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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

NRIC DOME Crane Trade Study and Recommendation

The National Reactor Innovation Center (NRIC) is a national program that was established as part of the Nuclear Energy Innovation Capabilities Act (NEICA). NRIC’s mission is to accelerate the demonstration and deployment of advanced nuclear energy through its mission to inspire stakeholders and the public, empower innovators, and deliver successful outcomes through efficient coordination of partners and resources. NRIC is designed to bridge the gap between research, development, and the marketplace to help convert some of the nation’s most promising advanced nuclear reactors into commercial applications. The NRIC Demonstration of Microreactor Experiments (DOME) facility, formerly known as Experimental Breeder Reactor II (EBR II), located at the Materials and Fuels Complex (MFC) at the Idaho National Laboratory (INL) is intended to allow industrial and other partners the opportunity to test Advanced Microreactors up to 20MW thermal power. The 75-ton capacity polar crane located in the EBR II facility was rendered inoperable to support planned facility demolition in 2015; small holes were flame cut in the girders; hoists and cables were removed; oil was drained from gear boxes; trolley drive, and then filled with absorbent; and the electrical and control umbilical’s were disconnected, removed and disposed. Subsequently, the decision was made to convert the EBR II facility into the DOME test bed.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2024

Machine tool cross beam design, fabrication, and testing using metal big area additive manufacturing

This paper describes the application of metal Big Area Additive Manufacturing (mBAAM) to the fabrication of a machine tool cross beam. The replacement of a traditional box design weldment with a new design printed by wire arc additive manufacturing using the MedUSA system at Oak Ridge National Laboratory (ORNL) is detailed. This requires a new design strategy based on the unique mBAAM capabilities. The intent of the new design is to reduce mass, while maintaining the dynamic stiffness. To compare the two designs, the natural frequencies and mode shapes are measured using impact testing and predicted using finite element analysis. It is confirmed that the printed structure dynamics agreed with the numerical model predictions, which demonstrates that it is feasible to model a large-scale mBAAM part and understand its behavior prior to printing. Another notable outcome of this study is that the significant residual stress and distortion in the print indicate that knowledge gaps remain for widespread implementation of mBAAM.

42 ENGINEERING↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

58 GEOSCIENCES↗
DOE OSTI2025

Eucalyptus – An Analysis Suite for Fault Trees with Uncertainty Quantification

Eucalyptus is a novel code developed at Lawrence Livermore National Laboratory to incorporate uncertainty quantification into Fault Tree Analysis (FTA). This tool addresses the challenge of imperfect knowledge in “grey-box” systems by allowing analysts to incorporate and propagate uncertainty from component-level assessments to system-level effects. Eucalyptus facilitates a consistent evaluation of the impact of subject matter expert judgment and knowledge gaps on overall system response by Monte Carlo generation of possible system fault trees, sampling probabilities of the existence of subsystems and components. Here, the code supports the specification of fault trees through text and allows export to various formats, including auto-generated images, easing analysis and reducing errors. It has undergone extensive verification testing, demonstrating its reliability and readiness for deployment, and leverages on-node parallelism for rapid analysis. Example analyses are shown that include the identification of system failure paths and quantification of the value of further information about system components.

Fault Tree Analysis↗
DOE OSTI2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

In-situ electrochemical optical techniques in the investigation of lithium interfacial phenomena with a liquid and a solid-state electrolyte

An in-situ electrochemical optical diagnosis is the key to the investigation of electrode interface during a redox reaction. Because the morphology changes particularly, dendrite formation, dendrite shapes, solid electrolyte interface formation and gas generation can be revealed visually. The challenge of ensuring uniform current density on a flat Li anode in a liquid electrolyte was addressed and uniform Li plating was demonstrated. The dendrite shape change under different reduction current density was discussed. Here the Li dendrite shape change and the performance of Li anodes with a surface lamination of graphite and red phosphate were used as examples to demonstrate the capability of the in-situ optical cell. An in-situ electrochemical optical cell used in the investigation of the increasingly popular solid-state Li batteries has its own challenges. Due to the untransparent nature of a solid-state electrolyte, an optical investigation on a solid-state electrolyte Li battery needs to be done by exposing the cross-section of the cell. In addition, it is very difficult to assemble an optical cell with a brittle and fragile solid-state electrolyte in a glove box. A set of formation and transfer dies, and an optical cell were introduced. The Li dendrite growth at the interface can be observed in a solid-state Li cell.

