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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
DOE OSTI2021

Active Optical Control with Machine Learning: A Proof of Concept for the Vera C. Rubin Observatory

Abstract The Active Optics System of the Vera C. Rubin Observatory (Rubin) uses information provided by four wave front sensors to determine deviations between the reconstructed wave front and the ideal wave front. The observed deviations are used to adjust the control parameters of the optical system to maintain image quality across the 3.°5 field of view. The baseline approach from the project is to obtain amplitudes of the Zernike polynomials describing the distorted wave front from out-of-focus images collected by the wave front sensors. These Zernike amplitudes are related via an “influence matrix” to the control parameters necessary to correct the wave front. In this paper, we use deep-learning methods to extract the control parameters directly from the images captured by the wave front sensors. Our neural net model uses anti-aliasing pooling to boost performance, and a domain-specific loss function to aid learning and generalization. The accuracy of the control parameters derived from our model exceeds Rubin requirements even in the presence of full-moon background levels and mis-centering of reference stars. Although the training process is time consuming, model evaluation requires only a few milliseconds. This low latency should allow for the correction of the optical configuration during the readout and slew interval between successive exposures.

Astronomy & Astrophysics↗
DOE OSTI2021

Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous U.S.

The second phase of the North America Multi-Model Ensemble (NMME-2) provides globally available Subseasonal-to-Seasonal (S2S) precipitation forecasts with a daily resolution. The S2S precipitation forecasts are getting increasing attention for their potentials in providing hydrometeorological forcing information for water resources planning at an extended range. However, the forecast skills of many existing S2S forecast products will significantly decrease when the lead time increases, hindering their applicability for watershed-scale hydrologic modeling. Therefore, forecast validation and large-scale evaluation are of great importance for water resources planning and hydrological applications. In this study, we comprehensively evaluate the S2S precipitation forecasts from the NMME-2 dataset over the contiguous United States (CONUS) and during the study period from 1982 to 2011. Three aspects of precipitation forecast capabilities are compared and analyzed: bias, skill scores, and the ability to predict extreme precipitation events. The Parameter-elevation Regressions on Independent Slopes Model (PRISM) is used as ground truth reference. Differs from other regional forecast validation study, we further examined and analyzed the dependences of NMME-2 precipitation forecast skills according to different seasonality, geographical locations, and lead times. Results show that the forecast biases are not sensitive to lead times but are seasonally dependent of all NMME-2 models. Overestimations are found in the Western U.S. in cooler seasons while underestimations are observed in the central regions of the U.S. in warmer seasons. The forecast skill of all individual NMME-2 models generally decreases as increases of lead times. The simple model averaging (SMA) of five NMME-2 models demonstrates a higher forecast skill than any individual NMME-2 models. Spatially, the highest forecast skill scores are observed at coastal areas in the Western U.S. with an one-week lead time. As compared to the historical resampled forecasts, NMME-2 also shows better performance in predicting extreme precipitation events above 99% percentiles and below 1% percentiles with higher probability of detections and lower false alarm ratios. Finally, the obtained results suggest the great potentials of NMME-2 precipitation forecasts in assisting ensemble hydrologic forecasts at the S2S scale over the CONUS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Discovering the nature of neutrinos: How nuclear theory will advance this grand endeavor. Final report

Why is there more matter than antimatter in the visible universe? Are neutrinos their own antiparticles? These are some of the most profound questions in science. The detection of a (0vββ) neutrinoless doublebeta decay and the measurement of its half-life is one of the most important experimental endeavors in science because it will answer these questions. Paramount to these measurements are accurate predictions with quantified uncertainties of the 0vββ-decay nuclear transitions, especially for the germanium-76 and xeon-136 nuclei, two candidate materials for building the next generation of DOE funded 0vββ-decay experiments. These predictions will guide the Office of Science’s down selection of experiments as well as aid in determining how much material is needed by these experiments. We studied a specific difficult to assess source of uncertainty that will impact coming microscopic predictions, uncertainty which arises from an incomplete yet systematically improvable model of interactions between nucleons and of nucleons with the other particles involved in decay process, referred to as the theory uncertainty. We employed a surrogate model of the germanium-76 nucleus, and its decay products, just detailed enough to adequately mimic the decaying nucleons need for a reasonable measure of this uncertainty and nothing more, avoiding the immense computational expense required in quantifying uncertainties of the full calculation. We found that microscopic calculations of this process can be expected to achieve relative theory uncertainties around 30%, including an unconstrained contribution that had the potential to spoil microscopic calculations. This results in a nearly order of magnitude on previously estimated uncertainties for this rare decay process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

