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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Uncertainty Quantification via Stable Distribution Propagation

We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal approximation in terms of total variation distance for the ReLU non-linearity. This allows propagating Gaussian and Cauchy input uncertainties through neural networks to quantify their output uncertainties. To demonstrate the utility of propagating distributions, we apply the proposed method to predicting calibrated confidence intervals and selective prediction on out-of-distribution data. The results demonstrate a broad applicability of propagating distributions and show the advantages of our method over other approaches such as moment matching.

Artificial Intelligence (cs.AI)↗
DOE OSTI2025

Reducing the Cost of Energy Differences in Variational Monte Carlo with Spotlight Sampling

Here, we investigate an approximate sampling scheme that can significantly reduce the cost scaling of variational Monte Carlo when it is employed to predict the energy differences associated with local chemical changes. Inspired by side-chaining and embedding methods, this spotlight sampling approach adopts an approximate fragmented Hamiltonian and correlated sampling to reduce cost scaling to the point that it is essentially linear with system size, with the potential to go sublinear if certain conditions are met. In tests on bond stretching energies in alcohols, hydrogen dimer chains, and molecules with various degrees of π-system delocalization, we observe the anticipated linear scaling and an explicit cost crossover with standard variational Monte Carlo.

Bumann, Sonja [University of California, Berkeley,↗
DOE OSTI2021

The dominant contribution of Southern Ocean heat uptake to time - evolving radiative feedback in CESM

Radiative feedbacks are found to vary with time in both historical records and future warming projections. Previous studies proposed two factors that determine the variation of radiative feedbacks: (i) the evolution of tropical sea surface warming patterns and (ii) the tropical-extratropical contrast of ocean heat uptake. Our results bridge the two factors by evaluating the remote impact from the extratropical ocean on tropical temperature patterns, accounting for the changes in radiative feedbacks. Based on the Green’s Function approach that quantifies the non-local contributions of regional ocean heat uptake, we show that the net radiative feedback evolution in CESM can be mostly attributed to the heat uptake variations in the Southern Ocean. The enhanced surface warming associated with the weakened heat uptake decades after quadrupling CO2 is not confined over the Southern Ocean, but extends to tropical Southeastern Pacific, which leads to decreasing tropospheric stability and more positive cloud feedbacks.

Lin, Yuan-Jen↗
DOE OSTI2020

Fine Root and Soil Organic Carbon Depth Distributions are Inversely Related Across Fertility and Rainfall Gradients in Lowland Tropical Forests

Humid tropical forests contain some of the largest soil organic carbon (SOC) stocks on Earth. Much of this SOC is in subsoil, yet variation in the distribution of SOC through the soil profile remains poorly characterized across tropical forests. We used a correlative approach to quantify relationships among depth distributions of SOC, fine root biomass, nutrients and texture to 1 m depths across 43 lowland tropical forests in Panama. The sites span rainfall and soil fertility gradients, and these are largely uncorrelated for these sites. We used fitted b parameters to characterize depth distributions, where b is a numerical index based on an asymptotic relationship, such that larger b values indicate greater concentrations of root biomass or SOC at depth in the profile. Root b values ranged from 0.82 to 0.95 and were best predicted by soil pH and extractable potassium (K) stocks. For example, the three most acidic (pH < 4) and Kpoor (< 20 g K m -2 ) soils contained 76 ± 5% of fine root biomass from 0 to 10 cm depth, while the three least acidic (pH > 6.0) and most K-rich (> 50 g K m -2 ) soils contained only 41 ± 9% of fine root biomass at this depth. Root b and SOC b values were inversely related, such that a large fine root biomass in surface soils corresponded to large SOC stocks in subsoils (50–100 cm). SOC b values were best predicted by soil pH and base cation stocks, with the three most base-poor soils containing 34 ± 8% of SOC from 50 to 100 cm depth, and the three most base-rich soils containing just 9 ± 2% of SOC at this depth. Nutrient depth distributions were not related to Root b or SOC b values. Here, these data show that large surface root biomass stocks are associated with large subsoil C stocks in strongly weathered tropical soils. Further studies are required to evaluate why this occurs, and whether changes in surface root biomass, as may occur with global change, could in turn influence SOC storage in tropical forest subsoils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Transforming the electronic properties of phosphorene through charge transfer superatomic doping