25 ENERGY STORAGE↗
DOE OSTI2020

The JA‐responsive MYC2‐ BADH ‐ like transcriptional regulatory module in Poncirus trifoliata contributes to cold tolerance by modulation of glycine betaine biosynthesis

Summary Glycine betaine (GB) is known to accumulate in plants exposed to cold, but the underlying molecular mechanisms and associated regulatory network remain unclear. Here, we demonstrated that PtrMYC2 of Poncirus trifoliata integrates the jasmonic acid (JA) signal to modulate cold‐induced GB accumulation by directly regulating PtrBADH‐l , a betaine aldehyde dehydrogenase (BADH)‐like gene. PtrBADH‐l was identified based on transcriptome and expression analysis in P. trifoliata . Overexpression and VIGS (virus‐induced gene silencing)‐mediated knockdown showed that PtrBADH‐l plays a positive role in cold tolerance and GB synthesis. Yeast one‐hybrid library screening using PtrBADH‐l promoter as baits unraveled PtrMYC2 as an interacting candidate. PtrMYC2 was confirmed to directly bind to two G‐box cis ‐acting elements within PtrBADH‐l promoter and acts as a transcriptional activator. In addition, PtrMYC2 functions positively in cold tolerance through modulation of GB synthesis by regulating PtrBADH‐l expression. Interestingly, we found that GB accumulation under cold stress was JA‐dependent and that PtrMYC2 orchestrates JA‐mediated PtrBADH‐l upregulation and GB accumulation. This study sheds new light on the roles of MYC2 homolog in modulating GB synthesis. In particular, we propose a transcriptional regulatory module PtrMYC2‐PtrBADH‐l to advance the understanding of molecular mechanisms underlying the GB accumulation under cold stress.

Ming, Ruhong↗
DOE OSTI2025

Explainable Machine Learning for Functional Data

Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).

Elastic Shape Analysis↗
DOE OSTI2020

DECENTRALIZED APPROACH TO MULTI-ZONE GREY-BOX MODELING FOR MODEL-BASED PREDICTIVE CONTROL

This study aims to improve the easiness of utilizing the grey-box model (i.e., Resistance-Capacitance circuit) for Model-based Predictive Control (MPC). The primary barrier of implementing the MPC is estimating the control-oriented building model that needs to be computationally inexpensive and quick but reasonably precise in predicting building load and indoor conditions. The estimating of the model parameters becomes more complicated when the building scale is larger; e.g., multi-zone building. In this study, a decentralized approach is introduced; each zone is split and individually estimated with measured boundary temperature from adjacent zones integrated into one single system model. The proposed decentralized method is demonstrated with experimental data from a full-scale multi-zone test cell compared with the centralized reference case.

Joe, Jaewan↗
DOE OSTI2021

Enabling Long-range Exploration in Minimization of Multimodal Functions

We consider the problem of minimizing multi-modal loss functions with a large number of local optima. Since the local gradient points to the direction of the steepest slope in an infinitesimal neighborhood, an optimizer guided by the local gradient is often trapped in a local minimum. To address this issue, we develop a novel nonlocal gradient to skip small local minima by capturing major structures of the loss’s landscape in black-box optimization. The nonlocal gradient is defined by a directional Gaussian smoothing (DGS) approach. The key idea of DGS is to conducts 1D long-range exploration with a large smoothing radius along d orthogonal directions in Rd, each of which defines a nonlocal directional derivative as a 1D integral. Such long-range exploration enables the nonlocal gradient to skip small local minima. The d directional derivatives are then assembled to form the nonlocal gradient. We use the Gauss-Hermite quadrature rule to approximate the d 1D integrals to obtain an accurate estimator. The superior performance of our method is demonstrated in three sets of examples, including benchmark functions for global optimization, and two real-world scientific problems.

