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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast

Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at suboptical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based autosegmentation pipeline to segment and label cellular structures in hundreds of cells across three Saccharomyces cerevisiae strains. This task-based pipeline uses manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the three-dimensional (3D) whole-cell morphometric characteristics of wild-type, VPH1-GFP, and vac14 strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single-cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts.

Chen, Jianhua [Lawrence Berkeley National Laborato↗
DOE OSTI2025

Exponentially Reduced Circuit Depths Using Trotter Error Mitigation

Product formulas are a popular class of digital quantum simulation algorithms due to their conceptual simplicity, low overhead, and performance, which often exceeds theoretical expectations. Recently, Richardson extrapolation and polynomial interpolation have been proposed to mitigate the Trotter error incurred by the use of these formulas. This work provides a rigorous, general analysis of these techniques for computing time-evolved observables, simplifying the interpolation algorithm in the process, and shows that extrapolation generically improves the performance of product formulas for this task. We demonstrate that, to achieve error 𝜖 in a simulation of time 𝑇 using a 𝑝 ⁢th-order product formula with extrapolation, circuit depths of 𝑂⁡(𝑇 1+1/𝑝 ⁢polylog (1/𝜖)) are sufficient—an exponential improvement in the precision over product formulas alone. Furthermore, we prove that these algorithms achieve commutator scaling, and improve the 𝑇 complexity for the interpolation algorithm. By relaxing the requirement of performing exact Chebyshev interpolation, our simplified algorithm eliminates the need for fractional implementations of Trotter steps, reducing computational overhead. Finally, we show these techniques can be combined with the classical shadows method to estimate many time-evolved local observables. Taken together, our findings provide the strongest evidence yet for the utility of Trotter error-mitigation techniques in algorithmic applications.

quantum algorithms & computation↗
DOE OSTI2021

Extending C++ for Heterogeneous Quantum-Classical Computing

In this report we present qcor - a language extension to C++ and compiler implementation that enables heterogeneous quantum-classical programming, compilation, and execution in a single-source context. Our work provides a first-of-its-kind C++ compiler enabling high-level quantum kernel (function) expression in a quantum-language agnostic manner, as well as a hardware-agnostic, retargetable compiler workflow targeting a number of physical and virtual quantum computing backends. qcor leverages novel Clang plugin interfaces and builds upon the XACC system-level quantum programming framework to provide a state-of-the-art integration mechanism for quantum-classical compilation that leverages the best from the community at-large. qcor translates quantum kernels ultimately to the XACC intermediate representation, and provides user-extensible hooks for quantum compilation routines like circuit optimization, analysis, and placement. This work details the overall architecture and compiler workflow for qcor, and provides a number of illuminating programming examples demonstrating its utility for near-term variational tasks, quantum algorithm expression, and feed-forward error correction schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Polaris Studio

SF-24-040 Polaris-studio is the Python entry point/front end for using the Polaris transportation simulator. The package contains the code used to create new models from open and user-provided data, an extensive range of convenience tools for data preparation and result analysis and the ability of designing, launching and tracking studies combining multiple individual Polaris simulations from a Python terminal. The Polaris Studio software contains several modules for accomplishing the above tasks, including the PolarisLib - the Polaris object model, Polaris Manager - code for setting up and running Polaris studies, QPOLARIS - an add-on for the QGis software to allow model files to be created and edited, PolarisVis - a juptyer-based postprocessing library, and Polaris Tools - a useful collection of supplemental tools for interacting with Polaris files.