HostSub_GP: Precise Galaxy Background Subtraction in Transient Long-slit Spectroscopy with Gaussian Processes

We present a novel host galaxy subtraction technique in long-slit spectroscopy for extragalactic transients. Unlike classic methods which generally estimate the background using simple interpolation of local galaxy flux in the 2D spectrum, our approach leverages multi-band archival images of the host galaxies to model the background emission from the galaxy in the 2D spectrum. Such imaging encodes the wavelength-dependent galaxy profile along the slit, and is readily accessible through wide-field imaging surveys. We construct a smooth prior for the 2D galaxy profile with a Gaussian process (GP) based on these reference images, and use another GP to model the correlated deviations from the prior in the observed spectrum. This enables accurate inference of the galaxy flux blended with the transient. On synthetic long-slit data of a spiral galaxy extracted from a Multi Unit Spectroscopic Explorer hyper-spectral cube, the GP method remains robust as long as the host galaxy is spatially resolved and consistently outperforms classic methods. We apply the method to archival Keck spectra of two real transients, SN 2019eix and AT 2019qiz, to further demonstrate how the method uniquely recovers weak spectral features amid strong galaxy contamination, enabling refined constraints on the properties of both transients. We have released the software implementation, HostSub_GP, a scalable toolkit that leverages JAX, with an MIT license.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Benchmark of numerical modeling approaches on the systematic performance evaluation of wave energy converters

Different numerical modeling methods have been developed and applied to evaluate a variety of performance indicators of wave energy converters (WECs), including the power performance, structural loads, levelized cost of energy, etc. Based on the modeling fidelity, the commonly used numerical modeling approaches can be classified as linear modeling, weakly nonlinear modeling and fully nonlinear modeling approaches. Each method differs in accuracy and computational efficiency, making them suitable for different stages of WEC design. However, the selection of modeling approach could significantly impact evaluation outcomes. For instance, simplified linear models may underestimate structural loads or overestimate energy production in some operational conditions, potentially leading to less cost-effective designs. Given the widespread utilization of these models, it is essential to understand the uncertainties brought by them in performance evaluations. This work is dedicated to benchmarking different linear-potential-flow-based numerical models for evaluating the systematic performance of WECs. Three representative numerical modeling approaches are considered in this work, including linear frequency-domain modeling, statistically linearized spectral-domain modeling and Cummins equation-based nonlinear time-domain modeling. A generic point absorber WEC is considered as the research reference in this work, and different sea sites are taken into account. The numerical models are utilized to predict critical performance indicators, including power performance, the annual energy production, the capacity factor, the levelized cost of energy and the PTO fatigue loads. By comparing the results, this work identifies the uncertainties associated with different modeling approaches in evaluating WEC performance.

Fatigue↗
DOE OSTI2026

Finite deformation implementation of a mixed-mode single-integral type cohesive zone with reorienting surfaces of separation

To model material ductile failure and crack propagation, cohesive zone elements can be embedded along potential fracture paths in a finite element simulation. When damage criteria are met, elements in the mesh decohere, simulating the formation and propagation of a crack. In this paper, we present a novel computational algorithm based on finite deformation theory, essential to modeling crack initiation and growth in solids undergoing large deformations. This new algorithm was formulated within a Lagrangian frame of reference to extend previous cohesive zone algorithms to include modeling crack growth in finite deformation contexts. The local coordinate system, necessary for defining an embedded cohesive zone, is constructed based upon the current configuration and is updated within the nonlinear iteration process, thereby resulting in the convergence of the solution for a growing crack in a large deformation quasi-static setting. The model’s accuracy was demonstrated by comparing finite element model simulation results with the analytic case of a constant surface separation, as shown in the verification examples. The power and efficacy of the algorithm to capture large deformations during crack growth were then demonstrated with a double cantilever beam example case. It indicates that the model can be applied to a variety of physical circumstances for predicting crack initiation and growth with delamination and fracture.

