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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A fast computational framework for the design of solvent-based plastic recycling processes

Multicomponent plastics cannot be processed using mechanical recycling technologies, hindering efforts to deal with plastic waste. Multicomponent plastics include multilayer plastic films, which are widely used for food and healthcare packaging. Multilayer films combine several layers (potentially dozens) of different polymers to protect products from external factors (e.g., oxygen, water, temperature, shock, and light). Solvent-based separation processes have emerged as a promising alternative to recycle these complex materials. For instance, the Solvent-Targeted Recovery and Precipitation (STRAP TM ) process uses sequential solvent washes to selectively dissolve and separate constituent polymers from multicomponent plastic waste, including films. STRAP TM process design (separation sequence, type of solvents, and operating conditions) changes significantly depending on the design of the multilayer plastic film (e.g., number, types, and proportions of polymers). The ability to quickly quantify the economic and environmental benefits of diverse STRAP TM process designs is essential to accelerate the development of sustainable recycling processes and more recyclable multilayer film products. In this work, we present a fast computational framework that integrates molecular-scale models, process modeling, and techno-economic and life cycle analysis to quickly evaluate STRAP TM designs. The computational framework is general and can be used to study the processing of complex multilayer plastic waste streams that contain many layers. Furthermore, we highlight the different uses of the framework via targeted case studies.

Computational framework↗
DOE OSTI2020

Notch insensitivity in fatigue failure of chopped carbon fiber chip-reinforced composites using experimental and computational analysis

This study focuses on the notch effect on the fatigue failure of chopped carbon fiber chip-reinforced sheet molding compound (SMC) composites by using both experimental and numerical methods. The S-N diagrams of notched specimens are first obtained experimentally, which exhibit large scatter and are shown to depend on the spatial distribution of local fiber orientations. After considering the effect of this spatial distribution by adopting a new fatigue damage parameter to normalize the results, we achieve a much better correlation in the normalized S-N diagram for all the notched specimens with different hole diameters. Next, compared with the fatigue results of smooth specimens without notches, we find that SMC composites under tension-tension fatigue loading exhibit notch-insensitivity, which renders this type of composites a better candidate in joints or similar applications. Meanwhile, the fatigue failure mechanisms of notched SMC composites are explored through a microstructure characterization procedure. On this basis, a computational micromechanics model considering the notch effect and an improved multi-scale progressive damage fatigue law, calibrated by coupon-level fatigue tests of smooth SMC composites under different loading conditions, are employed to predict the fatigue behavior of notched SMC composites. We show that detailed failure processes and fatigue life of notched SMC composites can be efficiently and adequately predicted by this computational framework.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Parallel computing for power system climate resiliency: Solving a large-scale stochastic capacity expansion problem with mpi-sppy

Here we propose a nodal stochastic generation and transmission expansion planning model that incorporates the output from high-resolution global climate models through load and generation availability scenarios. We implement our model in Pyomo and perform computational studies on a realistically-sized test case of the California electric grid in a high performance computing environment. We propose model reformulations and algorithm tuning to efficiently solve this large problem using a variant of the Progressive Hedging Algorithm. We utilize the parallelization capabilities and overall versatility of mpi-sppy, exploiting its hub-and-spoke architecture to concurrently obtain inner and outer bounds on an optimal expansion plan. Initial results show that instances with 360 representative days on a system with over 8,000 buses can be solved to within 5% of optimality in under 4 h of wall clock time, a first step towards solving a large-scale power system expansion planning problem across a wide range of climate-informed operational scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Recombinant expression of computationally designed peptide-bundlemers in Escherichia coli

Computational design of fully artificial peptides is extensively researched by material scientists and engineers for the construction of novel nanostructures and biomaterials. Such design has yielded a peptide-based building block or bundlemer, a coiled coil peptide assembly that undergoes further physical-covalent interactions to form 1D, 2D and, potentially, 3D hierarchical assemblies and displays targeted and biomimetic material properties. Recombinant expression is a convenient, flexible tool to synthesize such artificial and modified peptides in large quantities while also enabling economical synthesis of isotopically labeled peptides and longer protein-like artificial peptides. This report describes the protocol for recombinant expression of a 31-amino acid, computationally designed bundlemer-forming peptide in Escherichia coli. Peptide yields of 10 mgs per liter of media were achieved which highlights complementary advantages of recombinant expression technique relative to conventional laboratory-scale synthesis, such as solid-phase peptide synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Development of a multi-layer canopy model for E3SM Land Model with support for heterogeneous computing

