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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Time-resolved turbulent dynamo in a laser plasma

Understanding magnetic-field generation and amplification in turbulent plasma is essential to account for observations of magnetic fields in the universe. A theoretical framework attributing the origin and sustainment of these fields to the so-called fluctuation dynamo was recently validated by experiments on laser facilities in low-magnetic-Prandtl-number plasmas (Pm<1). However, the same framework proposes that the fluctuation dynamo should operate differently when Pm≳1, the regime relevant to many astrophysical environments such as the intracluster medium of galaxy clusters. This paper reports an experiment that creates a laboratory Pm≳1 plasma dynamo. We provide a time-resolved characterization of the plasma’s evolution, measuring temperatures, densities, flow velocities, and magnetic fields, which allows us to explore various stages of the fluctuation dynamo’s operation on seed magnetic fields generated by the action of the Biermann-battery mechanism during the initial drive-laser target interaction. The magnetic energy in structures with characteristic scales close to the driving scale of the stochastic motions is found to increase by almost three orders of magnitude and saturate dynamically. It is shown that the initial growth of these fields occurs at a much greater rate than the turnover rate of the driving-scale stochastic motions. Our results point to the possibility that plasma turbulence produced by strong shear can generate fields more efficiently at the driving scale than anticipated by idealized magnetohydrodynamics (MHD) simulations of the nonhelical fluctuation dynamo; this finding could help explain the large-scale fields inferred from observations of astrophysical systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Large scale multi-node simulations of $\mathbb{Z}_2$ gauge theory quantum circuits using Google Cloud Platform

Simulating quantum field theories on a quantum computer is one of the most exciting fundamental physics applications of quantum information science. Dynamical time evolution of quantum fields is a challenge that is beyond the capabilities of classical computing, but it can teach us important lessons about the fundamental fabric of space and time. Whether we may answer scientific questions of interest using near-term quantum computing hardware is an open question that requires a detailed simulation study of quantum noise. Here we present a large scale simulation study powered by a multi-node implementation of qsim using the Google Cloud Platform. We additionally employ newly-developed GPU capabilities in qsim and show how Tensor Processing Units -- Application-specific Integrated Circuits (ASICs) specialized for Machine Learning -- may be used to dramatically speed up the simulation of large quantum circuits. We demonstrate the use of high performance cloud computing for simulating $\mathbb{Z}_2$ quantum field theories on system sizes up to 36 qubits. We find this lattice size is not able to simulate our problem and observable combination with sufficient accuracy, implying more challenging observables of interest for this theory are likely beyond the reach of classical computation using exact circuit simulation.

Gustafson, Erik↗
DOE OSTI2023

A Lattice Boltzmann Method for Predicting Porous Transport Layer Performance During Electrolysis

Electrolysis, the splitting of water into oxygen and hydrogen using electricity, is a sustainable way to produce green hydrogen for energy storage. In polymer electrolyte membrane (PEM) water electrolysis, water is brought into contact with charged catalyst layers and electrochemically separated into oxygen and hydrogen. The hydrogen product formed at the cathode is carried through the catalyst layer for eventual collection, while the oxygen by-product formed at the anode is removed from the surface via a multiphase interaction with circulating water and a solid porous transport layer (PTL). The design of this PTL aids in the detachment and advection of the oxygen by-product and thereby plays a role in the overall efficacy of the catalyst. In this presentation, we present our initial results modeling this multiphase system using a single-component, multiphase lattice Boltzmann method. We use the Shan-Chen model describing inter-particle forces to capture both the cohesion of the water (liquid) and oxygen (gas) phases and their interaction with the PTL (solid) (Shan and Chen, 1993). We use a Carnahan-Starling equation of state to model the effective density governing these inter-particle interactions which allows us to model this relatively high density ratio system (Carnahan and Starling, 1969). With these simulations, we show that the geometry and heterogeneity of the PTL geometry plays a large role in its ability to move oxygen away from the catalyst layer and the resulting bubble structures that are formed within the PTL. The current work demonstrates these effects using synthesized PTL geometries and 2D physics, which will be extended to experimentally-imaged PTL sections and 3D algorithms in the near future.

