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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
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NASA NTRS1977

Description of a Computer Program Written for Approach and Landing Test Post Flight Data Extraction of Proximity Separation Aerodynamic Coefficients and Aerodynamic Data Base Verification

A computer program written to calculate the proximity aerodynamic force and moment coefficients of the Orbiter/Shuttle Carrier Aircraft (SCA) vehicles based on flight instrumentation is described. The ground reduced aerodynamic coefficients and instrumentation errors (GRACIE) program was developed as a tool to aid in flight test verification of the Orbiter/SCA separation aerodynamic data base. The program calculates the force and moment coefficients of each vehicle in proximity to the other, using the load measurement system data, flight instrumentation data and the vehicle mass properties. The uncertainty in each coefficient is determined, based on the quoted instrumentation accuracies. A subroutine manipulates the Orbiter/747 Carrier Separation Aerodynamic Data Book to calculate a comparable set of predicted coefficients for comparison to the calculated flight test data.

Homan, D. J.↗
NASA NTRS2003

Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.

Korsmeyer, David↗
NASA NTRS2002

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

Rajkumar, T.↗
NASA NTRS1992

Identification of aerodynamic coefficients using computational neural networks

Precise, smooth aerodynamic models are required for implementing adaptive, nonlinear control strategies. Accurate representations of aerodynamic coefficients can be generated for the complete flight envelope by combining computational neural network models with an Estimation-Before-Modeling paradigm for on-line training information. A novel method of incorporating first-partial-derivative information is employed to estimate the weights in individual feedforward neural networks for each aerodynamic coefficient. The method is demonstrated by generating a model of the normal force coefficient of a twin-jet transport aircraft from simulated flight data, and promising results are obtained.

Linse, Dennis J.↗
NASA NTRS2002

Prediction of Aerodynamic Coefficients using Neural Networks for Sparse Data

Basic aerodynamic coefficients are modeled as functions of angles of attack and sideslip with vehicle lateral symmetry and compressibility effects. Most of the aerodynamic parameters can be well-fitted using polynomial functions. In this paper a fast, reliable way of predicting aerodynamic coefficients is produced using a neural network. The training data for the neural network is derived from wind tunnel test and numerical simulations. The coefficients of lift, drag, pitching moment are expressed as a function of alpha (angle of attack) and Mach number. The results produced from preliminary neural network analysis are very good.

Rajkumar, T.↗
NASA NTRS2016

Incremental Aerodynamic Coefficient Database for the USA2

In March through May of 2016, a wind tunnel test was conducted by the Aerosciences Branch (EV33) to visually study the unsteady aerodynamic behavior over multiple transition geometries for the Universal Stage Adapter 2 (USA2) in the MSFC Aerodynamic Research Facility's Trisonic Wind Tunnel (TWT). The purpose of the test was to make a qualitative comparison of the transonic flow field in order to provide a recommended minimum transition radius for manufacturing. Additionally, 6 Degree of Freedom force and moment data for each configuration tested was acquired in order to determine the geometric effects on the longitudinal aerodynamic coefficients (Normal Force, Axial Force, and Pitching Moment). In order to make a quantitative comparison of the aerodynamic effects of the USA2 transition geometry, the aerodynamic coefficient data collected during the test was parsed and incorporated into a database for each USA2 configuration tested. An incremental aerodynamic coefficient database was then developed using the generated databases for each USA2 geometry as a function of Mach number and angle of attack. The final USA2 coefficient increments will be applied to the aerodynamic coefficients of the baseline geometry to adjust the Space Launch System (SLS) integrated launch vehicle force and moment database based on the transition geometry of the USA2.