The vertical structure of vegetation canopies creates micro-climates. However, the land components of most Earth System Models, including the Energy Exascale Earth System Model (E3SM), typically neglect vertical canopy structure by using a single layer big-leaf representation to simulate water, CO 2 , and energy exchanges between the land and the atmosphere. In this study, we developed a Multi-Layer Canopy Model for the E3SM Land Model to resolve the micro-climate created by vegetation canopies. The model developed in this study re-implements the CLM-ml_v1 to support heterogeneous computing architectures consisting of CPUs and GPUs and includes three additional optimization-based stomatal conductance models. The use of Portable, Extensible Toolkit for Scientific Computation provides a speedup of 25–50 times on a GPU relative to a CPU. The numerical implementation of the model was verified against CLM-ml_v1 for a month-long simulation using data from the Ameriflux US-University of Michigan Biological Station site. Model structural uncertainty was explored by performing control simulations for five stomatal conductance models that exclude and include the control of plant hydrodynamics (PHD) on photosynthesis. The bias in simulated sensible and latent heat fluxes was lower when PHD was accounted for in the model. Additionally, six idealized simulations were performed to study the impact of three environmental variables (i.e. air temperature, atmospheric CO 2 , and soil moisture) on canopy processes (i.e. net CO 2 assimilation, leaf temperature, and leaf water potential). Increasing air temperature reduced net CO 2 assimilation and increased air temperature. Net CO 2 assimilation increased at higher atmospheric CO 2 , while decreasing soil moisture resulted in lower leaf water potential.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Fourier method for 3-dimensional data fusion of X-ray Computed Tomography and ultrasound

X-ray Computed Tomography (CT) is essential for nondestructive inspection of many manufactured components but is susceptible to various forms of artifacts and noise. In particular, multi-detector row cone-beam CT systems can suffer from so called “cone-beam” artifacts and partial volume effects, particularly on planar edges at the periphery of the field of view in the cone angle dimension. Uni-directional ultrasonic testing methods generally have extremely accurate in-plane depth resolution but poorer lateral resolution due to physical and geometric constraints. This paper presents a novel technique called Computed Tomographic Fusion (CT-F) which uses three-dimensional Fourier filtering to combine x-ray cone-beam CT reconstructions and ultrasound data in the frequency space. The result is a single image with improved accuracy and quality. CT-F mitigates artifacts while allowing for rapid, accurate characterization of large three-dimensional CT volumes. Finally, using simulations and experiments, we demonstrate artifact reduction and edge contrast improvement in volumetric reconstructions.

42 ENGINEERING↗
DOE OSTI2023

Coregistered positron emission particle tracking (PEPT) and X-ray computed tomography (CT) for engineering flow measurements

Increasingly, fully 3D experimental measurements of flow in complex engineering geometries are required to validate computational fluid dynamics models that support and inform reactor design and licensing. One barrier to such measurements is the complexity of typical reactor components and subsequent lack of optical access in these systems. To overcome this, the deployment of coregistered positron emission particle tracking (PEPT) and X-ray computed tomography (CT) is explored for flow measurement in reactor thermal hydraulic components and model (scaled) systems. Through this methodology, fully 3D flow information (via PEPT) and detailed internal geometry (via CT) are captured in opaque systems such as pipes, rod bundles, packed beds, etc. The reconstructed flow field and geometry can then be overlain to reveal detailed flow features around internal structures within a given test section. This is enabled through the use of a combined preclinical PET/CT scanner with overlapping PET and CT fields of view. Such measurements are useful for characterizing flow inside such intricate nuclear thermal hydraulic components as core geometries and heat exchangers, among others, and providing valuable 3D validation data for CFD models. In this work, basic tests of this 3D flow/geometry mapping are presented, and the implications of such measurements are discussed. Further, preliminary measurements are made with both point sources and flow in a simple pipe flow geometry to evaluate the capabilities of this technique. PEPT and CT features are coregistered with up to 0.1 mm precision, and pipe flow mean velocity and Reynolds stresses are reconstructed with similar accuracy to previous PEPT demonstrations. The utility of PEPT/CT is shown herein, and suggestions for future measurements are made.

