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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
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Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Unmitigated uncertainties are known to have previously led to failed development programs; in order to combat these uncertainties, risks and their impacts must be understood and handled to ensure program success. In this paper, a probabilistic methodology to handle uncertainties is demonstrated on a three-element Human Landing System (HLS) concept, which allows tracking of current best estimates of the vehicle’s performance and assessment of its robustness against uncertainties. This methodology has two key parts: first, the creation of a dynamic architecture model of a three-element HLS concept; and second, its use with surrogate modeling and range estimating techniques to capture and propagate uncertainties. The DYnamic Rocket EQuation Tool (DYREQT), a space systems synthesis and sizing framework used by NASA, was used as to model the HLS architecture. For the probabilistic analysis, uncertainties of interest within the HLS concept were enumerated and represented as parameters within the DYREQT model as inputs for vehicle stages or mission profile events. Range estimating — a probabilistic method that combines Monte Carlo sampling, focus on critical parameters, and heuristics to assess risk and opportunities — is then adapted with operational parameters as well as vehicle parameters in the DYREQT model to capture mission uncertainty alongside vehicle uncertainty. To perform the range estimation portion of this methodology, the DYREQT model was sampled using a Design of Experiments (DoE) to efficiently explore the architecture design space with respect to the set of uncertainty parameters. Then, the results were used to create surrogate models, multivariate regressions that can visualize hypercube trends in the design space, of the architecture with respect to the uncertainty parameters. Using a correlation matrix constructed for the uncertainty parameters, previously independent samples were transformed to perform a Correlated Monte Carlo on the surrogate models. This probabilistic methodology was proved to provide insight into the underlying uncertainties of the three-element HLS architecture.

Stephanie Y. Zhu↗
DOE OSTI2024

Measurement of D-T Neutron Source Flux by SelfNeutron Activation Analysis with a LaBr3 Detector

Conclusions: • Decay of 78Br produced from the 79Br(n,2n) reaction in LaBr3 is present in the spectrum plotted in the time domain. • The neutron production rate reconstructed from preliminary LaBr3 estimate is smaller than the generator output. • Generator characterization may become more routine if a simple and robust method is made available for applications. Ongoing work: • Implementing bootstrapping to improve error estimates. • Examining threshold and edge effects of LaBr3

SMITH, MURRY↗
NASA NTRS1990

An optimal estimation approach for enhanced identification of large space structures

A very robust technique for the realization/identification of structural dynamic systems is presented. This methodology combines the eigensystem realization algorithm (ERA) and minimum model error (MME) techniques to develop an extremely robust algorithm able to distinguish modes whose amplitudes are small fractions of the noise amplitude. As the amplitude of the noise grows, the enhanced robustness of the combined ERA/MME algorithm becomes more significant. This is a result of the MME being able to produce simulated measurements of much greater accuracy than the original measurements, for input to the ERA. The new method was able to determine correct model order at noise levels many times higher than the original ERA for the realization problem in which the model order must be derived.

Mook, D. Joseph↗
DOE OSTI2024

Physics-Informed Active Learning With Simultaneous Weak-Form Latent Space Dynamics Identification

The parametric greedy latent space dynamics identification (gLaSDI) framework has demonstrated promising potential for accurate and efficient modeling of high-dimensional nonlinear physical systems. However, it remains challenging to handle noisy data. Here, to enhance robustness against noise, we incorporate the weak-form estimation of nonlinear dynamics (WENDy) into gLaSDI. In the proposed weak-form gLaSDI (WgLaSDI) framework, an autoencoder and WENDy are trained simultaneously to discover intrinsic nonlinear latent-space dynamics of high-dimensional data. Compared with the standard sparse identification of nonlinear dynamics (SINDy) employed in gLaSDI, WENDy enables variance reduction and robust latent space discovery, therefore leading to more accurate and efficient reduced-order modeling. Furthermore, the greedy physics-informed active learning in WgLaSDI enables adaptive sampling of optimal training data on the fly for enhanced modeling accuracy. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including viscous and inviscid Burgers' equations, time-dependent radial advection, and the Vlasov equation for plasma physics. With data that contains 5%–10% Gaussian white noise, WgLaSDI outperforms gLaSDI by orders of magnitude, achieving 1%–7% relative errors. Compared with the high-fidelity models, WgLaSDI achieves 121 to 1779x speed-up.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Survival of Immature Gopher Tortoises Recruited into a Translocated Population

