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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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An economics-by-design approach to a radiant integrated thermophotovoltaic microreactor system

This paper presents an application of the economics-by-design approach to the Radiant Integrated Thermo-photovoltaic Microreactor System (RITMS). The RITMS design is unique in that it directly couples a critical fission reactor with thermo-photovoltaic (TPV) panels for high efficiency energy conversion. This significant shift from electric conversion using traditional dynamic heat pumps leads to a simpler and more reliable system without turbomachinery and high pressurization. In working towards wrapping up the early design work, the economics-by-design approach, which centers economic competitiveness as the optimization parameter, is well suited in making a final determination on viability. This paper describes the computational sequence that was developed to couple the radiative and conductive heat transfer and feed operational performance to cost estimation. The framework was applied to maximize the power of the system, while minimizing the fuel enrichment. This method is applied to a reference RITMS design as part of a parametric sensitivity study, which revealed that the system is under moderated and that single unit plants could produce power as low as 300 dollar/MWh. This price point supports the notion that early in the RITMS design implementation, adoption into niche markets as a first-of-a-kind technology is possible. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

High-Fidelity Accelerated Design of High-performance Electrochemical Systems

Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, meso-scale and continuum simulations. To accelerate this process, our team has developed the AutoMat platform. AutoMat can accelerate development of new electrochemical materials along two avenues: first, automated input generation and management of simulations at multiple lengthscales as well as “handoff” of outputs from one lengthscale as inputs to the next; and second, replacement of the most computationally intensive simulation processes with machine-learned surrogate models. The crux of our team’s effort was not “reinventing the wheel” by developing entirely new techniques, but rather building a “superhighway” that allows existing state-of-the-art techniques to run faster and more smoothly than before. AutoMat can utilize tools spanning from first-principles quantum chemistry computations to automated robotic experimentation, and is driven by design space search techniques to reduce the number of iterations through the full simulation loop by rapidly targeting promising regions of design spaces such as single-atom alloy catalysts or blends of liquid electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2020

A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration

A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, here we propose a cost-effective, data-driven approach to assessing a power system's load margin probabilistically. Using actual wind data, a kernel density estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of traditional Monte-Carlo simulations that process a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian-learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate, which serves as a highly efficient, parametric Bayesian prior, is developed. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Results of simulations that are performed on several test systems reveal the impressive performance of the proposed method in the probabilistic load-margin assessment.

17 WIND ENERGY↗
DOE OSTI2016

An Efficient Multicore Implementation of a Novel HSS-Structured Multifrontal Solver Using Randomized Sampling

Here, we present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination and exploits low-rank approximation of the resulting dense frontal matrices. We use hierarchically semiseparable (HSS) matrices, which have low-rank off-diagonal blocks, to approximate the frontal matrices. For HSS matrix construction, a randomized sampling algorithm is used together with interpolative decompositions. The combination of the randomized compression with a fast ULV HSS factoriz ation leads to a solver with lower computational complexity than the standard multifrontal method for many applications, resulting in speedups up to 7 fold for problems in our test suite. The implementation targets many-core systems by using task parallelism with dynamic runtime scheduling. Numerical experiments show performance improvements over state-of-the-art sparse direct solvers. The implementation achieves high performance and good scalability on a range of modern shared memory parallel systems, including the Intel Xeon Phi (MIC). The code is part of a software package called STRUMPACK - STRUctured Matrices PACKage, which also has a distributed memory component for dense rank-structured matrices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Maximization of permanent trapping of CO{sub 2} and co-contaminants in the highest-porosity formations of the Rock Springs Uplift (Southwest Wyoming): experimentation and multi-scale modeling

