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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Gross spectral differences between bright and dim gamma-ray bursts

We find that dim gamma-ray bursts (GRBs) are softer than bright GRBs, as indicated on average by data from the Burst and Transient Source Experiment (BATSE) on board the Compton Gamma Ray Observatory (CGRO). We show that this correlation is statistically significant with respect to variations due to random differences between GRBs. This effect is discernible using a variety of methods and data sets, including public domain data. We analyze several types of systematic errors and selection effects in the BATSE data and conclude that the observed effect is not dominated by any of them. We therefore assert that this dim/soft effect is a real property of GRBs. It is possible that this correlation is a consequence of the time dilation and that the burst sources are located at cosmological distances.

Nemiroff, R. J.↗
NASA NTRS2001

Structural Qualification Testing of the WindSat Payload Using Sine Bursts Near Structural Resonance

Sine burst tests are often used for structural qualification of space flight hardware. In most instances, the driving frequency of the shaker is specified far below the structure's first resonant mode, such that the entire test article sees uniform acceleration. For large structures, this limits qualification testing to lower parts of the structure, or else it over-tests the lower structure to achieve qualification of the upper structure. The WindSat payload, a 10.5 foot tall graphite/epoxy, titanium, and aluminum radiometer, experiences accelerations at the six foot diameter reflector nearly four times that at the spacecraft interface. Due to size of the payload, the number of bonded joints, and the lightweight reflector support structure design and construction, using static pull testing to qualify all of the bonded joints in the upper structure would result in large, expensive, and extensive test fixturing. Sine burst testing near the first two structural resonant modes was performed on the WindSat payload to achieve the correct load factor distribution up the stack for structural qualification. In this presentation, how finite element method (FEM) sine burst predictions were used in conjunction with low level random and sine burst tests to achieve correct qualification test load factor distribution on the WindSat payload is discussed. Also presented is the risk mitigation approach for using the uncorrelated FEM in this procedure.

Pontius, Jim↗
NASA NTRS2008

Quantum-Inspired Maximizer

A report discusses an algorithm for a new kind of dynamics based on a quantum- classical hybrid-quantum-inspired maximizer. The model is represented by a modified Madelung equation in which the quantum potential is replaced by different, specially chosen 'computational' potential. As a result, the dynamics attains both quantum and classical properties: it preserves superposition and entanglement of random solutions, while allowing one to measure its state variables, using classical methods. Such optimal combination of characteristics is a perfect match for quantum-inspired computing. As an application, an algorithm for global maximum of an arbitrary integrable function is proposed. The idea of the proposed algorithm is very simple: based upon the Quantum-inspired Maximizer (QIM), introduce a positive function to be maximized as the probability density to which the solution is attracted. Then the larger value of this function will have the higher probability to appear. Special attention is paid to simulation of integer programming and NP-complete problems. It is demonstrated that the problem of global maximum of an integrable function can be found in polynomial time by using the proposed quantum- classical hybrid. The result is extended to a constrained maximum with applications to integer programming and TSP (Traveling Salesman Problem).

Zak, Michail↗
NASA NTRS2016

CLARREO: Reference Inter-Calibration on Orbit With Reflected Solar Spectrometer

The CLARREO approach for reference intercalibration is based on obtaining coincident highly accurate spectral reflectance and reflected radiance measurements, and establish an on-orbit reference for existing Earth viewing reflected solar radiation sensors: CERES and VIIRS on JPSS satellites, AVHRR and follow-on imagers on MetOp, and imagers on GEO platforms. The mission goal is to be able to provide CLARREO RS reference observations that are matched in space, time, and viewing angles with measurements from the aforementioned instruments, with sampling sufficient to overcome the random error sources from imperfect data matching and instrument noise. The intercalibration method is to monitor over time changes in targeted sensor response function parameters: effective offset, gain, nonlinearity, spectral degradation, and sensitivity to polarization of optics.