Zhang, Jiaxin↗
DOE OSTI2026

Input specific neural networks

Neural networks have emerged as powerful tools for mapping between inputs and outputs. However, their black-box nature limits the ability to encode or impose specific structural relationships between inputs and outputs. Many scientific and engineering problems, such as constitutive modeling in solid mechanics, require networks that can enforce convexity, monotonicity, or other structural constraints to ensure physical consistency. Here, we introduce the Input Specific Neural Network (ISNN), a new architecture that enables multiple, distinct constraints to be imposed on different input subsets for scalar-valued outputs. This framework unifies convex, monotone–convex, monotone, and arbitrary mappings within a single network for the first time. Two ISNN architectures with analytical first- and second-order derivatives are developed. We demonstrate the performance on synthetic toy problems, inverse problems in isotropic hyperelasticity, and finite element simulations. ISNNs achieve improved extrapolation behavior, require fewer invariant inputs than standard input convex networks for polyconvex potentials, and enable significant computational savings via manual differentiation. We also show how ISNNs can be used to learn structural relationships between inputs and outputs via a binary gating mechanism. Particularly, ISNNs are employed to model a homogenized anisotropic free energy potential in a decoupled multiscale setting. The network learns whether or not the potential should be modeled as polyconvex and retains only the relevant layers while using the minimum number of inputs. ISNNs provide a flexible foundation for embedding structural priors into neural networks, enhancing both interpretability and stability. They are broadly applicable across computational mechanics and other scientific domains requiring constrained functional relationships.

Jadoon, Asghar A. [Univ. of Texas, Austin, TX (Uni↗
DOE OSTI2025

Nonintrusive projection-based reduced order modeling using stable learned differential operators

Nonintrusive projection-based reduced order models (ROMs) are essential for dynamics prediction in multi-query applications where underlying governing equations are known but the access to the source of the underlying full order model (FOM) is unavailable; that is, FOM is a glass-box. This article proposes a learn-then-project approach for nonintrusive model reduction. In the first step of this approach, high-dimensional stable sparse learned differential operators (S-LDOs) are determined using the generated data. In the second step, the ordinary differential equations, comprising these S-LDOs, are used with suitable dimensionality reduction and low-dimensional subspace projection methods to provide equations for the evolution of reduced states. This approach allows easy integration into the existing intrusive ROM framework to enable nonintrusive model reduction while allowing the use of Petrov–Galerkin projections. The applicability of the proposed approach is demonstrated for Galerkin and LSPG projection-based ROMs through four numerical experiments: 1-D scalar advection, 1-D Burgers, 2-D scalar advection and 1-D scalar advection–diffusion–reaction equations. In conclusion, the results indicate that the proposed nonintrusive ROM strategy provides accurate and stable dynamics prediction.

42 ENGINEERING↗
DOE OSTI2025

The GRAS protein RAM1 interacts with WRI transcription factors to regulate plant genes required for arbuscule development and function

During arbuscular mycorrhiza (AM) symbiosis AM fungi form tree-shaped structures called arbuscules in root cortex cells of host plants. Arbuscules and their host cells are central for reciprocal nutrient exchange between the symbionts.REQUIRED FOR ARBUSCULAR MYCORRHIZATION1(RAM1) encodes a GRAS protein crucial for transcriptionally regulating plant genes needed for arbuscule development and nutrient exchange. Similar to other GRAS proteins, RAM1 likely does not bind to DNA and how RAM1 activates its target promoters remained elusive. Here, we demonstrate that RAM1 interacts with five AM-induced APETALA 2 (AP2) transcription factors of the WRINKLED1-like family called CTTC MOTIF-BINDING TRANSCRIPTION FACTOR1 (CBX1), WRI3, WRI5a, WRI5b, and WRI5c via a C-terminal domain containing the M2/M2a motif. This motif is conserved and enriched in WRI proteins encoded by genomes of AM-competent plants. RAM1 together with any of these WRI proteins activates the promoters of genes required for symbiotic nutrient exchange, namelyRAM2,STUNTED ARBUSCULES (STR),andPHOSPHATE TRANSPORTER 4 (PT4), inNicotiana benthamianaleaves. This activation as well as target promoter induction inLotus japonicushairy roots depends onMYCS(MYCORRHIZA SEQUENCE)-elements andAW-boxes, previously identified as WRI-binding sites. TheWRIgenes are activated in two waves: Transcription ofRAM1,CBX1,andWRI3is coregulated by calcium- and calmodulin-dependent protein kinase-activated CYCLOPS, through theAMCYC-REin their promoter, and DELLA, whileWRI5a,b,andcpromoters containMYCS-elements andAW-boxes and can be activated by RAM1 heterocomplexes with CBX1 or WRI3. We propose that RAM1 provides an activation domain to DNA-binding WRI proteins to activate genes with central roles in AM development and function.

