Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Advanced Computing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Advanced Reaction System Program: Materials for ARS Reaction Chambers (FY18 Milestone, Subtask 3.3)

The objective of this effort is to identify materials and manufacturing technology that will allow the rapid construction and evaluation of computer modeled advanced reactor system designs to process specific carbon feedstock materials. Before full scale reaction systems can be built, prototypes need to be constructed and operated to validate the proposed new system performance against target criteria. The first steps, however, are to understand what type of materials are needed that can withstand the targeted process environments and determine how structures using those materials can be built. A wide range of reaction vessel conditions may exist in the processes being considered that could involve temperatures up to 1200°C, abrasive materials (high velocity coal and coal ash), different carbon feedstock materials (coal, biomass, and process wastes such as petcoke), corrosive and oxidizing/reducing gases, different material phases (solid, liquid, and gas), cyclic temperatures, microwave energy, customized vessel construction material needs (process stability, cost, weight, and compatibility with other materials), along with other known/unknown variables – all of which must be considered in selecting materials and considering manufacturing processes. The current focus of the research is to develop a reaction vessel sidewall structure consisting of a metal outer support and an inner ceramic (or cermet) protective surface. Ceramic coatings are desired because of their good corrosion and wear resistance, and because they are the material of choice to reduce metal shell temperature in high temperature processes. However significant property mismatches between metals and ceramics have been a roadblock to the joining of metals and ceramics.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Analysis of the VERA Core Physics Benchmark Problems using cosRMC Code

In reactor physics, two kinds of numerical methods are generally employed to solve the neutron transport equations, one of which is the deterministic method and the other one is the Monte Carlo method. Monte Carlo is widely used to provide reference solution because of its ability to deal with continuous energy cross sections and complex geometry. Just like any statistical process, the Monte Carlo method requires repetition to achieve a small relative uncertainty, and therefore may inevitably have larger simulation times than deterministic method. Fortunately, the great advancements of computer technology make it possible to apply Monte Carlo method in reactor core analysis. To promote nuclear power plant system design and simulation software self-reliance development, a Monte Carlo neutron/photon transport code cosRMC has been developed jointly by State Nuclear Power Software Development Center (SNPSDC) and Tsinghua University. The cosRMC is aimed at achieving full core calculations and analysis with high fidelity and efficiency by means of advanced methodologies and algorithms as well as high performance computing techniques. The cosRMC has many functions as criticality calculation, fixed-source calculation, burnup calculation, etc. In order to ensure its reliability, several types of verification and validation (V and V) work have been and are being carried out for cosRMC. The simulation results of cosRMC based on Problem 1 to 3 of VERA Core Physics Benchmark showed its good capability of 2D lattice and 3D assembly modeling. In this paper, some progressive verification results of multi-assembly and full core problems are presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

The Science of Scientific Software Development and Use

Increasingly powerful and affordable computing has revolutionized scientific and scholarly discovery across a broad range of fields. Computing relies on software, which has been rapidly growing in scope, diversity, and complexity. At the same time, the methods, processes, and tools used to produce and utilize this essential software are often ad hoc, and the study and improvement of them is often done without the benefit of direct funding or prioritization. Consequently, concerns are growing about the productivity of the developers and users of scientific software, its sustainability, and the trustworthiness of the results that it produces. The US Department of Energy Office of Science (DOE-SC) is at the forefront of modern software-enabled scientific discovery across numerous areas of computational, experimental, and observational science, including major investments in national user facilities that support these activities. In December 2021, the DOE-SC Office of Advanced Scientific Computing Research (ASCR) convened a workshop on basic research needs for the Science of Scientific Software Development and Use (SSSDU). Through keynote presentations, lightning talks, and breakout groups, participants discussed the current practice of software development, maintenance, evolution, and use, and considered how the scientific method could be used to examine these practices and develop more evidence-based approaches to enhance the impact of software and computing on all areas of science. Workshop participants identified three priority research directions (PRDs) and three important crosscutting themes that center on the following overarching insight: software has become an essential part of modern science that impacts new discovery, policy, and technological development. To have full confidence in science delivered via software, we must improve the processes and tools that help us create and use it, and this enhancement requires a deep understanding of the diverse array of teams and individuals doing the work. The full workshop report will be available at https://doi.org/10.2172/1846009.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. Here, we finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Compressed Optimization of Device Architectures for Semiconductor Quantum Devices

