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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Bayesian Adaptive Polynomial Chaos Expansions

Polynomial chaos expansions (PCEs) are widely used for uncertainty quantification (UQ) tasks, particularly in the applied mathematics community. However, PCE has received comparatively less attention in the statistics literature, and fully Bayesian formulations remain rare—especially with implementations in R. Motivated by the success of adaptive Bayesian machine learning models such as BART, BASS and BPPR, we develop a new fully Bayesian adaptive PCE method with an efficient and accessible R implementation: khaos. Our approach includes a novel proposal distribution that enables data-driven interaction selection and supports a modified g-prior tailored to PCE structure. Through simulation studies and real-world UQ applications, we demonstrate that the Bayesian adaptive PCE provides competitive performance for surrogate modeling, global sensitivity analysis and ordinal regression tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Chapter 6: An Improved Leaf Protoplast System for Highly Efficient Transient Expression in Switchgrass (Panicum virgatum L.)

As a robust perennial C4-type monocot plant and a native species to North America, switchgrass (Panicum virgatum) has been evaluated and designated as a strong candidate bioenergy crop by the U.S. DOE. Although genetic modifications of switchgrass have been used to successfully reduce the recalcitrance of switchgrass biomass for biofuel production, the generation of transgenic switchgrass is still a slow and laborious process. A transient protoplast system can provide an excellent platform to accelerate the selection of genes-of-interest for tailoring switchgrass biomass. However, partially due to the lack of the complete genomic information, the attempts to optimize the transient protoplast system for switchgrass remain scarce. In this chapter, we provide an improved protocol for switchgrass protoplast isolation, increased transformation efficiency using CsCl gradient ultracentrifugation-derived plasmid DNA and extended application of the transient switchgrass protoplast system to analyze protein expression using western blot.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2020

An Integrated Computer-Aided Design and Manufacturing Workflow for Synthetic Biology

Biological computer-aided design and manufacturing (bioCAD/CAM) tools facilitate the design and build processes of engineering biological systems using iterative design-build-test-learn (DBTL) cycles. In this book chapter, we highlight some of the bioCAD/CAM tools developed and used at the US Department of Energy (DOE) Joint Genome Institute (JGI), Joint BioEnergy Institute (JBEI), and Agile BioFoundry (ABF). We demonstrate the use of these bioCAD/CAM tools on a common workflow for designing and building a multigene pathway in a hierarchical fashion. Additionally, each tool presented in this book chapter is specifically tailored to support one or more specific steps in a workflow, can be integrated with the others into design and build workflows, and can be deployed at academic, government, or commercial entities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Sparse Matrix-Based HPC Tomography

Tomographic imaging has benefited from advances in X-ray sources, detectors and optics to enable novel observations in science, engineering and medicine. These advances have come with a dramatic increase of input data in the form of faster frame rates, larger fields of view or higher resolution, so high performance solutions are currently widely used for analysis. Tomographic instruments can vary significantly from one to another, including the hardware employed for reconstruction: from single CPU workstations to large scale hybrid CPU/GPU supercomputers. Furthermore, flexibility on the software interfaces and reconstruction engines are also highly valued to allow for easy development and prototyping. This paper presents a novel software framework for tomographic analysis that tackles all aforementioned requirements. The proposed solution capitalizes on the increased performance of sparse matrix-vector multiplication and exploits multi-CPU and GPU reconstruction over MPI. Furthermore, the solution is implemented in Python and relies on CuPy for fast GPU operators and CUDA kernel integration, and on SciPy for CPU sparse matrix computation. As opposed to previous tomography solutions that are tailor-made for specific use cases or hardware, the proposed software is designed to provide flexible, portable and high-performance operators that can be used for continuous integration at different production environments, but also for prototyping new experimental settings or for algorithmic development. The experimental results demonstrate how our implementation can even outperform state-of-the-art software packages used at advanced X-ray sources worldwide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

3D Characterization of the Columnar-to-Equiaxed Transition in Additively Manufactured Inconel 718

Additive manufacturing (AM) provides enormous processing flexibility, enabling novel part geometries and optimized designs. Access to a local heat source further permits the potential for local microstructure control on the scale of individual melt pools, which can enable local control of part properties. In order to design tailored processing strategies for target microstructures, models predicting the columnar-to-equiaxed transition must be extended to the high solidification velocities and complex thermal histories present in AM. Here, we combine 3D characterization with advanced modeling techniques to develop a more complete understanding of the solidification process and evolution of microstructure during electron beam melting (EBM) of Inconel 718. Full calibration of existing microstructure prediction models demonstrates the differences between AM processes and more conventional welding techniques, underlying the need for accurate determination of key parameters that can only be measured directly in 3D. The ability to combine multisensor data in a consistent 3D framework via data fusion algorithms is essential to fully leverage these advanced characterization approaches. Thermal modeling provides insight on microstructure development within isolated solidification events and demonstrates the role of Marangoni effects on controlling solidification behavior.

