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Results for “PERFORMANCE PREDICTION”

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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Initial Results of Reynolds Number Testing at LaRC's NTF Using the 2.2% Reference H Model

To develop full scale flight performance predictions an understanding of Reynolds number effects on HSCT-class configurations is essential. A wind tunnel database utilizing a 2.2% scale Reference H model in NASA Langley Research Centers National Transonic Facility is being developed to assess these Reynolds number effects. In developing this database temperature and aeroelastic corrections to the wind tunnel data have been identified and are being analyzed. Once final corrections have been developed and applied, then pure Reynolds number effects can be determined. In addition, final corrections will yield the data required for CFD validation at q = 0. Presented in this report are the results of seven tests involving the wing/body configuration. This includes summaries of data acquired in these tests, uncorrected Reynolds number effects, and temperature and aeroelastic corrections. The data presented herein illustrates the successes achieved to date as well as the challenges that will be faced in obtaining full scale flight performance predictions.

Owens, Lewis R., Jr.↗
DOE OSTI2025

Bayesian D‐Optimal Designs for Gaussian Process Surrogate Models

Computer experiments often employ space-filling strategies to create surrogate models with strong predictive performance. The impact of model parameter estimation for Gaussian process surrogates, however, is often overlooked. Obtaining a better initial estimate of the covariance lengthscale parameter, θ, can greatly improve the resulting Gaussian process fit through more effective sequential acquisitions during active learning. In this work, we propose a novel initial design maximizing the Bayesian D-optimality criterion of the Gaussian process lengthscale parameter. Previously published results have shown the emphasis on lengthscale estimation to be promising, but relied on an empirically driven design creation process. Our Bayesian D-optimal designs are rooted in information theory and lead to more informative sequential acquisitions by improving lengthscale estimation. In many cases, these gains eventually result in better surrogates than those seeded with space-filling initial designs. Furthermore, Bayesian D-optimal designs can be tailored to either isotropic or anisotropic covariance structures, and the Bayesian framework enables the inclusion of prior knowledge in the design process, offering greater flexibility and adaptability. Through several simulation studies, we demonstrate the advantages of Bayesian D-optimal designs in terms of both lengthscale estimation accuracy and predictive performance during active learning.

Bayesian experimental design↗
NASA NTRSDate not supplied

PALMO: An OVERFLOW Machine Learning Airfoil Performance Database

The OVERFLOW Machine Learning Airfoil Performance (PALMO) database has been created to enable robust modeling of airfoil performance in a variety of applications. The PALMO database uses OVERFLOW simulation data second-order accurate in time and fourth-order accurate in space with Spalart-Allmaras turbulence closure. The foundation of the in-development PALMO database is the airfoil base cube. Each base cube includes simulation data parametrized over a range of Mach numbers, Reynolds numbers, and angles-of-attack. This database includes the NACA 4-series airfoils, with parametrization in airfoil thickness and camber from an NACA 0006 to an NACA 4424. In total, 52,480 NACA 4-series OVERFLOW calculations were run on the NASA High-End Compute Capability (HECC) supercomputer. This provides high-order-accurate simulation data covering a wide range of aerospace design applications, which enables users to develop accurate airfoil performance look-up tables without additional high-performance computing. In addition to engineering design and analysis of aerospace vehicles, PALMO is well suited to be a benchmark dataset for the development and testing of machine learning methods in aerospace engineering. This work presents an example PALMO surrogate model that enables accurate airfoil performance predictions for any arbitrary combination of camber, thickness, Mach number, Reynolds number, and angle of attack within the bounds of the database. Airfoil performance tables predicted for an airfoil not used in training the model are used in three-dimensional OVERFLOW simulations to quantify the downstream accuracy on aggregate rotor performance metrics. For the NACA 3415 airfoil, which had no common thickness or camber with the training data, the surrogate predicted and CFD generated tables were within 2.1% of each other in the forward flight lift to drag metric. This suggests that performance tables generated for airfoils within the bounds of the PALMO database will yield aggregate rotor performance predictions on par with tables generated from directly running OVERFLOW airfoil calculations. The PALMO airfoil performance coefficients are available publicly.