Recent advances in nanotechnology have enabled researchers to manipulate small collections of quantum-mechanical objects with unprecedented accuracy. In semiconductor quantum-dot qubits, this manipulation requires controlling the dot orbital energies, the tunnel couplings, and the electron occupations. These properties all depend on the voltages placed on the metallic electrodes that define the device, the positions of which are fixed once the device is fabricated. While there has been much success with small numbers of dots, as the number of dots grows, it will be increasingly useful to control these systems with as few electrode voltage changes as possible. Here, we introduce a protocol, which we call the "compressed optimization of device architectures" (CODA), in order both to efficiently identify sparse sets of voltage changes that control quantum systems and to introduce a metric that can be used to compare device designs. As an example of the former, we apply this method to simulated devices with up to 100 quantum dots and show that CODA automatically tunes devices more efficiently than other common nonlinear optimizers. To demonstrate the latter, we determine the optimal lateral scale for a triple quantum dot, yielding a simulated device that can be tuned with small voltage changes on a limited number of electrodes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Developing High-Performance Computing Model of Vapor Transition Through Advanced Membranes for Applications in 7AC Liquid Desiccant Air Conditioners

This poster for the 2019 Advanced Manufacturing Office Peer Review covers the high-performance computing model development for applications in Liquid Desiccant Air Conditioners. Conventional compressor-based cooling has undergone incremental changes over the past 100 years. Further improvements in efficiency require evermore complex systems, especially under humid conditions. Microporous membranes offer unique opportunities for efficient humidity control in buildings using an absorbent desiccant solution, but membranes are not designed for this purpose. This project used molecular dynamic (MD) simulations to determine the effect of membrane properties on important metrics for these air-conditioning applications, focusing on the membrane properties at the membrane/liquid/air interface. Improved membranes can enable smaller, more durable, and less costly designs for these membrane air conditioning systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Comparison of atmospheric radionuclide dispersion models for a risk-informed consequence-driven advanced reactor licensing framework

Current nuclear facility emergency planning zones (EPZs) are based on outdated distance-based criteria, predating comprehensive dose and risk-informed frameworks. Recent advancements in simulation tools have permitted the development of site-specific, dose, and risk-based consequence-driven assessment frameworks. This study investigated the computation of advanced reactor (AR) EPZs using two atmospheric dispersion models: a straight-line Gaussian plume model (GPM) and a semi-Lagrangian Particle in Cell (PIC). Two case studies were conducted: (1) benchmarking the NRC SOARCA study for the Peach Bottom Nuclear Generating Station and (2) analyzing an advanced INL Heat Pipe Design A microreactor's end-of-cycle inventory. The dose criteria for both cases were 10 mSv at mean weather conditions and 50 mSv at 95th percentile weather conditions at 96 h post-release. Results demonstrated that GPM and PIC estimated similar mean peak dose levels for large boiling water reactors in the farfield case, placing EPZ limits beyond current regulations. For ARs with source terms remaining in the nearfield, PIC modeling without specific nearfield considerations could result in excessively high doses and inaccurate EPZ designations. PIC dispersion demonstrated an order of magnitude higher estimate of nearfield inhalation dose contribution when compared to GPM results. Furthermore, both models significantly reduced EPZ sizing within the nearfield. Thus, reductions in the AR source term may eliminate the need for a separate EPZ.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2017

Verification of Electromagnetic Physics Models for Parallel Computing Architectures in the GeantV Project

