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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Integrated Design Engineering Analysis (IDEA) Environment - Aerodynamics, Aerothermodynamics, and Thermal Protection System Integration Module

This report documents the work performed during from March 2010 October 2011. The Integrated Design and Engineering Analysis (IDEA) environment is a collaborative environment based on an object-oriented, multidisciplinary, distributed environment using the Adaptive Modeling Language (AML) as the underlying framework. This report will focus on describing the work done in the area of extending the aerodynamics, and aerothermodynamics module using S/HABP, CBAERO, PREMIN and LANMIN. It will also detail the work done integrating EXITS as the TPS sizing tool.

Kamhawi, Hilmi N.↗
NASA NTRS2013

Integrated Design and Engineering Analysis (IDEA) Environment - Propulsion Related Module Development and Vehicle Integration

This report documents the work performed during the period from May 2011 - October 2012 on the Integrated Design and Engineering Analysis (IDEA) environment. IDEA is a collaborative environment based on an object-oriented, multidisciplinary, distributed framework using the Adaptive Modeling Language (AML). This report will focus on describing the work done in the areas of: (1) Integrating propulsion data (turbines, rockets, and scramjets) in the system, and using the data to perform trajectory analysis; (2) Developing a parametric packaging strategy for a hypersonic air breathing vehicles allowing for tank resizing when multiple fuels and/or oxidizer are part of the configuration; and (3) Vehicle scaling and closure strategies.

Kamhawi, Hilmi N.↗
NASA NTRS2016

Dynamic Filtering Improves Attentional State Prediction with fNIRS

Brain activity can predict a person's level of engagement in an attentional task. However, estimates of brain activity are often confounded by measurement artifacts and systemic physiological noise. The optimal method for filtering this noise - thereby increasing such state prediction accuracy - remains unclear. To investigate this, we asked study participants to perform an attentional task while we monitored their brain activity with functional near infrared spectroscopy (fNIRS). We observed higher state prediction accuracy when noise in the fNIRS hemoglobin [Hb] signals was filtered with a non-stationary (adaptive) model as compared to static regression (84% +/- 6% versus 72% +/- 15%).

Harrivel, Angela R.↗
NASA NTRS2014

M-MRAC With Normalization

This paper presents a normalization based modified reference model adaptive control method for multi-input multi-output (MIMO) uncertain systems in the presence of bounded external disturbances. It has been shown that desired tracking performance can be achieved for the system's output and input signals with the proper choice of design parameters. The resulting adaptive control signal satisfies a second order linear time invariant (LTI) system, which is the effect of the normalization term in the adaptive laws. This LTI system provides the guideline for the design parameter selection. The theoretical findings are confirmed via a simulation example.

Stepanyan, Vahram↗
NASA NTRS2018

Performance of Cassini Reaction Wheel Friction Compensation Scheme During Spin Rate Zero-Crossing and Drag Spikes

Cassini uses reaction wheels to achieve the spacecraft pointing stability that is needed during imaging operations of several science instruments. The Cassini flight software makes inflight estimates of reaction wheel bearing drag torque and the reaction wheel controller uses these estimates to achieve a high level of spacecraft pointing stability. However, the Cassini drag torque estimator was designed to accurately track the bearing drag torque only in the steady state. When the physical drag torque changes abruptly (for example, during a reaction wheel spin rate reversal or when wheel bearings experienced drag spikes), the drag estimator will not be able to track the physical drag closely. This will lead to a degradation in the spacecraft pointing stability performance. For Cassini, this was not a problem because of the significant performance margin in pointing stability. However, for missions that have very challenging pointing stability requirements and that must perform well in the presence of frequent wheel rate reversals, alternative drag-compensating control schemes must be considered. To this end, alternative drag torque compensating control schemes (such as the adaptive model reference control scheme) are briefly reviewed in this paper. Selected design features used in these friction compensation schemes may be incorporated in reaction wheel controller design to improve the robustness of spacecraft pointing stability performance with regard to a wide range of reaction wheel drag torque anomalous behavior.