Database↗
DOE OSTI2026

Automated ICRF heating surrogate modeling via machine learning

This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.

Sanchez-Villar, Alvaro [Princeton Plasma Physics L↗
NASA NTRS1973

Extravehicular mobility unit thermal simulator

The analytical methods, thermal model, and user's instructions for the SIM bay extravehicular mobility unit (EMU) routine are presented. This digital computer program was developed for detailed thermal performance predictions of the crewman performing a command module extravehicular activity during transearth coast. It accounts for conductive, convective, and radiative heat transfer as well as fluid flow and associated flow control components. The program is a derivative of the Apollo lunar surface EMU digital simulator. It has the operational flexibility to accept card or magnetic tape for both the input data and program logic. Output can be tabular and/or plotted and the mission simulation can be stopped and restarted at the discretion of the user. The program was developed for the NASA-JSC Univac 1108 computer system and several of the capabilities represent utilization of unique features of that system. Analytical methods used in the computer routine are based on finite difference approximations to differential heat and mass balance equations which account for temperature or time dependent thermo-physical properties.

Hixon, C. W.↗
NASA NTRS1972

A simple second-order digital phase-locked loop.

A simple second-order digital phase-locked loop has been designed for the Viking Orbiter 1975 command system. Excluding analog-to-digital conversion, implementation of the loop requires only an adder/subtractor, two registers, and a correctable counter with control logic. The loop considers only the polarity of phase error and corrects system clocks according to a filtered sequence of this polarity. The loop is insensitive to input gain variation, and therefore offers the advantage of stable performance over long life. Predictable performance is guaranteed by extreme reliability of acquisition, yet in the steady state the loop produces only a slight degradation with respect to analog loop performance.

Tegnelia, C. R.↗
NASA NTRS1977

Use of sensitivity analysis to predict pilot performance as a function of different displays

A technique for objectively evaluating different displays by sensitivity analysis is described. First, the mathematical model used to analyze static displays is developed. The technique is based on formulating functional relationships between the state variables and the variables observable in the display. The matrix of the partial derivatives of the display variables with respect to the state variables, together with the observer's acuity function, is used to calculate expected errors in the state vector estimation. The technique is expanded by the use of Kalman filtering to process a time series of observation vectors. This provides a tool for analyzing displays of dynamic processes by means of a dynamic display evaluation computer program. Results are reported using this program to simulate an Instrument Landing System approach.

Mout, M. L.↗
NASA NTRS1981

A Simulation System for Validating the Analytical Prediction of Performance of the Convolutional Encoded and Symbol Interleaved TDRSS S-band Return Link Service in a Pulsed RFI Environment

A hardware integrated convolutional coding/symbol interleaving and integrated symbol deinterleaving/Viterbi decoding simulation system is described. Validation on the system of the performance of the TDRSS S-band return link with BPSK modulation, operating in a pulsed RFI environment is included. The system consists of three components, the Fast Linkabit Error Rate Tester (FLERT), the Transition Probability Generator (TPG), and a modified LV7017B which includes rate 1/3 capability as well as a periodic interleaver/deinterleaver. Operating and maintenance manuals for each of these units are included.

