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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Simulations of space charge in the Fermilab Main Injector

The Fermilab Project X plan for future high intensity operation relies on the Main Injector as the engine for delivering protons in the 60-120 GeV energy range. Project X plans call for increasing the number of protons per Main Injector bunch from the current value of 1.0 x 10{sup 11} to 3.0 x 10{sup 11}. Space charge effects at the injection energy of 8 GeV have the potential to seriously disrupt operations. We report on ongoing simulation efforts with Synergia, MARYLIE/Impact, and IMPACT, which provide comprehensive capabilities for parallel, multi-physics modeling of beam dynamics in the Main Injector including 3D space-charge effects.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Scientific machine learning for modeling and simulating complex fluids

The formulation of rheological constitutive equations—models that relate internal stresses and deformations in complex fluids—is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these “digital fluid twins” to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation—a task that is not achievable using any previously developed data-driven rheological equation of state.

Science & Technology - Other Topics↗
DOE OSTI2016

PROPAGATION OF RELATIVISTIC, HYDRODYNAMIC, INTERMITTENT JETS IN A ROTATING, COLLAPSING GRB PROGENITOR STAR

The prompt emission of gamma-ray bursts (GRBs) is characterized by rapid variabilities, which may be a direct reflection of the unsteady central engine. We perform a series of axisymmetric 2.5-dimensional simulations to study the propagation of relativistic, hydrodynamic, intermittent jets through the envelope of a GRB progenitor star. A realistic rapidly rotating star is incorporated as the background of jet propagation, and the star is allowed to collapse due to the gravity of the central black hole. By modeling the intermittent jets with constant-luminosity pulses with equal on and off durations, we investigate how the half period, T , affects the jet dynamics. For relatively small T values (e.g., 0.2 s), the jet breakout time t {sub bo} depends on the opening angle of the jet, with narrower jets more penetrating and reaching the surface at shorter times. For T ≤ 1 s, the reverse shock (RS) crosses each pulse before the jet penetrates through the stellar envelope. As a result, after the breakout of the first group of pulses at t {sub bo}, several subsequent pulses vanish before penetrating the star, causing a quiescent gap. For larger half periods ( T = 2.0 and 4.0 s), all the pulses can successfully penetrate through the envelope, since each pulse can propagate through the star before the RS crosses the shell. Our results may interpret the existence of a weak precursor in some long GRBs, given that the GRB central engine injects intermittent pulses with a half period T ≤ 1 s. The observational data seem to be consistent with such a possibility.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

High Fidelity Dynamic Simulation of Cycling in a Natural Gas Combined Cycle Power Plant

The CRADA objective is to use a high-fidelity natural gas combined cycle (NGCC) dynamic simulator deployed at the National Energy Technology Laboratory (NETL) to produce practical engineering ideas that would either reduce the impact on equipment resulting from cycling, or improve certain plant operational modes. The goal is to demonstrate to National Rural Electric Cooperative Association (NRECA) that a high-fidelity dynamic simulator offers a relatively low-cost option for investigation and testing without direct plant experimentation. Initial operation of the simulator revealed the potential for saturation conditions in the final high pressure superheater as the attemperator tried to control temperature at the superheater outlet during gas turbine loading and unloading. Subsequent plant operational data confirmed simulation results. Thus, this particular area was chosen for further investigation. Multiple simulations were performed during loading and unloading of the gas turbine in order to determine operational strategies that prevented saturation and increased the approach to saturation temperature. The results of these simulations are presented in this report. The summary at the end of this report suggests trials that could be performed at the plant to confirm the benefits seen by simulator investigations, such as changes to the attemperator temperature control setpoints and strategic control of the steam turbine inlet pressure control valve.