An intensive R&D and programming effort is required to accomplish new challenges posed by future experimental high-energy particle physics (HEP) programs. The GeantV project aims to narrow the gap between the performance of the existing HEP detector simulation software and the ideal performance achievable, exploiting latest advances in computing technology. The project has developed a particle detector simulation prototype capable of transporting in parallel particles in complex geometries exploiting instruction level microparallelism (SIMD and SIMT), task-level parallelism (multithreading) and high-level parallelism (MPI), leveraging both the multi-core and the many-core opportunities. We present preliminary verification results concerning the electromagnetic (EM) physics models developed for parallel computing architectures within the GeantV project. In order to exploit the potential of vectorization and accelerators and to make the physics model effectively parallelizable, advanced sampling techniques have been implemented and tested. In this paper we introduce a set of automated statistical tests in order to verify the vectorized models by checking their consistency with the corresponding Geant4 models and to validate them against experimental data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Management and Storage of Scientific Data

Scientific discoveries rely heavily on efficient access, search, and management of massive data sets. Data management technologies have, for decades, provided foundational capabilities for scientific computing. Just as storage, input/output (I/O), and data management have been fundamental to simulation-based science for many years, so too are capable data-management technologies key to the success of today’s scientific workflows utilizing data intensive and machine learning (ML) techniques. The Department of Energy, Office of Science, Advanced Scientific Computing Research (ASCR) program has invested broadly in data-management research focused on high-performance computing (HPC) systems, from parallel file systems that store data to application software that makes these systems more productive. Still, advances in technology combined with growing diversity of supported science strongly motivate continued investment in this area. In January 2022, ASCR convened a workshop to identify priority research directions in the area of data management for high-performance and scientific computing. Attendees were challenged to identify promising approaches that would support the breadth of the DOE mission, including the explosion of artificial intelligence (AI) uses and the growing needs of experimental and observational science. Technological and science drivers were identified and considered as they relate to key aspects of data management such as interfaces, architectural design, and FAIR principles (Findable, Accessible, Interoperable, and Reusable). The thoughts of the workshop participants were distilled into a set of four priority research directions with the potential for high impact on DOE science. These research directions are summarized in the following pages.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Advancing Fusion with Machine Learning Research Needs Workshop

The pursuit of fusion energy has required extensive experimental and theoretical science activities to develop the knowledge needed that will enable design of successful fusion power plants. Even today, following decades of research in many key areas including plasma physics and material science, much remains to be learned to enable optimization of the tokamak or other paths to fusion energy. Data science methods from the fields of machine learning and artificial intelligence (ML/AI) offer opportunities for enabling or accelerating progress toward the realization of fusion energy by maximizing the amount and usefulness of information extracted from experimental and simulation output data. Jointly supported by the Department of Energy Offices of Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR), a workshop was organized to identify Priority Research Opportunities (PRO’s) for application of ML/AI methods to enable accelerated solution of fusion problems. The resulting “Advancing Fusion with Machine Learning Research Needs Workshop,” held in Gaithersburg, MD, April 30 – May 2, 2019, brought together ~ 60 experts in fields spanning fusion science, data science, statistical inference and mathematics, machine learning, and artificial intelligence, along with DOE program managers and technical experts, to identify key PRO’s.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

MOCUM Verification with a Heterogeneous MOX Whole Core C5G7 Benchmark Problem

Recent advancements in computer technology has allowed for the possibility to solve complex reactor problems without homogenization, but the present benchmark problems are all limited to small scale with few fuel assemblies and homogeneity at the core level. Therefore, to verify the capabilities of the advanced neutronics codes, heterogeneous full core model shall be developed. Recently several whole-core benchmark problems such as BEAVRS and VERA are developed with measured data to which computational codes can be compared. With a particular research interest on the MOX fuel, this study develops a two-dimensional (2-D) whole core MOX neutronics benchmark based on the C5G7 benchmark concept. The developed MOX core benchmark will then be used to verify the accuracy of the MOCUM code, which is developed using the method of characteristic (MOC) as the flux solver with an advanced geometry processor designed for arbitrary core geometries. MOCUM results will be compared against MCNP5 results as the reference solutions. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

