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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Musculoskeletal Modeling Component of the NASA Digital Astronaut Project

The NASA Digital Astronaut Project s (DAP) objective is to provide computational tools that support research of the physiological response to low gravity environments and analyses of how changes cause health and safety risks to the astronauts and to the success of the mission. The spaceflight risk associated with muscle atrophy is impaired performance due to reduced muscle mass, strength and endurance. Risks of early onset of osteoporosis and bone fracture are among the spaceflight risks associated with loss of bone mineral density. METHODS: Tools under development include a neuromuscular model, a biomechanical model and a bone remodeling model. The neuromuscular model will include models of neuromuscular drive, muscle atrophy, fiber morphology and metabolic processes as a function of time in space. Human movement will be modeled with the biomechanical model, using muscle and bone model parameters at various states. The bone remodeling model will allow analysis of bone turnover, loss and adaptation. A comprehensive trade study was completed to identify the current state of the art in musculoskeletal modeling. The DAP musculoskeletal models will be developed using a combination of existing commercial software and academic research codes identified in the study, which will be modified for use in human spaceflight research. These individual models are highly dependent upon each other and will be integrated together once they reach sufficient levels of maturity. ANALYSES: The analyses performed with these models will include comparison of different countermeasure exercises for optimizing effectiveness and comparison of task requirements and the state of strength and endurance of a crew member at a particular time in a mission. DISCUSSION: The DAP musculoskeletal model has the potential to complement research conducted on spaceflight induced changes to the musculoskeletal system. It can help with hypothesis formation, identification of causative mechanisms and supplementing small data samples.

Lewandowski, B. E.↗
NASA NTRS2010

SIM-Lite Mission Spectral Calibration Sensitivities and Refinements

SIM-Lite missions will perform astrometry at microarcsecond accuracy using star light interferometry. For typical baselines that are shorter than 10 meters, this requires to measure optical path difference (OPD) accurate to tens of picometers calling for highly accurate calibration. A major challenge is to calibrate the star spectral dependency in fringe measurements -- the spectral calibration. Previously, we have developed a spectral calibration and estimation scheme achieving picometer level accuracy. In this paper, we present the improvements regarding the application of this scheme from sensitivity studies. Data from the SIM Spectral Calibration Development Unit (SCDU) test facility shows that the fringe OPD is very sensitive to pointings of both beams from the two arms of the interferometer. This sensitivity coupled with a systematic pointing error provides a mechanism to explain the bias changes in 2007. Improving system alignment can effectively reduce this sensitivity and thus errors due to pointing errors. Modeling this sensitivity can lead to further improvement in data processing. We then investigate the sensitivity to a model parameter, the bandwidth used in the fringe model, which presents an interesting trade between systematic and random errors. Finally we show the mitigation of calibration errors due to system drifts by interpolating instrument calibrations. These improvements enable us to use SCDU data to demonstrate that SIM-Lite missions can meet the 1pm noise floor requirement for detecting earth-like exoplanets.

wave front aberration↗
DOE OSTI2021

Hydrodechlorination of 1,2-Dichloroethane on Platinum Catalysts: Insights from Reaction Kinetics Experiments, Density Functional Theory, and Microkinetic Modeling

Catalytic hydrodechlorination is a promising strategy for treating industrial 1,2-dichloroethane wastes, for which Pt and Pt-based alloy catalysts are widely used. Here, we performed a detailed mechanistic study for 1,2-dichloroethane hydrodechlorination on Pt using a synergistic approach combining density functional theory (DFT) calculations, reaction kinetics experiments, and microkinetic modeling. Using planewave DFT calculations, we evaluated the reaction energy and activation energy barrier of each elementary step involved in the reaction network on Pt(111). The calculated energetics were then incorporated into a comprehensive mean-field microkinetic model accounting for a total of 65 elementary steps. The model-predicted reaction rates were compared with the results from our reaction kinetics experiments on SiO2-supported Pt catalysts. Our results indicated that the hydrodechlorination of 1,2-dichloroethane on Pt(111) starts with a H-removal step; then, it proceeds through a sequence of alternating dechlorination and dehydrogenation steps until vinylidene (CH 2 C*) is formed; finally, CH 2 C* is hydrogenated to the final product, ethane, sequentially via vinyl (CH 2 CH*), ethylene, and ethyl (CH 3 CH 2 *) intermediates. After model parameter adjustments, we achieved good agreement between our theoretical model and experimental results; the adjustments to the calculated parameters are consistent with the typically anticipated coverage effects. Furthermore, our study offers valuable mechanistic insights, which are useful for improving catalysts for this chemistry.

