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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Gutzwiller hybrid quantum-classical computing approach for correlated materials

Rapid progress in noisy intermediate-scale quantum (NISQ) computing technology has led to the development of novel resource-efficient hybrid quantum-classical algorithms, such as the variational quantum eigensolver (VQE), that can address open challenges in quantum chemistry, physics, and material science. Proof-of-principle quantum chemistry simulations for small molecules have been demonstrated on NISQ devices. While several approaches have been theoretically proposed for correlated materials, NISQ simulations of interacting periodic models on current quantum devices have not yet been demonstrated. Here, we develop a hybrid quantum-classical simulation framework for correlated electron systems based on the Gutzwiller variational embedding approach. We implement this framework on Rigetti quantum processing units (QPUs) and apply it to the periodic Anderson model, which describes a correlated heavy electron band hybridizing with noninteracting conduction electrons. Our simulation results quantitatively reproduce the known ground state quantum phase diagram including metallic, Kondo and Mott insulating phases. This is the first fully self-consistent hybrid quantum-classical simulation of an infinite correlated lattice model executed on QPUs, demonstrating that the Gutzwiller hybrid quantum-classical embedding framework is a powerful approach to simulate correlated materials on NISQ hardware. This benchmark study also puts forth a concrete pathway towards practical quantum advantage on NISQ devices.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Behavior, Energy, Autonomy, Mobility Modeling Framework

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility-maximizing evolutionary algorithm that minimizes each individual's cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within-day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing.

Bae, Sangjae↗
DOE OSTI2025

Python Library for Monte Carlo Simulations with Ab Initio and Machine-Learned Interatomic Potentials

There is a growing need in the simulation community for software that provides a transparent, reproducible, usable, and extensible (TRUE) Monte Carlo (MC) simulation framework employing energies from ab initio methods and machine-learning interatomic potentials (MLIPs). We introduce a Python library (ASE-MC) that adds Monte Carlo functionality to the Atomic Simulation Environment (ASE) package. Now, we can combine the powerful tools used to build systems and perform ab initio and MLIP in ASE with MC simulation algorithms to sample the configurational space with a concise Python script. After presenting the design philosophy, we demonstrate the flexibility of our approach using selected examples. These example simulations include liquid water described with a message-passing MLIP in the canonical and isothermal–isobaric ensembles, sampling the characteristic dihedral angle of biphenyl and comparing an MLIP to first-principles calculations, and a grand canonical Monte Carlo simulation of ammonia adsorption on Pt(111). These examples showcase the main features of the software, which include flexibility in the choice of ab initio or MLIP engine, ab initio or MLIP grand canonical MC with cavity bias insertions and deletions, the ability to add custom MC moves to the move set, and how users can condense complex MC workflows into a single Python script. Finally, this library serves as a framework for reproducible Monte Carlo simulations, facilitating easy reproduction of the work and application to new systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗
DOE OSTI2017

Umbra (core)

Umbra is a flexible simulation framework for complex systems that can be used by itself for modeling, simulation, and analysis, or to create specific applications. It has been applied to many operations, primarily dealing with robotics and system of system simulations. This version, from 4.8 to 4.8.3b, incorporates bug fixes, refactored code, and new managed C++ wrapper code that can be used to bridge new applications written in C# to the C++ libraries. The new managed C++ wrapper code includes (project/directories) BasicSimulation, CSharpUmbraInterpreter, LogFileView, UmbraAboutBox, UmbraControls, UmbraMonitor and UmbraWrapper.

Bradley, Jon David↗
DOE OSTI2017

Process Modeling and Validation for Metal Big Area Additive Manufacturing

Metal Big Area Additive Manufacturing (mBAAM) is a new additive manufacturing (AM) technology based on the metal arc welding. A continuously fed metal wire is melted by an electric arc that forms between the wire and the substrate, and deposited in the form of a bead of molten metal along the predetermined path. Objects are manufactured one layer at a time starting from the base plate. The final properties of the manufactured object are dependent on its geometry and the metal deposition path, in addition to depending on the basic welding process parameters. Computational modeling can be used to accelerate the development of the mBAAM technology as well as a design and optimization tool for the actual manufacturing process. We have developed a finite element method simulation framework for mBAAM using the new features of software ABAQUS. The computational simulation of material deposition with heat transfer is performed first, followed by the structural analysis based on the temperature history for predicting the final deformation and stress state. In this formulation, we assume that two physics phenomena are coupled in only one direction, i.e. the temperatures are driving the deformation and internal stresses, but their feedback on the temperatures is negligible. The experiment instrumentation (measurement types, sensor types, sensor locations, sensor placements, measurement intervals) and the measurements are presented. The temperatures and distortions from the simulations show good correlation with experimental measurements. Ongoing modeling work is also briefly discussed.