CRiSPPy: An advanced hydropower scheduling tool for the Colorado River Storage Project

The Western Area Power Administration (WAPA) plays a vital role in delivering reliable and cost-effective hydroelectric power to millions of customers across the western United States. The Colorado River Storage Project (CRSP) carries out WAPA’s mission in Arizona, Utah, Colorado, New Mexico, Nevada, Wyoming and Texas. Achieving this mission requires effective management of the Colorado River system, and depends on the use of advanced analytical tools and modeling methodologies. For many years, CRSP has relied on the Generation and Transmission Maximization Superlite (GTMax SL) model for its mid-term and long-term hydroscheduling needs. However, the evolving energy market, power system operations, environmental rules, and hydrology conditions, coupled with advancements in computational capabilities, have necessitated the development of a more modern and robust solution. This report introduces the Colorado River Storage Project Python-based (CRiSPPy) model, a new, advanced hydropower scheduling tool developed to address CRSP ever-evolving challenges. CRiSPPy represents a significant leap forward in our ability to model and optimize the operation of the Colorado River system. It incorporates state-of-the-art optimization algorithms, enhanced data management capabilities, and an advanced graphical user interface, providing WAPA CRSP personnel with unprecedented insights and decision-making support. This document details the development, capabilities, and implementation of CRiSPPy. It is intended to serve as a comprehensive resource for WAPA staff, stakeholders, and anyone interested in the future of hydropower scheduling in the Colorado River Basin. We are confident that CRiSPPy will enhance WAPA's mission while adapting to the challenges of a dynamic and increasingly complex environment. The version of CRiSPPy described in this report is the version 2.3. New versions of CRiSPPy will be developed as the tool keeps evolving to address CRSP challenges.

13 HYDRO ENERGY↗
DOE OSTI2017

Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model

Objectives: Sepsis affects nearly 1 million people in the United States per year, has a mortality rate of 28–50% and requires more than $20 billion a year in hospital costs. Over a quarter century of research has not yielded a single reliable diagnostic test or a directed therapeutic agent for sepsis. Central to this insufficiency is the fact that sepsis remains a clinical/physiological diagnosis representing a multitude of molecularly heterogeneous pathological trajectories. Advances in computational capabilities offered by High Peformance Computing (HPC) platforms call for an evolution in the investigation of sepsis to attempt to define the boundaries of traditional research (bench, clinical and computational) through the use of computational proxy models. We present a novel investigatory and analytical approach, derived from how HPC resources and simulation are used in the physical sciences, to identify the epistemic boundary con- ditions of the study of clinical sepsis via the use of a proxy agent-based model of systemic inflammation. Design: Current predictive models for sepsis use correlative methods that are limited by patient heterogeneity and data sparseness. We address this issue by using an HPC version of a system-level validated agent-based model of sepsis, the Innate Immune Response ABM (IIRBM), as a proxy system in order to identify boundary conditions for the possible behavioral space for sepsis. We then apply advanced analysis derived from the study of Random Dynamical Systems (RDS) to identify novel means for characterizing system behavior and providing insight into the tractability of traditional investigatory methods. Results: The behavior space of the IIRABM was examined by simulating over 70 million sepsis patients for up to 90 days in a sweep across the following parameters: cardio-respiratory-metabolic resilience; microbial invasiveness; microbial toxigenesis; and degree of nosocomial exposure. In addition to using established methods for describing parameter space, we developed two novel methods for characterizing the behavior of a RDS: Probabilistic Basins of Attraction (PBoA) and Stochastic Trajectory Analysis (STA). Computationally generated behavioral landscapes demonstrated attractor structures around stochastic re- gions of behavior that could be described in a complementary fashion through use of PBoA and STA. The stochasticity of the boundaries of the attractors highlights the challenge for correlative attempts to characterize and classify clinical sepsis. Conclusions: HPC simulations of models like the IIRABM can be used to generate approximations of the behavior space of sepsis to both establish “boundaries of futility” with respect to existing investi- gatory approaches and apply system engineering principles to investigate the general dynamic properties of sepsis to provide a pathway for developing control strategies. Here, the issues that bedevil the study and treatment of sepsis, namely clinical data sparseness and inadequate experimental sampling of system behavior space, are fundamental to nearly all biomedical research, manifesting in the “Crisis of Reproducibility” at all levels. HPC-augmented simulation-based research offers an investigatory strategy more consistent with that seen in the physical sciences (which combine experiment, theory and simulation), and an opportunity to utilize the leading advances in HPC, namely deep machine learning and evolutionary computing, to form the basis of an iterative scientific process to meet the full promise of Precision Medicine (right drug, right patient, right time).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Quantum computation of silicon electronic band structure

