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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 1,531 records · Page 85

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NASA NTRS1986

Micromechanisms of thermomechanical fatigue: A comparison with isothermal fatigue

Thermomechanical Fatigue (TMF) experiments were conducted on Mar-M 200, B-1900, and PWA-1480 (single crystals) over temperature ranges representative of gas turbine airfoil environments. The results were examined from both a phenomenological basis and a micromechanical basis. Depending on constituents present in the superalloy system, certain micromechanisms dominated the crack initiation process and significantly influenced the TMF lives as well as sensitivity of the material to the type TMF cycle imposed. For instance, high temperature cracking around grain boundary carbides in Mar-M 200 resulted in short in-phase TMF lives compared to either out-of-phase or isothermal lives. In single crystal PWA-1480, the type of coating applied was seen to be the controlling factor in determining sensitivity to the type of TMF cycle imposed. Micromechanisms of deformation were observed over the temperature range of interest to the TMF cycles, and provided some insight as to the differences between TMF damage mechanisms and isothermal damage mechanisms. Finally, the applicability of various life prediction models to TMF results was reviewed. Current life prediction models based on isothermal data must be modified before being generally applied to TMF.

Bill, R. C.↗

NASA NTRS1995

Heating of the solar middle chromosphere by large-scale electric currents

A global resistive, two-dimensional, time-dependent magnetohydrodynamic (MHD) model is used to introduce and support the hypothesis that the quiet solar middle chromosphere is heated by resistive dissipation of large-scale electric currents which fill most of its volume. The scale height and maximum magnitude of the current density are 400 km and 31.3 m/sq m, respectively. The associated magnetic field is almost horizontal, has the same scale height as the current density, and has a maximum magnitude of 153 G. The current is carried by electrons flowing across magnetic field lines at 1 m/s. The resistivity is the electron contribution to the Pedersen resitivity for a weakly ionized, strongly magnetized, hydrogen gas. The model does not include a driving mechanism. Most of the physical quantities in the model decrease exponentially with time on a resistive timescale of 41.3 minutes. However, the initial values and spatial; dependence of these quantities are expected to be essentially the same as they would be if the correct driving mechanism were included in a more general model. The heating rate per unit mass is found to be 4.5 x 10(exp 9) ergs/g/s, independent of height and latitude. The electron density scale height is found to be 800 km. The model predicts that 90% of the thermal energy required to heat the middle chromosphere is deposited in the height range 300-760 km above the temperature minimum. It is shown to be consistent to assume that the radiation rate per unit volume is proportional to the magnetic energy density, and then it follows that the heating rate per unit volume is also proportional to the energy from the photosphere into the overlying chromosphere are briefly discussed as possible driving mechanisms for establishing and maintaining the current system. The case in which part of or all of the current is carried by protons and metal ions, and the contribution of electron-proton scattering to the current are also considered, with the conclusion that these effects do not change the qualitative prediction of the model, but probably change the quantitative predictions slightly, mainly by increasing the maximum magntiude of the current density and magnetic field to at most approximately 100 mA/m and approximately 484 G, respectively. The heating rate per unit mass, current density scale height, magnetic field scale height, temperatures, and pressures are unchanged or are only slightly changed by including these additional effects due to protons and ions.

Goodman, M. L.↗

NASA NTRS2002

Bubble Formation and Detachment in Reduced Gravity Under the Influence of Electric Fields

The objective of the study is to investigate the behavior of individual air bubbles injected through an orifice into an electrically insulating liquid under the influence of a static electric field. Both uniform and nonuniform electric field configurations were considered. Bubble formation and detachment were recorded and visualized in reduced gravity (corresponding to gravity levels on Mars, on the Moon as well as microgravity) using a high-speed video camera. Bubble volume, dimensions and contact angle at detachment were measured. In addition to the experimental studies, a simple model, predicting bubble characteristics at detachment was developed. The model, based on thermodynamic considerations, accounts for the level of gravity as well as the magnitude of the uniform electric field. Measured data and model predictions show good agreement and indicate that the level of gravity and the electric field magnitude significantly affect bubble shape, volume and dimensions.