Simunovic, Srdjan↗
DOE OSTI2024

Behavior, Energy, Autonomy, Mobility Modeling Framework (BEAM) v1.0

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility- maximizing evolutionary algorithm that minimizes each individual’s cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within- day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing. It is designed to integrate with other open source transportation models, such as ActivitySim.

Lazarus, Jessica↗
DOE OSTI2024

Kinetic Plasma Simulation in the MOOSE Framework: Verification of Electrostatic Particle In Cell Capabilities

In magnetic confinement nuclear fusion reactors, the interaction between the plasma edge and plasma facing components is extremely important. At the plasma edge, a kinetic representation such as particle-in-cell (rather than a fluid representation) is required to accurately capture the plasma behavior. General purpose particle-in-cell plasma simulation capabilities have been developed in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. This new capability is a part of the development of a new MOOSE-based framework for modeling plasma facing components, the Fusion ENergy Integrated multiphys-X (FENIX) framework. In this work, the verification of foundational particle-in-cell capabilities in FENIX is presented. This new plasma simulation capability has three main components: moving particles in discrete steps on the finite element mesh, mapping charge density from the particle's location to the finite element mesh, and solving for the electrostatic potential based on the charge density mapped from particles to the mesh. In this paper, simple verification problems demonstrating each of these new capabilities are presented, and future work includes electromagnetic capabilities and Monte Carlo collisions with neutral gas particles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

LOCOMOTIVES - Comprehensive Impact and Cost Assessment Framework of Carbon Lowering Approaches for the US Rail Freight System

The goal of this project is to develop a tool to aid railroads and other stakeholders assess and approach the decarbonization of freight rail operations by identifying new, viable low-carbon energy storage and conversion systems for future locomotive systems and how they should be deployed on the existing US freight rail network. In the first quarter, the project focused on collecting data, establishing a simulation workflow, and engaging industry through the creation of the Industry Advisory Board (IAB). In the second quarter, the project focused on selecting fuel pathways and powertrain technologies, setting performance targets, conducting a techno-economic analyses, and developing the simulation framework that would serve as the backbone of the future toolhead. The third quarter involved developing an industry-oriented interactive dashboard powered by a five-step sequential framework, as well as holding industry advisory board meetings as per the initial technology-to-market plan. In the remaining project quarters, the NUFRIEND dashboard were fine-tuned with the help of IAB member feedback and in-depth scenario analyses were conducted to support the techno-economic analysis of energy sources. Additionally, dashboard documentation, project insights, and open-source code on GitHub were prepared and released. Throughout the project, the team completed testing and analysis of all model components, integrated all initial test scenarios, and conducted stakeholder engagement. Lower-carbon drop-in fuels can be deployed as admixtures and are considered uniform across the network at a desired penetration rate, while hydrogen and battery-electric technology deployment poses a more complex problem as they require significant investments to be made in the siting of refueling/charging facilities and the replacement of locomotive fleets. Thus, strategies for locating and sizing refueling/charging facilities on a railroad’s network to meet their energy demands were developed to inform deployment decisions. To address this challenge, the Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework presents a five-step sequential framework to select O-D paths, locate facilities, reroute flows, size facilities, and evaluate the deployment for alternative energy sources that require locomotive powertrains to be converted and new refueling infrastructure to be deployed. The NUFRIEND Framework is an industry-oriented tool for simulating the deployment of new energy technologies across the US freight rail network. The framework provides a comprehensive network-level optimization and scenario simulation tool for decarbonizing the freight rail sector, addressing the uncertainties surrounding technological developments by supporting sensitivity analyses for different operational and technological parameters through a transparent and flexible input module. It offers practical alternatives to diesel locomotives and can be applied for any railroad considering the specific network structure and freight demand, outputting evaluation metrics for the associated emissions and costs relative to diesel operations. A number of relevant simulation scenarios were run and analyzed for key insights on the value of different alternative technologies for freight rail decarbonization. The project developments and findings have been presented at numerous conferences and events.