Development of quantum architectures during the last decade has inspired hybrid classical–quantum algorithms in physics and quantum chemistry that promise simulations of fermionic systems beyond the capability of modern classical computers, even before the era of quantum computing fully arrives. Strong research efforts have been recently made to obtain minimal depth quantum circuits which could accurately represent chemical systems. Here, we show that unprecedented methods used in quantum chemistry, designed to simulate molecules on quantum processors, can be extended to calculate properties of periodic solids. In particular, we present minimal depth circuits implementing the variational quantum eigensolver algorithm and successfully use it to compute the band structure of silicon on a quantum machine for the first time. We are convinced that the presented quantum experiments performed on cloud-based platforms will stimulate more intense studies towards scalable electronic structure computation of advanced quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Scaling up to address data science challenges

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithms and procedures for efficient processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Force Field Development for Actinyl Ions via Quantum Mechanical Calculations: An Approach to Account for Many Body Solvation Effects

Advances in computational algorithms and methodologies make it possible to use highly accurate quantum mechanical calculations to develop force fields (pair-wise additive intermolecular potentials) for condensed phase simulations. Despite these advances, this approach faces numerous hurdles for the case of actinyl ions, AcO2n+ (high-oxidation-state actinide dioxo cations), mainly due to the complex electronic structure resulting from an interplay of s, p, d, and f valence orbitals. Traditional methods use a pair of molecules (“dimer”) to generate a potential energy surface (PES) for force field parametrization based on the assumption that many body polarization effects are negligible. We show that this is a poor approximation for aqueous phase uranyl ions and present an alternative approach for the development of actinyl ion force fields that includes important many body solvation effects. Force fields are developed for the UO22+ ion with the SPC/Fw, TIP3P, TIP4P, and TIP5P water models and are validated by carrying out detailed molecular simulations on the uranyl aqua ion, one of the most characterized actinide systems. It is shown that the force fields faithfully reproduce available experimental structural data and hydration free energies. Failure to account for solvation effects when generating PES leads to overbinding between UO22+ and water, resulting in incorrect hydration free energies and coordination numbers. A detailed analysis of arrangement of water molecules in the first and second solvation shell of UO22+ is presented. The use of a simple functional form involving the sum of Lennard-Jones + Coulomb potentials makes the new force field compatible with a large number of available molecular simulation engines and common force fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Performance analysis and acceleration of explicit integration for large kinetic networks using batched GPU computations

Abstract—We demonstrate the systematic implementation of recently-developed fast explicit kinetic integration algorithms that solve efficiently N coupled ordinary differential equations (subject to initial conditions) on modern GPUs. We take representative test cases (Type Ia supernova explosions) and demonstrate two or more orders of magnitude increase in efficiency for solving such systems (of realistic thermonuclear networks coupled to fluid dynamics). This implies that important coupled, multiphysics problems in various scientific and technical disciplines that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible. As examples of such applications we present the computational techniques developed for our ongoing deployment of these new methods on modern GPU accelerators. We show that similarly to many other scientific applications, ranging from national security to medical advances, the computation can be split into many independent computational tasks, each of relatively small-size. As the size of each individual task does not provide sufficient parallelism for the underlying hardware, especially for accelerators, these tasks must be computed concurrently as a single routine, that we call batched routine, in order to saturate the hardware with enough work.

Haidar Ahmad, Azzam↗