Lukashin, C.↗
NASA NTRS2016

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Safe and efficient surface operations↗
NASA NTRS2019

Astrophysics and Technical Study of a Solar Neutrino Spacecraft: NIAC (NASA Innovative Advanced Concepts) Grant - NASA 2018-Phase-1

We report on our study of the design of a neutrino detector, shielding and veto array needed to operate a neutrino detector in space close to the Sun. This study also took into account the expected rates of Galactic gamma and cosmic rays in addition to the particles from the Sun.These preliminary studies show that we can devise a detector such that a small signal of neutrino interactions can be extracted from a large random number of events from the background sources using a double timing method from the conversion electron produced in the neutrino interaction and a secondary delayed signal from the nuclear excited state produced from the initial neutrino interaction; in our case the conversion of Ga 69 or 71 into Ge 69 or 71, but this method could apply to other nuclei with large neutrino cross sections such as Ir 115. Although these types of events need to be above 0.405 megaelectronvolt (MeV) neutrino energy and are only 66 percent of all conversion neutrino interactions on Gallium, this is a small price to pay for an increase of 10,000 by going close to the Sun to enhance the neutrino rate over the background combatorical fake-signal events. The conclusion of this Phase-1 study is very positive in that we can get the backgrounds less than 20 percent fake signals, and in addition to this we have devised another shielding method that makes the Galactic gamma-ray rate a hundred fold less which will make further improvements over these initial estimates. Although these studies are very encouraging it suggests that the next step is a NIAC Phase-II to actually build a test device,measuring basic principles such as light attention within the scintillator with high dopants and to take data in the lab with a cosmic-ray test stand and triggered X-ray source for comparison with simulated expected performance of the detector. This would be the perfect lead into a future proposal beyond a NIAC (NASA Innovative Advanced Concepts) Phase-II for a test flight of a small one-pint detector in orbit of the detector concept beyond Earth outside of the radiation belts.

Astrophysics↗
NASA NTRS2017

Thermal Expansion from Stochastic Nonlinear Acoustic Fields

The methods of stochastic mechanics are applied to nonlinear acoustic fields generated by random acoustic radiation sources associated with oscillating lattice sites in nonlinear lattices. The assumption of stochastically independent zero-point and temperature-dependent acoustic fields leads to expressions of the thermodynamic internal and Helmholtz free energies per unit mass in terms of modal energies per unit mass that account for the nonlinearity of the propagation modes. The thermodynamic state functions canonically transform to the familiar results of a system of quantized, simple harmonic oscillators in the linear field limit. The thermal expansion coefficient, derived from the Helmholtz free energy per unit mass, is obtained as a sum of the zero-point modal nonlinearity parameters, weighted by temperature-dependent, modal heat capacities. The relationship is fully anharmonic, in contrast to commonly used quasi-harmonic models, and predicts with excellent agreement the experimentally observed null thermal expansion of vitreous silica at the ‘cross-over’ temperature corresponding to the balance between long wavelength modes that contribute negative nonlinearity parameters to the modal sum and short wavelength modes that contribute positive nonlinearity parameters.

Cantrell, John H., Jr.↗
NASA NTRS2024

Impact of Simulated Microgravity on Mesenchymal Stem Cell Proliferation

Background Long term exposure to microgravity has adverse effects on human body such as bone loss. Due to the vital role of mesenchymal stem cell (MSC) in bone regeneration, studying MSC under microgravity conditions has been of interest during the past decade. However, high cost and limited access to space flight can dampen enthusiasm of researchers. Microgravity simulators provide a way to investigate the effects of microgravity in human physiology. Methods In this study, we compared MSC cultures under simulated microgravity using random positioning machine (RPM) and Gravite 3D simulators. Two culture durations, 1 week and 2 weeks, were selected for MSC cultures without media change. To evaluate impact of simulated microgravity on cell proliferation, cell counting was performed using trypan blue exclusion assay. Additionally, RNA samples and conditioned media were collected for further analysis. Results After 1 week and 2 weeks, no cell morphology change was observed from RPM and Gravite cultures compared to control. Notably, distinct lower cell confluence at the edge of the flasks on the RPM was observed, which could be caused by fluid sheer forces. MSCs cultured on both microgravity simulators showed decreased cell proliferation after 1-week culture. After 2-week culture, cells grown on the RPM showed no significant difference in cell proliferation compared with control while cells cultured on the Gravite bioreactor showed significantly enhanced MSC proliferation. Conclusion Type of microgravity simulator and duration of culture significantly affect MSC proliferation. Further investigation into how simulated microgravity influences MSC gene expression and cytokine secretion related to bone loss is currently on going in our lab.