Auld, Joshua↗
DOE OSTI2023

Structure-informed clustering for population stratification in association studies

Background: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. Results: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. Conclusions: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Policy and Regulatory Environment for Utility-Scale Energy Storage: India

This report is part of a series investigating the potential for utility-scale energy storage in South Asia. The first part of the work is focused on opportunities and barriers for energy storage within existing policy and regulatory frameworks in the region. This report applies the previously-developed Energy Storage Readiness Assessment to evaluate the policy and regulatory environment for energy storage in India. Forthcoming reports will expand this analysis to other countries in South Asia. These evaluations are designed to provide insights on the opportunities and barriers for energy storage growth and deployment in each country. The second part of this work consists of techno-economic analysis to understand the drivers of energy storage investments in South Asia. Using NREL’s power system planning and operational models of South Asia, this analysis will identify potential storage applications and growth under various cost, policy, and demand growth scenarios. In addition, the regulatory and policy barriers and incentive mechanisms identified in the first task will be incorporated into the modeling task to understand their impact on energy storage deployment and operation. Together these studies will inform the applications and value of energy storage on South Asia power systems and policy and regulatory pathways to realize this value.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

MIDAS: Modeling Individual Differences using Advanced Statistics

This research explores novel methods for extracting relevant information from EEG data to characterize individual differences in cognitive processing. Our approach combines expertise in machine learning, statistics, and cognitive science, advancing the state-of-the art in all three domains. Specifically, by using cognitive science expertise to interpret results and inform algorithm development, we have developed a generalizable and interpretable machine learning method that can accurately predict individual differences in cognition. The output of the machine learning method revealed surprising features of the EEG data that, when interpreted by the cognitive science experts, provided novel insights to the underlying cognitive task. Additionally, the outputs of the statistical methods show promise as a principled approach to quickly find regions within the EEG data where individual differences lie, thereby supporting cognitive science analysis and informing machine learning models. This work lays methodological ground work for applying the large body of cognitive science literature on individual differences to high consequence mission applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Process Intensification for Nanostructure Aluminum Extrusions

A new technology called Shear Assisted Processing and Extrusion (ShAPE™), pioneered at the Pacific Northwest National Laboratory (PNNL), has the potential to revolutionize the US extrusion industry. Maturation of the ShAPE™ process would create a new cross cutting US manufacturing technology that advances three key the objectives within AMO; namely, nanomaterials processing, process intensification, and materials for extreme and harsh environments. PNNL and SCM Metals Products, Inc. (SCM), a division of Kymera International, will collaborate to mature the ShAPE™ extrusion process beyond TRL 3 to increase the likelihood of its adoption by industry. PNNL’s custom (one-of-a-kind in the world) ShAPE™ machine will be used to extrude round rods of high strength aluminum alloy feedstock powder provided by SCM. As proof-of-concept, PNNL has previously extruded 5 mm diameter rods with SCM aluminum alloy powder AL-12.4TM. This project aims to scale up the process to a diameter of 25 mm while retaining the excellent mechanical properties achieved for the 5 mm diameter rods. The degree of process intensification with ShAPE™ will also be estimated. The primary objectives of this project are as follows. 1) Fabricate 25 mm diameter nanostructured extrusions in bulk, directly from AL-12.4TM aluminum alloy powder without the need for canning, degassing, compaction, and other intermediate steps typical of powder metallurgy extrusion. 2) Achieve a microstructure with an ultrafine grained (UFG <1 µm) aluminum matrix having refined and homogenously distributed nanoscale second phases. 3) Show that the ram force can be significantly reduced compared to conventional powder metallurgy extrusion of the same material and estimate the extent of process intensification that may be possible with ShAPE™. 4) Demonstrate improved high-temperature mechanical properties compared to conventional extrusion of the same material. The following tasks will be undertaken to achieve these objectives. PNNL will develop the die set and process parameters necessary to fabricate Al-12.4TM extrusions with a 25 mm diameter. Microstructural analysis will be performed by PNNL while mechanical testing will be conducted by SCM. With information learned from the process development and characterization, PNNL will perform an estimate of the degree of process intensification that may be possible with ShAPE™.