Boltzmann↗
DOE OSTI2022

Quantification of Dead Lithium on Graphite Anode under Fast Charging Conditions

A series of computational and experimental studies were conducted to understand the onset of lithium plating and subsequent quantification of dead lithium on graphite electrodes in the design of fast charging batteries. The experiments include titration and relaxation studies for detecting initiation of lithium metal plating for various SOC and C-rates, which are compared against the thermodynamically consistent phase field computational results. The collaborative study on "model graphite electrode" with 2.18 mAh cm-2 nominal capacity at 25 degrees C demonstrates: (1) the macroscopic voltage response during relaxation studies indicate the reintercalation of plated lithium into the graphite anode; (2) for SOC below 60% and low C-Rates, there is no dead lithium; (3) for SOC between 60% to 80%, and C-Rates in the range of 4C-6C show dead lithium both in experiments and simulations; (4) at 100% SOC and 4C-6C rates, large amounts of dead lithium are observed. The study presented here allows us to evaluate the effects of the physical properties of the electrochemical system on plating and stripping kinetics and the amount of dead lithium on graphite electrodes, which determines the cell capacity loss under fast charge.

batteries↗
DOE OSTI2024

Evidence of Directional Structural Superlubricity and Lévy Flights in a van der Waals Heterostructure

Abstract Structural superlubricity is a special frictionless contact in which two crystals are in incommensurate arrangement such that relative in‐plane translation is associated with vanishing energy barrier crossing. So far, it has been realized in multilayer graphene and other van der Waals (2D crystals with hexagonal or triangular crystalline symmetries, leading to isotropic frictionless contacts. Directional structural superlubricity, to date unrealized in 2D systems, is possible when the reciprocal lattices of the two crystals coincide in one direction only. Here, directional structural superlubricity a α‐bismuthene/graphite van der Waals system is evidenced, manifested by spontaneous hopping of the islands over hundreds of nanometers at room temperature, resolved by low‐energy electron microscopy and supported by registry simulations. Statistical analysis of individual and collective α‐bismuthene islands populations reveal a heavy‐tailed distribution of the hopping lengths and sticking times indicative of Lévy flight dynamics, largely unobserved in condensed‐matter systems.

Le Ster, Maxime↗
DOE OSTI2022

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. Further, the best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Foundation models for atomistic simulation of chemistry and materials

Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models — pre-trained on massive datasets using transformer architectures — have revolutionized many fields. It is thus interesting to ask whether a foundation model — subject to suitable data, parameter scaling and training — could enable learned simulations of chemistry and materials. Here, in this study, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on small datasets corresponding to specific, targeted atomistic simulation tasks. We provide specific criteria for creating such large-scale MLIP foundation models, coordinated strategies for their development, evaluation and deployment, and highlight potential emergent capabilities that could transform predictive simulations in chemistry and materials science and accelerate discovery across multiple technological domains.

Yuan, Eric C.-Y. [University of California, Berkel↗
DOE OSTI2022

Tausch: A halo exchange library for large heterogeneous computing systems using MPI, OpenCL, and CUDA

Exchanging halo data is a common task in modern scientific computing applications and efficient handling of this operation is critical for the performance of the overall simulation. Tausch is a novel header-only library that provides a simple API for efficiently handling these types of data movements. Tausch supports both simple CPU-only systems, but also more complex heterogeneous systems with both CPUs and GPUs. It currently supports both OpenCL and CUDA for communicating with GPGPU devices, and allows for communication between GPGPUs and CPUs. The API allows for drop-in replacement in existing codes and can be used for the communication layer in new codes. This paper provides an overview of the approach taken in Tausch, and a performance analysis that demonstrates expected and achieved performance. Here, we highlight the ease of use and performance with three applications: First Tausch is compared to the halo exchange framework from two Mantevo applications, HPCCG and miniFE, and then it is used to replace a legacy halo exchange library in the flexible multigrid solver framework Cedar.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Full-Scale Ab Initio Simulation of Magic-Angle-Spinning Dynamic Nuclear Polarization

Theoretical models aimed at describing magic-angle-spinning (MAS) dynamic nuclear polarization (DNP) NMR have great potential in facilitating the in silico design of DNP polarizing agents and formulations. These models must typically face a trade-off between the accuracy of a strict quantum mechanical description and the need for using realistically large spin systems, for instance, using phenomenological models. In this paper, we show that the use of aggressive state-space restrictions and an optimization strategy allows full-scale ab initio MAS-DNP simulations of spin systems containing thousands of nuclei. Our simulations are shown to reproduce experimental DNP enhancements quantitatively, including their MAS rate dependence, for both frozen solutions and solid materials. They also reveal the importance of a previously unrecognized structural feature found in some polarizing agents that helps minimize the sensitivity losses imposed by the spin diffusion barrier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Generic Advanced Computing Framework for Executing Windows-based Dynamic Contingency Analysis Tool in Parallel on Cluster Machines