03 NATURAL GAS↗
DOE OSTI2020

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Investigating PVC polymer–plasticizer interactions with atomistic MD simulations and potential of mean force calculations

For this work, atomistic molecular dynamics (MD) simulations coupled with potential of mean force (PMF) calculations were employed to investigate the interactions between PVC polymer chains containing 6–20 repeating units and various plasticizers, with the goal of identifying potential replacements for the toxic plasticizer di(2-ethylhexyl)phthalate (DEHP) used in blood bags. The selected plasticizers belong to various chemical families, such as orthophthalates, citrates, adipates, and the terephthalate DEHT. Both the polymer and the plasticizers lack ionizable groups, and their interactions are primarily governed by van der Waals and electrostatic forces. A model correlating PMF profiles with interaction forces was developed and validated across polyvinyl chloride (PVC) polymers of different lengths and all investigated plasticizers. This model provides insight into how structural variations in plasticizers influence their respective PMF values. The study ranks the investigated plasticizers based on binding affinity, identifying TOTM (tris(2-ethylhexyl) trimellitate, TEHTM), BTHC (butyryl trihexyl citrate, Citroflex B-6), ATHC (acetyl trihexyl citrate, Citroflex A-6, CA-6), and DEHT (bis(2-ethylhexyl)terephthalate, DOTP/DEHTP) as promising alternatives for further investigation. This comprehensive study encompasses PVC polymers of four different lengths and 14 plasticizers, with all data averaged over 30 independent simulations. The approach provides a deeper understanding of molecular interactions, enabling the tailoring of polymer–plasticizer systems for diverse applications, including extractables and leachables, with relevance spanning materials science to biomedical engineering, and also serves as a basis for developing coarse-grained simulation protocols. Overall, this study provides valuable insights for designing safer and more efficient plasticizer substitutes and is well supported by other studies.

Shet, Sai Athmeeya G. [Sri Sathya Sai Institute of↗
DOE OSTI2018

A parametric study of ignition dynamics at ECN Spray A thermochemical conditions using 2D DNS

The ignition process in diesel engines is highly complex and incompletely understood. Here, two-dimensional direct numerical simulations are performed to investigate the ignition dynamics and their sensitivity to thermochemical and mixing parameters. The thermochemical and mixing conditions are matched to the benchmark Spray A experiment from the Engine Combustion Network. The results reveal a complex ignition process with overlapping stages of: low-temperature ignition (cool flames), rich premixed ignition, and nonpremixed ignition, which are qualitatively consistent with prior experimental and numerical investigations, however, this is the first time that fully-resolved simulations have been reported at the actual Spray A thermochemical condition. Parametric variations are then performed for the Damköhler number Da, oxidiser temperature, oxygen concentration, and peak mixture fraction (a measure of premixedness), to study their effect on the ignition dynamics. It is observed that with both increasing oxidiser temperature and decreasing oxygen concentration, that the cool flame moves to richer mixtures, the overlap in the ignition stages decreases, and the (nondimensional) time taken to reach a fully burning state increases. With increasing Da, the cool-flame speed is decreased due to lower mean mixing rates, which causes a delayed onset of high-temperature ignition. With increasing peak mixture fraction, the onset of each stage of ignition is not affected, but the overall duration of the ignition increases leading to a longer burn duration. In conclusion, the results suggest that turbulence–chemistry interactions play a significant role in determining the timing and location in composition space of the entire ignition process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Transactive Systems Simulation and Valuation Platform Trial Analysis

Transactive energy systems use principles of value to coordinate responsive supply and demand in energy systems. Work continues within the Transactive Systems Program, which is funded by the U.S. Department of Energy at Pacific Northwest National Laboratory, to understand the value of, understand the theory behind, and simulate the behaviors of transactive energy systems. This report summarizes recent advances made by this program. The main capability advances include a more comprehensive valuation model, including recommended documentation that should make valuation studies of all sorts more transparent, definition of economic metrics with which transactive mechanisms can be evaluated, and multiple improvements to the time-simulation environment that is being used to evaluate transactive scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Nuclear Energy Advanced Modeling and Simulation (NEAMS) Accident Tolerant Fuels High Impact Problem: FeCrAl Modeling Capabilities