Lee, Allan Y↗
NASA NTRSDate not supplied

Developing and Testing a Physics Guided Machine Learning NeuralNetwork to Predict Tonal Noise Emitted by a Propeller

Artificial neural networks offer a highly nonlinear and adaptive model for predicting complex interactions between input-output parameters. However, these networks require large datasets which often exceed practical considerations in modeling experimental results. To alleviate the dataset size requirement, a method known as physics guided machine learning has been applied to construct several neural networks for predicting propeller tonal noise in the time domain over a broad range of flight conditions. Three space-filling designs, namely, Latin-Hypercube, Sphere-Packing, and Grid-Space, were used to distribute points throughout the input parameter space encompassing nondimensional flight conditions and observer geometry. Each neural network’s performance was validated by conditions outside of the training set and compared to the Propeller Analysis System tool from the NASA Aircraft Noise Prediction Program. Compared to the Grid-Space input design, the Latin-Hypercube and the Sphere-Packing designs provided a better representation of the domain for training. Regarding the network archetype, a fully connected perceptron was found to outperform the partially connected perceptron in their ability to predict tonal noise for small datasets. The black-box nature of these neural networks was also explored to understand how the networks constructed the waveform and understand why some network designs produce better models.

Propeller noise↗
NASA NTRS2022

Solar Polar Flux Redistribution Based on Observed Coronal Holes

We explore the use of observed polar coronal holes (CHs) to constrain the flux distribution within the polar regions of global solar magnetic field maps in the absence of reliable quality polar field observations. Global magnetic maps, generated by the Air Force Data Assimilative Photospheric flux Transport (ADAPT) model, are modified to enforce field unipolarity thresholds both within and outside observed CH boundaries. The polar modified and unmodified maps are used to drive Wang–Sheeley–Arge (WSA) models of the corona and solar wind (SW). The WSA-predicted CHs are compared with the observations, and SW predictions at the WIND and Ulysses spacecraft are also used to provide context for the new polar modified maps. We find that modifications of the polar flux never worsen and typically improve both the CH and SW predictions. We also confirm the importance of the choice of the domain over which WSA generates the coronal magnetic field solution but find that solutions optimized for one location in the heliosphere can worsen predictions at other locations. Finally, we investigate the importance of low-latitude (i.e., active region) magnetic fields in setting the boundary of polar CHs, determining that they have at least as much impact as the polar fields themselves.

Solar coronal holes↗
DOE OSTI2026

How does drought affect residential water demand and price elasticity?

Urban water scarcity is an important social and economic concern, particularly as the intensity, duration, and frequency of droughts is increasing in many regions. We consider whether drought induces changes to water demand and the price elasticity of demand for water that may last beyond a drought’s official end date. If drought shocks prompt long-term changes in water demand behavior, and these changes occur at broad geographic scale, they could have important implications for modeling adaptive responses to water scarcity. We assemble a novel dataset on residential water demand and pricing in the western United States to test empirically for effects of drought on water demand and price elasticity. We perform our analysis with aggregate quantity, price, and drought data, accounting for endogenous prices under increasing-block water tariffs and using both average and marginal water fees in estimating water demand functions. Results are consistent with the hypothesis that households may become less price-sensitive after exposure to drought. However, we find no systematic evidence of long-run, drought-related reductions in water demand, itself.

demand hardening↗
NASA NTRS1987

An adaptive grid, unsteady model for two-dimensional magnetohydrodynamic (MHD) flow

An adaptive grid finite difference method for solving multi-dimensional, time-dependent, magnetohydrodynamic (MHD) equations is developed. The method is capable of solving problems that include high gradients due to geometry, propagation of shock waves and unsteady boundary conditions. The grid generation technique is based on variational principles with direct control over grid concentration, smoothness and skewness. An example for a two-dimensional MHD simulation of the propagation of a solar-flare-generated shock wave in solar wind flow in the heliographic equatorial plane is selected for illustration of this method.