Science & Technology - Other Topics↗
DOE OSTI2020

Limitations to the ‘basic’ HOD model and beyond

ABSTRACT We use the IllustrisTNG cosmological, hydrodynamical simulations to test fundamental assumptions of the mass-based halo occupation distribution (HOD) approach to modelling the galaxy–halo connection. By comparing the clustering of galaxies measured in the 300 Mpc TNG box (TNG300) with that predicted by the standard (basic) HOD model, we find that, on average, the ‘basic’ HOD model underpredicts the real-space correlation function in the TNG300 box by ∼15 per cent on scales of $1 \,\,\lt\,\, r \,\,\lt\,\, 20 \ {\rm Mpc}\, h^{-1}$, which is well beyond the target precision demanded of next-generation galaxy redshift surveys. We perform several tests to establish the robustness of our findings to systematic effects, including the effect of finite box size and the choice of halo finder. In our exploration of ‘secondary’ parameters with which to augment the ‘basic’ HOD, we find that the local environment of the halo, the velocity dispersion anisotropy, β, and the product of the half-mass radius and the velocity dispersion, σ2Rhalfmass, are the three most effective measures of assembly bias that help reconcile the ‘basic’ HOD-predicted clustering with that in TNG300. In addition, we test other halo properties such as halo spin, formation epoch, and halo concentration. We also find that at fixed halo mass, galaxies in one type of environment cluster differently from galaxies in another. We demonstrate that a more complete model of the galaxy–halo connection can be constructed if we combine both mass and local environment information about the halo.

42 ENGINEERING↗
DOE OSTI2024

An Accelerated Clip Algorithm for Unstructured Meshes: A Batch-Driven Approach

The clip technique is a popular method for visualizing complex structures and phenomena within 3D unstructured meshes. Meshes can be clipped by specifying a scalar isovalue to produce an output unstructured mesh with its external surface as the isovalue. Similar to isocontouring, the clipping process relies on scalar data associated with the mesh points, including scalar data generated by implicit functions such as planes, boxes, and spheres, which facilitates the visualization of results interior to the grid. In this paper, we introduce a novel batch-driven parallel algorithm based on a sequential clip algorithm designed for high-quality results in partial volume extraction. Our algorithm comprises five passes, each progressively processing data to generate the resulting clipped unstructured mesh. The novelty lies in the use of fixed-size batches of points and cells, which enable rapid workload trimming and parallel processing, leading to a significantly improved memory footprint and run-time performance compared to the original version. On a 32-core CPU, the proposed batch-driven parallel algorithm demonstrates a run-time speed-up of up to 32.6x and a memory footprint reduction of up to 4.37x compared to the existing sequential algorithm. The software is currently available under an open-source license in the VTK visualization system.

Tsalikis, Spiros↗
DOE OSTI2021

Indra: a public computationally accessible suite of cosmological N -body simulations

ABSTRACT Indra is a suite of large-volume cosmological N-body simulations with the goal of providing excellent statistics of the large-scale features of the distribution of dark matter. Each of the 384 simulations is computed with the same cosmological parameters and different initial phases, with 10243 dark matter particles in a box of length 1 h−1 Gpc, 64 snapshots of particle data and halo catalogues, and 505 time-steps of the Fourier modes of the density field, amounting to almost a petabyte of data. All of the Indra data are immediately available for analysis via the SciServer science platform, which provides interactive and batch computing modes, personal data storage, and other hosted data sets such as the Millennium simulations and many astronomical surveys. We present the Indra simulations, describe the data products and how to access them, and measure ensemble averages, variances, and covariances of the matter power spectrum, the matter correlation function, and the halo mass function to demonstrate the types of computations that Indra enables. We hope that Indra will be both a resource for large-scale structure research and a demonstration of how to make very large data sets public and computationally accessible.

Falck, Bridget↗