Iron-chromium-aluminum alloys are a potential candidate replacement for zirconium-based claddings in light water reactor fuel rods due to their increased strength (lower creep rates and higher yield stress) and slower oxidation kinetics with steam. Their larger neutron absorption cross-section necessitates thinner claddings and larger fuel pellets to maintain the same as-fabricated fuel-to-clad gap. The aggressive development schedule of the United States Department of Energy's (DOE) Advanced Fuels Campaign (AFC) to have potential accident tolerant fuels (ATF) concepts in lead test rods or assemblies by 2022 has necessitated the need for advanced modeling and simulation techniques to investigate the behavior of these materials under normal operating and accident conditions. This report presents the modeling capabilities for FeCrAl claddings at the engineering (continuum) scale developed as part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) high impact problem (HIP). Sensitivity studies of select material parameters have been completed to determine their significance in predictions of important fuel performance parameters such as fuel centerline and surface temperatures, cladding stresses and strains, fission gas released, and rod internal pressure. Comparative analyses of FeCrAl and Zircaloy-4 claddings for separate effects conditions are presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Efficiency-Optimized Dual Fuel Engine with In-Cylinder Gasoline/CNG Blending

The objective of the proposed project is to demonstrate targeted blending of gasoline and natural gas as a means to reduce petroleum consumption of light-duty spark-ignition engine vehicles by at least 50%. This goal will be achieved through petroleum displacement with natural gas (NG) combined with a relative engine efficiency improvement in excess of 10% while meeting retrofitability requirements with an anticipated payback period of less than 36 months. Further through NG direct injection (DI) a 10% improvement in power density over the PFI baseline will be demonstrated. The proposed project employs a sound R&D approach and combines experimental work with state-of-the-art computational modeling to be able to demonstrate the ambitious project goals. The project spans a 30 month period and is divided in 3 phases with the initial phase focused on experimental characterization of the gaseous jet structure of the high-pressure NG DI injector which is used to validate the 3D-CFD simulation tool. The phase also includes an experimental demonstration of the fundamental feasibility of the proposed dual-fuel approach on an automotive single-cylinder research engine. The scope of the second phase includes optimization of the combustion system by means of virtual design combined with 3D-CFD analysis. The resulting optimized combustion system will be built and experimentally evaluated by the end of the 12 month period. The third and final phase expands the scope by integrating the engine results in a vehicle level simulation tool with the objective of developing an optimized engine control strategy that maximizes the efficiency benefits attainable with the limited on-board NG supplies. A detailed cost analysis to verify that the 36 month amortization period requirement is met will conclude the proposed effort.

42 ENGINEERING↗
DOE OSTI2025

Breaking the mold: Overcoming the time constraints of molecular dynamics on general-purpose hardware

The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved with computing power along the three principal dimensions of accuracy, atom count (spatial scale), and duration (temporal scale). Since the mid-2000s, computer platforms have, however, failed to provide strong scaling for MD, as scale-out central processing unit (CPU) and graphics processing unit (GPU) platforms that provide substantial increases to spatial scale do not lead to proportional increases in temporal scale. Important scientific problems therefore remained inaccessible to direct simulation, prompting the development of increasingly sophisticated algorithms that present significant complexity, accuracy, and efficiency challenges. While bespoke MD-only hardware solutions have provided a path to longer timescales for specific physical systems, their impact on the broader community has been mitigated by their limited adaptability to new methods and potentials. In this work, we show that a novel computing architecture, the Cerebras wafer scale engine, completely alters the scaling path by delivering unprecedentedly high simulation rates up to 1.144 M steps/s for 200 000 atoms whose interactions are described by an embedded atom method potential. This enables direct simulations of the evolution of materials using general-purpose programmable hardware over millisecond timescales, dramatically increasing the space of direct MD simulations that can be carried out. In this paper, we provide an overview of advances in MD over the last 60 years and present our recent result in the context of historical MD performance trends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