42 ENGINEERING↗
DOE OSTI2024

Shielding Thickness Recommendations for an Activated Target Segment Vertical Stave

This analysis examines the shielding requirements needed to plan maintenance and handling operations of the target segment assembly vertical stave. It provides the analyses and results that fulfill the requirements of Task Order TO-150. The vertical stave is a stainless-steel welded assembly that carries cooling water to and from the target block. In this report, we characterize the dose rates and shielding requirements for the vertical stave only, i.e., separated from the target block. Shielding and dose rates for the target block only have been calculated in response to TO-008.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Materials Characterization, Prediction, and Control Project: Characterization of 316L Stainless Steel after Solid Phase Processing using Ultrasonic NDE Method

The Pacific Northwest National Laboratory undertook the Materials Characterization, Prediction, and Control Laboratory Directed Research and Development Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems produced via advanced manufacturing methods, such as solid phase processing, for use in national security and advanced energy applications (Smith 2021). A motivation of the Materials Characterization, Prediction, and Control Project was to demonstrate ultrasonic testing as a nondestructive evaluation method to complement traditional destructive methods for characterizing material microstructure with emphasis on grain size determination using a method that may have future applications for real-time inline process monitoring. The objective of the work described in this report is to establish the process and an analysis method for measuring grain sizes of polycrystalline metals with ultrafine grains using ultrasonic shear wave backscattering, building on prior studies on coarser-grained material. The work involves five tasks: Measured ultrasonic backscattering experimentally for a series of 316L stainless steel specimens with various grain sizes made by friction stir processing. Calculated ultrasonic backscattering coefficients from experimental data based on a physical measurement model. Measured ground truth grain sizes of the specimens from electron backscatter diffraction grain boundary images using a generalization of the ASTM E112 (ASTM 2021) intercept method. Built a curve of ultrasonic backscattering coefficients versus the ground truth intercept-based grain sizes to determine the correlation between mean grain sizes and ultrasonic measurements. Demonstrated the ability of using the correlation curve to deduce grain sizes with measured ultrasonic backscattering coefficients for a few 316L stainless steel specimens whose grain sizes were unknown beforehand but were targeted to be an extrapolation to larger grain sizes than used to formulate the correlation curves. Experimental procedures and computational algorithms are developed and validated for these tasks. This work establishes an ultrasonic technique for characterizing material microstructure with ultrafine grains that are often resulted by solid-phase processing. The technique is nondestructive, and it has the potential to be used for real time inline process monitoring. This work successfully demonstrates the viability of an ultrasonic nondestructive evaluation method for microstructural characterization of material having ultrafine grain structure (as small as 1?mm) and produced by an advanced manufacturing method. This includes a demonstration of the method to extrapolate to other conditions. While not demonstrated here, the method is expected to be viable for in-line, or near-inline, process monitoring in advanced manufacturing applications with suitable consideration for access of instrumentation to the material being manufactured.

316 L Stainless Steel↗
DOE OSTI2021

Risk Informed Timelines (RIT) v.1.0

Risk Informed Timelines (RIT) is a more modern and holistic method for understanding how access delay timelines are developed and utilized in physical security system evaluations. The software utilizes Bayesian methods to combine subject matter expert judgement and small performance test datasets in a consistent and defensible way. It allows a more holistic view of delay performance that provides distributions of task times and task success probabilities to account for tasks that, if failed, would result in failure of the attack. The RIT methodology is meant to enable security risk managers to begin the transition into more comprehensive delay timeline analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-8294 O