Source record↗
NASA NTRS1983

Requirements for a mobile communications satellite system. Volume 3: Large space structures measurements study

This study report defines a set of tests and measurements required to characterize the performance of a Large Space System (LSS), and to scale this data to other LSS satellites. Requirements from the Mobile Communication Satellite (MSAT) configurations derived in the parent study were used. MSAT utilizes a large, mesh deployable antenna, and encompasses a significant range of LSS technology issues in the areas of structural/dynamics, control, and performance predictability. In this study, performance requirements were developed for the antenna. Special emphasis was placed on antenna surface accuracy, and pointing stability. Instrumentation and measurement systems, applicable to LSS, were selected from existing or on-going technology developments. Laser ranging and angulation systems, presently in breadboard status, form the backbone of the measurements. Following this, a set of ground, STS, and GEO-operational were investigated. A third scale (15 meter) antenna system as selected for ground characterization followed by STS flight technology development. This selection ensures analytical scaling from ground-to-orbit, and size scaling. Other benefits are cost and ability to perform reasonable ground tests. Detail costing of the various tests and measurement systems were derived and are included in the report.

Akle, W.↗
NASA NTRS1988

Computer-aided modeling and prediction of performance of the modified Lundell class of alternators in space station solar dynamic power systems

The main purpose of this project is the development of computer-aided models for purposes of studying the effects of various design changes on the parameters and performance characteristics of the modified Lundell class of alternators (MLA) as components of a solar dynamic power system supplying electric energy needs in the forthcoming space station. Key to this modeling effort is the computation of magnetic field distribution in MLAs. Since the nature of the magnetic field is three-dimensional, the first step in the investigation was to apply the finite element method to discretize volume, using the tetrahedron as the basic 3-D element. Details of the stator 3-D finite element grid are given. A preliminary look at the early stage of a 3-D rotor grid is presented.

Demerdash, Nabeel A. O.↗
NASA NTRS1990

Developments in REDES: The rocket engine design expert system

The Rocket Engine Design Expert System (REDES) is being developed at the NASA-Lewis to collect, automate, and perpetuate the existing expertise of performing a comprehensive rocket engine analysis and design. Currently, REDES uses the rigorous JANNAF methodology to analyze the performance of the thrust chamber and perform computational studies of liquid rocket engine problems. The following computer codes were included in REDES: a gas properties program named GASP, a nozzle design program named RAO, a regenerative cooling channel performance evaluation code named RTE, and the JANNAF standard liquid rocket engine performance prediction code TDK (including performance evaluation modules ODE, ODK, TDE, TDK, and BLM). Computational analyses are being conducted by REDES to provide solutions to liquid rocket engine thrust chamber problems. REDES is built in the Knowledge Engineering Environment (KEE) expert system shell and runs on a Sun 4/110 computer.

Davidian, Kenneth O.↗
NASA NTRS1990

Developments in REDES: The Rocket Engine Design Expert System

The Rocket Engine Design Expert System (REDES) was developed at NASA-Lewis to collect, automate, and perpetuate the existing expertise of performing a comprehensive rocket engine analysis and design. Currently, REDES uses the rigorous JANNAF methodology to analyze the performance of the thrust chamber and perform computational studies of liquid rocket engine problems. The following computer codes were included in REDES: a gas properties program named GASP; a nozzle design program named RAO; a regenerative cooling channel performance evaluation code named RTE; and the JANNAF standard liquid rocket engine performance prediction code TDK (including performance evaluation modules ODE, ODK, TDE, TDK, and BLM). Computational analyses are being conducted by REDES to provide solutions to liquid rocket engine thrust chamber problems. REDES was built in the Knowledge Engineering Environment (KEE) expert system shell and runs on a Sun 4/110 computer.

Davidian, Kenneth O.↗
NASA NTRS1992

High efficiency low cost monolithic module for SARSAT distress beacons

The program objectives were to develop a highly efficient, low cost RF module for SARSAT beacons; achieve significantly lower battery current drain, amount of heat generated, and size of battery required; utilize MMIC technology to improve efficiency, reliability, packaging, and cost; and provide a technology database for GaAs based UHF RF circuit architectures. Presented in viewgraph form are functional block diagrams of the SARSAT distress beacon and beacon RF module as well as performance goals, schematic diagrams, predicted performances, and measured performances for the phase modulator and power amplifier.

Petersen, Wendell C.↗