Polonsky, Andrew↗
DOE OSTI2022

The SENSEI Generic In Situ Interface: Tool and Processing Portability at Scale [Book Chapter]

One key challenge when doing in situ processing is the investment required to add code to numerical simulations needed to take advantage of in situ processing. Such instrumentation code is often specialized, and tailored to a specific in situ method or infrastructure. Then, if a simulation wants to use other in situ tools, each of which has its own bespoke API [4], then the simulation code team will quickly become overwhelmed with having a different set of instrumentation APIs, one per in situ tool or method. In an ideal situation, such instrumentation need happen only once, and then the instrumentation API provides access to a large diversity of tools. In this way, a data producer’s instrumentation need not be modified if the user desires to take advantage of a different set of in situ tools. The SENSEI generic in situ interface addresses this challenge, which means that SENSEI-instrumented codes enjoy the benefit of being able to use a diversity of tools at scale, tools that include Libsim, Catalyst, Ascent, as well as user-defined methods written in C++ or Python. SENSEI has been shown to scale to greater than 1M-way concurrency on HPC platforms, and provides support for a rich and diverse collection of common scientific data models. Furthermore, this chapter presents the key design challenges that enable tool and processing portability at scale, some performance analysis, and example science applications of the methods.

Bethel, E. Wes↗
DOE OSTI2020

First-Principles Quantum Transport Modeling of Spin-Transfer and Spin-Orbit Torques in Magnetic Multilayers

A unified approach for computing (i) spin-transfer torque in magnetic trilayers like spin valve and magnetic tunnel junction, where injected charge current flows perpendicularly to interfaces, and (ii) spin-orbit torque in magnetic bilayers of the type ferromagnet/spin-orbit-coupled material, where injected charge current flows parallel to the interface, was reviewed. The experimentally explored and technologically relevant spin-orbit-coupled materials include 5d heavy metals, topological insulators, Weyl semimetals, and transition metal dichalcogenides. This approach requires to construct the torque operator for a given Hamiltonian of the device and the steady-state nonequilibrium density matrix, where the latter is expressed in terms of the nonequilibrium Green’s functions and split into three contributions. Tracing these contributions with the torque operator automatically yields field-like and damping-like components of spin-transfer torque or spin-orbit torque vector, which is particularly advantageous for spin-orbit torque where the direction of these components depends on the unknown-in-advance orientation of the current-driven nonequilibrium spin density in the presence of spin-orbit coupling. Illustrative examples are provided by computing spin-transfer torque in a one-dimensional toy model of a magnetic tunnel junction and realistic Co/Cu/Co spin valve, both of which are described by first-principles Hamiltonians obtained from noncollinear density functional theory calculations, as well as by computing spin-orbit torque in a ferromagnetic layer described by a tight-binding Hamiltonian which includes spin-orbit proximity effect within ferromagnetic monolayers assumed to be generated by the adjacent monolayer transition metal dichalcogenide. In addition, it is shown here how spin-orbit proximity effect, quantified by computing (via first-principles retarded Green’s function) spectral functions and spin textures on monolayers of realistic ferromagnetic material like Co in contact with heavy metal or monolayer transition metal dichalcogenide, can be tailored to enhance the magnitude of spin-orbit torque. Furthermore, errors made in the calculation of spin-transfer torque are quantified when using Hamiltonian from collinear density functional theory, with rigidly rotated magnetic moments to create noncollinear magnetization configurations, instead of proper (but computationally more expensive) self-consistent Hamiltonian obtained from noncollinear density functional theory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Model selection and signal extraction using Gaussian Process regression

We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression — a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Phenomenology of inclusive $ \overline{B}\to {X}_s{\mathrm{\ell}}^{+}{\mathrm{\ell}}^{-} $ for the Belle II era

With the first data being recorded at Belle II, we are at the brink of a new era in quark flavour physics. The many exciting new opportunities for Belle II include a full angular analysis of inclusive B ¯ → X s ℓ + ℓ - which has the potential to reveal new physics, in particular by its interplay with the exclusive b → s ℓ + ℓ - counterparts studied extensively at LHCb. In this paper, we present fully updated Standard Model predictions for all angular observables necessary for this endeavour. These predictions are tailored to Belle II and include an elaborate study of the treatment of collinear photons which become crucial when aiming for the highest precision. In addition, we present a phenomenological study of the potential for Belle II to reveal possible new physics in the inclusive decay channel, both in an independent manner and in combination with exclusive modes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Generalized thermo-mechanical framework for heterogeneous materials through asymptotic homogenization