Cuiping Zhang↗
DOE OSTI2024

Quaternized polyaromatics for use in electrochemical devices

Disclosed herein in various embodiments are aryl-ether free polyaromatic polymers based on random copolymer architecture with two, three, or more aromatic ring components and methods of preparing those polymers. The polymers of the present disclosure can be used as ion exchange membranes, e.g., as anion exchange membranes, and ionomer binders in alkaline electrochemical devices.

Bae, Chulsung↗
DOE OSTI2025

Power System Feature-Based Event Classification by Means of Multiple PMU Data

Abstract—Phasor Measurement Units (PMUs) provide time synchronized measurements across the power grid, enabling data driven event detection and classification for enhanced system monitoring and situational awareness. However, variations in event duration, spatial extent, and severity, along with coincident events, pose challenges for conventional classification models that require fixed-size inputs. This paper presents a feature-based framework that aggregates diverse attributes from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, and Multilayer Perceptron. A probabilistic post-processing scheme is further introduced to enable multi-label classification in the presence of overlapping events. Experiments using real-world PMU data demonstrate that the Random Forest model achieves 95% accuracy, while the proposed post-processing method yields an additional 3% improvement.

Nematirad, Reza↗
DOE OSTI2026

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗
DOE OSTI2022

The influence of random packed column parameters on the liquid holdup and interfacial area

Abstract Carbon dioxide capture via solvent absorption in packed columns has emerged as a potential technology to mitigate coal‐fired power plant CO 2 emissions. Parameters, including packing types, solvent properties, and operating conditions, could potentially affect the packed column CO 2 capture efficiency. To understand the importance of those parameters and help packed column optimization, a design of experiments (DoEs) method was proposed to generate input parameter matrix. Combined with multiphase computational fluid dynamics (CFD), the random packed column parameter influence on the liquid holdup and interfacial area can be efficiently investigated. Surrogate‐based sensitivity analysis shows that the solvent flow rate and contact angle are key factors dictating liquid holdup and interfacial area. On the other hand, solvent viscosity has a marginal impact on the interfacial area. The sensitivity scores were calculated for each input parameter to guide the selection of dimensionless numbers for the liquid holdup and interfacial area correlation development.

42 ENGINEERING↗
NASA NTRS1984

Application of multiple input random and polyreference analysis techniques to the Galileo spacecraft modal test

An experimental modal analysis of the Galileo spacecraft was required to verify a finite element model used in loads analysis. Multiple input random and polyreference analysis techniques were applied in this program to demonstrate their effectiveness in determining the modal characteristics of a complex space structure. The methods were successful in determining an accurate set of modal data from two days of data acquisition. A complete set of results was available within 24 hours of test completion. Final analysis shows the modes from the multiple input random tests to be more complete and orthogonal than those obtained from classical sine dwell methods.

Chen, J. C.↗
DOE OSTI2024

A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

Power flow computations are fundamental to many power system studies. Obtaining a converged power flow case is not a trivial task especially in large power grids due to the non-linear nature of the power flow equations. One key challenge is that the widely used Newton based power flow methods are sensitive to the initial voltage magnitude and angle estimates, and a bad initial estimate would lead to non-convergence. This paper addresses this challenge by developing a random-forest (RF) machine learning model to provide better initial voltage magnitude and angle estimates towards achieving power flow convergence. This method was implemented on a real ERCOT 6102 bus system under various operating conditions. By providing better Newton-Raphson initialization, the RF model precipitated the solution of 2,106 cases out of 3,899 non-converging dispatches. These cases could not be solved from flat start or by initialization with the voltage solution of a reference case. Finally, results obtained from the RF initializer performed better when compared with DC power flow initialization, Linear regression, and Decision Trees.