42 ENGINEERING↗
DOE OSTI2022

Thermal Pressure in the Thermal Equation of State for Solid and a Proposed Substitute

Abstract The thermal equation of state (TEOS) for solids is a mathematic model among pressure, temperature and density, and is essential for geophysical, geochemical, and other high pressure–temperature (high P–T) researches. However, in the last few decades, there has been a growing concern about the accuracy of the pressure scales of the calibrants, and efforts have been made to improve it by either introducing a reference standard or building new thermal pressure models. The existing thermal equation of state, P ( V , T ) = P ( V , T 0 ) + P th ( V , T ), consists of an isothermal compression and an isochoric heating, while the thermal pressure is the pressure change in the isochoric heating. In this paper, we demonstrate that, for solids in a soft pressure medium in a diamond anvil cell, the thermal pressure can neither be determined from a single heating process, nor from the thermal pressure of its calibrant. To avoid the thermal pressure, we propose to replace the thermal pressure with a well-known thermal expansion model, and integrate it with the isothermal compression model to yields a Birch–Murnaghan-expansion TEOS model, called VPT TEOS. The predicted pressure of MgO and Au at ambient pressure from Birch–Murnaghan-expansion VPT TEOS model matches the experimental pressure of zero (0) GPa very well, while the pressure prediction from the approximated Anderson PVT TEOS exhibit a big deviation and a wrong trend.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Logistic Regression in Clinical Studies

• A logistic regression model is used when the outcome of interest is binary. The term “logistic” refers to the underlying “logit” (log odds) function that is used to model the binary outcome. • Odds ratios are produced from a logistic regression model and have a useful interpretation. • Tips, tricks and concepts used to fit logistic regression models are similar to those used in linear regression models. • Modeling building that is knowledge-based rather than automatic is preferred in most applications of logistic regression. • A logistic regression model that is overparameterized (ie, too many variables for too few events) can result in odds ratios that are implausibly large and confidence intervals that are wide and uninterpretable. These types of “overfitted” models should be avoided. • Logistic regression models can be fit using most standard statistical software.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

MELCOR 2.2 Benchmarks of Peach Bottom NUREG/CR 7155 Uncertainty Analysis

The U.S. Nuclear Regulatory Commission (NRC) performed a first-of-a-kind uncertainty analysis (UA) of the accident progression, radiological releases, and offsite consequences for the State-of- the-Art Reactor Consequence Analyses (SOARCA) of an unmitigated long-term station blackout (LTSBO) severe accident scenario at the Peach Bottom Atomic Power Station. The objective of the UA was to evaluate the robustness of the SOARCA deterministic "best estimate results and conclusions documented in NUREG-1935, and to develop insight into the overall sensitivity of the SOARCA results to uncertainty in key modeling inputs. The study was completed in 2015 and documented in NUREG/CR-7155. Since 2015, two other SOARCA UAs were completed for two pressurized water reactor (PWR) plants. The PWR UAs incrementally updated the approach and methodology, including using the latest release of the MELCOR 2.2 computer code. There were also advances made in the state-of-the-art modeling related to NRC efforts using the Peach Bottom model in NUREG-2206, which provided the technical basis for the containment protection and release reduction rulemaking for boiling water reactors with Mark I and Mark II containments. This report documents the input model changes from the NUREG/CR-7155 study and performs a small number of reference calculations to assess the changes of the new computer code and the model input updates. The objective of the work is to verify whether the updated Peach Bottom MELCOR model and updated version of MELCOR support the conclusions formed in the Peach Bottom SOARCA UA by performing these representative calculations.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

A State-Space Model for Stability Boundary Analysis of Grid-Following Voltage Source Converters Considering Grid Conditions