Abstract A fundamental understanding of the interaction between microstructure and underlying physical mechanisms is essential, especially for developing more accurate multi-physics models for heterogeneous materials. Effects of microstructure on the material response at the macroscale are modeled by using the generalized thermomechanics. In this study, strain gradient theory is employed as a higher-order theory on the macroscale with thermodynamics modeled as a first-order theory on the microscale. Hence, energy depends only on the temperature such that we circumvent an extension of Fourier’s law and analyze the “simplest” thermo-mechanical model in strain gradient elasticity. Developing multiphysics models for heterogeneous materials is indeed a challenge and even this “simplest” model in generalized thermomechanics creates dozens of parameters to be determined. We develop a thermo-mechanical framework, in which microstructure is modeled as a periodic structure and through asymptotic homogenization approach, higher-order parameters at macroscopic scale are calculated. To illustrate the importance of higher-order parameters in overall thermo-mechanical response of a heterogeneous materials, finite element method (FEM) is employed with the aid of open-source codes (FEniCS). Verification example of a bulk system and several case studies of porous structures demonstrate how such numerical framework can be beneficial in the design of materials with tailored microstructures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Scale-bridging with the extended/generalized finite element method for linear elastodynamics

This paper presents an extended/generalized finite element method for bridging scales in linear elastodynamics in the absence of scale separation. More precisely, the GFEMgl framework is expanded to enable the numerical solution of multiscale problems through the automated construction of specially-tailored shape functions, thereby enabling high-fidelity finite element modeling on simple, fixed finite element meshes. Furthermore, this introduces time-dependencies in the shape functions in that they are subject to continuous adaptation with time. The temporal aspects of the formulation are investigated by considering the Newmark-β time integration scheme, and the efficacy of mass lumping strategies is explored in an explicit time-stepping scheme. This method is demonstrated on representative wave propagation examples as well as a dynamic fracture problem to assess its accuracy and flexibility.

36 MATERIALS SCIENCE↗
DOE OSTI2021

MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources

We present an approach for constructing a surrogate from ensembles of information sources of varying cost and accuracy. The multifidelity surrogate encodes connections between information sources as a directed acyclic graph, and is trained via gradient-based minimization of a nonlinear least squares objective. While the vast majority of state-of-the-art assumes hierarchical connections between information sources, our approach works with flexibly structured information sources that may not admit a strict hierarchy. The formulation has two advantages: (1) increased data efficiency due to parsimonious multifidelity networks that can be tailored to the application; and (2) no constraints on the training data—we can combine noisy, non-nested evaluations of the information sources. Finally, numerical examples ranging from synthetic to physics-based computational mechanics simulations indicate the error in our approach can be orders-of-magnitude smaller, particularly in the low-data regime, than single-fidelity and hierarchical multifidelity approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Tuning working potential of silicon-phosphorus anode via microstructure control for high-energy lithium-ion batteries

The primary research on anode materials of lithium-ion batteries have been focused on increasing the specific capacity, while the working potential that is also closely related to the practical energy density of batteries has been paid much less attention. In this paper, starting from micrometer-sized silicon and black phosphorus, we have reported a high-energy silicon-phosphorus/carbon anode (denoted as mSPC) via a high-energy ball milling process, which demonstrates an average discharge working potential of 0.3 volts versus lithium, together with a high reversible capacity of > 2000 mAh/g, high initial coulombic efficiency of 84%, excellent cycle stability, and superior rate capability up to 15 A/g. Furthermore, in situ focused-ion-beam scanning electron microscopy reveals that the volume change of the mSPC anode during repeated (de) lithiation is effectively alleviated. In contrast, starting from nanometer-sized silicon, the resulted anode (denoted as nSPC) not only presents a lower reversible capacity (~1200 mAh/g), but also exhibits a higher charge/discharge working potential, leading to reduced energy density. Our results indicate the importance of composition/structure control in tailoring the working potential and specific capacity of alloying-type anodes towards high-energy lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

An infeasible-start framework for convex quadratic optimization, with application to constraint-reduced interior-point and other methods

A framework is proposed for solving general convex quadratic programs (CQPs) from an infeasible starting point by invoking an existing feasible-start algorithm tailored for inequality-constrained CQPs. The central tool is an exact penalty function scheme equipped with a penalty-parameter updating rule. The feasible-start algorithm merely has to satisfy certain general requirements, and so is the updating rule. Under mild assumptions, the framework is proved to converge on CQPs with both inequality and equality constraints and, at a negligible additional cost per iteration, produces an infeasibility certificate, together with a feasible point for an (approximately) ℓ 1 -least relaxed feasible problem, when the given problem does not have a feasible solution. The framework is applied to a feasible-start constraint-reduced interior-point algorithm previously proved to be highly performant on problems with many more inequality constraints than variables (“imbalanced”). Numerical comparison with popular codes (OSQP, qpOASES, MOSEK) is reported on both randomly generated problems and support-vector machine classifier training problems. The results show that the former typically outperforms the latter on imbalanced problems. Finally, application of the proposed infeasible-start framework to other feasible-start algorithms is briefly considered, and is tested on a simplex iteration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Lowly Fused Non-Fullerene Acceptors Towards Efficient Organic Solar Cells Enabled by Isomerization