Dynamic contingency analysis tool (DCAT) has been developed to assess the impact and likelihood of extreme contingencies and potential cascading events across their systems and interconnections. By including more customized protection models and corrective actions into the Windows-based commercial tools, DCAT can help operators understand the cascading behavior and find mitigation approaches to reduce the risk of cascading outages in a more realistic manner. In order to further enhance the capability of DCAT, this paper presents a design of an advanced computing framework that enables DCAT to run on a cluster machine to improve its computational performance. This framework is generic and can be applied to other Windows-based simulation tools to fill the technical gap of applying advanced computing technology to vendors' Windows-based tools. The preliminary tests using medium to large power systems have shown the effectiveness of this framework and its potential for accelerating the adoption of advanced computing in utilities.

Advanced computing, dynamic contingency analysis, ↗
DOE OSTI2024

Simulation of DIII-D disruption with argon pellet injection and runaway electron beam

Abstract The next generation of large tokamaks, including ITER, will be equipped with a disruption mitigation system (DMS) that can be activated if a disruption is deemed to be imminent. Introducing impurities by pellet (large or shattered) or massive gas injection has been shown to be an effective mitigation mechanism on many tokamaks. The goal of the mitigation is to lessen the thermal and electromagnetic loads from the disruption without generating enough high-energy (runaway) electrons to damage the device. Variations of this mitigation process with impurity injection are presently being tested on many experiments. We have modeled one such impurity injection experiment on DIII-D using the M3D-C1 nonlinear 3D extended MHD code (Jardin et al 2012 Comput. Sci. Discovery 6 014002), The model includes an argon large pellet injection and ablation model, impurity ionization, recombination, and radiation, and runaway electron formation and subsequent evolution, including both Dreicer and avalanche sources. We obtain reasonable agreement with the experimental results for the timescale of the thermal and current quench and for the magnitude of the runaway electron plateau formed during the mitigation. This is the first 3D full MHD simulation with pellets and REs to simulate the disruption process and it also provides a partial validation of the M3D-C1 DMS model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Active learning for robust, high-complexity reactive atomistic simulations

Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. In this paper, we present an active learning approach based on cluster analysis and inspired by Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. The use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training database management approach enables development of models exhibiting excellent agreement with Kohn–Sham density functional theory in terms of structure, dynamics, and speciation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Efficient Treatment of Large Active Spaces through Multi-GPU Parallel Implementation of Direct Configuration Interaction

In this study, we have extended our graphical processing unit (GPU)-accelerated direct configuration interaction program to multiple devices, reducing iteration times for configuration spaces of 165 million determinants to only 3 s using NVIDIA P100 GPUs. Similar improvements in the one- and two-particle reduced density matrix formation allow for fast analytical energy gradients and electronic properties. Our parallel algorithm enables the calculation of arbitrarily large configuration spaces (limited only by available system memory), with iteration times of 13 min for an active space of 18 electrons in 18 orbitals (2.4 billion determinants) using six consumer grade NVIDIA 1080Ti GPUs. These advances enable routine molecular dynamics simulations, geometry optimizations, and absorption spectrum calculations for molecules with large configuration spaces, a task that has heretofore required massive computational effort. In this work, we demonstrate the utility of our program by generating the absorption spectrum for diphenyl acetylene at the floating occupation molecular orbital complete active space configuration interaction level of theory. Lastly, several active spaces were investigated to assess the dependence of spectral features on orbital space dimension.