Population manipulations such as translocation and head-starting are increasingly used as recovery tools for chelonians. However, evaluating success of individual projects can require decades of monitoring to detect population trends in these long-lived species. Furthermore, there are often few benchmarks from stable, unmanipulated populations against which to compare demographic rates, particularly for the immature stages. Here, we used 8 years of mark-recapture data to estimate apparent survival of immature gopher tortoises recruited into an introduced population of gopher tortoises (Gopherus polyphemus) first established on St. Catherines Island, Georgia, USA in 1987. During 2006 29 -2013, we conducted targeted trapping of immature gopher tortoises and compared survival of the hatchling, juvenile and subadult stages among treatments: 1) individuals released shortly after hatching from eggs obtained from gravid female founders (‘direct releases’); 2) individuals reared in captivity for 6-9 months following hatching (‘head-starts’); and 3) individuals first encountered as free-ranging, wild-recruited offspring (‘wild recruits’). Among the candidate models we examined, the best fit model included additive effects of tortoise stage and treatment, however, overlapping 95% credible intervals among treatments (CI) suggested that survival did not vary significantly among treatments. Annual apparent survival increased over the immature period, highlighting the importance of calculating separate estimates for the different immature stages. Across all treatments, the additive model estimated annual apparent survival probability to be 0.37 (CI: 0.25 40 – 0.48) for hatchlings, 0.71 (CI: 0.61 – 0.81) for juveniles, and 0.83 (CI: 0.74 – 0.94) for subadults. Our study, in combination with previous monitoring efforts at St. Catherines Island, provides strong evidence that the translocation and subsequent population augmentation efforts have been successful in establishing a robust population of gopher tortoises. Additionally, our results provide estimates of demographic rates for life stages that are poorly understood but critical to understanding population dynamics of this imperiled species.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1989

Automated frequency domain system identification of a large space structure

This paper presents the development and experimental results of an automated on-orbit system identification method for large flexible spacecraft that yields estimated quantities to support on-line design and tuning of robust high performance control systems. The procedure consists of applying an input to the plant, obtaining an output, and then conducting nonparametric identification to yield the spectral estimate of the system transfer function. A parametric model is determined by curve fitting the spectral estimate to a rational transfer function. The identification method has been demonstrated experimentally on the Large Spacecraft Control Laboratory in JPL.

Yam, Y.↗
NASA NTRS1996

Accuracy Studies of a Magnetometer-Only Attitude-and-Rate-Determination System

A personal computer based system was recently prototyped that uses measurements from a three axis magnetometer (TAM) to estimate the attitude and rates of a spacecraft using no a priori knowledge of the spacecraft's state. Past studies using in-flight data from the Solar, Anomalous, and Magnetospheric Particles Explorer focused on the robustness of the system and demonstrated that attitude and rate estimates could be obtained accurately to 1.5 degrees (deg) and 0.01 deg per second (deg/sec), respectively, despite limitations in the data and in the accuracies of te truth models. This paper studies the accuracy of the Kalman filter in the system using several orbits of in-flight Earth Radiation Budget Satellite (ERBS) data and attitude and rate truth models obtained from high precision sensors to demonstrate the practical capabilities. This paper shows the following: Using telemetered TAM data, attitude accuracies of 0.2 to 0.4 deg and rate accuracies of 0.002 to 0.005 deg/sec (within ERBS attitude control requirements of 1 deg and 0.0005 deg/sec) can be obtained with minimal tuning of the filter; Replacing the TAM data in the telemetry with simulated TAM data yields corresponding accuracies of 0.1 to 0.2 deg and 0.002 to 0.005 deg/sec, thus demonstrating that the filter's accuracy can be significantly enhanced by further calibrating the TAM. Factors affecting the fillter's accuracy and techniques for tuning the system's Kalman filter are also presented.