Noel, ToddG.↗
DOE OSTI2025

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗
DOE OSTI2023

Novel Chalcopyrites for Advanced Photoelectrochemical Water Splitting

With the support of DoE’s EERE office, our team has established a unique tool-chest of capabilities, including theoretical modeling (Lawrence Livermore National Laboratory: LLNL), state-of-the-art synthesis (Hawaii Natural Energy Institute: HNEI, Stanford, and the National Renewable Energy Laboratory: NREL) and advanced materials and interfaces characterization (University of Nevada, Las Vegas: UNLV, and Lawrence Berkeley National Laboratory: LBNL), to accelerate the development of high efficiency and durable chalcopyrite materials for advanced photoelectrochemical (PEC) water splitting. Using this synergistic approach, we have successfully created new wide bandgap chalcopyrite photocathodes generating over 10 mA/cm 2 , developed innovative strategies to protect them from corrosion, and engineered novel integration methods to circumvent thin film materials mechanical, chemical and thermal incompatibility. In Task 1 “Modeling and synthesis of chalcopyrite photocathodes”, we expanded our library of wide bandgap chalcopyrites for PEC water splitting. With support from LLNL’s “Computational Materials Diagnostics and Optimization of PEC Devices”, LBNL’s “photophysical” and NREL’s “I-III-VI Compound Semiconductors for Water-Splitting” nodes, we investigated two new chalcopyrite candidates for PEC water splitting: Cu(In,Al)Se 2 and Cu(In,B)Se 2 . We also further developed ordered vacancy compounds, such as CuGa 3 Se 5 , with unprecedented durability during PEC waters splitting in acidic solutions. In Task 2 “Interfaces engineering for enhanced efficiency and durability”, we addressed both the non-ideal band-edge positions of chalcopyrites with respect to water redox potentials, as well as their chemical instability under PEC water splitting, with a buried-junctions approach. With help from NREL’s “High-Throughput Experimental Thin Film Combinatorial Capabilities” and “Corrosion Analysis of Materials” nodes, we engineered environmentally friendly n-type buffers, including Mn x Zn 1-x O, to adjust the chalcopyrite band-edge positions and achieved photovoltages as high as 925 mV. Also, we integrated non-precious catalytic-protecting layers, such as WO 3 , to enhance the water splitting long-term stability of chalcopyrite absorbers. Finally, in Task 3 “Hybrid photoelectrode device integration”, we proposed an innovative method to bond wide bandgap photocathodes onto narrow bandgap PV drivers at room temperature using conductive polymers. Our semi-monolithic approach addressed fundamental processing incompatibility issues, as both the photocathode and the PV driver are processed separately. Proof-of-concept whole-chalcopyrite tandems were obtained by consecutive exfoliation and transfer of fully integrated 1.85 eV CuGa 3 Se 5 and 1.13 eV CuInGaSe 2 stacks from their Mo/SLG substrates onto a new single FTO host substrate.

08 HYDROGEN↗
DOE OSTI2021

Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.

42 ENGINEERING↗
DOE OSTI2022

Gamma-ray bursts, supernovae Ia, and baryon acoustic oscillations: A binned cosmological analysis

Abstract Cosmological probes at any redshift are necessary to reconstruct consistently the cosmic history. Studying properly the tension on the Hubble constant, H0, obtained by supernovae type Ia (SNe Ia) and the Planck measurements of the cosmic microwave background radiation would require complete samples of distance indicators at any epoch. Gamma-ray bursts (GRBs) are necessary for the aforementioned task because of their huge luminosity that allows us to extend the cosmic ladder to very high redshifts. However, using GRBs alone as standard candles is challenging, because their luminosity varies widely. To this end, we choose a reliable correlation for GRBs with a very small intrinsic scatter: the so-called fundamental plane correlation for GRB afterglows corrected for selection biases and redshift evolution. We choose a well defined sample: the platinum sample, composed of 50 long GRBs. To further constrain the cosmological parameters, we use baryon acoustic oscillations (BAOs) given their reliability as standard rulers. Thus, we have applied GRBs, SNe Ia, and BAOs in a binned analysis in redshifts so that the GRB contribution is fully included in the last redshift bin, which reaches z = 5. We use the fundamental plane correlation (also known as the 3D Dainotti relation), together with SNe Ia and BAOs, to constrain H0 and the density matter today, ΩM. This methodology allows us to assess the role of GRBs combined with SNe Ia and BAOs. We have obtained results for H0 and ΩM using GRBs+SNe Ia+BAOs with better precision than SNe Ia alone for every bin, thus confirming the beneficial role of BAOs and GRBs added together. In addition, consistent results between GRBs+SNe Ia+BAOs are obtained when compared with SNe Ia+BAOs, showing the importance of GRBs since the distance ladder is extended up to z = 5 with a similar precision obtained with other probes without including GRBs.

