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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Development and Evaluation of Titanium Spacesuit Bearings

The Z-2 Prototype Planetary Extravehicular Space Suit Assembly is a continuation of NASA's Z-series of spacesuits, designed with the intent of meeting a wide variety of exploration mission objectives, including human exploration of the Martian surface. Incorporating titanium bearings into the Z-series space suit architecture allows us to reduce mass by an estimated 23 lbs per suit system compared to the previously used stainless steel bearing race designs, without compromising suit functionality. There are two obstacles to overcome when using titanium for a bearing race- 1) titanium is flammable when exposed to the oxygen wetted environment inside the space suit and 2) titanium's poor wear properties are often challenging to overcome in tribology applications. In order to evaluate the ignitability of a titanium space suit bearing, a series of tests were conducted at White Sands Test Facility (WSTF) that introduced the bearings to an extreme test profile, with multiple failures imbedded into the test bearings. The testing showed no signs of ignition in the most extreme test cases; however, substantial wear of the bearing races was observed. In order to design a bearing that can last an entire exploration mission (approx. 3 years), design parameters for maximum contact stress need to be identified. To identify these design parameters, bearing test rigs were developed that allow for the quick evaluation of various bearing ball loads, ball diameters, lubricants, and surface treatments. This test data will allow designers to minimize the titanium bearing mass for a specific material and lubricant combination and design around a cycle life requirement for an exploration mission. This paper reviews the current research and testing that has been performed on titanium bearing races to evaluate the use of such materials in an enriched oxygen environment and to optimize the bearing assembly mass and tribological properties to accommodate for the high bearing cycle life for an exploration mission.

Rhodes, Richard↗
DOE OSTI2023

Spin-squeezing-enhanced dual-species atom interferometric accelerometer employing large momentum transfer for precision test of the equivalence principle

Here, we theoretically investigate the feasibility of applying spin squeezing to a light pulse atom interferometer in the presence of large momentum transfer using off resonant Raman transitions, in order to enhance the sensitivity of accelerometry close to the Heisenberg limit. We also show how to implement this scheme in a dual-species atom interferometer for a precision test of the equivalence principle by measuring the Eötvös parameter, and to identify the spin squeezing protocol that is best suited for such an experiment. For a space borne platform in low Earth orbit, such a scheme may eventually enable the measurement of the Eötvös parameter with a sensitivity of the order of 10 -20 within 150 days when 10 5 atoms are employed in each cycle of the experiment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Veneziano variations: how unique are string amplitudes?

String theory offers an elegant and concrete realization of how to consistently couple states of arbitrarily high spin. But how unique is this construction? In this paper we derive a novel, multi-parameter family of four-point scattering amplitudes exhibiting i) polynomially bounded high-energy behavior and ii) exchange of an infinite tower of high-spin modes, albeit with a finite number of states at each resonance. These amplitudes take an infinite-product form and, depending on parameters, exhibit mass spectra that are either unbounded or bounded, thus corresponding to generalizations of the Veneziano and Coon amplitudes, respectively. For the bounded case, masses converge to an accumulation point, a peculiar feature seen in the Coon amplitude but more recently understood to arise naturally in string theory [1]. Importantly, our amplitudes contain free parameters allowing for the customization of the slope and offset of the spin-dependence in the Regge trajectory. We compute all partial waves for this multi-parameter class of amplitudes and identify unitary regions of parameter space. For the unbounded case, we apply similar methods to derive new deformations of the Veneziano and Virasoro-Shapiro amplitudes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

HIPED: Machine learning framework for spherical tokamak pedestal prediction and optimization

We introduce a Machine Learning framework, HIPED (HeIght and width Predictor for Edge Dynamics), for predicting and optimizing pedestal and core performance in spherical tokamak plasmas. Trained on pedestal and core datasets from the third MAST-U campaign, HIPED provides accurate estimates of pedestal height and width. The results reveal notable differences compared with conventional aspect-ratio studies; for instance, a simple power-law relation between pedestal width and height has very low accuracy. Instead, additional parameters such as normalized plasma pressure, elongation, and Greenwald fraction significantly improve accuracy. HIPED can also be trained only on `control room parameters' to inform experimentalists of which controllable parameters to adjust for improving core-integrated performance. The framework further includes a multi-objective optimization scheme that helps guide experimental planning and optimization. We find Pareto-optimal discharges with respect to various features, including distance from edge-localized modes and normalized plasma pressure, track their parameter trajectories over time, and identify the control room parameters required for these Pareto-optimal discharges. This provides a framework for systematically optimizing core and edge performance according to different experimental priorities.