1,2-dichloroethane↗
NASA NTRS2007

Mechanical Analog Approach to Parameter Estimation of Lateral Spacecraft Fuel Slosh

The nutation (wobble) of a spinning spacecraft in the presence of energy dissipation is a well-known problem in dynamics and is of particular concern for space missions. Even with modern computing systems, CFD type simulations are not fast enough to allow for large scale Monte Carlo analyses of spacecraft and launch vehicle dynamic behavior with slosh included. Simplified mechanical analogs for the slosh are preferred during the initial stages of design to reduce computational time and effort to evaluate the Nutation Time Constant (NTC). Analytic determination of the slosh analog parameters has met with mixed success and is made even more difficult by the introduction of propellant management devices such as elastomeric diaphragms. By subjecting full-sized fuel tanks with actual flight fuel loads to motion similar to that experienced in flight and measuring the forces experienced by the tanks, these parameters can be determined experimentally. Currently, the identification of the model parameters is a laborious trial-and-error process in which the hand-derived equations of motion for the mechanical analog are evaluated and their results compared with the experimental results. Of particular interest is the effect of diaphragms and bladders on the slosh dynamics and how best to model these devices. An experimental set-up is designed and built to include a diaphragm in the simulated spacecraft fuel tank subjected to lateral slosh. This research paper focuses on the parameter estimation of a SimMechanics model of the simulated spacecraft propellant tank with and without diaphragms using lateral fuel slosh experiments. Automating the parameter identification process will save time and thus allow earlier identification of potential vehicle problems.

Chatman, Yadira↗
DOE OSTI2025

A statistical approach to screening isotopic signatures in monitoring for underground nuclear explosions

The ability to differentiate between atmospheric radionuclide signatures from underground nuclear explosions (UNEs) and signals from other sources, such as medical isotope-production facilities and nuclear reactors, can be critical to the detection and monitoring of unannounced, low-yield nuclear events. Signatures having anomalously high amplitudes, compared to background levels, remain the best indicator in screening for a UNE. However, isotopic composition can further validate a suspected UNE signature, but separation from any atmospheric background composition is first necessary. To date, evaluating the challenges of performing this separation has typically involved comparing an observed background with a highly idealized deterministic model of radioxenon signature production by a UNE that does not consider the influence of post-detonation chemical/physical processes in the detonation cavity or the subsequent gas transport mechanisms that can also affect the isotopic composition of the detected gas signature. In addition, purely deterministic models, as previously employed, overlook the uncertainty inherent in estimating critical parameters characterizing the UNE and its detonation environment. In this paper, we create detailed, multi-parameter models of radionuclide evolution using the widely accepted England and Rider post-detonation radionuclide decay-chain network coupled to detailed models simulating physical production and transport processes affecting the gas signature. Because these models are governed by uncertain parameters including barometric fluctuations, realistic ranges of variation for each of the parameters influencing isotopic composition are then defined. A Latin-Hypercube sampling approach is used to obtain a random distribution of isotopic production and gas transport results associated with a given value of each parameter. We apply these results to background histories of two stations, one providing 4-isotope background measurements and the other providing two-isotope measurements associated with the 2013 DPRK announced UNE.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Jipole: A Differentiable ipole-based Code for Radiative Transfer in Curved Spacetimes

Recent imaging of supermassive black holes by the Event Horizon Telescope has relied on exhaustive parameter-space searches, matching observations to large, precomputed libraries of theoretical models. As observational data become increasingly precise, the limitations of this computationally expensive approach grow more acute, creating a pressing need for more efficient methods. In this work, we present Jipole, an automatically differentiable (AD), ipole-based code for radiative transfer in curved spacetimes, designed to compute image gradients with respect to underlying model parameters. These gradients quantify how parameter changes—such as the black hole’s spin or the observer’s inclination—affect the image, enabling more efficient parameter estimation and reducing the number of required images. We validate Jipole against ipole in two analytical tests and then compare pixelwise intensity derivatives from AD with those from finite-difference methods. We then demonstrate the utility of these gradients by performing parameter recovery for an analytical model in three increasingly complex cases for the injected image: ideal, blurred, and blurred with added noise. In most cases, high-accuracy fits are obtained in only a few optimization steps, failing only in cases with extremely low signal-to-noise ratios. These results highlight the potential of AD-based methods to accelerate robust, high-fidelity model-data comparisons in current and future black hole imaging efforts.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2000