Richardson, Annie Catherine↗
NASA NTRS2017

Estimates for the Aerodynamic Coefficients of Ringsail and Disk-Gap-Band Parachutes Operating on Mars

Models are presented for the aerodynamic coefficients of Supersonic Ringsail and Disk-Gap-Band parachutes as functions of total porosity, Lambda(sub t), Mach number, M, and total angle of attack, Alpha(sub t) (when necessary). The source aerodynamic coefficients data used for creating these models were obtained from a wind tunnel test of subscale parachutes. In this wind tunnel test, subscale parachutes of both parachute types were fabricated from two different fabrics with very different permeabilities. By varying the fabric permeability, while maintaining the parachute geometry constant, it was possible to vary Alpha(sub t). The fabric permeability test data necessary for the calculation of Alpha(sub t) were obtained from samples of the same fabrics used to fabricate the subscale parachutes. Although the models for the aerodynamic coefficients are simple polynomial functions of Alpha(sub t) and M, they are capable of producing good reproductions of the source data. The (Alpha(sub t), M) domains over which these models are applicable are clearly defined. The models are applicable to flight operations on Mars.

Cruz, Juan R.↗
NASA NTRS2017

Estimates for the Aerodynamic Coefficients of Ringsail and Disk-Gap-Band Parachutes Operating on Mars

The creation of flight dynamics simulations is often part of the design, development, and flight operations of missions that include entry, descent, and landing (EDL) in their concept of operation. Such flight dynamics simulations include models for many of the EDL sub-systems. For EDL systems that include a parachute, models of the parachute’s aerodynamic characteristics must be created for inclusion in the flight dynamics simulations. Models are presented for the aerodynamic coefficients (i.e., C(D),C(Tot),C(T),C(N), and C(m,SLCP)) of Supersonic Ringsail (SSRS) and Disk-Gap-Band (DGB) parachutes as functions of total porosity, λ(T), Mach number, M, and total angle of attack, λ(T) (when necessary). The source aerodynamic coefficients data used for creating these models were obtained from a wind tunnel test of subscale parachutes. In this wind tunnel test, subscale parachutes of both parachute types were fabricated from two different fabrics with very different permeabilities. By varying the fabric permeability, while maintaining the parachute geometry constant, it was possible to vary λ(T). The fabric permeability test data necessary for the calculation of λ(T) were obtained from samples of the same fabrics used to fabricate the subscale parachutes. Although the models for the aerodynamic coefficients are simple polynomial functions of λ(T) and M, they are capable of producing good reproductions of the source data. The (λ(T), M) domains over which these models are applicable are clearly defined. The models and their domains are applicable to flight operations on Mars. The models presented in this report are not unique – other models could be created from the source data. To make the creation of alternative numerical models, the aerodynamic coefficients source data are included in this report.

Cruz, Juan R.↗
NASA NTRS2017

Aerodynamic Coefficients from Aeroballistic Range Testing of Deployed- and Stowed-SIAD SFDT Models

This report documents a ballistic-range test campaign conducted in 2012 in order to estimate the aerodynamic stability characteristics of two configurations of the Supersonic Flight Dynamics Test (SFDT) vehicle prior to its initial flight in 2014. The SFDT vehicle was a test bed for demonstrating several new aerodynamic decelerator technologies then being developed under the Low-Density Supersonic Decelerator (LDSD) Project. Of particular interest here is the Supersonic Inflatable Aerodynamic Decelerator (SIAD), an inflatable attached torus used to increase the drag surface area of an entry vehicle during the supersonic portion of the entry trajectory. Two model configurations were tested in the ballistic range: one representing the SFDT vehicle prior to deployment of the SIAD, and the other representing the nominal shape with the SIAD inflated. Both models were fabricated from solid metal, and therefore, the effects of the flexibility of the inflatable decelerator were not considered. The test conditions were chosen to match, as close as possible, the Mach number, Reynolds number, and motion dynamics expected for the SFDT vehicle in flight, both with the SIAD stowed and deployed. For SFDT models with the SIAD stowed, 12 shots were performed covering a Mach number range of 3.2 to 3.7. For models representing the deployed SIAD, 37 shots were performed over a Mach number range of 2.0 to 3.8. Pitch oscillation amplitudes covered a range from 0.7 to 20.6 degrees RMS. Portions of this report (data analysis approach, aerodynamic modeling, and resulting aerodynamic coefficients) were originally published as an internal LDSD Project report [1] in 2012. In addition, this report provides a description of the test design approach, the test facility, and experimental procedures. Estimated non-linear aerodynamic coefficients, including pitch damping, for both model configurations are reported, and the shot-by-shot trajectory measurements, plotted in comparison with calculated trajectories based on the derived non-linear aerodynamic coefficients, are provided as appendices. Since the completion of these tests, two full-scale SFDT flights have been successfully conducted: one in June 2014 [2, 3], and one in June 2015 [3].