LAROMance Grade 91 Model Integration in NEML2

New reactor designs are targeting higher operating temperatures for increased thermal efficiency when compared to the current fleet of light water reactors. Designing structural components for these high temperature environments with reliable long-term operations requires material models that can accurately capture the deformation mechanisms active in these environments. The LAROMance surrogate material models are based on a database of mechanistic crystal plasticity simulations for high-temperature conditions. Inputs to the LAROMance models reflect the microstructural pedigree of the material, like dislocation densities and precipitate contents. Based on the evolution of these microstructural features, the LAROMance model provides the engineering scale constitutive model response. The LAROMance model was recently parameterized for Grade 91, a high temperature alloy. In the present work, the Grade 91 LAROMance model is implemented in the New Material Model Library, version 2 (NEML2). NEML2 provides a modular way to build material models from smaller blocks and was developed to vectorize the material update to efficiently run on modern computational architectures with graphics processing unit accelerators. NEML2 constitutive models can be used in simulations based on the multiphysics object-oriented simulation environment (MOOSE). This report provides details on the implementation of the Grade 91 LAROMance model in NEML2 and its verification of engineering scale finite element simulations in MOOSE.

42 - ENGINEERING↗
DOE OSTI2014

The Metropolis Monte Carlo method with CUDA enabled Graphic Processing Units

We present a CPU–GPU system for runtime acceleration of large molecular simulations using GPU computation and memory swaps. The memory architecture of the GPU can be used both as container for simulation data stored on the graphics card and as floating-point code target, providing an effective means for the manipulation of atomistic or molecular data on the GPU. To fully take advantage of this mechanism, efficient GPU realizations of algorithms used to perform atomistic and molecular simulations are essential. Our system implements a versatile molecular engine, including inter-molecule interactions and orientational variables for performing the Metropolis Monte Carlo (MMC) algorithm, which is one type of Markov chain Monte Carlo. By combining memory objects with floating-point code fragments we have implemented an MMC parallel engine that entirely avoids the communication time of molecular data at runtime. Our runtime acceleration system is a forerunner of a new class of CPU–GPU algorithms exploiting memory concepts combined with threading for avoiding bus bandwidth and communication. The testbed molecular system used here is a condensed phase system of oligopyrrole chains. A benchmark shows a size scaling speedup of 60 for systems with 210,000 pyrrole monomers. Our implementation can easily be combined with MPI to connect in parallel several CPU–GPU duets. -- Highlights: •We parallelize the Metropolis Monte Carlo (MMC) algorithm on one CPU—GPU duet. •The Adaptive Tempering Monte Carlo employs MMC and profits from this CPU—GPU implementation. •Our benchmark shows a size scaling-up speedup of 62 for systems with 225,000 particles. •The testbed involves a polymeric system of oligopyrroles in the condensed phase. •The CPU—GPU parallelization includes dipole—dipole and Mie—Jones classic potentials.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Uncertainty Quantification by Monte Carlo Analysis using CFD Simulations for GEMIX Benchmark Activities

Uncertainty quantification (UQ) methods can be applied to computational fluid dynamics (CFD) simulations for determining the uncertainty bands of the simulations results. In order to determine the applicability of these different methods, simple benchmark activities have been conducted by various group for comparison. The GEMIX (Generic Mixing experiment) test facility was used to develop benchmark data for this purpose by the Paul Scherrer Institute (PSI). The facility involves the mixing of two fluids that are initially separated. One of the two fluids is water with sugar dissolved in it to create a fluid with similar fluid properties to water, except for density. The other fluid was distilled water to reduce the uncertainty in the fluid's properties. During the experiments, velocity fields and concentration measurements were acquired at several planes of interest. The velocity fields were acquired using particle image velocimetry (PIV) and concentrations of each fluid were acquired using laser induced fluorescence (LIF). The inlet velocity profiles were acquired using laser Doppler velocimetry (LDV). The activity provided the opportunity to vary turbulence models, meshing methodologies, and numerical schemes for creation of uncertainty estimates. The present study investigates the usage of a simple Monte Carlo (MC) technique for varying the inlet boundary conditions of each inlet. The velocity profiles and densities of each inlet were specifically varied based on experiment measurement uncertainty. The intent was to isolate the uncertainty due to inlet quantities and not related to meshing, numerical scheme, or turbulence models. The comparisons of experimental and simulation results were found to be reasonably accurate. Though deficiencies were identified in the modeling approach to be corrected in future works by the authors. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