Herman, Cila↗

NASA NTRS2004

Malaria Modeling using Remote Sensing and GIS Technologies

Malaria has been with the human race since the ancient time. In spite of the advances of biomedical research and the completion of genomic mapping of Plasmodium falciparum, the exact mechanisms of how the various strains of parasites evade the human immune system and how they have adapted and become resistant to multiple drugs remain elusive. Perhaps because of these reasons, effective vaccines against malaria are still not available. Worldwide, approximately one to three millions deaths are attributed to malaria annually. With the increased availability of remotely sensed data, researchers in medical entomology, epidemiology and ecology have started to associate environmental and ecological variables with malaria transmission. In several studies, it has been shown that transmission correlates well with certain environmental and ecological parameters, and that remote sensing can be used to measure these determinants. In a NASA project, we have taken a holistic approach to examine how remote sensing and GIs can contribute to vector and malaria controls. To gain a better understanding of the interactions among the possible promoting factors, we have been developing a habitat model, a transmission model, and a risk prediction model, all using remote sensing data as input. Our objectives are: 1) To identify the potential breeding sites of major vector species and the locations for larvicide and insecticide applications in order to reduce costs, lessen the chance of developing pesticide resistance, and minimize the damage to the environment; 2) To develop a malaria transmission model characterizing the interactions among hosts, vectors, parasites, landcover and environment in order to identify the key factors that sustain or intensify malaria transmission, and 3) To develop a risk model to predict the occurrence of malaria and its transmission intensity using epidemiological data and satellite-derived or ground-measured environmental and meteorological data.

Kiang, Richard↗

NASA NTRS2002

Electric Field Effects on an Injected Air Bubble at Detachment in a Low Gravity Environment

The objective of the study is to investigate the behavior of individual air bubbles injected through an orifice into an electrically insulating liquid under the influence of a static and uniform electric field. Bubble formation and detachment were visualized and recorded in microgravity using a high-speed video camera. Bubble volume, dimensions and contact angle at detachment were measured. In addition to the experimental studies, a simple model, predicting bubble characteristics at detachment was developed. The model, based on thermodynamic considerations, accounts for the level of gravity as well as the magnitude of the uniform electric field. Measured data and model predictions show good agreement, and indicate that the level of gravity and the electric field magnitude significantly affect bubble shape, volume and dimensions.

Iacona, Estelle↗

NASA NTRS2015

Evaluation of Variable-Depth Liner Configurations for Increased Broadband Noise Reduction

This paper explores the effects of variable-depth geometry on the amount of noise reduction that can be achieved with acoustic liners. Results for two variable-depth liners tested in the NASA Langley Grazing Flow Impedance Tube demonstrate significant broadband noise reduction. An impedance prediction model is combined with two propagation codes to predict corresponding sound pressure level profiles over the length of the Grazing Flow Impedance Tube. The comparison of measured and predicted sound pressure level profiles is sufficiently favorable to support use of these tools for investigation of a number of proposed variable-depth liner configurations. Predicted sound pressure level profiles for these proposed configurations reveal a number of interesting features. Liner orientation clearly affects the sound pressure level profile over the length of the liner, but the effect on the total attenuation is less pronounced. The axial extent of attenuation at an individual frequency continues well beyond the location where the liner depth is optimally tuned to the quarter-wavelength of that frequency. The sound pressure level profile is significantly affected by the way in which variable-depth segments are distributed over the length of the liner. Given the broadband noise reduction capability for these liner configurations, further development of impedance prediction models and propagation codes specifically tuned for this application is warranted.