Modeled and Empirical Approaches for Retrieving Columnar Water Vapor from Solar Transmittance Measurements in the 0.72, 0.82, and 0.94 Micrometer Absorption Bands

A Sun photometer (18 channels between 300 and 1024 nm) has been used for measuring the columnar content of atmospheric water vapor (CWV) by solar transmittance measurements in absorption bands with channels centered at 719, 817, and 946 nm. The observable is the band-weighted transmittance function defined by the spectral absorption of water vapor and the spectral features of solar irradiance and system response. The transmittance function is approximated by a three-parameter model. Its parameters are determined from MODTRAN and LBLRTM simulations or empirical approaches using CWV data of a dual-channel microwave radiometer (MWR) or a Fourier transform spectrometer (FTS). Data acquired over a 2-year period during 1996-1998 at two different sites in Switzerland, Bern (560 m above sea level (asl)) and Jungfraujoch (3580 m asl) were compared to MWR, radiosonde (RS), and FTS retrievals. At the low-altitude station with an average CWV amount of 15 mm the LBLRTM approach (based on recently corrected line intensities) leads to negligible biases at 719 and 946 nm if compared to an average of MWR, RS, and GPS retrievals. However, at 817 nm an overestimate of 2.7 to 4.3 mm (18-29%) remains. At the high-altitude station with an average CWV amount of 1.4 mm the LBLRTM approaches overestimate the CWV by 1.0, 1.4. and 0.1 mm (58, 76, and 3%) at 719, 817, and 946 nm, compared to the ITS instrument. At the low-altitude station, CWV estimates, based on empirical approaches, agree with the MWR within 0.4 mm (2.5% of the mean); at the high-altitude site with a factor of 10 less water vapor the agreement of the sun photometers (SPM) with the ITS is 0.0 to 0.2 mm (1 to 9% of the mean CWV there). Sensitivity analyses show that for the conditions met at the two stations with CWV ranging from 0.2 to 30 mm, the retrieval errors are smallest if the 946 nm channel is used.

Ingold, T.↗
NASA NTRS1988

JPL pulsar timing observations. V - Macro and microjumps in the Vela pulsar 0833-45

In this paper, 14.5 yr of JPL timing data are analyzed to study the spindown of the Vela pulsar. Discontinuities in the spin frequency and its derivatives have bimodal amplitude distributions. Macrojumps are characterized by relative changes in spin rate of about 10 to the -6th with a derivative of 0.01, while microjumps have relative changes in spin rate of less than about 10 to the -9th with derivative of less than about 0.0001. Macrojumps display a (+,-) signature with a well-defined ratio of the amplitudes. Microjumps, however, show all possible signs of events and a larger range in signature. Six macrojumps and their associated transients are quantified using a 10-parameter model for each jump. Amplitude and decay time parameters vary by a factor of about five over the set of jumps. The results are discussed in terms of superfluid vortex models. The macrojumps are consistent with a model in which angular momentum is suddenly transferred to the crust from a more rapidly rotating superfluid core, followed by relaxation of the spin-down rate.

Cordes, J. M.↗
DOE OSTI2026

A Perspective on Traditional and Data Driven Electrochemical Modeling and Analysis

To understand the behavior of electrochemical systems, we need to reduce the dimensionality of the measured current-voltage-time (I-V-t) data by fitting models, thus enabling us to analyze and compare the governing physics. Traditionally, the process for this is an 'expert first' approach: defining the model and its explicit assumptions based on inductive reasoning or empirical observation, fitting small portions of the I-V-t data where assumptions are most valid or carefully designing experiments to enforce key assumptions, and then interpreting the model parameters. However, modern data-driven methods enable a new paradigm: a 'data first' approach, where the latent behaviors governing the system's measured response are identified directly using machine-learning models that optimize both model structure and parameters from the I-V-t data, guaranteeing that the learned model explains as much of the observed system response as possible. After model identification, the model can then be interrogated by an expert to connect observed behaviors with underlying physics. This talk will review several different types of electrochemical analysis (electrochemical impedance, differential voltage-capacity, electrochemical kinetics) and compare the traditional and data-driven methods for analyzing the data.