08 HYDROGEN↗
DOE OSTI2023

Virtual Volumetric Additive Manufacturing (VirtualVAM)

Abstract Tomographic volumetric additive manufacturing (VAM) produces arbitrary 3D geometries by exposure of a rotating volume of photopolymer resin to tomographically‐patterned illumination. This enables high speed, layer‐less printing of parts from a wide range of photopolymers not amenable to layer‐by‐layer processes. Since the entire geometry is produced at once over the course of a few seconds to minutes, molecular diffusion length scales become significant to the printing process. Understanding these molecular reaction and diffusion processes is imperative for advancing VAM to a usable technology. These processes are experimentally very difficult to monitor and measure. Herein, VirtualVAM ‐ a simulation framework for modeling the tomographic VAM process, is developed and experimentally validated. VirtualVAM simulates reaction, diffusion, and heat generation processes over the course of a print with single‐voxel resolution. From a few experimentally‐determined input parameters and a set of images for projection, VirtualVAM is able to generate a large spatio‐temporal data set for any given tomographic VAM print. Using VirtualVAM, a number of experimentally‐unattainable aspects of the VAM process are investigated such as single‐voxel conversion profiles, effect of molecular oxygen, and stopping time determination. VirtualVAM also enables the optimization of exposure patterns to further improve contrast between in‐part and out‐of‐part delivered dose.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies

Electrification and the growth of distributed energy resources (DERs), including flexible loads, are changing the energy landscape of electric distribution systems and creating new challenges and opportunities for electric utilities. Changes in demand profiles require improvements in distribution system load models, which have not historically accounted for device controllability or impacts on customer comfort. Although building modeling research has focused on these features, there is a need to incorporate them into distribution load models that include DERs and can be used to study grid-interactive buildings. In this paper, we present the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) model. OCHRE is a controllable thermal-electric residential energy model that captures building thermal dynamics, integrates grid-dependent electrical behavior, contains models for common DERs and end-use loads, and simulates at a time resolution down to 1 minute. It includes models for space heaters, air conditioners, water heaters, electric vehicles, photovoltaics, and batteries that are externally controllable and integrated in a co-simulation framework. Using a proposed zero energy ready community in Colorado, we co-simulate a distribution grid and 498 all-electric homes with a diverse set of efficiency levels and equipment properties. We show that controllable devices can reduce peak demand within a neighborhood by up to 73% during a critical peak period without sacrificing occupant comfort. We also demonstrate the importance of modeling load diversity at a high time resolution when quantifying power and voltage fluctuations across a distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Interactions Between Climate Policy and Technology-influenced Travel Behavior: Mitigating Induced Demand from CACC

Advances in vehicle technology have influenced the development of automated vehicle systems, where vehicles that do not require human intervention are already deployed in the roadway networks. While these advances are proved to increase roadway safety and highway capacity, more research is needed to understand the long-term and regional-level impacts on mobility, land use, energy consumption, and emissions. This study proposes a multi-model approach to analyze the effect of vehicle automation and deep decarbonization policies over a period from 2020 to 2040 in Austin, Texas. We use the Global Change Analysis Model (GCAM) to develop internally the scenarios that are then passed to the SMART Mobility modeling workflow, a large-scale simulation framework combining the POLARIS activity-based travel demand model and mesoscopic traffic simulator with the Autonomie vehicle energy consumption model and the UrbanSim land use simulator. Results suggest that the introduction of vehicles with advanced automation could increase fuel consumption when no decarbonization policies are implemented. Also, advances in vehicle technology research and development could lead to a decline in energy use in the long-term. Energy pricing and vehicle electrification incentives could help reduce the impact of vehicle automation. Finally, our analysis indicates the relevance of introducing land use processes in longterm vehicle automation studies.

land use↗
DOE OSTI2019

Nuclear Thermal Propulsion Dynamic Modeling with Modelica

Oak Ridge National Laboratory (ORNL) is participating in the nuclear thermal propulsion (NTP) research and development effort supported by the National Aeronautics and Space Administration (NASA). This effort involves collaboration between multiple research groups, representing various government agencies and industry partners. ORNL has developed a Modelica-based modeling package for dynamic system modeling of nuclear reactors called the Transient Simulation Framework of Reconfigurable Models (TRANSFORM). While this software has been successfully demonstrated in simulations of traditional pressurized-water reactors, boiling-water reactors, liquid-metal reactors and molten-salt reactors, it has also been adapted for non-traditional use in modeling hybrid energy systems, and tritium transport. This versatility is being applied to the current NTP project, where specific modules within TRANSFORM are being developed for and applied to transient modeling of the NASA NTP design. This paper presents the current state of the ORNL-NTP model, the utility of TRANSFORM methodologies in NTP transient simulations, the ability to develop NTP-specific modules, and future work for the model.