Jones, M. G.↗

DOE OSTI2025

Data-Enabled Fusion Technology (Final Scientific/Technical Report)

Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2018

Evaluation of the Validity of Bio-Mathematical Models in Predicting Fatigue in an Operational Environment

During long-duration spaceflight missions, crewmembers and ground-support staff experience irregular sleep schedules, erratic natural light patterns, and high workload due to mission demands. Such conditions can cause circadian misalignment and sleep loss, which in turn cause deficits in cognitive performance. While bio-mathematical models have been implemented within workplace settings to predict fatigue profiles, the accuracy of sleep-wake models under conditions of non-traditional shiftwork is little known. Thus, the present study aims to evaluate the validity of four sleep-wake models (e.g., SAFTE-FAST, the Unified Model of Performance, the Adenosine-Circadian Model, and the State-Space Model) designed to predict human performance and fatigue levels against objective measures of performance in a spaceflight analog. To accomplish this aim, we will collect Psychomotor Vigilance Task (PVT) data from four crews (n=16) in the Human Exploration Research Analog (HERA) over 45 days. HERA is a closed, 3-story habitat at Johnson Space Center where inhabitants are exposed to extreme space exploration scenarios under varying sleep-wake conditions. The PVT is a simple reaction time test that involves minimal learning, making it sensitive to the effects of sleep loss and circadian misalignment. Findings from this study will help inform work scheduling and implementation of effective countermeasures (e.g., caffeine, lighting) to improve work efficiency and combat fatigue, as well as offer valuable insight into the applicability of bio-mathematical fatigue models in future space exploration missions.

fatigue↗

DOE OSTI2020

A phenomenological rate of injection model for predicting fuel injection with application to mixture formation in light-duty diesel engines

Fuel injection rate laws are one of the most important pieces of information needed when modeling engine combustion with computational fluid dynamics. In this study, a simple phenomenological model of a common-rail injector was developed and calibrated for the Bosch CRI2.2 platform. The model requires three tunable parameter fits, making it relatively easy to calibrate and suitable for injector modeling when high-fidelity information about the internal injector’s geometry and electrical circuit details are not available. Each injection pulse is modeled as a sequence of up to four stages: an injection needle mechanical opening transient, a full-lift viscous flow inertial transient, a Bernoulli steady-state stage, and a needle descent transient. Parameters for each stage are obtained as polynomial fits from measured injection rate properties. The model enforces total injected mass, and the intermediate stages are only introduced if the injection pulse duration is long enough. Experimental rates of injection from two separate campaigns on the same injector were used to calibrate the model. The model was first validated against measured injection rate laws featuring pilot injections, short partially premixed combustion pulses, and conventional diesel combustion injection strategies. Then, it was employed as an input to engine computational fluid dynamics simulations, which were run to simulate experiments of mixture formation in an optically accessible light-duty diesel engine. It was found that, though simple, this model is capable of predicting both pilot and main injection pulse mass flow rates well: the simulations yielded accurate predictions of in-cylinder equivalence ratio distributions from injection strategies for both partially premixed combustion and pilot injections. Also, once calibrated, the model produced appropriate results for a wide range of injected mass and rail pressure values. Finally, it was observed that usage of such a relatively simple model can be a good choice when high-fidelity injection rate input and highly detailed information of the injector’s geometry and operation are not available, particularly as noticeable discrepancies can be present also among different experimental campaigns on similar hardware.

42 ENGINEERING↗

DOE OSTI2024

Preventive Power Outage Estimation Based on a Novel Scenario Clustering Strategy

The increasing occurrence of extreme weather events is challenging power grid operation. For extreme weather events, the system operator is responsible for estimating the power outages and scheduling the restoration resources. This paper proposes an outage evaluation framework to identify the possible unserved load profiles, vulnerable areas, and mobile energy adequacy. The outputs of an outage prediction model tool are used to generate numerous faulted line scenarios. Next, each scenario's nodal unserved load profile is obtained by solving a three-phase restoration model that considers repair crews and mobile energy resources (MERs). Then, a novel scenario clustering strategy is developed to cluster the unserved load profiles into multiple representative profiles which the system operator can focus on. Finally, case studies on a distribution system evaluate the damage caused by an extreme weather event and verify the effectiveness of the proposed scenario clustering strategy.

MATHEMATICS AND COMPUTING,POWER TRANSMISSION AND D↗

DOE OSTI2017

Evaluation of scenario-specific modeling approaches to predict plane of array solar irradiation

The two main objectives of this study are to evaluate the extent to which the prediction of plane of array irradiance is improved when diffuse radiation is predicted using location-specific regression models developed from onsite measured data as against using generalized models and to estimate the expected uncertainties associated with plane of array irradiance predictions under different data collection scenarios likely to be encountered in practical situations.

