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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

WHONDRS-GUI: a web application for global survey of surface water metabolites

Background The Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) is a consortium that aims to understand complex hydrologic, biogeochemical, and microbial connections within river corridors experiencing perturbations such as dam operations, floods, and droughts. For one ongoing WHONDRS sampling campaign, surface water metabolite and microbiome samples are collected through a global survey to generate knowledge across diverse river corridors. Metabolomics analysis and a suite of geochemical analyses have been performed for collected samples through the Environmental Molecular Sciences Laboratory (EMSL). The obtained knowledge and data package inform mechanistic and data-driven models to enhance predictions of outcomes of hydrologic perturbations and watershed function, one of the most critical components in model-data integration. To support efforts of the multi-domain integration and make the ever-growing data package more accessible for researchers across the world, a Shiny/R Graphical User Interface (GUI) called WHONDRS-GUI was created. Results The web application can be run on any modern web browser without any programming or operational system requirements, thus providing an open, well-structured, discoverable dataset for WHONDRS. Together with a context-aware dynamic user interface, the WHONDRS-GUI has functionality for searching, compiling, integrating, visualizing and exporting different data types that can easily be used by the community. The web application and data package are available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1484811 , which enables users to simultaneously obtain access to the data and code and to subsequently run the web app locally. The WHONDRS-GUI is also available for online use at Shiny Server ( https://xmlin.shinyapps.io/whondrs/ ).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Future energy infrastructure, energy platform and energy storage

Today fossil energy dominates energy consumption across the world. There has been an increasing momentum to reduce fossil energy consumption and increase renewable energy utilization to more than 70%. Such high penetrations of distributed renewable resources bring large uncertainty and complexity that cannot be easily handled by the current infrastructure. For the last two decades, telecommunication industry has gone through a similar transition from centralized to distributed systems, and more recently to platform approaches, which become the corner stone of today’s vibrant platform economy. Based on telecommunication history, we believe that a platform-based approach, called the energy platform, is a viable solution for addressing the renewable energy challenges. The energy platform consists of an array of computational algorithms, sensing and control technologies for key industry, energy generators and users to jointly manage and control the complex energy infrastructure. It includes the following key components: (1) the hardware and software to generate, store, control and transmit electricity/data (the energy cloud), (2) the digital platforms and tools for key energy producers, digital, service and communication industry to share the assets, participate in and manage the complex infrastructure, and for customers to participate in energy transactions, (3) the transaction platform for new service and business models. Additionally, the energy platform requires breakthroughs in large scale energy storage and many other areas including efficient power electronics, sensors and controls, new mathematical and computational tools, and deep integration of energy technologies and information sciences to control and stabilize such complex chaotic systems.

25 ENERGY STORAGE↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Computational chemistry for NH 3 synthesis, hydrotreating, and NO x reduction: Three topics of special interest to Haldor Topsøe

Computational chemistry, especially density functional theory, has experienced a remarkable growth in terms of application over the last few decades. This is attributed to the improvements in theory and computing infrastructure that enable the analysis of systems of unprecedented size and detail at an affordable computational expense. In this perspective, we discuss recent progress and current challenges facing electronic structure theory in the context of heterogeneous catalysis. We specifically focus on the impact of computational chemistry in elucidating and designing catalytic systems in three topics of interest to Haldor Topsøe – ammonia, synthesis, hydrotreating, and NO x reduction. Furthermore, we then discuss the common tools and concepts in computational catalysis that underline these topics and provide a perspective on the challenges and future directions of research in this area of catalysis research.

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

Equation-based and data-driven modeling: Open-source software current state and future directions

Here, a review of current trends in scientific computing reveals a broad shift to open-source and higher-level programming languages such as Python and growing career opportunities over the next decade. Open-source modeling tools accelerate innovation in equation-based and data-driven applications. Significant resources have been deployed to develop data-driven tools (PyTorch, TensorFlow, Scikit-learn) from tech companies that rely on machine learning services to meet business needs while keeping the foundational tools open. Open-source equation-based tools such as Pyomo, CasADi, Gekko, and JuMP are also gaining momentum according to user community and development pace metrics. Integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to radically accelerate progress. However, long-term support mechanisms are still necessary to sustain the momentum and maintenance of critical foundational packages.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Eulerian simulation of complex suspensions and biolocomotion in three dimensions

Significance Fluid–structure interactions are ubiquitous in many natural and man-made environments. They are difficult to study analytically, and therefore accurate and flexible computational methods are an indispensable tool in the field. Typically, fluids are simulated with a fixed background computational mesh, whereas a solid is simulated with a mesh that moves with it, making it challenging to couple the two. Here we develop a three-dimensional computational method where both fluid and solid can be represented on a fixed computational grid, which simplifies the coupling between the two phases considerably. Our method can simulate scenarios that are difficult to do with other methods, such as complex suspensions containing soft particles that are both heavier and lighter than the fluid.