Rader, Jordan↗
DOE OSTI2021

Search for Lightly Ionizing Particles in SuperCDMS and simulation of neutron backgrounds

The Super Cryogenic Dark Matter Search (SuperCDMS) is a direct-detection dark matter search experiment that primarily aims to search for Weakly Interacting Massive Particles (WIMPs) using state-of-the-art solid-state detection technology. During its operation at the Soudan underground laboratory, germanium detectors were operated with high bias voltage mode known as the CDMS low ionization threshold experiment (CDMSlite) to achieve below-keV thresholds. CDMSlite, for being able to measure small energy depositions in detectors, also provides sensitivity to Lightly Ionizing Particles (LIPs) with very small fractional charges. This thesis will discuss an analysis to search LIPs with the data acquired in CDMSlite mode. An important component for LIPs search is the expected energy-deposition distributions for LIPs falling on the CDMSlite detector. In this thesis, a simulation framework to calculate the energy-deposition distributions is developed. This thesis presents first direct-detection limits on the intensity of cosmogenic LIPs with electric charges smaller than e /(3 × 10 5 ) as well as the strongest limits for charges ≤ e /160, with a minimum intensity of 1.36 × 10 -7 cm -2 s -1 sr -1 at charge e /160.In any rare-event search experiment, understanding background is crucial. Neutrons capable of mimicking dark matter signals are a major background for any dark matter search experiment. A simulation study to estimate the neutron background for an India based dark matter search experiment at Jaduguda Underground Science Laboratory (JUSL) is performed. The experiment at JUSL will be the first phase of a proposed Dark matter search at India-based Neutrino Observatory (DINO). It will be a direct detection experiment with primary aims to search for WIMPs as dark matter candidates. In this thesis, we discuss the methodology of estimating neutron flux at JUSL and report the results. The total neutron flux reaching the laboratory above 1 MeV energy threshold is found to be 5.76(±0.69) × 10 -6 cm -2 s -1 . The impact of neutron background on the sensitivity of the experiment to detect dark matter at JUSL is also discussed. The thesis is organized as follows. Chapter 1 provides a brief introduction to the Lightly Ionizing Particle (LIPs). The analysis to search LIPs in SuperCDMS is briefly outlined in the chapter. This chapter also discusses the importance of neutron background estimates in a dark matter search experiment, more specifically, in the context of a proposed India-based dark matter search experiment at Jaduguda Underground Science Laboratory. In Chapter 2, the SuperCDMS experiment is introduced. In Chapter 3, the framework developed to perform simulations for Lightly Ionizing Particles is presented. In Chapter 4, the LIPs search analysis with the CDMSlite data and the results are discussed. In Chapter 5, the simulation of neutron background and the feasibility of dark-matter search at JUSL is discussed. Finally, conclusions from all the results discussed in this thesis are presented in Chapter 6.

Banik, Samir↗
DOE OSTI2016

Trace Replay and Network Simulation Tool

TraceR is a trace reply tool built upon the ROSS-based CODES simulation framework. TraceR can be used for predicting network performances and understanding network behavior by simulating messaging in High Performance Computing applications on interconnection networks.

Acun, Bilge↗
DOE OSTI2017

Trace Replay and Network Simulation Tool

TraceR Is a trace replay tool built upon the ROSS-based CODES simulation framework. TraceR can be used for predicting network performance and understanding network behavior by simulating messaging In High Performance Computing applications on interconnection networks.

Jain, Nikhil↗
DOE OSTI2021

Standard Behaviors - Umbra Package

This package contains the standard commands that characters can perform within the Umbra simulation framework, including the managers and supporting behaviors which enable commands to be initialized and simulated. Commands can be specified for a list of characters or a team. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-3055 O

Hart, BrianE.↗