Moslehi, Salim↗

DOE OSTI2022

Training calibration-based counterfactual explainers for deep learning models in medical image analysis

The rapid adoption of artificial intelligence methods in healthcare is coupled with the critical need for techniques to rigorously introspect models and thereby ensure that they behave reliably. This has led to the design of explainable AI techniques that uncover the relationships between discernible data signatures and model predictions. In this context, counterfactual explanations that synthesize small, interpretable changes to a given query while producing desired changes in model predictions have become popular. This under-constrained, inverse problem is vulnerable to introducing irrelevant feature manipulations, particularly when the model’s predictions are not well-calibrated. Hence, in this paper, we propose the TraCE (training calibration-based explainers) technique, which utilizes a novel uncertainty-based interval calibration strategy for reliably synthesizing counterfactuals. Given the wide-spread adoption of machine-learned solutions in radiology, our study focuses on deep models used for identifying anomalies in chest X-ray images. Using rigorous empirical studies, we demonstrate the superiority of TraCE explanations over several state-of-the-art baseline approaches, in terms of several widely adopted evaluation metrics. Our findings show that TraCE can be used to obtain a holistic understanding of deep models by enabling progressive exploration of decision boundaries, to detect shortcuts, and to infer relationships between patient attributes and disease severity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

Machine Learning Models for Predicting Molecular UV–Vis Spectra with Quantum Mechanical Properties

Accurate understanding of Ultraviolet–visible (UV–Vis) spectra is critical for highthroughput design of compounds for drug discovery. Experimentally determining UV–Vis spectra can become expensive when dealing with a large quantity of novel molecules. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning. In this work, we use both Quantum Mechanically (QM) predicted and measured UV–Vis spectra as input to modify four different machine learning architectures: UVvis-SchNet, UVvis- DTNN, UVvis-Transformer, and UVvis-MPNN. Here we find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UVVisible spectra with a training RMSE of 0.06 and validation RMSE of 0.08.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Facilitating better and faster simulations of aerosol-cloud interactions in Earth system models

Focal Area(s): 1. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising a hierarchy of models. 2. Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: One major challenge that Earth system models (ESMs) face in providing credible prediction of the Earth system and its water cycle characteristics (e.g., mean state, variability, and extreme events) is to accurately simulate aerosol-cloud interactions (ACI). The physical, chemical, and dynamical processes affecting ACI are extremely complex and they range from nanoscale to planetary scale. In each model development cycle, scientists spend significant efforts investigating model deficiencies and uncertainties associated with aerosols (e.g., emissions, chemical processes, aerosol microphysics, and transport) and clouds (e.g., macrophysics, microphysics, turbulence, and large-scale circulation) in order to develop improved treatments. However, despite decades of active research, ACI is still a major source of uncertainty in climate projections, even though great progress has been made. Specific scientific challenges include: (i) Parameterizations are developed based on limited data; (ii) The complexity of a parameterization required for accurate predictions is not understood; (iii) Incomplete and unknown physics leads to errors in the fully coupled Earth system; and (iv) Complex physics is computationally too expensive to employ in ESMs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Measurements of atoms and metastable species in N 2 and H 2 –N 2 nanosecond pulse plasmas