42 ENGINEERING↗
NASA NTRS2011

System Identification Applied to Dynamic CFD Simulation and Wind Tunnel Data

Demanding aerodynamic modeling requirements for military and civilian aircraft have provided impetus for researchers to improve computational and experimental techniques. Model validation is a key component for these research endeavors so this study is an initial effort to extend conventional time history comparisons by comparing model parameter estimates and their standard errors using system identification methods. An aerodynamic model of an aircraft performing one-degree-of-freedom roll oscillatory motion about its body axes is developed. The model includes linear aerodynamics and deficiency function parameters characterizing an unsteady effect. For estimation of unknown parameters two techniques, harmonic analysis and two-step linear regression, were applied to roll-oscillatory wind tunnel data and to computational fluid dynamics (CFD) simulated data. The model used for this study is a highly swept wing unmanned aerial combat vehicle. Differences in response prediction, parameters estimates, and standard errors are compared and discussed

Murphy, Patrick C.↗
DOE OSTI2021

An initial framework for the rapid qualification of long-term creep rupture strength via microstructural modeling

This report describes the development and testing of a new method for extrapolating short-term creep rupture test data to predict long-term rupture strength. The goal of this work is to reduce the time required to qualify new materials for nuclear service by reducing the lead time required for dedicated, long-term material testing to establish key long-term material properties. The new approach described here uses a physics-based model to predict the long-term creep rupture strength of 316H stainless steel using only short-term test data. The key idea is to use Bayesian inference to find the statistical distribution of the model parameters that best explain the short-term rupture data. Because the model is physics-based these parameters are all microstructural quantities that can be measured through detailed material characterization experiments. The Bayesian prior distributions provide a means for incorporating this characterization data into the final model to improve the accuracy of the long-term model predictions. However, where such data is not available the process still produces an accurate model based on an uniformed prior. Our hypothesis is that this approach more accurately extrapolates the short-term test data when compared to current, empirical methods. The report proves this hypothesis using actual long-term rupture data available for 316H, including tests with rupture times greater than 200,000 hours. The general approach developed here could be applied to other materials and other time-dependent material properties. Applying this new technique to develop long-term qualified material properties, potentially in conjunction with other accelerated qualification approaches like staggered qualification test programs, could greatly reduce the time required to qualify new materials for nuclear service.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Multiprobe cosmology from the abundance of SPT clusters and DES galaxy clustering and weak lensing

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the Universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3 × 2 ⁢pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining Λ cold dark matter (Λ ⁢CDM) parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure Ω m = 0.300 ± 0.017 and 𝜎 8 = 0.797 ± 0.026. Compared to constraints from Planck primary cosmic microwave background (CMB) anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 (1.2⁢𝜎) for the two-parameter difference. We further obtain 𝑆 8 ≡𝜎 8 ⁢(Ω m /0.3) 0.5 = 0.796 ± 0.013 which is lower than the Planck measurement at the 1.6⁢𝜎 level. The combined SPT cluster, DES 3 ×2 ⁢pt, and Planck datasets mildly prefer a nonzero positive neutrino mass, with a 95% upper limit ∑ 𝑚 𝜈 < 0.25 eV on the sum of neutrino masses. Assuming a 𝑤⁢CDM model, we constrain the dark energy equation of state parameter 𝑤 = −1.1⁢5$^{+0.23}_{−0.17}$ and when combining with Planck primary CMB anisotropies, we recover 𝑤 = −1.2⁢0$^{+0.15}_{−0.09}$, a 1.7⁢𝜎 difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology and our analysis paves the way for upcoming joint analyses of next-generation datasets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

CMPLE: Correlation Modeling to Decode Photosynthesis Using the Minorize–Maximize Algorithm