Under this project, a multidisciplinary team of researchers at the University of Wyoming combined state-of-the-art experimental studies, numerical pore- and reservoir-scale modeling, and high performance computing to investigate trapping mechanisms relevant to geologic storage of mixed scCO{sub 2} in deep saline aquifers. The research included investigations in three fundamental areas: (i) the experimental determination of two-phase flow relative permeability functions, relative permeability hysteresis, and residual trapping under reservoir conditions for mixed scCO{sub 2}-­brine systems; (ii) improved understanding of permanent trapping mechanisms; (iii) scientifically correct, fine grid numerical simulations of CO{sub 2} storage in deep saline aquifers taking into account the underlying rock heterogeneity. The specific activities included: (1) Measurement of reservoir-­conditions drainage and imbibition relative permeabilities, irreducible brine and residual mixed scCO{sub 2} saturations, and relative permeability scanning curves (hysteresis) in rock samples from RSU; (2) Characterization of wettability through measurements of contact angles and interfacial tensions under reservoir conditions; (3) Development of physically-­based dynamic core-­scale pore network model; (4) Development of new, improved high-­performance modules for the UW-­team simulator to provide new capabilities to the existing model to include hysteresis in the relative permeability functions, geomechanical deformation and an equilibrium calculation (Both pore-­ and core-­scale models were rigorously validated against well-­characterized core-­ flooding experiments); and (5) An analysis of long term permanent trapping of mixed scCO{sub 2} through high-­resolution numerical experiments and analytical solutions. The analysis takes into account formation heterogeneity, capillary trapping, and relative permeability hysteresis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Parallel State Estimation Assessment with Practical Data

This paper presents a full-cycle parallel state estimation (PSE) implementation using a preconditioned conjugate gradient algorithm. The developed code is able to solve large-size power system state estimation within 5 seconds using real-world data, comparable to the Supervisory Control And Data Acquisition (SCADA) rate. This achievement allows the operators to know the system status much faster to help improve grid reliability. Case study results of the Bonneville Power Administration (BPA) system with real measurements are presented. The benefits of fast state estimation are also discussed.

State Estimation, Preconditioned Conjugate Gradien↗
DOE OSTI2020

Collaborative Research: Improved Efficiency and Coupling of the Radiation Code in the ACME Earth System Model. Final Report

This final report details all work performed on the project by both project partners. This project provided support to properly couple RTE+RRTMGP, a high-performance broadband radiation code, within DOE’s Energy Exascale Earth System Model (E3SM). RTE+RRTMGP is a successor to the RRTMG radiation code, which has been widely accepted for its speed and accuracy by the global modeling community, and has been in use in the NCAR CESM for many years and was implemented in the initial version of E3SM. However, the computational cost of RRTMG remains high relative to other components in part due to its complexity and to its inefficient use of modern optimization strategies, issues that were rectified by the development of RTE+RRTMGP. Many of the accomplishment in this project necessitated significant collaboration with the E3SM development team. One focus of the project was to enhance the code’s optimization on the limited number of emerging computing systems on which the model is expected be used, including Many Integrated Core (MIC) architectures and Graphics Processing Unit (GPU) hardware. We also developed additional capabilities for RTE+RRTMGP that E3SM scientists identified as important for the planned applications of the model. The result of our project was optimization of a key physical component (radiative transfer calculations) of E3SM, directly supporting E3SM’s overarching global modeling objectives. More broadly, this project provided overall advancements in the use of radiative transfer calculations in atmospheric modeling and simulation, particularly for climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Insights into mixing of non-isothermal multi-polymer melts for complex plastics recycling