3D fluid-structure interaction↗
DOE OSTI2023

Applying Quantum Computing to Simulate Power System Dynamics

Power system dynamics are generally modeled by high dimensional nonlinear differential-algebraic equations due to a large number of generators, loads, and transmission lines. Thus, its computational complexity grows exponentially with the system size. This paper demonstrates the potential use of quantum computing algorithms to model the power system dynamics. Leveraging a symbolic programming framework, we equivalently convert the power system dynamics’ differential algebraic equations (DAEs) into ordinary differential equations (ODEs), where the data of the state vector can be encoded into quantum computers via amplitude encoding. The system's nonlinearity is captured by Taylor polynomial expansion, the quantum state tensor, and Hamiltonian simulation, whereas state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can simulate the dynamics of the power system with high accuracy, whereas its complexity is polynomial in the logarithm of the system dimension. Our work also illustrates the use of scientific machine learning tools for implementing scientific computing concepts, e.g., Taylor expansion, DAEs/ODEs transform, and quantum computing solver, in the field of power engineering.

Tran, Huynh↗
DOE OSTI2013

A Complex-Geometry Validation Experiment for Advanced Neutron Transport Codes

The Idaho National Laboratory (INL) has initiated a focused effort to upgrade legacy computational reactor physics software tools and protocols used for support of core fuel management and experiment management in the Advanced Test Reactor (ATR) and its companion critical facility (ATRC) at the INL.. This will be accomplished through the introduction of modern high-fidelity computational software and protocols, with appropriate new Verification and Validation (V&V) protocols, over the next 12-18 months. Stochastic and deterministic transport theory based reactor physics codes and nuclear data packages that support this effort include MCNP5[1], SCALE/KENO6[2], HELIOS[3], SCALE/NEWT[2], and ATTILA[4]. Furthermore, a capability for sensitivity analysis and uncertainty quantification based on the TSUNAMI[5] system has also been implemented. Finally, we are also evaluating the Serpent[6] and MC21[7] codes, as additional verification tools in the near term as well as for possible applications to full three-dimensional Monte Carlo based fuel management modeling in the longer term. On the experimental side, several new benchmark-quality code validation measurements based on neutron activation spectrometry have been conducted using the ATRC. Results for the first four experiments, focused on neutron spectrum measurements within the Northwest Large In-Pile Tube (NW LIPT) and in the core fuel elements surrounding the NW LIPT and the diametrically opposite Southeast IPT have been reported [8,9]. A fifth, very recent, experiment focused on detailed measurements of the element-to-element core power distribution is summarized here and examples of the use of the measured data for validation of corresponding MCNP5, HELIOS, NEWT, and Serpent computational models using modern least-square adjustment methods are provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Extending the Capabilities of the Mooring Analysis Program: A Survey of Dynamic Mooring Line Theories for Integration into FAST: Preprint

Techniques to model dynamic mooring lines come in various forms. The most widely used models include either a heuristic representation of the physics (such as a Lumped-Mass, LM, system), a Finite-Element Analysis (FEA) discretization of the lines (discretized in space), or a Finite-Difference (FD) model (which is discretized in both space and time). In this paper, we explore the features of the various models, weigh the advantages of each, and propose a plan for implementing one dynamic mooring line model into the open-source Mooring Analysis Program (MAP). MAP is currently used as a module for the FAST offshore wind turbine computer-aided engineering (CAE) tool to model mooring systems quasi-statically, although dynamic mooring capabilities are desired. Based on the exploration in this manuscript, the lumped-mass representation is selected for implementation in MAP based on its simplicity, computational cost, and ability to provide similar physics captured by higher-order models.