3D flow measurement↗
DOE OSTI2020

A parallel strategy for density functional theory computations on accelerated nodes

Using the Löwdin orthonormalization of tall-skinny matrices as a proxy-app for wavefunction-based Density Functional Theory solvers, we investigate a distributed memory parallel strategy focusing on Graphics Processing Unit (GPU)-accelerated nodes as available on some of the top ranked supercomputers at the present time. Here we present numerical results in the strong limit regime, as it is particularly relevant for First-Principles Molecular Dynamics. We also examine how matrix product-based iterative solvers provide a competitive alternative to dense eigensolvers on GPUs, allowing to push the strong scaling limit of these computations to a larger number of distributed tasks. Our strategy, which relies on replicated Gram matrices and efficient collective communications using the NCCL library, leads to a time-to-solution under 0.5 s for the Löwdin orthonormalization of a tall-skinny matrix of 3000 columns on Summit at Oak Ridge Leadership Facility (OLCF). Given the similarity in computational operations between one iteration of a DFT solver and this proxy-app, this shows the possibility of solving accurately the DFT equations well under a minute for 3000 electronic wave functions, and thus perform First-Principles molecular dynamics of physical systems much larger than traditionally solved on CPU systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modeling information flow in a computer processor with a multi-stage queuing model

In this paper, we introduce a nonlinear stochastic model to describe the propagation of information inside a computer processor. In this model, a computational task is divided into stages, and information can flow from one stage to another. The model is formulated as a spatially-extended, continuous-time Markov chain where space represents different stages. This model is equivalent to a spatially-extended version of the M/M/s queue. The main modeling feature is the throttling function which describes the processor slowdown when the amount of information falls below a certain threshold. We derive the stationary distribution for this stochastic model and develop a closure for a deterministic ODE system that approximates the evolution of the mean and variance of the stochastic model. In conclusion, we demonstrate the validity of the closure with numerical simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. Here, the quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Recent advances in computational mathematics and applications

We are honored to bring you this special issue dedicated to recent advances in computational mathematics and applications in science and engineering. The papers in this special issue were presented in ”Conference on Computational Mathematics and Applications (CCMA)” held at the University of Nevada Las Vegas (UNLV) during October 25–27, 2019. The conference has attracted about 90 attendees from six countries. The 15 papers in the special issue comprise a diverse collection of theoretical numerical analysis as well as applications in various subjects. Topics cover optimal control of PDEs, discontinuous Galerkin methods, fluid flow in porous media, turbulence flow, static and moving interface problems, complex fluids composed by the mixture of Newtonian fluid and nematic (liquid crystal) flows, modeling of ocean–atmosphere system, image segmentation algorithm, uncertainty quantification problems for turbulence model and Maxwell’s equations, iterative solver for systems resulting from mixed methods, and novel methods for solving systems of nonlinear equations. As the Guest Editors we would like to thank Dr. Leland Jameson at NSF (National Science Foundation) for kindly supporting our conference proposal which made this conference possible. We would also like to thank Darren Sugrue from Elsevier who provided partial support for our conference, and Thennarasu Gunasekaran and his production team from Elsevier who produced this nice special issue. We are grateful to many people (Dr. Zhijian Wu, Lori Ornelas, Elsa Juarez, and many Ph.D. students) at the Department of Mathematical Sciences of UNLV who provided tremendous support for the conference. Finally, we very much appreciate all authors who contributed their precious results to this special issue.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Simplifying computational workflows with the Multiscale Atomic Zeolite Simulation Environment (MAZE)

Zeolites, an important class of 3-dimensional nanoporous materials, have been widely explored for a variety of applications including gas storage, separations, and catalysis. As the properties of these aluminosilicate materials depend on a number of factors (e.g., framework topology, Si/Al ratio, extra-framework cations etc.), detailed experiments (e.g., catalytic properties, adsorption capacities etc.) are often limited to only a handful of materials. Computational methods have played an important role in (1) providing molecular level insights to rationalize experimental observations, and (2) screening large libraries of zeolites to identify promising candidates for experimental synthesis and validation. Different levels of theory and computational chemistry codes are necessary to describe the range of relevant phenomena such as adsorption (e.g., grand canonical Monte Carlo), diffusion (e.g., molecular dynamics), and chemical reactions (e.g., density functional theory). Manipulation of atomic structures, handling of input files, and developing robust workflows becomes quite cumbersome. To mitigate these challenges, we describe the development of the Multiscale Atomic Zeolite Simulation Environment (MAZE) – a Python package that simplifies zeolite-specific calculation workflows by providing a user-friendly interface for systematically manipulating zeolite structures