Here two lowly fused non-fullerene acceptors (NFAs) with isomeric structures, named as BTP-out-4F and BTP-in-4F, were developed by tailoring the fused 7-ring central core of Y6 into a lowly fused 5-ring linked with two octyloxythiophene bridges. BTP-out-4F with octyloxy side chains away from the central core exhibited large steric hindrance that restrained the rotational freedom between the thiophene bridge and end group but maintained free rotation between the central core and the thiophene bridge. In contrast, BTP-in-4F with octyloxy side chains close to the central core had much lower rotation freedom due to the non-covalent S … O interactions locked the central core, thiophene bridge and end group simultaneously, making BTP-in-4F have higher molecular crystallinity. On the other hand, the optical properties, energy levels and the blend morphology properties were significantly influenced, leading to distinctive photovoltaic performances. BTP-out-4F formed favorable energy level alignment and morphology when matching with PBDB-T donor, thus its device realized a much higher PCE of 13.32%, which was over 13 times than that of BTP-in-4F based device (PCE=0.97%).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effectiveness and outcomes of invasive species removal in Hawaiian streams

Despite aquatic invasive species (AIS) being a widely recognized threat to native biodiversity in streams on subtropical and tropical oceanic islands across the Pacific, AIS management has been constrained by concerns that methods of removal could result in more harm than good. In this study, we assessed the effectiveness and outcomes of combining hand-netting and streamside sequestration of native species with AIS removal via three-pass electrofishing tailored for Hawaiian Island watersheds. Removals were conducted in 13 watersheds on the island of O‘ahu following a before-after, control-impact design to assess how AIS removal and post-removal community reassembly varied by surface flow and target species of interest. We found that removals resulted in a sharp reduction in AIS densities without altering native species densities. Removal efficacy was greatest in streams with intermediate mean discharge irrespective of target group or pre-removal AIS densities. Long-term monitoring demonstrated that removals led to persistent shifts in community composition reflecting sustained reductions in AIS densities. These findings indicate that electrofishing can be a valuable tool for AIS management in Hawaiian and other subtropical Pacific island streams, so long as steps are taken to minimize potential harm to at-risk and culturally important native species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Automated Fire Detection for Industrial Settings with Pretrained Convolutional Networks

Early fire detection in industrial environments is critical to preventing equipment damage, personal injury, and operational disruptions. Traditional smoke detectors, while effective, often experience delays due to the time required for smoke to reach sensors, allowing fires to spread. Manual fire watch operations and human surveillance of camera feeds are resource-intensive and prone to human error. To address these challenges, this paper explores the application of convolutional neural networks for automated fire detection, specifically in industrial settings. By leveraging 11 different pre-trained machine vision models from TensorFlow and enhancing them with transfer learning on a custom-built industrial fire dataset, we optimized fire detection performance. Here, we analyzed each machine vision model architecture in terms of its depth, width, and input image resolution, considering both resource requirements and detection accuracy. We further explored the option of combining multiple models into an ensemble classifier to evaluate whether the performance improvements could justify the much greater computational complexity and other practical impacts. A cost-benefit analysis is presented to evaluate the trade-offs between performance and computational expense. Our findings identify that EfficientNetV2L, specifically tailored for industrial applications, provides the optimal balance between costs involved in training and using the model versus the overall fire detection performance. Additionally, we present a qualitative analysis of model performance using the technique of gradient-based class activation mapping to provide explainability by visualizing model decisions.

artificial intelligence↗
DOE OSTI2020

A step forward from high-entropy ceramics to compositionally complex ceramics: a new perspective

High-entropy ceramics (HECs) have quickly gained attention since 2015. To date, nearly all work has focused on five-component, equimolar compositions. This perspective article briefly reviews different families of HECs and selected properties. Following a couple of our most recent studies, we propose a step forward to expand HECs to compositionally-complex ceramics (CCCs) to include medium-entropy and/or non-equimolar compositions. Here, using defective fluorite and ordered pyrochlore oxides as two primary examples, we further consider the complexities of aliovalent cations and anion vacancies as well as ordered structures with two cation sublattices. Better thermally-insulating yet stiff CCCs have been found in non-equimolar compositions with optimal amounts of oxygen vacancies and in ordered pyrochlores with large size disorder; it is demonstrated that medium-entropy ceramics (MECs) can prevail over their high-entropy counterparts. The diversifying classes of CCCs provide even more possibilities than HECs to tailor the composition, defects, disorder/order, and consequently various properties.

36 MATERIALS SCIENCE↗