Parisi, Jason F. [Princeton Plasma Physics Laborat↗
DOE OSTI2013

Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses

We developed a Microbial-ENzyme-mediated Decomposition (MEND) model, based on the Michaelis-Menten kinetics, that describes the dynamics of physically defined pools of soil organic matter (SOC). These include particulate, mineral-associated, dissolved organic matter (POC, MOC, and DOC, respectively), microbial biomass, and associated exoenzymes. The ranges and/or distributions of parameters were determined by both analytical steady-state and dynamic analyses with SOC data from the literature. We used an improved multi-objective parameter sensitivity analysis (MOPSA) to identify the most important parameters for the full model: maintenance of microbial biomass, turnover and synthesis of enzymes, and carbon use efficiency (CUE). The model predicted an increase of 2 C (baseline temperature =12 C) caused the pools of POC-Cellulose, MOC, and total SOC to increase with dynamic CUE and decrease with constant CUE, as indicated by the 50% confidence intervals. Regardless of dynamic or constant CUE, the pool sizes of POC, MOC, and total SOC varied from 8% to 8% under +2 C. The scenario analysis using a single parameter set indicates that higher temperature with dynamic CUE might result in greater net increases in both POC-Cellulose and MOC pools. Different dynamics of various SOC pools reflected the catalytic functions of specific enzymes targeting specific substrates and the interactions between microbes, enzymes, and SOC. With the feasible parameter values estimated in this study, models incorporating fundamental principles of microbial-enzyme dynamics can lead to simulation results qualitatively different from traditional models with fast/slow/passive pools.

09 BIOMASS FUELS↗
NASA NTRS2012

Validation of Slosh Model Parameters and Anti-Slosh Baffle Designs of Propellant Tanks by Using Lateral Slosh Testing

The slosh dynamics of propellant tanks can be represented by an equivalent pendulum-mass mechanical model. The parameters of this equivalent model, identified as slosh model parameters, are slosh mass, slosh mass center of gravity, slosh frequency, and smooth-wall damping. They can be obtained by both analysis and testing for discrete fill heights. Anti-slosh baffles are usually needed in propellant tanks to control the movement of the fluid inside the tank. Lateral slosh testing, involving both random testing and free-decay testing, are performed to validate the slosh model parameters and the damping added to the fluid by the anti-slosh baffles. Traditional modal analysis procedures are used to extract the parameters from the experimental data. Test setup of sub-scale test articles of cylindrical and spherical shapes will be described. A comparison between experimental results and analysis will be presented.

Perez, Jose G.↗
DOE OSTI2024

Development of a Method for Shape Optimization for a Gas Turbine Fuel Injector Design Using Metal-Additive Manufacturing

Adjoint shape optimization has enabled physics-based optimal designs for aerodynamic surfaces. Additive manufacturing (AM) makes it possible to manufacture complex shapes. However, there has been a gap between optimal and manufacturable surfaces due to the inherent limitations of commercial computational fluid dynamics (CFD) codes to implement geometric constraints during adjoint computation. In such cases, the design sensitivities are exported and used to perform constrained shape modifications using parametric information stored in computer aided design (CAD) files to satisfy manufacturability constraints. However, modifying the design using adjoint methods in CFD solvers and performing constrained shape modification in CAD can lead to inconsistencies due to different shape parameterization schemes. This paper describes a method to enable the simultaneous optimization of the fluid domain and impose AM manufacturability constraints, resolving one of the key issues of geometry definition for isogeometric analysis. Similar to a grid convergence study, the proposed method verifies the consistencies between shape parameterization techniques present within commercial CAD and CFD software during mesh movement as a part of the adjoint shape optimization routine. By identifying the appropriate parameters essential to a shape optimization study, the error metric between the different parameterization techniques converges to demonstrate sufficient consistencies for justifiable exchange of data between CAD and CFD. For the identified shape optimization parameters, the error metric to measure the deviation between the two parameterization schemes lies within the AM laser-powder bed fusion (L-PBF) process tolerance. Additionally, comparison for subsequent objective function calculations between iterations of the optimization loop showed acceptable differences within 1% variation between the modified geometries obtained using the two parameterization schemes. This method provides justification for the use of multiphysics guided adjoint design sensitivities computed in CFD software to perform shape modifications in CAD to incorporate AM manufacturability constraints during the shape optimization loop such that optimal designs are also additively manufacturable.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Heavy-Duty Vehicle Air Drag Coefficient Estimation: From an Algebraic Perspective

When a heavy-duty vehicle (HDV) operates at the nominal highway speed, over two-thirds of its total resistive force comes from the air drag, contributing to more than half of its fuel consumption. One effective countermeasure to reduce the fuel consumption of HDVs is platooning, which employs connectivity and automated driving technologies to link two or more HDVs in convoy. Platooning allows HDVs to drive closer together and yields improved fuel economy and less CO2 emission thanks to the reduced air drag. Maximizing the energy benefits of an HDV platoon requires quantifying the drag interaction between vehicles. In practice, modeling the drag reduction in a platoon boils down to identifying the relationship between the air drag coefficient C d and the inter-vehicle distance d. Existing approaches to identify C d (d) include vehicle field tests, wind tunnel experiments, and computational fluid dynamics simulation, which can howbeit be time-consuming and cost prohibitive. In contrast, this paper proposes an algebraic approach, which relies on onboard-measurable variables, to estimate the air drag coefficient of an HDV in a platoon. Its algebraic nature avoids the classical persistence of excitation condition for parameter identification and can yield the identified parameter almost instantaneously. Simulation results demonstrate its effectiveness and the improved estimation speed over a recursive least squares identifier.