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Computational Data-Driven Materials Discovery

Machine learning from large materials datasets enables accelerated materials discovery. Currently, the most accessible way to generate uniform, well curated, voluminous datasets is by the application of high-throughput first principles computations. In this work, we present the guiding principles of using computational data and machine learning to drive new materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with up 85 compatible production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Altogether, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Optimal experimental design: Formulations and computations

Questions of ‘how best to acquire data’ are essential to modelling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental design (OED) formalizes these questions and creates computational methods to answer them. This article presents a systematic survey of modern OED, from its foundations in classical design theory to current research involving OED for complex models. We begin by reviewing criteria used to formulate an OED problem and thus to encode the goal of performing an experiment. We emphasize the flexibility of the Bayesian and decision-theoretic approach, which encompasses information-based criteria that are well-suited to nonlinear and non-Gaussian statistical models. We then discuss methods for estimating or bounding the values of these design criteria; this endeavour can be quite challenging due to strong nonlinearities, high parameter dimension, large per-sample costs, or settings where the model is implicit. A complementary set of computational issues involves optimization methods used to find a design; we discuss such methods in the discrete (combinatorial) setting of observation selection and in settings where an exact design can be continuously parametrized. Finally we present emerging methods for sequential OED that build non-myopic design policies, rather than explicit designs; these methods naturally adapt to the outcomes of past experiments in proposing new experiments, while seeking coordination among all experiments to be performed. Throughout, we highlight important open questions and challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Harnessing the Second-Order Metal−Insulator Transition for Neuromorphic Computing

Vanadium oxides are widely studied phase change materials for brain-inspired computing architectures. Systems like VO 2 and V 2 O 3 exhibit first-order metal−insulator transitions (MITs) with hysteresis and percolative switching, increasing stochasticity and device variability. Here, we focus on the less explored Magnéli phase V 4 O 7 , which undergoes a continuous, non-hysteretic, second-order MIT. This surprisingly enables highly reproducible volatile resistive switching in spiking-neuron-type devices. We synthesize V 4 O 7 films, characterize their structural and transport properties, and demonstrate voltage and current-driven threshold switching with electrothermal feedback. In a Pearson–Anson oscillator, V 4 O 7 devices produce stable, tunable spiking across 20–200 kHz, with consistent operation among multiple devices. We introduce a numerical analog leaky-integrate-and-fire (aLIF) model that captures waveform shapes and their dependence on load resistance, temperature, and voltage. Furthermore, these findings suggest that second-order MIT materials like V 4 O 7 are promising for deterministic, scalable spiking neuron arrays for neuromorphic computing.

V4O7↗
DOE OSTI2025

Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing

Spiking neural networks seek to emulate biological computation through interconnected artificial neuron and synapse devices. Spintronic neurons can leverage magnetization physics to mimic biological neuron functions, such as integration tied to magnetic domain wall (DW) propagation in a patterned nanotrack and firing tied to the resistance change of a magnetic tunnel junction (MTJ), captured in the domain wall-magnetic tunnel junction (DW-MTJ) device. Leaking, relaxation of a neuron when it is not under stimulation, is also predicted to be implemented based on DW drift as a DW relaxes to a low energy position, but it has not been well explored or demonstrated in device prototypes. Here, in this work, we study DW-MTJ artificial neurons capable of leaky integrate-and-fire (LIF) behavior and demonstrate geometry-dependent leaking dynamics that results in repeatable, tunable LIF operation. Studying the behavior of five different device designs, we show tuning the geometry, stimulating fields and currents, and location of electrical contacts results in a wide range of neuron behavior. Additionally, implementation of an asymmetric notch allows for nonlinear pinning which increased expressivity without sacrificing leaking. The measured behavior is implemented in a simulated spiking neural network that outperforms a 1D model of continuous DW motion and approaches the performance of an ideal LIF activation function. The results show that the analog LIF capability of DW-MTJ neurons combines many desirable neuron functions into a single device, which can result in varied forms of multifunctional neuromorphic computing.

42 ENGINEERING↗