17 WIND ENERGY↗
DOE OSTI2017

Z-checker: A framework for assessing lossy compression of scientific data

Because of the vast volume of data being produced by today’s scientific simulations and experiments, lossy data compressor allowing user-controlled loss of accuracy during the compression is a relevant solution for significantly reducing the data size. However, lossy compressor developers and users are missing a tool to explore the features of scientific data sets and understand the data alteration after compression in a systematic and reliable way. To address this gap, we have designed and implemented a generic framework called Z-checker. On the one hand, Z-checker combines a battery of data analysis components for data compression. On the other hand, Z-checker is implemented as an open-source community tool to which users and developers can contribute and add new analysis components based on their additional analysis demands. In this article, we present a survey of existing lossy compressors. Then, we describe the design framework of Z-checker, in which we integrated evaluation metrics proposed in prior work as well as other analysis tools. Specifically, for lossy compressor developers, Z-checker can be used to characterize critical properties (such as entropy, distribution, power spectrum, principal component analysis, and autocorrelation) of any data set to improve compression strategies. For lossy compression users, Z-checker can detect the compression quality (compression ratio and bit rate) and provide various global distortion analysis comparing the original data with the decompressed data (peak signal-to-noise ratio, normalized mean squared error, rate–distortion, rate-compression error, spectral, distribution, and derivatives) and statistical analysis of the compression error (maximum, minimum, and average error; autocorrelation; and distribution of errors). Z-checker can perform the analysis with either coarse granularity (throughout the whole data set) or fine granularity (by user-defined blocks), such that the users and developers can select the best fit, adaptive compressors for different parts of the data set. Z-checker features a visualization interface displaying all analysis results in addition to some basic views of the data sets such as time series. To the best of our knowledge, Z-checker is the first tool designed to assess lossy compression comprehensively for scientific data sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Coupled Mechanical-Electrochemical-Thermal Modeling for Accelerated Design of EV Batteries

This presentation provides an overview of the mechanical electrochemical-thermal (M-ECT) modeling efforts. The physical phenomena occurring in a battery are many and complex and operate at different scales (particle, electrodes, cell, and pack). A better understanding of the interplay between different physics occurring at different scales through modeling could provide insight to design improved batteries for electric vehicles. Work funded by the U.S. DOE has resulted in development of computer-aided engineering (CAE) tools to accelerate electrochemical and thermal design of batteries; mechanical modeling is under way. Three competitive CAE tools are now commercially available.

25 ENERGY STORAGE↗
DOE OSTI2017

WEC3: Wave Energy Converter Code Comparison Project: Preprint

This paper describes the recently launched Wave Energy Converter Code Comparison (WEC3) project and present preliminary results from this effort. The objectives of WEC3 are to verify and validate numerical modelling tools that have been developed specifically to simulate wave energy conversion devices and to inform the upcoming IEA OES Annex VI Ocean Energy Modelling Verification and Validation project. WEC3 is divided into two phases. Phase 1 consists of a code-to-code verification and Phase II entails code-to-experiment validation. WEC3 focuses on mid-fidelity codes that simulate WECs using time-domain multibody dynamics methods to model device motions and hydrodynamic coefficients to model hydrodynamic forces. Consequently, high-fidelity numerical modelling tools, such as Navier-Stokes computational fluid dynamics simulation, and simple frequency domain modelling tools were not included in the WEC3 project.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

PluginPlay: Enabling exascale scientific software one module at a time

For many computational chemistry packages, being able to efficiently and effectively scale across an exascale cluster is a heroic feat. Collective experience from the Department of Energy’s Exascale Computing Project suggests that achieving exascale performance requires far more planning, design, and optimization than scaling to petascale. In many cases, entire rewrites of software are necessary to address fundamental algorithmic bottlenecks. This in turn requires a tremendous amount of resources and development time, resources that cannot reasonably be afforded by every computational science project. It thus becomes imperative that computational science transition to a more sustainable paradigm. Key to such a paradigm is modular software. While the importance of modular software is widely recognized, what is perhaps not so widely appreciated is the effort still required to leverage modular software in a sustainable manner. The present manuscript introduces PluginPlay, https://github.com/NWChemEx-Project/PluginPlay, an inversion-of-control framework designed to facilitate developing, maintaining, and sustaining modular scientific software packages. Here this manuscript focuses on the design aspects of PluginPlay and how they specifically influence the performance of the resulting package. Although, PluginPlay serves as the framework for the NWChemEx package, PluginPlay is not tied to NWChemEx or even computational chemistry. We thus anticipate PluginPlay to prove to be a generally useful tool for a number of computational science packages looking to transition to the exascale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