Phosphorene is a two-dimensional electron poor p-type semiconductor with great promise for applications in electronics and optoelectronics. Here, in this work, we propose how the two most important properties of a semiconductor, the band gap energy and the nature of carriers, can be controlled by changing the dimensionality or through charge transfer doping with metal-chalcogenide superatoms. Our studies on nanoribbons examine how the band gap can be changed by controlling the width of the ribbons. We investigate the stability and properties of bare and H-terminated nanoribbons. We show that small non-passivated ribbons can be metallic while the passivated ribbons show variation in the band gap energy as a function of the width of the ribbon. We next investigate an alternative approach to band gap and carrier control via doping with ligated metal-chalcogenide superatom clusters whose redox properties can be changed by ligand exchange. Our results obtained from deposition of Co 6 S 8 (PH 3 ) 5-n (CO) n clusters on a phosphorene support showed that the band gap energy can be controlled by exchanging the electron donating phosphine ligands with electron withdrawing CO. We then show that by depositing Re 6 Se 8 (PH 3 ) 5-n Cl n clusters, phosphorene can be converted into a p- or n- type semiconductor as the relative composition of ligands is changed. Our studies provide a novel approach to controlling carrier type and band gap in phosphorene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Global stochastic optimization of stellarator coil configurations

In the construction of a stellarator, the manufacturing and assembling of the coil system is a dominant cost. These coils need to satisfy strict engineering tolerances, and if those are not met the project could be cancelled as in the case of the National Compact Stellarator Experiment (NCSX) project. Therefore, our goal is to find coil configurations that increase construction tolerances without compromising the performance of the magnetic field. In this paper, we develop a gradient-based stochastic optimization model which seeks robust stellarator coil configurations in high dimensions. In particular, we design a two-step method: first, we perform an approximate global search by a sample efficient trust-region Bayesian optimization; second, we refine the minima found in step one with a stochastic local optimizer. To this end, we introduce two stochastic local optimizers: BFGS applied to the sample average approximation; and Adam, equipped with a control variate for variance reduction. Numerical simulations performed on a W7-X-like coil configuration demonstrate that our global optimization approach finds a variety of promising local solutions at less than 0.1% of the cost of previous work, which considered solely local stochastic optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Validation of the DESI DR2 measurements of baryon acoustic oscillations from galaxies and quasars

The Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2) galaxy and quasar clustering data represents a significant expansion of data from Data Release 1 (DR1), providing improved statistical precision in baryon acoustic oscillation (BAO) constraints across multiple tracers, including bright galaxies, luminous red galaxies, emission line galaxies, and quasars. In this paper, we validate the BAO analysis of DR2. We present the results of robustness tests on the blinded DR2 data and, after unblinding, consistency checks on the unblinded DR2 data. All results are compared with those obtained from a suite of mock catalogs that replicate the selection and clustering properties of the DR2 sample. We confirm the consistency of DR2 BAO measurements with DR1 while achieving a reduction in statistical uncertainties due to the increased survey volume and completeness. The combined BAO precision, including both statistical and systematic errors, improves from ∼0.52% in DR1 to 0.30% in DR2—a factor of 1.7 gain. We assess the impact of analysis choices, including different data vectors (correlation function vs power spectrum), modeling approaches and systematics treatments, and an assumption of the Gaussian likelihood, finding that our BAO constraints are stable across these variations and assumptions with a few minor refinements to the baseline setup of the DR1 BAO analysis. We summarize a series of pre-unblinding tests that confirmed the readiness of our analysis pipeline, the final systematic errors, and the DR2 BAO analysis baseline. The successful completion of these tests led to the unblinding of the DR2 BAO measurements, ultimately leading to the DESI DR2 cosmological analysis, with their implications for the expansion history of the Universe and the nature of dark energy presented in the DESI key paper (companion paper).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Isogeometric large-eddy simulations of turbulent particle-laden flows