Dainotti, Maria Giovanna↗
DOE OSTI2022

Implementation of an Orificing Optimization Algorithm in the DASSH Subchannel Analysis Code

The Ducted Assembly Steady-State Heat transfer code (DASSH) performs full-core subchannel thermal hydraulics calculations in liquid metal fast reactors. One of the applications of subchannel codes is to optimize coolant flow orificing. As a design activity, the primary task is to determine the best way to divide assemblies into groups and distribute coolant flow rates among them. This report documents an algorithm implemented in DASSH to automatically optimize coolant orificing. Over the course of multiple iterations, DASSH determines the orifice grouping and flow distribution that minimizes peak coolant, clad, or fuel temperatures across all timesteps for a user-specified number of assembly groups. The total coolant flow rate in the reactor is constrained to achieve the specified core-average outlet temperature. The flow rate to each orifice group may also be constrained by the allowable pressure drop. The distribution of coolant flow among groups is accelerated using a predictor-corrector algorithm based on interpolated results from single-assembly parametric calculations. The assembly orificing grouping is initially predicted based on assembly power but can be refined if results demonstrate that an assembly would fit better in another group. The algorithm is demonstrated with two case studies. The first is a simple model for a reactor core consisting of just fuel assemblies; the pin power distributions are specified to create a situation where the initial assembly grouping prediction is suboptimal. This example is used to describe the initial grouping, demonstrate convergence over multiple iterations, and highlight the impact of regrouping. Then, the algorithm is applied to minimize peak clad and fuel temperatures in an example sodium-cooled fast reactor, the Versatile Test Reactor. The multicycle optimization confirms prior calculations for the reference core design. The example highlights how optimizing for different peak temperatures affects the results and demonstrates the use of the pressure drop constraint to limit the maximum flow rate.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Parametrizing the detector response with neural networks

In high energy physics, characterizing the response of a detector to radiation is one of the most important and fundamental experimental tasks. In many cases, this task is accomplished by parameterizing summary statistics of the full detector response probability density. The parameterized detector response can then be used for calibration as well as for directly improving physics analysis sensitivity. This paper reports on how to parameterize summary statistics of the detector response using neural networks. Specifically, neural networks are powerful tools for incorporating multidimensional data and the loss function used during training determines which summary statistic is learned. One common summary statistic that has not been combined with deep learning (as far as the authors are aware) is the mode. A neural network-based approach to mode learning is proposed and empirically demonstrated in the context of high energy jet calibrations. Altogether, the neural network-based toolkit for detector response parameterization can enhance the utility of data collected at high energy physics experiments and beyond.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Monolithic Multigrid for a Reduced-Quadrature Discretization of Poroelasticity

Advanced finite-element discretizations and preconditioners for models of poroelasticity have attracted significant attention in recent years. The equations of poroelasticity offer significant challenges in both areas, due to the potentially strong coupling between unknowns in the system, saddle-point structure, and the need to account for wide ranges of parameter values, including limiting behavior such as incompressible elasticity. This paper was motivated by an attempt to develop monolithic multigrid preconditioners for the discretization developed in [C. Rodrigo et al., Comput. Methods App. Mech. Engrg, 341 (2018), pp. 467--484]; we show here why this is a difficult task and, as a result, we modify the discretization in [Rodrigo et al.] through the use of a reduced-quadrature approximation, yielding a more “solver-friendly” discretization. Local Fourier analysis is used to optimize parameters in the resulting monolithic multigrid method, allowing a fair comparison between the performance and costs of methods based on Vanka and Braess--Sarazin relaxation. Further, numerical results are presented to validate the local Fourier analysis predictions and demonstrate efficiency of the algorithms. Finally, a comparison to existing block-factorization preconditioners is also given.

97 MATHEMATICS AND COMPUTING↗