Wang, Zejiang↗
DOE OSTI2024

Impact of the Exciter and Governor Parameters on Forced Oscillations

In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability.

42 ENGINEERING↗
DOE OSTI2024

Augmented Human Analysis (AHA)

Radio frequency (RF) signal monitoring generally emphasizes intentionally generated signals, such as WiFi, Bluetooth, or cellular transmissions. However, electronic devices also produce unintended radiated emissions (UREs), which could also be useful in RF spectrum analysis. In either case, deriving intelligence from RF signals is typically a human-intensive process requiring significant domain knowledge. In the Augmented Human Analysis (AHA) project, we investigate the utility of dimensionally aligned signal projection (DASP) and machine learning (ML) algorithms for accelerating RF analysis workflows. We find that while DASP algorithms can indeed highlight signal characteristics relevant for classification tasks, the choice of algorithmic hyperparameters greatly affects performance. To address this challenge, we evaluate the quality of DASP outputs using the silhouette score, which measures how well data points cluster; high silhouette scores indicate good clustering, and thus good hyperparameter values. This approach is critical for machine learning pipelines as the DASP parameters cannot be directly optimized during model training. By identifying good DASP parameters, and thus good DASP outputs, as a preprocessing step, we can decrease the amount of effort required for downstream ML model training. We demonstrate our workflow using a dataset of UREs from common household devices, showing that even without the aid of ML, proper selection of DASP parameters enables clustering by device type.

42 ENGINEERING↗
NASA NTRS1988

A multiple-vortex-ring model of the DFW microburst

A multiple-vortex-ring model of the winds associated with a microburst is verified by matching the model-generated winds to those encountered at the Dallas-Ft. Worth (DFW) microburst. The basis of the model consists of time-invariant vortex ring filaments embedded in irrotational flow. Each ring's viscous core is modeled by distributing the vorticity over a small distance (relative to the ring diameter) radially from the filaments. Parameters such as the size and strength of the vortex rings are identified using a modified Newton-Raphson technique. The parameters identified from the analysis of the DFW microburst encounter indicate a large ring with a radius of 8500 ft and a smaller ring with a radius of 1700 ft.

Schultz, Thomas A.↗
NASA NTRS2000

Nonlinear Bulging Factors for Longitudinal and Circumferential Cracks in Cylindrical Shells Subjected to Combined Loads

Results of a geometrically nonlinear finite element parametric study to determine curvature correction factors or bulging factors that account for increased crack-tip stresses due to curvature for longitudinal and circumferential cracks in unstiffened cylindrical shells subjected to combined loads are presented. Nondimensional parameters varied in the study include the shell curvature parameter, l , which is a function of the shell radius, the shell wall thickness, and the crack length; a pressure loading parameter, h , which is a function of the shell geometry, material properties and the applied internal pressure; and a biaxial loading parameter, c , which is the ratio of the farfield axial stress to the farfield circumferential stress. The major results are presented in the form of contour plots of the bulging factor as a function of these three nondimensional parameters. These plots identify the ranges of the shell curvature and loading parameters for which the effects of geometric nonlinearity are significant, and show the effect of the biaxial loads on the value of the bulging factor. Simple empirical expressions for the bulging factor are then derived from the numerical results and are shown to predict accurately the nonlinear response of shells with longitudinal and circumferential cracks.

Young, Richard D.↗
DOE OSTI2020

Stochastic Virtual Battery Modeling of Uncertain Electrical Loads using Variational Autoencoder

Effective utilization of flexible loads for grid services, while satisfying end-user preferences and constraints, requires an accurate estimation of the aggregated predictive flexibility offered by the electrical loads. Recently, there have been efforts to quantify the predictive flexibility of thermostatic loads (e.g. residential air-conditioners, electric water-heaters) using the notion of virtual battery (VB), whose state evolution is governed by a first order dynamics including self-dissipation rate, and power and energy capacities. Identifying the VB model parameters for a collection of thermostatic loads, however, is challenging primarily due to uncertainties and lack of information regarding the end-user behavior, underlying device models and parameters. In this paper, we propose a \textit{variational autoencoder}-based deep learning algorithm to identify the parameters of the VB model. Using available sensors and meters data, the proposed algorithm generates not only point estimates of the VB parameters, but also confidence intervals around those values. Effectiveness of the proposed frameworks is demonstrated on a collection of electric water-heater loads, whose operation is driven by uncertain water usage profiles.

virtual battery, deep learning algorithms↗
NASA NTRS1983

On parameter identification for large space structures

The design of a controller for large space structures (LSS) based on the LQG theory requires the knowledge of the LSS parameters. Since apriori knowledge of the parameters is usually not reliable, the parameters must be identified prior to the controller synthesis, using methods such as the maximum likelihood technique. An expression is obtained for the Fisher information matrix for LSS, from which Cramer-Rao bounds can be obtained in order to determine the accuracy with which the parameters can be identified.