In recent years, isogeometric analysis (IGA) has attracted significant attention from the computational mechanics community due to its ability to integrate design and analysis. Besides, IGA is also a higher-order discretization technique for solving partial differential equations, showing high approximation capability per degree of freedom. In this paper, we extend the application realm of IGA to particle-laden flows based on Eulerian–Eulerian description that couples Navier–Stokes equations with a density transport equation through a Boussinesq approximation. The coupled systems are solved by using quadratic non-uniform rational B-spline (NURBS) functions and a recently developed residual-based variational multiscale (VMS) formulation, which introduces coupling between the fine velocity scales and density equation residuals. We deploy the proposed approach to perform large-eddy simulations (LES) of dilute particle-laden flows over a flat surface at Reynolds number = 10,000. We compare the simulation results against direct numerical simulation (DNS) results from the literature. We find that combining VMS and IGA, the proposed approach enables accurate prediction of a wide range of flow/particle statistics with a relatively lower mesh resolution.

Mathematics↗
DOE OSTI2022

Fuel Qualification for Molten Salt Reactors

This report supports the development and review of an efficient methodology or process for liquid salt fuel system qualification. The report describes the technical issues encountered when developing an adequate understanding of the fuel salt’s chemical and physical behavior under both normal and accident conditions. The concepts presented in this report align with the fundamental safety functions (FSFs) approach of the more general non-light water reactor (nonLWR) fuel qualification guidance provided in NUREG-2246. However, because liquid salt fuel has substantial technical differences from solid fuels, this report describes tailored approaches for achieving the common safety intents when using liquid salt. The purpose of this report is to illustrate how variations in fuel salt properties impact achievement of FSFs at liquid-salt fueled molten salt reactors (MSRs). The focus on FSFs supports methodology compatibility with both current and developing licensing processes. Customized fuel qualification guidance for liquid salt–fueled Nuclear Power Plants (NPPs) are also provided in this report to supplement the generic non-LWR guidance recently developed by the NRC. As with the broader non-LWR fuel qualification, this report focuses on identification and understanding of fuel life-limiting failure and fuel salt property degradation mechanisms that occur as a result of irradiation during reactor operation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

REBOUND: Reverse Engineering Bidirectional Outflow Under Non-Equilibrium Diffusion

Rare-earth elements (REEs) are essential for electronics, renewable energy, and defense technologies. However, the current supply of REEs relies on mining concentrated in a few countries and energy-intensive separations. DOE’s Basic Energy Sciences (BES) program has launched a grand challenge which aims to ensure a sustainable supply of critical REEs by developing innovative and environmentally friendly separation methods. As an alternative to costly and harmful traditional methods, the Non-Equilibrium Transport Driven Separations (NETS) initiative has created a microfluidic Y-channel co-flow method that applies external fields to exploit magneto- and electrohydrodynamic effects for separating dilute REE ions from complex feedstocks. Computational fluid dynamics (CFD) studies have identified a few operating conditions with promising ion selectivity and separation efficiency. However, challenges remain regarding Y-channel versatility across feedstocks and accurate incorporation of physical phenomena into CFD models. In this work, we develop a multi-fidelity modelling approach which integrates experimental results with CFD simulation to build a surrogate model for the dependence of separation efficiency to variation of design parameters. The surrogate model enables a reinforcement learning (RL) method to adaptively launch CFD and experimental runs, improving model fidelity around optimal Y-channel parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2020

On the elastocaloric effect in CuAlBe shape memory alloys: A quantitative phase-field modeling approach

We report the reversible stress-induced phase transformation in shape memory alloys (SMAs) is a dissipative process during which heat is absorbed or released. The inherent temperature variations inside the material has an elastocaloric effect (eCE) with appealing applications in solid-state cooling technology such as compact and efficient on-board refrigeration system for eletronic devices. In this manuscript, we conduct the first study of eCE of CuAlBe SMAs utilizing phase-field modeling. For an applied stress of 500 MPa, the results for polycrystalline Cu-Al11-2Be (at. %) show a minimum adiabatic unloading temperature change of -10 K over a pseudoelastic window of 40 K. In the absence of plastic deformation, the material demonstrates good reproducibility of the eCE over a few loading–unloading cycles. The presence of plastic deformation is found to cause functional fatigue that deteriorates the cooling capacity; however, the coefficient of performance only decreases from 9.04 to 8.03, which is still a very good value. These results place CuAlBe as a frontrunner SMA for solid-state cooling compared to the expensive NiTi.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nondestructive Evaluation of Concrete: Elastic Property Imaging Through Full-Waveform Inversion

