research Center. LEAPS will enable the analysis of advanced aircraft concepts and architec- tures that include electric and hybrid-electric propulsion systems. The development of LEAPS is motivated by the analysis gaps found in traditional aircraft analysis tools such as the Flight Optimization System (FLOPS). FLOPS has been the tool of choice of the ASAB for over 30 years and has proven to be a reliable analysis tool for conventional aircraft. However, FLOPS is not suitable to analyze the cur- rent unconventional vehicles that are of interest to industry, government agencies, and academia. In contrast, LEAPS is being developed with a flexible architecture that leverages new analysis methodologies that will enable the analysis of unconven- tional aircraft. This paper presents the first complete working version of LEAPS by showing the analysi at include fuel-based and hybrid-electric conceptual aircraft.
Capristan, Francisco M.↗
Machine Learning Approach for Aircraft Performance Model Parameter Estimation for Trajectory Prediction Applications
Inaccurate prediction of aircraft trajectory by ground-based decision support tools (DST) is a major concern in air traffic management (ATM). Aircraft trajectory prediction tools rely on a simplified point-mass aircraft performance model (APM) to make their predictions. Even though the performance coefficients and weight of an aircraft are a vital part of the APM’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to DSTs. Current ATM research focuses on improving the estimate of some APM parameters by freezing all other coefficients. This simplified approach introduces unwanted sources of bias and negatively impacts the accuracy of the performance model. In this paper, we apply machine learning (ML) techniques for the simultaneous prediction of three key APM parameters (two drag coefficients and the initial aircraft weight). To accomplish this, we employ an ordinary differential equation (ODE) fitting approach to generate optimized APM parameter labels customized to each individual flight record. Subsequently, we train ML models to capture the relationship between the historical data and the optimized APM parameters. Two different ML model solutions are applied and APM coefficients are predicted for unseen flights. The results indicate that the ML models are able to capture the relationship between APM parameters and flight-related features with good accuracy.
trajectory prediction, machine learning, aircraft ↗
Numerical Stability and Control Analysis Towards Falling-Leaf Prediction Capabilities of Splitflow for Two Generic High-Performance Aircraft Models
Aerodynamic analysis are performed using the Lockheed-Martin Tactical Aircraft Systems (LMTAS) Splitflow computational fluid dynamics code to investigate the computational prediction capabilities for vortex-dominated flow fields of two different tailless aircraft models at large angles of attack and sideslip. These computations are performed with the goal of providing useful stability and control data to designers of high performance aircraft. Appropriate metrics for accuracy, time, and ease of use are determined in consultations with both the LMTAS Advanced Design and Stability and Control groups. Results are obtained and compared to wind-tunnel data for all six components of forces and moments. Moment data is combined to form a "falling leaf" stability analysis. Finally, a handful of viscous simulations were also performed to further investigate nonlinearities and possible viscous effects in the differences between the accumulated inviscid computational and experimental data.
Charlton, Eric F.↗