Panitchob, Supat↗
NASA NTRS1990

Dynamic modeling, property investigation, and adaptive controller design of serial robotic manipulators modeled with structural compliance

Research results on general serial robotic manipulators modeled with structural compliances are presented. Two compliant manipulator modeling approaches, distributed and lumped parameter models, are used in this study. System dynamic equations for both compliant models are derived by using the first and second order influence coefficients. Also, the properties of compliant manipulator system dynamics are investigated. One of the properties, which is defined as inaccessibility of vibratory modes, is shown to display a distinct character associated with compliant manipulators. This property indicates the impact of robot geometry on the control of structural oscillations. Example studies are provided to illustrate the physical interpretation of inaccessibility of vibratory modes. Two types of controllers are designed for compliant manipulators modeled by either lumped or distributed parameter techniques. In order to maintain the generality of the results, neither linearization is introduced. Example simulations are given to demonstrate the controller performance. The second type controller is also built for general serial robot arms and is adaptive in nature which can estimate uncertain payload parameters on-line and simultaneously maintain trajectory tracking properties. The relation between manipulator motion tracking capability and convergence of parameter estimation properties is discussed through example case studies. The effect of control input update delays on adaptive controller performance is also studied.

Tesar, Delbert↗
NASA NTRS1988

Model reference, sliding mode adaptive control for flexible structures

A decentralized model reference adaptive approach using a variable-structure sliding model control has been developed for the vibration suppression of large flexible structures. Local models are derived based upon the desired damping and response time in a model-following scheme, and variable structure controllers are then designed which employ colocated angular rate and position feedback. Numerical simulations have been performed using NASA's flexible grid experimental apparatus.

Yurkovich, S.↗
NASA NTRSDate not supplied

Time-Varying Reference Model Modification of Adaptive Control with Multi-Objective Performance Optimization

This paper presents an adaptive control approach that modifies a reference model in order to satisfy a multi-objective optimization of two performance signals: a linear signal and a quadratic signal. The time varying reference model modification is accomplished by the real-time solutions of the time-varying Riccati and Sylvester equations coupled with the least-squares parameter estimation of the sensitivities of the performance signals to be minimized. The effectiveness of the proposed method is to be demonstrated by a flight control application of maneuver load alleviation and drag minimization.

Adaptive Control↗
DOE OSTI2025

Shift Happens: Building Robust AI Models with Domain Adaptation

Artificial Intelligence (AI) is revolutionizing physics research—from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data—exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This summer school session will introduce students to common challenges in applying AI across domains and present solutions based on domain adaptation—a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we'll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery. The concepts discussed are broadly applicable across physics and other scientific disciplines, making this a valuable topic for anyone interested in building robust, transferable AI models for science.

Ciprijanovic, A. [Fermilab] (ORCID:000000031281719↗
NASA NTRS2022

New Insights into the First Two PSP Solar Encounters Enabled by Modeling Analysis with ADAPT-WSA

Parker Solar Probe's (PSP's) unique orbital path allows us to observe the solar wind closer to the Sun than ever before. Essential to advancing our knowledge of solar wind and energetic particle formation is identifying the sources of PSP observations. We report on results for the first two PSP solar encounters derived using the Wang–Sheeley–Arge (WSA) model driven by Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model maps. We derive the coronal magnetic field and the 1 R⊙ source regions of the PSP-observed solar wind. We validate our results with the solar wind speed and magnetic polarity observed at PSP. When modeling results are very reliable, we derive time series of model-derived spacecraft separation from the heliospheric current sheet, magnetic expansion factor, coronal hole boundary distance, and photospheric field strength along the field lines estimated to be connected to the spacecraft. We present new results for Encounter 1, which show time evolution of the far-side mid-latitude coronal hole that PSP corotates with. We discuss how this evolution coincides with solar wind speed, density, and temperature observed at the spacecraft. During Encounter 2, a new active region emerges on the solar far side, making it difficult to model. We show that ADAPT-WSA output agrees well with PSP observations once this active region rotates onto the near side, allowing us to reliably estimate the solar wind sources retrospectively for most of the encounter. We close with ways in which coronal modeling enables scientific interpretation of these encounters that would otherwise not have been possible.

Solar wind↗