Concrete is a vital material in construction—especially in the nuclear industry, where it is used in critical structures such as containment vessels. Over time, concrete can degrade due to harsh operational and environmental conditions, necessitating that its elastic properties be accurately evaluated to ensure structural integrity and safety. Traditional nondestructive evaluation methods such as ultrasound-based techniques often rely on simplifying assumptions that may not hold true for concrete. This paper presents an advanced ultrasound-based method that uses elastic full-waveform inversion (EFWI) to create detailed images of concrete’s mechanical properties. By accurately modeling wave behaviors such as scattering and reflection, we aim to overcome the limitations of conventional ultrasonic-based methods. In this work, the imaging problem involved reconstructing the various elastic properties of a heterogenous concrete block with three steel rebars embedded in it. The ultrasonic measurements were synthetically generated from multiple sources and receivers, and the reconstruction process was performed using a gradient-based optimization algorithm. Our approach leveraged EFWI to reconstruct high-resolution images of the pressure wave speed, shear wave speed, and density. Multiple misfit functions—including L2-norm, cross-correlation (CC), and L1-norm—combined with total variation (TV) regularization and parameter constraints using a Sigmoid function—were explored for the reconstruction. The results demonstrated that using the L1-norm misfit function in conjunction with TV regularization and Sigmoid constraints significantly improved the reconstruction quality in comparison to traditional methods. This approach provided clearer images with fewer artifacts and better captured background heterogeneity. Our findings highlight that, when properly designed, EFWI carries great potential for providing comprehensive, more accurate, and more reliable assessments of concrete conditions, as is crucial for the maintenance and safety of nuclear power plant structures.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Dynamics-Based Peptide–MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO

An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen–major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we apply CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach, to identify per-residue antigen binding contributions and then design novel antigens of increased MHC-II binding affinity for a type 1 diabetes-implicated system. We build upon a small-molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across four antigens and four HLA serotypes. We develop several new machine learning metrics including a structure-based anchor residue classification model as well as cluster comparison scores. ML-MD predictions agree well with experimental binding results and free energy perturbation-predicted binding affinities. Moreover, ML-MD metrics are independent of traditional MD stability metrics such as contact area and root-mean-square fluctuations (RMSF), which do not reflect binding affinity data. Finally, our work supports the role of structure-based deep learning techniques in antigen-specific immunotherapy design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

ABC -Auxetics: An Implicit Design Approach for Negative Poisson's Ratio Materials

A novel methodology is introduced for designing auxetic (negative Poisson's ratio) structures based on topological principles and is demonstrated by investigating a new class of auxetics based on two-dimensional (2D) textile weave patterns. Conventional methodology for designing auxetic materials typically involves determining a single deformable block (a unit cell) of material whose shape results in auxetic behavior. Consequently, patterning such a unit cell in a 2D (or 3D) domain results in a larger structure that exhibits overall auxetic behavior. Such an approach naturally relies on some prior intuition and experience regarding which unit cells may be auxetic. Second, tuning the properties of the resulting structures is typically limited to parametric variations of the geometry of a specific type of unit cell. Thus, most of the currently known auxetic structures belong to a selected few classes of unit cell geometries that are explicitly defined in accordance with a specified topological (i.e., grid structure). Herein, a new class of auxetic structures is demonstrated that, while periodic, can be generated implicitly, i.e., without reference to a specific unit cell design. The approach leverages weave-based parameters (A–B–C), resulting in a rich design space for auxetics that is previously unexplored.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The multifaceted role of c-di-AMP signaling in the regulation of Porphyromonas gingivalis lipopolysaccharide structure and function