Challa, M.↗
DOE OSTI2025

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic

Here, this article presents a novel hierarchical speed planning framework for variable speed limits in mixed-autonomy traffic environments, leveraging server-side macroscopic control and vehicle-side microscopic execution. The framework integrates real-time traffic state estimation (TSE) and reinforcement learning (RL)-based control to mitigate congestion and improve traffic flow. A TSE enhancement module combines macroscopic data from sources like INRIX with high-resolution observations from connected autonomous vehicles (CAVs), enabling predictive modeling to address latency and noise. The target speed design module employs kernel smoothing and a buffer zone strategy to optimize traffic density and flow around bottlenecks. The proposed system was validated in the largest open-road test to date with 100 CAVs, demonstrating an overall 8% traffic density decrease, with a specific decrease of 7% upstream, 10% downstream, and a 52% decrease during the congestion formation phase at bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Success of digital adiabatic simulation with large Trotter step

The simulation of adiabatic evolution has deep connections with adiabatic quantum computation, the quantum approximate optimization algorithm, and adiabatic state preparation. Here we address the error analysis problem in quantum simulation of adiabatic process using Trotter formulas. Here we show that with additional conditions, the circuit depth can be linear in simulation time T. The improvement comes from the observation that the fidelity error here can't be estimated by the norm distance between evolution operators. This phenomenon is termed the robustness of discretization in digital adiabatic simulation. It can be explained in three steps, from analytical and numerical evidence: (1) The fidelity error should be estimated by applying adiabatic theorem on the effective Hamiltonian instead. (2) Because of the specialty of Riemann-Lebesgue lemma, most adiabatic process is naturally robust against discretization. (3) As the Trotter step gets larger, the spectral gap of effective Hamiltonian tends to close, which results in the failure of digital adiabatic simulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Analytical-based simulation approach for an anion exchange membrane fuel cell

An analytical and empirical-based 1-D, non-isothermal, steady-state model for anion exchange membrane fuel cell capable of capturing two-phase phenomena is presented in this study. Coupled multi-physics including mass and charge transport, electrochemical reactions, heat transfer, and two-phase water transport are considered in the model and the simulated results are compared to experimental data. To better represent actual material properties and localized conditions, the model applies multilayer discretization in the gas diffusion electrode to enhance prediction accuracy. The model successfully predicts the baseline performance at 70 °C, 131 kPa abs., 92% RH with pure H 2 /O 2 gas as well as the limiting current at 10% H 2 . The robust simulation approach allows for simplistic and accurate estimation of cell performance without the complications of applying two-phase parameters and expensive computational need for numerical models. In addition, the results from the sensitivity studies of material properties and operating conditions provide valuable insights on water management strategies and optimal component design for advancing anion exchange membrane fuel cell technology.

1-D model↗
DOE OSTI2022

The PDF4LHC21 combination of global PDF fits for the LHC Run III

A precise knowledge of the quark and gluon structure of the proton, encoded by the parton distribution functions (PDFs), is of paramount importance for the interpretation of high-energy processes at present and future lepton–hadron and hadron–hadron colliders. Motivated by recent progress in the PDF determinations carried out by the CT, MSHT, and NNPDF groups, we present an updated combination of global PDF fits: PDF4LHC21. It is based on the Monte Carlo combination of the CT18, MSHT20, and NNPDF3.1 sets followed by either its Hessian reduction or its replica compression. Extensive benchmark studies are carried out in order to disentangle the origin of the differences between the three global PDF sets. In particular, dedicated fits based on almost identical theory settings and input datasets are performed by the three groups, highlighting the role played by the respective fitting methodologies. We compare the new PDF4LHC21 combination with its predecessor, PDF4LHC15, demonstrating their good overall consistency and a modest reduction of PDF uncertainties for key LHC processes such as electroweak gauge boson production and Higgs boson production in gluon fusion. We study the phenomenological implications of PDF4LHC21 for a representative selection of inclusive, fiducial, and differential cross sections at the LHC. The PDF4LHC21 combination is made available via the LHAPDF library and provides a robust, user-friendly, and efficient method to estimate the PDF uncertainties associated to theoretical calculations for the upcoming Run III of the LHC and beyond.