random forest↗
DOE OSTI2020

The Specific Carbohydrate Diet and Diet Modification as Induction Therapy for Pediatric Crohn’s Disease: A Randomized Diet Controlled Trial

Background: Crohn’s disease (CD) is a chronic inflammatory intestinal disorder associated with intestinal dysbiosis. Diet modulates the intestinal microbiome and therefore has a therapeutic potential. The aim of this study is to determine the potential efficacy of three versions of the specific carbohydrate diet (SCD) in active Crohn’s Disease. Methods: 18 patients with mild/moderate CD (PCDAI 15–45) aged 7 to 18 years were enrolled. Patients were randomized to either SCD, modified SCD(MSCD) or whole foods (WF) diet. Patients were evaluated at baseline, 2, 4, 8 and 12 weeks. PCDAI, inflammatory labs and multi-omics evaluations were assessed. Results: Mean age was 14.3 ± 2.9 years. At week 12, all participants (n = 10) who completed the study achieved clinical remission. The C-reactive protein decreased from 1.3 ± 0.7 at enrollment to 0.9 ± 0.5 at 12 weeks in the SCD group. In the MSCD group, the CRP decreased from 1.6 ± 1.1 at enrollment to 0.7 ± 0.1 at 12 weeks. In the WF group, the CRP decreased from 3.9 ± 4.3 at enrollment to 1.6 ± 1.3 at 12 weeks. In addition, the microbiome composition shifted in all patients across the study period. While the nature of the changes was largely patient specific, the predicted metabolic mode of the organisms increasing and decreasing in activity was consistent across patients. Conclusions: This study emphasizes the impact of diet in CD. Each diet had a positive effect on symptoms and inflammatory burden; the more exclusionary diets were associated with a better resolution of inflammation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Data-assisted combustion simulations with dynamic submodel assignment using random forests

This investigation outlines a data-assisted approach that employs random forest classifiers for local and dynamic submodel assignment in turbulent-combustion simulations. This method is demonstrated in simulations of a single-element GOX/GCH4 rocket combustor; a priori as well as a posteriori assessments are conducted to (i) evaluate the accuracy and adjustability of the classifier for targeting different quantities of interest (QoIs), and (ii) assess improvements, resulting from the data-assisted combustion model assignment, in predicting target QoIs during simulation runtime. Results from the a priori study show that random forests, trained with local flow properties as input variables and combustion model errors as training labels, assign three different combustion models – finite-rate chemistry (FRC), flamelet progress variable (FPV) model, and inert mixing (IM) – with reasonable classification performance even when targeting multiple QoIs. Applications in a posteriori studies demonstrate improved predictions from data-assisted simulations, in temperature and CO mass fraction, when compared with monolithic FPV calculations. An additional a posteriori data-assisted simulation of a modified configuration demonstrates that the present approach can be successfully applied to different configurations, as long as thermophysical behavior can be represented by the training data. Furthermore, these results demonstrate that this data-driven framework holds promise for dynamic combustion submodel assignments in reacting flow simulations.

42 ENGINEERING↗
DOE OSTI2025

Source Analysis of Ozone Pollution in Liaoyuan City’s Atmosphere Based on Machine Learning Models and HYSPLIT Clustering Method

Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O 3 ) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O 3 concentrations. Finally, the HYSPLIT clustering method and CMAQ model are utilized to analyze inter-regional transport characteristics, identifying the causes of O 3 pollution. The results indicate that O 3 pollution in Liaoyuan exhibits a distinct seasonal pattern, with the highest concentrations found in spring and summer, peaking in the afternoon. Among the three ML models, the random forest model demonstrates the best predictive performance (R 2 = 0.9043). Feature importance identifies NO 2 as the primary driving factor, followed by meteorological conditions in the second quarter and land surface characteristics. Furthermore, regional transport significantly contributes to O 3 pollution, with approximately 80% of air mass trajectories in heavily polluted episodes originating from adjacent industrial areas and the sea. The combined effects of transboundary precursors and O 3 transport with local emissions and meteorological conditions further increase the O 3 pollution level. This study highlights the need to strengthen coordinated NO X and VOCs emission reductions and enhance regional joint prevention and control strategies in China.

HYSPLIT clustering↗