Joshi, S. M.↗
NASA NTRS1981

A data collection scheme for identification of parameters in a driver model

A high gain steering controller to compensate for limitations in a handicapped driver's range of motion is employed when adapting vehicle to his use. A driver/vehicle system can become unstable as vehicle speed is increased, therefore it is desirable to use a computer simulation of the driver/vehicle combination as a design tool to investigate the system response prior to construction of a controller and road testing. Unknown driver parameters must be identified prior to use of the model for system analysis. A means to collect the data necessary for identification of these driver model parameters without extensive instrumentation of a vehicle to measure and record vehicle states is addressed. Initial tests of the procedure identified all of the driver parameters with errors of 6% or less.

Mooring, B. W.↗
DOE OSTI2019

Machine Learning Applied to a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures [Machine Learning a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures]

Alloy oxide heterostructures are essential for a wide variety of energy applications, including anticorrosion coatings, bifunctional catalysis, energy storage, as well as emerging platforms for multilevel nonvolatile memory and neuromorphic devices. The key role played by oxide composition, density, and stoichiometry in governing electrochemical reactions, interface structure, and ionic transport in alloy oxides remains largely unknown. This is primarily due to the lack of variable charge interatomic potential models that can adequately describe the various atomic/ionic interactions in alloy oxides within a unified framework. Here, we introduce a charge transfer ionic potential (CTIP) model for Cu/Hf/O alloy system and demonstrate its ability to accurately capture the complex potential energy surface owing to dynamically varying interactions, including metallic (Cu/Hf), ionic (Cu/Hf/Ox), mixed environment (interfaces). We leverage supervised machine learning methods powered by genetic algorithms coupled with local simplex optimization to efficiently navigate through a high-dimensional parameter space and identify an optimum set of an independent set of CTIP parameters. We train our model against an extensive first-principles based data set that includes lattice constants, cohesive energies, equations of state, and elastic constants of various experimentally observed polymorphs of hafnia, copper oxide, hafnium, as well as copper. Our machine-learned CTIP model captures the structure, elastic properties, thermodynamics, and energetic ordering of various polymorphs. It also accurately predicts the surface properties of both oxides and their metals. To demonstrate the suitability of our CTIP model for investigating dynamic processes, we employed it to identify the atomistic scale mechanisms associated with the initial nanoscale oxidation and surface oxide growth on Cu-doped hafnia surfaces. Here, our machine-learned CTIP model can be used to probe the dynamic response of Cu/Hf/O alloy interfaces subjected to external stimuli (e.g., electric field, pressure, temperature, strain, etc.) and a variety of atomistic phenomena including the dynamics of switching in emerging neuromorphic platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Study on the parameters of the scanning system for the 300 keV electron accelerator

This paper describes the method to identify the magnetic coil parameters of the scanning system. This locally designed low energy electron accelerator with the present energy of 140 keV will be upgraded to 300 keV. In this accelerator, scanning system is required to deflect the energetic electron beam across a titanium foil in vertical and horizontal direction. The excitation current of the magnetic coil is determined by the energy of the electron beam. Therefore, the magnetic coil parameters must be identified to ensure the matching of the beam energy and excitation coil current. As the result, the essential parameters of the effective lengths for X-axis and Y-axis have been found as 0.1198 m and 0.1134 m and the required excitation coil currents which is dependenton the electron beam energies have be identified.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Chaotic saddles in nonlinear modulational interactions in a plasma

A nonlinear model of modulational processes in the subsonic regime involving a linearly unstable wave and two linearly damped waves with different damping rates in a plasma is studied numerically. We compute the maximum Lyapunov exponent as a function of the damping rates in a two-parameter space, and identify shrimp-shaped self-similar structures in the parameter space. By varying the damping rate of the low-frequency wave, we construct bifurcation diagrams and focus on a saddle-node bifurcation and an interior crisis associated with a periodic window. We detect chaotic saddles and their stable and unstable manifolds, and demonstrate how the connection between two chaotic saddles via coupling unstable periodic orbits can result in a crisis-induced intermittency. The relevance of this work for the understanding of modulational processes observed in plasmas and fluids is discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