Ground Test↗
NASA NTRS1976

Determination of Orbiter and Carrier Aerodynamic Coefficients from Load Cell Measurements

A method of determining orbiter and carrier total aerodynamic coefficients from load cell measurements is required to support the inert and the captive active flights of the ALT program. A set of equations expressing the orbiter and carrier total aerodynamic coefficients in terms of the load cell measurements, the sensed dynamics of the Boeing 747 (carrier) aircraft, and the relative geometry of the orbiter/carrier is derived.

Glenn, G. M.↗
NASA NTRS1983

Calibration of the aerodynamic coefficient identification package measurements from the shuttle entry flights using inertial measurement unit data

The Aerodynamic Coefficient Identification Package (ACIP) is an instrument consisting of body mounted linear accelerometers, rate gyros, and angular accelerometers for measuring the Space Shuttle vehicular dynamics. The high rate recorded data are utilized for postflight aerodynamic coefficient extraction studies. Although consistent with pre-mission accuracies specified by the manufacturer, the ACIP data were found to contain detectable levels of systematic error, primarily bias, as well as scale factor, static misalignment, and temperature dependent errors. This paper summarizes the technique whereby the systematic ACIP error sources were detected, identified, and calibrated with the use of recorded dynamic data from the low rate, highly accurate Inertial Measurement Units.

Heck, M. L.↗
NASA NTRS2002

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

Rajkumar, T.↗
NASA NTRS1952

Pressure distribution and aerodynamic coefficients associated with heat addition to supersonic air stream adjacent to two-dimensional supersonic wing

The modifications in the pressure distributions and the aerodynamic coefficients associated with additions of heat to the two-dimensional supersonic in viscid flow field adjacetnt to the lower surface of of a 5-percent-thickness symmetrical circular-arc wing are presented in this report. The pressure distributions are obtained by the use of graphical method which gives the two-dimensional supersonic inviscid flow field obtained with moderate heat addition. The variation is given of the lift-drag ratio and of the aerodynamic coefficients of lift, drag, and moment with free stream Mach number, angle of attack, and parameters defining extent and amount of heat addition. The six graphical solutions used in this study included Mach numbers of 3.0 and 5.0 and angles of attack of 0 degrees and 2 degrees.

Pinkel, I Irving↗
NASA NTRS1985

Experimental aerodynamic coefficients on a Shuttle-like vehicle at Mach 6 and 10 and comparison to prediction

Aerodynamic coefficients measured for an Orbiter-like configuration are presented for a range of angle of attack from 15 deg to 45 deg at Mach 6 and 10. The low-gamma aspect of a real gas on aerodynamic characteristics and shock detachment distance were simulated by testing this configuration in Mach 6 air (freestream gamma equal to 1.4) and Mach 6 CF4 (freestream gamma equal to 1.17). The effects of Mach number, Reynolds number, and gamma on these measurements are examined and comparisons made to an inviscid flowfield computer code known as HALIS. Pitching-moment coefficients measured in CF4 revealed a nose-up increment when compared with measurements in perfect air, indicative of real-gas effects. In general, the HALIS code accurately predicted the measured aerodynamic coefficients in air and CF4.

Micol, J. R.↗
NASA NTRS1976

Comparison with test data and application of aerodynamic coefficients computed for unfinned axisymmetric bodies using a modified viscous crossflow method

Aerodynamic coefficients computed using a modified viscous crossflow method are verified with test data and shown to compare favorably for the general class of sounding rocket reentry bodies. Next, several reentry predictions are compared with the actual reentry plus a discussion of characteristic traits shown in both. Then a detailed study of the effects of initial conditions on maximum dynamic pressure (q), q at Mach 1 and at 20,000 feet (nominal drogue deployment altitude) will be presented and discussed. These results prove that reentry simulations using this technique are applicable to sounding rocket reentry bodies.

Laudadio, J. F.↗