quantum chromodynamics↗
DOE OSTI2022

Benchmarking Simulated Precipitation Variability Amplitude across Time Scales

Here, objective performance metrics that measure precipitation variability across time scales from subdaily to interannual are presented and applied to Historical simulations of Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. Three satellite-based precipitation estimates (IMERG, TRMM, and CMORPH) are used as reference data. We apply two independent methods to estimate temporal variability of precipitation and compare the consistency in their results. The first method is derived from power spectra analysis of 3-hourly precipitation, measuring forced variability by solar insolation (diurnal and annual cycles) and internal variability at different time scales (subdaily, synoptic, subseasonal, seasonal, and interannual). The second method is based on time averaging and facilitates estimating the seasonality of subdaily variability. Supporting the robustness of our metric, we find a near equivalence between the results obtained from the two methods when examining simulated-to-observed ratios over large domains (global, tropics, extratropics, land, or ocean). Additionally, we demonstrate that our model evaluation is not very sensitive to the discrepancies between observations. Our results reveal that CMIP5 and CMIP6 models in general overestimate the forced variability while they underestimate the internal variability, especially in the tropical ocean and higher-frequency variability. The underestimation of subdaily variability is consistent across different seasons. The internal variability is overall improved in CMIP6, but remains underestimated, and there is little evidence of improvement in forced variability. Increased horizontal resolution results in some improvement of internal variability at subdaily and synoptic time scales, but not at longer time scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Synthesis of observed and simulated rain microphysics to inform a new Bayesian statistical framework for microphysical parameterization in climate models (Final Scientific Report)

This project aimed to investigate a new approach to bulk microphysics schemes. We identified the need to move beyond the fixed structural assumptions and approximation of existing schemes. For example, most schemes assume some functional form for the rain drop size distribution (e.g. a gamma or exponential distribution). Most schemes then evolve some number of statistical moments of that distribution via various processes, such as evaporation, sedimentation, collision-coalescence, and collisional breakup. These microphysical processes, in turn, are typically some fixed functional form derived from either some other (more detailed) model, or using some single-particle rates that are then integrated over the size distribution. While these process rate functions may have some free parameters to adjust, in other cases doing so is impossible (e.g. when the process rates are analytical or piecewise solutions to some target function). Our goal was to build and test a scheme that assumed no size distribution form, and used a series of power laws as the basis for the process rates. Moments of the size distribution would be predicted, but no underlying size distribution would be specified. Any number or choice of size distribution moments could be used, and any number of power laws could be employed to model the process rates. Thus, our approach is flexible, and can seamlessly scale across levels of complexity, as demanded by the data. Bayesian inference then would provide the formalism to estimate the model parameters and structure, allowing for robust uncertainty quantification. We proposed testing this framework in idealized simulations using bin microphysical schemes as a data source. We also proposed using real data to inform our microphysics scheme, and also that we would integrate our scheme into WRF.

58 GEOSCIENCES↗
DOE OSTI2025

Development of the Well-to-Wake Life Cycle Assessment Model for Marine Fuels in R&D GREET 2025