Catalytic recycling or upcycling of plastics is often limited not by catalyst performance, but by transport, arising from highly viscous, non-Newtonian polymer melts. In this work, we develop a reactor-scale framework that integrates rheological measurements, constitutive modeling, computational fluid dynamics (CFD), and experiments to quantify mixing, heat transfer, and dispersion in surrogate hydrocarbon melts representing mixed plastics systems. Temperature- and shear rate-dependent viscosity of low-density polyethylene (LDPE) and high-density polyethylene (HDPE) is measured to create two surrogate polymers (PLD and PHD) that capture the dominant shear-thinning flow behavior while neglecting strong elastic effects, enabling tractable simulation of non-isothermal, polymer-melt mixing using a Carreau-Arrhenius generalized Newtonian framework. Three-dimensional CFD simulations are employed to evaluate impeller performance in PLD using mixing time, cavern volume, thermal uniformity, and interfacial area for regimes in which viscoelastic effects are not dominant. We show that magnetic stir bars commonly used in lab-scale studies produce large thermal gradients (~60 °C) and poor mixing, even under idealized power delivery and polymer flow conditions. In contrast, close-clearance anchor impellers achieve near-isothermal operation, reduce mixing times by up to 5×, and provide >90% active circulation volume. We further demonstrate that, at low pseudo-Deborah number (De*), motor power requirements can be predicted directly from shear rate-dependent rheology using the Carreau-Arrhenius framework, enabling rational selection of operating conditions. Extension to surrogate immiscible multi-polymer systems based on PLD and PHD shows that interfacial area is highly sensitive to operating conditions and impeller design, with coaxial anchor-turbine configurations enhancing dispersion by up to 4 × .

Close-clearance impellers↗
DOE OSTI2022

Data-driven surrogate modeling of hPIC ion energy-angle distributions for high-dimensional sensitivity analysis of plasma parameters' uncertainty

In this work, we present a data-driven strategy for effective construction of a surrogate model in high-dimensional parameter space for the ion energy-angle distribution (IEAD) output of hPIC simulations of plasma-surface interactions. The methodology is based on a bin-by-bin least-squares fitting of the IEAD in the parameter space. The fitting is performed in a transformed coordinate system to normalize the IEAD, and it employs sparse grids for sampling the parameter space to overcome sampling challenges in high dimensions. The surrogate model is significantly cheaper computationally than direct hPIC simulations yet maintains high fidelity to them, providing a fast emulator for hPIC simulations. Sensitivity analysis based on the surrogate model is utilized to characterize the dependence of the ion impact angle and energy moments on the physical parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Melt pool temperature measurement and monitoring during laser powder bed fusion based additive manufacturing via single-camera two-wavelength imaging pyrometry (STWIP)

Melt pool (MP) temperature is one of the determining factors and a key signature for evaluating the properties of printed components in metal additive manufacturing (AM). The state-of-the-art measurement systems are hindered, primarily by the large-scale data acquisition and processing demands. In this work, we introduce a novel coaxial, high-speed, single-camera two-wavelength imaging pyrometer (STWIP) system as opposed to the typical utilization of multiple cameras for measuring MP temperature profiles in laser powder bed fusion (LPBF) processes. Developed on a commercial LPBF machine (EOS M290), the STWIP system demonstrated its ability to quantitatively monitor the MP temperature and its variation for 50 layers at high framerates (>30,000 fps) for a real-world application (standard fatigue specimens) print. High performance computing is employed to analyze the acquired big data (MP images), for determining each MP's average temperature and 2D temperature profile. The MP temperature evolution in the gage section of a fatigue specimen is also examined at a temporal resolution of 1 ms, by evaluating the MP temperatures in the samples' first, middle, and last layers. This report is the first of its kind on monitoring MP temperature distribution and evolution at such a large, detailed scale for longer durations in practical applications.