With the growing significance of renewable energy resources and energy storage systems, the number of grid-connected inverters has been rising at an increasingly rapid pace. Generally, these inverters are directly integrated with the distribution network by synchronizing with the grid voltage at the point of common coupling. However, the low grid strength and varying R/X ratios, as the common characteristics of most distribution networks or weak grids, can lead to dynamic interactions that comprise stability and limit the power transfer capacity of grid-connected inverters. To ensure stable operation of the inverters, researchers must determine the stability boundary, described as the maximum power transfer capacity of grid-connected inverters under the premise of maintaining system small-signal stability. For this purpose, we propose to formulate a state-space model of the system in the synchronously rotating dq-frame of reference and perform eigenvalue analysis to determine the stability boundary. With a detailed model of the control structure and parameters of the grid-connected inverters, the stability boundary is identified as a surface with respect to different grid strengths and R/X ratios. Case study results of proposed eigenvalue analysis are compared with those of admittance model-based stability analysis as well as time-domain simulation using a switching model in Matlab/Simulink, validating the effectiveness and accuracy of the proposed eigenvalue analysis for stability boundary identification.

grid-connected inverters↗
DOE OSTI2023

Computational fluid dynamics investigations of flow, heat transfer, and oxidation in heat recovery steam generator

Modern heat recovery steam generators (HRSGs) operate at elevated temperatures, leading to the formation of oxides inside the tubes of heat exchangers (HXs). This oxide growth reduces the heat recovery efficiency. Moreover, after reaching a certain critical thickness, some oxide scales detach from the tube surface (exfoliation), causing erosion damage to the components downstream. Predicting the metal temperature distribution and associated oxide thickness in the HXs of an HRSG can aid in mitigating these problems. Here, a computational fluid dynamics (CFD) model was developed within the commercial code STAR-CCM+ for the prediction of fluid flow, conjugate heat transfer, and associated oxidation in HRSGs. Moreover, a new Porous Media Model (PMM) method was developed to model the fin effect on the heat transfer in HX, which can substantially reduce the prohibitive computational costs of fin meshes. The developed CFD model was used to conduct a high-fidelity simulation of a real-scale HRSG to investigate flow, heat transfer, and oxide growth. The calculated oxide thickness on different tubes can be used to identify HX regions that require oxide-resistant coatings to prevent exfoliation and ensuing damages. Furthermore, this CFD framework can serve as a reference for future studies that intend to model and investigate high-temperature oxidation in HXs used for any applications.

42 ENGINEERING↗
DOE OSTI2025

An adaptive, data-driven multiscale approach for dense granular flows

The accuracy of coarse-grained continuum models of dense granular flows is limited by the lack of high-fidelity closure models for granular rheology. One approach to addressing this issue, referred to as the hierarchical multiscale method, is to use a high-fidelity fine-grained model to compute the closure terms needed by the coarse-grained model. The difficulty with this approach is that the overall model can become computationally intractable due to the high computational cost of the high-fidelity model. In this work, we describe a multiscale modeling approach for dense granular flows that utilizes neural networks trained using high-fidelity discrete element method (DEM) simulations to approximate the constitutive granular rheology for a continuum incompressible flow model. Our approach leverages an ensemble of neural networks to estimate predictive uncertainty that allows us to determine whether the rheology at a given point is accurately represented by the neural network model. Additional DEM simulations are only performed when needed, minimizing the number of additional DEM simulations required when updating the rheology. This adaptive coupling significantly reduces the overall computational cost of the approach while controlling the error. In addition, the neural networks are customized to learn regularized rheological behavior to ensure well-posedness of the continuum solution. We first validate the approach using two-dimensional steady-state and decelerating inclined flows. We then demonstrate the efficiency of our approach by modeling three-dimensional sub-aerial granular column collapse for varying initial column aspect ratios, where our multiscale method compares well with the computationally expensive computational fluid dynamics (CFD)-DEM simulation.

Dense granular flows↗
DOE OSTI2021

Species Extrapolation of Propyl Acetate Dose Metrics

We demonstrate the ability of the propyl PBPK model to predict dose metrics of propyl acetate, propanol, and propionic acid from a standard 90-day subchronic inhalation study of propyl acetate in male and female rats. The model was used to predict the same dose metrics in “reference” male and female humans using the same exposure conditions. Finally, we used reverse dosimetry with the model to predict what exposure conditions would lead to the same dose metrics measured in rats. These extrapolations of internal dose metrics based on known species differences in physiology and measured differences in metabolism offer a more scientific species extrapolation than conventional uncertainty approaches, potentially of interest for risk assessment.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