We report time-resolved, absolute number densities of metastable N 2 (A 3 Σ u + , v = 0, 1) molecules, ground state N 2 and H atoms, and rotational–translational temperature have been measured by tunable diode laser absorption spectroscopy and two-photon absorption laser-induced fluorescence in diffuse N 2 and N 2 –H 2 plasmas during and after a nanosecond pulse discharge burst. Comparison of the measurement results with the kinetic modeling predictions, specifically the significant reduction of the N 2 (A 3 Σ u + ) populations and the rate of N atom generation during the burst, suggests that these two trends are related. The slow N atom decay in the afterglow, on a time scale longer than the discharge burst, demonstrates that the latter trend is not affected by N atom recombination, diffusion to the walls, or convection with the flow. This leads to the conclusion that the energy pooling in collisions of N 2 (A 3 Σ u + ) molecules is a major channel of N 2 dissociation in electric discharges where a significant fraction of the input energy goes to electronic excitation of N 2 . Additional measurements in a 1% H 2 –N 2 mixture demonstrate a further significant reduction of N 2 (A 3 Σ u + , v = 0, 1) populations, due to the rapid quenching by H atoms accumulating in the plasma. Comparison with the modeling predictions suggests that the N 2 (A 3 Σ u + ) molecules may be initially formed in the highly vibrationally excited states. The reduction of the N 2 (A 3 Σ u + ) number density also diminishes the contribution of the energy pooling process into N2 dissociation, thus reducing the N atom number density. The rate of N atom generation during the burst also decreases, due to its strong coupling to N 2 (A 3 Σ u + , v) populations. On the other hand, the rate of H atom generation, produced predominantly by the dissociative quenching of the excited electronic states of N 2 by H 2 , remains about the same during the burst, resulting in a nearly linear rise in the H atom number density. Comparison of the kinetic model predictions with the experimental results suggests that the yield of H atoms during the quenching of the excited electronic state of N 2 by molecular H 2 is significantly less than 100%. The present results quantify the yield of N and H atoms in high-pressure H 2 –N 2 plasmas, which have significant potential for ammonia generation using plasma-assisted catalysis

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Prospects of federated machine learning in fluid dynamics

Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due to the rapid developments in data science, processing units, neural network based technologies, and sensor adaptations. So far in many applications in fluid dynamics, machine learning approaches have been mostly focused on a standard process that requires centralizing the training data on a designated machine or in a data center. In this article, we present a federated machine learning approach that enables localized clients to collaboratively learn an aggregated and shared predictive model while keeping all the training data on each edge device. We demonstrate the feasibility and prospects of such a decentralized learning approach with an effort to forge a deep learning surrogate model for reconstructing spatiotemporal fields. Our results indicate that federated machine learning might be a viable tool for designing highly accurate predictive decentralized digital twins relevant to fluid dynamics.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Nanosecond Transient Validation of Surge Arrester Models to Predict Electromagnetic Pulse Response

The impact of high-altitude electromagnetic pulse events on the electric grid is not fully understood, and validated modeling of mitigations, such as lightning surge arresters (LSAs) is necessary to predict the propagation of very fast transients on the grid. Experimental validation of high frequency models for surge arresters is an active area of research. Further, this article serves to experimentally validate a previously defined ZnO LSA model using four metal-oxide varistor pucks and nanosecond scale pulses to measure voltage and current responses. The SPICE circuit models of the pucks showed good predictability when compared to the measured arrester response when accounting for a testbed inductance of approximately 100 nH. Additionally, the comparatively high capacitance of low-profile arresters show a favorable response to high-speed transients that indicates the potential for effective electromagnetic pulse mitigation with future materials design.

42 ENGINEERING↗

DOE OSTI2019

A closure model for predicting crack growth under creep-fatigue loading

A strip-yield model was formulated here to simulate creep-fatigue crack growth and to quantify the influence of hold time on plasticity-induced crack closure during creep-fatigue crack growth. Creep-fatigue experiments have shown that longer creep hold times result in faster crack growth rates in subsequent fatigue cycles. This model advances the idea that a decrease of plasticity-induced crack closure is experienced by the crack during fatigue loading when a longer hold time is applied each creep-fatigue cycle. Consequently, the crack tip experiences an increase in the effective stress intensity factor range causing faster growth rate during the fatigue loading. The weight function method was used to compute stress intensity factors and surface displacements for cracks embedded in a material experiencing elastic, plastic and creep deformations at elevated temperatures. It is shown that the longer the hold time, the larger the creep deformation and crack opening displacements in the near crack-tip region. In turn, this leads to a decrease in the crack-tip opening stress/load and faster crack growth rates during the subsequent fatigue cycle. The model was used to perform simulations of creep-fatigue crack growth at elevated temperatures in a nickel-base superalloy and AISI 316 austenitic steel.

42 ENGINEERING↗