42 ENGINEERING↗
DOE OSTI2019

Fast, Dynamic, and Scalable Algorithms for Large-Scale Constrained Optimization

Scientists and engineers constantly aim to optimize an objective subject to physical, environmental, and/or resource constraints. The technique of using mathematical models to formulate and find real solutions of such problems is known as mathematical optimization, a process that has become invaluable for design and discovery in numerous scientific fields. This project has focused on the design, analysis, and implementation of high-performance computing algorithms for solving cutting-edge optimization problems. These include problems that involve (1) data uncertainties, such as those in the future supply, demand, and capacity of a given power system, (2) extreme numbers of alternatives, such as in the design of electrical power grids to avoid network vulnerabilities, and (3) real-time decisions, such as in the control of chemical reactors. The key features of the new algorithms are that they are fast, dynamic, and scalable to meet the computational requirements of scientists and researchers working to optimize large-scale, complex systems. The algorithms that have been designed, analyzed, and tested as part of the project can be characterized based on the types of optimization problems they have been designed to solve. Much of the work has focused on algorithms for solving large-scale constrained optimization problems. Contributions have also been made in the design of algorithms for unconstrained optimization, with some specialized methods for solving problems that have inherent nonsmoothness. Contributions have also been made in areas that have grown to prominence in the field over the five-year award period. These include the design of algorithms for solving large-scale stochastic optimization problems, such as those that arise in large-scale machine learning, and in complexity analyses for solving nonconvex problems, which has been a focal point of much recent work in continuous optimization. Overall, the project has supported work that has led to almost 30 articles that have been published or accepted to top journals in the field of mathematical optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

5G Enabled Energy Innovation: Advanced Wireless Networks for Science (Workshop Report)

Rapidly expanding, new telecommunications infrastructure based on 5G technologies will disrupt and transform how we design, build, operate, and optimize scientific infrastructure and the experiments and services enabled by that infrastructure, from continental-scale sensor networks to centralized scientific user facilities, from intelligent Internet of Things devices to supercomputers. Concurrently, 5G will introduce, or exacerbate, challenges related to protecting infrastructure and associated scientific data as well as to fully leveraging opportunities related to expanded infrastructure scale and complexity. The U.S. Department of Energy (DOE) Office of Science operates scientific infrastructure, supporting some of the nation’s most advanced intellectual discoveries, spanning the country and including 30 world-class user facilities from supercomputers to accelerators. Along with field experiments and remote observatories, every aspect of DOE’s scientific enterprise will be affected by 5G, which amounts to a complete renovation of the underpinnings of the nation’s information infrastructure. In this report we explore the scientific opportunities and new research challenges associated with 5G, ranging from scalability to heterogeneity to cybersecurity. The rapid commercial deployment of 5G opens the opportunity to rethink and reinvent DOE’s scientific infrastructure and experimentation, from intelligent sensor networks at unprecedented scales to a digital continuum of cyberinfrastructure spanning low-power sensors, high-performance computing embedded within and at the edge of the network, and DOE’s large-scale user instrument and computing facilities. New programming paradigms, workflow and data frameworks, and AI-based system design, operation, and autonomous adaptation and optimization will be necessary in order to exploit these new opportunities. Field deployments and centralized scientific instruments can also be revolutionized, moving (without traditional performance penalties) from wired to wireless connectivity for data and control systems, improving flexibility, and opening new sensing modalities, including the use of the 5G electromagnetic spectrum itself as an environmental probe. For DOE science, in contrast to commercial 5G applications and settings, devices will be deployed in extreme environments such as cryogenically cooled instrument control systems and in remote settings with harsh conditions, requiring the design of new materials for RF communication and edge processing to operate in these regimes. Concurrently, 5G infrastructure comprises both hardware and sophisticated software systems - currently closed and proprietary. The cybersecurity challenges to 5G-empowered reinvention mirror the complexity and variety of new 5G features, from virtualization to private network slices to ubiquitous access. Research is also needed in order to accelerate the development of secure and open 5G software infrastructure, reducing reliance on hardware and software produced outside the United States and providing the transparency and rigorous evaluation and testing afforded through open software. Twelve broad research thrusts are laid out in four chapters, with a companion fifth chapter (and three additional research thrusts) underscoring the needs and opportunities for an aggressive testbed program co-designed by networking experts and scientists involved in the 15 research thrusts. The urgency of undertaking this research is fueled by a global, accelerating deployment of new telecommunications infrastructure that is designed for entertainment and commercial applications - barely scratching the surface of what 5G can do to extend U.S. leadership in scientific discovery.