The value of in-reservoir energy storage for flexible dispatch of geothermal power

Geothermal systems making use of advanced drilling and well stimulation techniques have the potential to provide tens to hundreds of gigawatts of clean electricity generation in the United States by 2050. With near-zero variable costs, geothermal plants have traditionally been envisioned as providing “baseload” power, generating at their maximum rated output at all times. However, as variable renewable energy sources (VREs) see greater deployment in energy markets, baseload power is becoming increasingly less competitive relative to flexible, dispatchable generation and energy storage. Herein we conduct an analysis of the potential for future geothermal plants to provide both of these services, taking advantage of the natural properties of confined, engineered geothermal reservoirs to store energy in the form of accumulated, pressurized geofluid and provide flexible load-following generation. We develop a linear optimization model based on multi-physics reservoir simulations that captures the transient pressure and flow behaviors within a confined, engineered geothermal reservoir. We then optimize the investment decisions and hourly operations of a power plant exploiting such a reservoir against a set of historical and modeled future electricity price series. Further, we find that operational flexibility and in-reservoir energy storage can significantly enhance the value of geothermal plants in markets with high VRE penetration, with energy value improvements of up to 60% relative to conventional baseload plants operating under identical conditions. Across a range of realistic subsurface and operational conditions, our modeling demonstrates that confined, engineered geothermal reservoirs can provide large and effectively free energy storage capacity, with round-trip storage efficiencies comparable to those of leading grid-scale energy storage technologies. Optimized operational strategies indicate that flexible geothermal plants can provide both short- and long-duration energy storage, prioritizing output during periods of high electricity prices. Sensitivity analysis assesses the variation in outcomes across a range of subsurface conditions and cost scenarios.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

A conservative, interface-resolved, compressible framework for the modeling and simulation of liquid/gas phase change

This paper presents a method for simulating evaporation in a compressible, interface-resolved framework appropriate for modeling problems of engineering interest. In order to achieve robustness and broad applicability, the method has been designed to discretely enforce consistent mass and thermal energy transport at the phase interface, to globally conserve mass, momentum, and energy, and to be capable of modeling compressible and incompressible systems. Verification is performed via the Sod-shock test, one-dimensional heat conduction, evaporation from a planar interface, and evaporation of three-dimensional droplets. Convergence with increasing mesh resolution is demonstrated in all tested configurations, and conservation is maintained near machine precision for a translating droplet. Conservation and accurate phase change rates are preserved at the low numerical resolutions commonly encountered in engineering calculations. Following verification, the method is validated by comparison to an empirical correlation for evaporating droplets in high temperature crossflow, and the presentation concludes with the simulation of an iso-octane spray at conditions representative of gasoline direct injection. In conclusion, successful verification, validation, and demonstrated practical utility suggest the method to be an accurate, efficient, and robust approach for the study of phase change in engineering systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Early Career Researcher Support for Foundations of Molecular Modeling and Simulation Conference

This project enabled 14 early career researchers, including graduate students and postdocs, to attend the 8th triennial conference on Foundations of Molecular Modeling and Simulation (FOMMS 2022), which took place in Delavan, WI (Lake Geneva area), from July 17 to 21, 2022. Molecular-level modeling and simulation, which serve as critical tools for advancing science and engineering in a myriad of applications, were at the forefront of discussions. These ranged from the design of new materials for various energy applications to insights into the interaction between drug molecules and protein receptors within the body.

97 MATHEMATICS AND COMPUTING↗