This study unveils the intricate functional association between cyclic di-3’,5’-adenylic acid (c-di-AMP) signaling, cellular bioenergetics, and the regulation of lipopolysaccharide (LPS) profile in Porphyromonas gingivalis, a Gram-negative obligate anaerobe considered as a keystone pathogen involved in the pathogenesis of chronic periodontitis. Previous research has identified variations in P. gingivalis LPS profile as a major virulence factor, yet the underlying mechanism of its modulation has remained elusive. We employed a comprehensive methodological approach, combining two mutants exhibiting varying levels of c-di-AMP compared to the wild type, alongside an optimized analytical methodology that combines conventional mass spectrometry techniques with a novel approach known as FLAT n . We demonstrate that c-di-AMP acts as a metabolic nexus, connecting bioenergetic status to nuanced shifts in fatty acid and glycosyl profiles within P. gingivalis LPS. Notably, the predicted regulator gene cdaR, serving as a potent regulator of c-di-AMP synthesis, was found essential for producing N-acetylgalactosamine and an unidentified glycolipid class associated with the LPS profile. The multifaceted roles of c-di-AMP in bacterial physiology are underscored, emphasizing its significance in orchestrating adaptive responses to stimuli. Furthermore, our findings illuminate the significance of LPS variations and c-di-AMP signaling in determining the biological activities and immunostimulatory potential of P. gingivalis LPS, promoting a pathoadaptive strategy. The study expands the understanding of c-di-AMP pathways in Gram-negative species, laying a foundation for future investigations into the mechanisms governing variations in LPS structure at the molecular level and their implications for host-pathogen interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High‐throughput combinatorial approach expedites the synthesis of a lead‐free relaxor ferroelectric system

Abstract Developing novel lead‐free ferroelectric materials is crucial for next‐generation microelectronic technologies that are energy efficient and environment friendly. However, materials discovery and property optimization are typically time‐consuming due to the limited throughput of traditional synthesis methods. In this work, we use a high‐throughput combinatorial synthesis approach to fabricate lead‐free ferroelectric superlattices and solid solutions of (Ba 0.7 Ca 0.3 )TiO 3 (BCT) and Ba(Zr 0.2 Ti 0.8 )O 3 (BZT) phases with continuous variation of composition and layer thickness. High‐resolution x‐ray diffraction (XRD) and analytical scanning transmission electron microscopy (STEM) demonstrate high film quality and well‐controlled compositional gradients. Ferroelectric and dielectric property measurements identify the “optimal property point” achieved at the composition of 48BZT–52BCT. Displacement vector maps reveal that ferroelectric domain sizes are tunable by varying {BCT–BZT} N superlattice geometry. This high‐throughput synthesis approach can be applied to many other material systems to expedite new materials discovery and properties optimization, allowing for the exploration of a large area of phase space within a single growth. image

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantifying Site Effects and Their Influence on Earthquake Source Parameter Estimations Using a Dense Array in Oklahoma

We investigate the effects of site response on source parameter estimates using earthquakes recorded by the LArge-n Seismic Survey in Oklahoma (LASSO). While it is well known that near-surface unconsolidated sediments can cause an apparent breakdown of earthquake self-similarity, the influence of laterally varying site conditions remains unclear. We analyze site conditions across the 1825-station array on a river plain within an area of 40 km by 23 km using vertical ground motions from 14 regional earthquakes. While the source radiation pattern controls P-wave ground motions below 8 Hz, the surface geology correlates with P-wave ground motions above 8 Hz and S-wave ground motions at 2–21 Hz. Stations installed in alluvial sediments have vertical ground motions that can exceed three times the array median. We use the variation of ground motion of regional earthquakes across the array as a proxy for site effects. The corner frequencies and stress drops of local earthquakes ( M L = 0.01–3) estimated using a standard single-spectra approach show negative correlations with the site-effect proxy, while the seismic moments show positive correlations. In contrast, the spectral-ratio approach effectively shows no correlation. The overall bias is small as expected for this relatively homogeneous structure; accurate estimation of site-related biases requires at least 30 stations. Correcting for site-related biases reduces the standard deviations of the source parameters by less than 13% of the total variations. Remaining variations are partially associated with source directivity and model misfits— as small earthquakes can have complex ruptures.

58 GEOSCIENCES↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