ACES: Infrastructure As Code. Model Optimization and Performance Capability

Infrastructure as Code (IaC) refers to managing infrastructure (networks, physical/virtual machines, storage, and connection topology) in a descriptive model/language, rather than configuring it manually or using interactive configuration tools. Just like source code can be compiled to generate the same binary code, IaC enables generating the same environment every time it is applied. IaC is a key DevOps practice and is generally used in conjunction with continuous integration (CI) and continuous delivery (CD). In CI, all code changes are merged into a mainline branch and validated multiple times a day as developers check in their changes to the source code. In CD on the other hand, code changes are automatically packaged for a new release-to-production on a regular basis. This typically enables teams to deliver software changes much more quickly and often. Developing and managing the ACES platform using (IaC) is vital for the robust deployment and continued sustainment of this foundational computing capability. IaC and DevOps practices will help us solve many of the common challenges often encountered in developing and maintaining compute infrastructure. First, it will make the provisioning, deployment, and maintenance of the compute infrastructure across multiple environments much more efficient. Second, these processes help make the overall system much more stable by continuously testing new changes as they are introduced to the system. Third, it allows us to be much more confident of the security controls in place since they can be tested as part of the CI process and all new changes can be audited and tracked. Finally, IaC enables the ACES Platform to be adaptable to the emerging technologies due to its ability to spin up different test beds to evaluate and incorporate these technologies. This document addresses common infrastructure-management challenges, describes what happens if they are not addressed, and highlights the value of utilizing IaC to tackle them. Finally, we will provide a high-level overview of the IaC and DevOps practices being utilized by the ACES Platform team.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

2023 FORCE Development Status Update

Technical and economic analysis of integrated energy systems (IES) using software models is a complex process requiring multiple commodity market decision analysis, optimal control, process modeling, and stochastic analysis. Many assumptions used in traditional energy analysis tools do not hold in future energy markets with significant storage and variable renewable energy sources (VRE), let alone with multiple commodity markets. Capturing these intricate elements for accurate techno-economic analysis of IES led to the development of the Framework for Optimization of ResourCes and Economics (FORCE) tool suite under the U.S. Department of Energy’s Integrated Energy Systems crosscutting technology program. With the aim of a full framework release in 2025, many improvements to the FORCE tool suite were developed in fiscal year 2023 (FY23). These improvements broadly fit into three focuses for development of FORCE: capability, accessibility, and reliability. Capability refers to the ability of FORCE to accurately model the technical and economic viability of various IES. Accessibility refers to ease-of-use for new and existing IES analysts to efficiently set up, analyze, and produce results using FORCE. Reliability refers to the consistency of the software, allowing consistency to analysis regardless of erstwhile changes to the software. In addition to many smaller changes, there are three major capability improvements in FORCE in FY23. In summary, FORCE developments in FY23 have moved us close to all the capability requirements for FORCE 1.0 to be delivered in FY25. Inclusion of Bayesian optimization, resilience metrics, and levelized cost analysis expand the capability, accessibility, and reliability of FORCE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Evaluating long-term model-based scenarios of the energy system

Energy-economic models are used to provide science-based decision support in a variety of contexts. Analyses using these tools often involve defining a “reference” scenario, which serves as a counterfactual against which alternative scenarios are compared. Evaluating scenarios, including reference scenarios, is critical for establishing the credibility of the analyses these models support. We propose a framework for evaluating energy system scenarios which consists of three parts – a qualitative storyline, quantitative metrics, and evaluation criteria. We apply this framework to the reference scenario for GCAM-USA, a version of the global human-Earth system model GCAM (Global Change Assessment Model) with state-level detail in the United States, focusing on the evolution of the electric power sector. We develop new visual analytic tools to facilitate the evaluation of model outcomes in 51 sub-national regions, and demonstrate how scenario performance can be tracked and compared across four quantifications of the GCAM-USA reference scenario.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Reference Architecture Discussion [Slides]

NREL is developing reference architectures - industry-validated and verified models - to provide a common operational view and emulation environment for analyzing risk to the energy sector. In this presentation, we overview our work in developing these architectures.

97 MATHEMATICS AND COMPUTING↗