42 ENGINEERING↗
DOE OSTI2020

BISON Capabilities for LWR Fuel Behavior Analysis During Accident and High-burnup Conditions

The U.S. Department of Energy (DOE)'s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities using computational methods for the analysis and design of advanced reactor and fuel cycle systems. This program has been supporting the development of BISON which is a high-fidelity and high-resolution fuel performance tool at the engineering scale. Increasing recent interest in applications at extended burnups motivated this study to incorporate more physically based models in BISON. This document details integration of newly implemented modeling capabilities into BISON, which includes (1) new thermal conductivity models that are valid up to 100 GWd/t, (2) models for the formation of the high-burnup structure (HBS), (3) two porosity correction methods beingapplied on the thermal conductivity due to the conducting pores during the HBS formation. BISON's results are verified and validated to test the new modeling capabilities

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Advances in GDSA Framework Development and Process Model Integration

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and highlevel nuclear waste (HLW). A high priority for SFWST disposal R&D is to develop a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2020 advances of the Geologic Disposal Safety Assessment (GDSA) Framework and PFLOTRAN development groups of the SFWST Campaign. The common mission of these groups is to develop a geologic disposal system modeling capability for nuclear waste that can be used to probabilistically assess the performance of disposal options and generic sites. The capability is a framework called GDSA Framework that employs high-performance computing (HPC) capable codes PFLOTRAN and Dakota.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Method to convert a written procedure to structured data, and related systems and methods

Various embodiments described in this disclosure relate to methods and computer-based systems that implement those methods for converting tasks performed as part of a procedure into dynamic procedure tasks. Such dynamic procedure tasks may be used by computer-based procedure solutions to create highly dynamic computer-based procedures.

Oxstrand, Johanna H.↗
DOE OSTI2025

High Velocity AI Cache (HVAC)

HVAC is a high-performance caching system designed for large-scale AI training workloads.

Khan, Awais [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2017

Xenon Recovery at Room Temperature using Metal-Organic Frameworks

Xenon is known to be a very efficient anesthetic gas but its cost prohibits the wider use in medical industry and other potential applications. It has been shown that Xe recovery and recycle from anesthetic gas mixture can significantly reduce its cost as anesthetic. The current technology uses series of adsorbent columns followed by low temperature distillation to recover Xe, which is expensive to use in medical facilities. Herein, we propose much efficient and simpler system to recover and recycle Xe from simulant exhale anesthetic gas mixture at room temperature using metal organic frameworks. Among the MOFs tested, PCN-12 exhibits unprecedented performance with high Xe capacity, Xe/N2 and Xe/O2 selectivity at room temperature. The in-situ synchrotron measurements suggest the Xe is occupied in the small pockets of PCN-12 compared to unsaturated metal centers (UMCs). Computational modeling of adsorption further supports our experimental observation of Xe binding sites in PCN-12.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa

Adventitious rooting (AR) is critical to the propagation, breeding, and genetic engineering of trees. The capacity for plants to undergo this process is highly heritable and of a polygenic nature; however, the basis of its genetic variation is largely uncharacterized. To identify genetic regulators of AR, we performed a genome-wide association study (GWAS) using 1148 genotypes of Populus trichocarpa. GWASs are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale; to help overcome this limitation, we developed a computer vision system to measure an array of traits related to adventitious root development in poplar, including temporal measures of lateral and basal root length and area. GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics and to gain statistical power. These analyses yielded a total of 277 unique associations, suggesting that genes that control rooting include regulators of hormone signaling, cell division and structure, reactive oxygen species signaling, and other processes with known roles in root development. Numerous genes with uncharacterized functions and/or cryptic roles were also identified. These candidates provide targets for functional analysis, including physiological and epistatic analyses, to better characterize the complex polygenic regulation of AR.

59 BASIC BIOLOGICAL SCIENCES↗