In plant genomic experiments, correlations among various biological traits (phenotypes) give new insights into how genetic diversity may have tuned biological processes to enhance fitness under diverse conditions. Consequently, knowing how the correlations are affected by genetic (G) and environmental (E) factors helps develop climate-resilient plants. However, the current literature lacks any method for assessing the effect of predictors on pairwise correlations among multiple phenotypes together with easily interpretable model parameters. To address this need, we propose to model pairwise correlations directly in terms of G and E and develop a computationally efficient inference procedure. Two major novelties in our methodology are (1) the use of a composite pairwise likelihood method to avoid the positive definiteness restriction on the correlation matrix and (2) the use of a novel Minorize–Maximize (MM) algorithm for the efficient estimation of a large number of parameters. The proposed method shows excellent numerical performance on synthetic datasets. Here, the analysis of the motivating data on cowpea reveals that the rates of solar energy storage by photosynthesis (the aggregate trait) are differentially affected by different genetic loci through two distinct processes: “photoinhibition” which results from photodamage caused by excess light, and “photoprotection” which protects plants from photodamage but also results in energy loss.

Correlation modeling↗
DOE OSTI2025

Galaxy cluster profiles: a Gaussian mixture model approach to halo miscentering

Measurements of the galaxy density and weak-lensing profiles of galaxy clusters typically rely on an assumed cluster center, which is taken to be the brightest cluster galaxy or other proxies for the true halo center defined as the minimum in the potential well. Departure of the assumed cluster center from the true halo center bias the resultant profile measurements, an effect known as miscentering bias. Currently, miscentering is typically modeled in stacked profiles of clusters with a two parameter model. We use an alternate approach in which the profiles of individual clusters are used with the corresponding likelihood computed using a Gaussian mixture model. We test the approach using halos and the corresponding subhalo profiles from the IllustrisTNG hydrodynamic simulations. We obtain significantly improved estimates of the miscentering parameters for both 3D and projected 2D profiles relevant for imaging surveys. We discuss applications to upcoming cosmological surveys. Our Python package for the Gaussian mixture model is publicly available at https://github.com/KyleMiller1/Halo-Miscentering-Mixture-Model.

Bayesian reasoning↗
DOE OSTI2026

HDSense: An efficient method for ranking observable sensitivity

Identifying which observables most effectively constrain model parameters can be computationally prohibitive when considering full likelihoods of many correlated observables. This is especially important for, e.g., hadronization models, where high precision is required to interpret the results of collider experiments. We introduce the High-Dimensional Sensitivity (HDSense) score, a computationally efficient metric for ranking observable sets using only one-dimensional histograms. Derived by profiling over unknown correlations in the Fisher information framework, the score balances total information content against redundancy between observables. We apply HDSense to rank a set observables in terms of their constraining power with respect to five parameters of the Lund string model of hadronization implemented in Pythia using simulated leptonic collider events at the $Z$ pole. Validation against machine-learning--based full-likelihood approximations demonstrates that HDSense successfully identifies near-optimal observable subsets. The framework naturally handles data from multiple experiments with different acceptances and incorporates detector effects. While demonstrated on hadronization models, the methodology applies broadly to generic parameter estimation problems where correlations are unknown or difficult to model.

Assi, Benoît [Cincinnati U.] (ORCID:00000003092433↗
NASA NTRS1983

Distributed system modeling of a large space antenna

A general approach for distributed parameter modeling of complex dynamical systems is described. The method consists of dividing the system in parts which can be modeled by simple partial differential equations and coupling the equations thus obtained by applying Hamilton's variational formalism to the entire system. The modeling of a large, offset-fed, wrap-rib antenna is presented to illustrate the approach. Although such models are perhaps not as precise as finite element models, they can be useful for initial physical insight and parametric design.

Hamidi, M.↗
DOE OSTI2023

Physics-based Models, Machine Learning, and Experiment: Towards Understanding Complex Electrode Degradation

Degradation phenomena in Li-ion batteries are highly complex, coupled, and sensitive to use history and operating conditions. In this study, we show how tracking model parameters in continuum-level physics-based models, expedited by machine learning, can be useful in testing hypotheses for degradation mechanisms. An exemplary analysis using this approach is presented for a set of lithium trivanadate ( L i x V 3 O 8 ) cathodes cycled over a range of current rates. A simple cell revival process is combined with the parameter estimates over the course of cycling to extract valuable insights into cathode evolution and eliminate hypothesized degradation mechanisms for these cathodes. The presented approach is expected to be broadly applicable for degradation analysis of other electrodes.

25 ENERGY STORAGE↗