The Marine analysis capabilities in the 2025 Research and Development Greenhouse gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) have been updated to incorporate the latest findings from the Fourth International Maritime Organization (IMO) GHG Study.1 To support these updates and enhance user flexibility, the former single marine module used in previous R&D GREET versions has been split into two workbook tabs: The Marine_Fuel tab, which covers the fuel-cycle, and the Marine_Trip tab, which covers the trip-cycle. In the Marine_Fuel tab, fuel cycle results for 39 marine fuels are available—assuming a slow speed diesel engine—and can be seen in Appendix A. However, there are options for changing feedstocks for multiple fuels, ability to view results with or without pilot fuels for fuels such as methanol and ammonia, and carbon capture options. All these combinations make the number of total possible pathways over one hundred. For each pathway, life cycle emission results are divided into feedstock, conversion, and combustion categories, along with pilot fuel supply chain emissions for fuels used in dual fuel engines. In the newly created Marine_Trip tab, users can view the life cycle emissions of using these fuels from a trip perspective. Compared to R&D GREET 2024, 16 new types of vessels are added in R&D GREET 2025. Examples of results assuming the default size for each vessel type are available in Appendix A. Additional information categories on vessels are included such as vessel size, main propulsion type, engine power rating, combustion cycle, etc. based on the Fourth IMO GHG Study. Operational mode of the trip, fuel consumption, and load calculation were updated based on the report. The fuel consumption calculation also incorporates low-load adjustment, speed–power correction, weather correction, and fouling correction factors. Furthermore, fuel consumption in boilers or steam turbines is also added for the first time, besides fuel use in main propulsion engines and auxiliary engines. This effort makes the R&D GREET Marine capabilities more robust than before. Users will be able to estimate fuel consumption more accurately for more diverse choices of vessel categories, vessel sizes, and energy converters.

09 BIOMASS FUELS↗
DOE OSTI2026

Measuring labor productivity dynamics in U.S. industrial and electric power sectors: a case study (2014–2023)

This study proposes a subsystem methodology for measuring labor productivity in the U.S. industrial and electric power sectors by leveraging public data available between 2014 and 2023. Building on Pasinetti’s framework and subsequent developments, the approach employs Vertically Integrated Sectors (VIS) to account for both direct and indirect productivity effects. The novelty of this work is twofold. First, it enables the estimation of productivity trends over time, providing a robust foundation for empirical analysis. Second, it applies the methodology to a case study of the electric generation sector, highlighting its practical relevance. Using data from the Bureau of Economic Analysis, the Bureau of Labor Statistics, and the Impact Analysis for Planning (IMPLAN) tool, the study reveals significant discrepancies between conventional productivity measures and those derived from the VIS approach. Furthermore, the proposed method aligns with the principles of Integrated Energy Systems by capturing the interrelations among generation, distribution, storage, and consumption. This alignment underscores its utility and applications for energy-related policy and planning. Overall, the findings contribute to more precise labor productivity assessments, supporting informed decision-making and future research. Additionally, the method highlights the importance of considering the whole supply chain, providing interrelated metrics for labor productivity which includes both, direct and indirect effects on the final labor productivity metric. By incorporating intersectoral dependencies, this method offers a more comprehensive and accurate measure of labor productivity compared to traditional metrics.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Dark Energy Survey Year 3 Results: Photometric Data Set for Cosmology

In this work, we describe the Dark Energy Survey (DES) photometric data set assembled from the first three years of science operations to support DES Year 3 cosmologic analyses, and provide usage notes aimed at the broad astrophysics community. Y3 GOLD improves on previous releases from DES, Y1 GOLD, and Data Release 1 (DES DR1), presenting an expanded and curated data set that incorporates algorithmic developments in image detrending and processing, photometric calibration, and object classification. Y3 GOLD comprises nearly 5000 deg 2 of grizY imaging in the south Galactic cap, including nearly 390 million objects, with depth reaching a signal-to-noise ratio ~10 for extended objects up to iAB ~ 23.0, and top-of-the-atmosphere photometric uniformity <3 mmag. Compared to DR1, photometric residuals with respect to Gaia are reduced by 50%, and per-object chromatic corrections are introduced. Y3 GOLD augments DES DR1 with simultaneous fits to multi-epoch photometry for more robust galactic color measurements and corresponding photometric redshift estimates. Y3 GOLD features improved morphological star–galaxy classification with efficiency >98% and purity >99% for galaxies with 19 < i AB < 22.5. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used to select the cosmologic analysis samples.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