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

ORNL_AISD_NiPt

This dataset describes the nickel-platinum (NiPt) solid solution binary alloy, where the two constituent elements nickel (Ni) and platinum (Pt) are randomly placed on the face centered cubic (FCC) crystal structure, with the lattice constant of 3.840 angstroms. The dataset comprises data for three different sizes of the crystal structure: 256 atoms, 864 atoms, and 2,048 atoms, each of which contains 1900 configurations. For each size of the crystal structure, the data set was generated for concentrations ranging from 0at% of Pt to 100at% of Pt in the NiPt binary system, with increasing the concentration of Pt in the system every 5at%. For each one of the chemical compositions, 100 random configurations were generated, each with a different random seed. Each of the output files contains the mass, type, atomic coordinates, energy per atom, and forces in x, y, and z directions respectively. For each atomic configuration, the output was collected every 150 steps during the minimization stage and every 1000 steps during the replica exchange stage. Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) [1], which is a molecular dynamics code, was used to generate data for NiPt alloy. The simulation used the interatomic potential for NiPt binary system MEAM_LAMMPS_KimSeolJi_2017_PtNi__MO_020840179467_001 [3] from the OpenKIM library (Open Knowledgebase of Interatomic Models) [2]. This potential was developed based on the second nearest-neighbor modified embedded-atom method (2NN MEAM). The simulation process begins with the generation of the random NiPt structure and follows with the short minimization and replica exchange simulation. The minimization procedure adjusts atomic coordinates and performs energy minimization, which typically leads to a local potential energy minimum. The method used for the minimization was the conjugate gradient algorithm. A short replica exchange (parallel tempering) simulation involves four replicas (ensembles) of a system and follows the minimization stage. Multiple snapshots of the configuration were collected during the minimization and replica exchange stages. NiPt alloy is interesting due to its magnetic and charge transfer properties [4]. The data is provided in three compressed zipped folders: atoms256.zip, atoms864.zip, atoms2048.zip Each zipped folder contains the data that describes crystals of size 256 atoms, 864 atoms, and 2,048 atoms respectively. Each one of the three zipped folders contains the data structured in the following way: -Ni_ground_state.cfg --> atomic configuration for the pure nickel -Pt_ground_state.cfg --> atomic configuration for the pure platinum -Pt#_filtered --> folders containing atomic configurations for #at% concentration of platinum. The folder contains 100 atomic configurations, each saved in a subfolder. Each subfolder named config* is associated with a specific atomic configuration. Each of these subfolders contains files with .cfg format, corresponding to outputs for each atomic configuration The total number of atomic configurations contained in atoms256.zip is 65,046. The total number of atomic configurations contained in atoms864.zip is 63,936. The total number of atomic configurations contained in atoms2048.zip is 61,997. The total number of atomic configurations spanned by the entire dataset is 190,979. References [1] https://www.lammps.org/ [2] https://openkim.org/ [3] https://openkim.org/id/MEAM_LAMMPS_KimSeolJi_2017_PtNi__MO_020840179467_001 [4] El-Gendy, Ahmed A. and Hampel, Silke and Büccchner, Bernd and Klingeler, Rüdiger, Tuneable magnetic properties of carbon-shielded NiPt-nanoalloys, RSC Adv., volume 6, issue 57, pages 52427-52433, 2016, The Royal Society of Chemistry, doi:10.1039/C6RA05910D

36 MATERIALS SCIENCE↗
DOE OSTI2023

ORNL_AISD_NiPt_108atoms

This dataset describes the nickel-platinum (NiPt) solid solution binary alloy, where the two constituent elements nickel (Ni) and platinum (Pt) are randomly placed on the face centered cubic (FCC) crystal structure, with the lattice constant of 3.840 angstroms. The dataset comprises data for crystal structures with 108 atoms with 1,900 configurations. The data set was generated for concentrations ranging from 0at% of Pt to 100at% of Pt in the NiPt binary system, with increasing the concentration of Pt in the system every 5at%. For each one of the chemical compositions, 100 random configurations were generated, each with a different random seed. Each of the output files contains the mass, type, atomic coordinates, energy per atom, and forces in x, y, and z directions respectively. For each atomic configuration, the output was collected every 150 steps during the minimization stage and every 1000 steps during the replica exchange stage. Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) [1], which is a molecular dynamics code, was used to generate data for NiPt alloy. The simulation used the interatomic potential for NiPt binary system 'MEAM_LAMMPS_KimSeolJi_2017_PtNi__MO_020840179467_001' [3] from the OpenKIM library (Open Knowledgebase of Interatomic Models) [2]. This potential was developed based on the second nearest-neighbor modified embedded-atom method (2NN MEAM). The simulation process begins with the generation of the random NiPt structure and follows with the short minimization and replica exchange simulation. The minimization procedure adjusts atomic coordinates and performs energy minimization, which typically leads to a local potential energy minimum. The method used for the minimization was the conjugate gradient algorithm. A short replica exchange (parallel tempering) simulation involves four replicas (ensembles) of a system and follows the minimization stage. Multiple snapshots of the configuration were collected during the minimization and replica exchange stages. NiPt alloy is interesting due to its magnetic and charge transfer properties [4]. The data is provided in a compressed zipped folders atoms108.zip. The zipped folder contains the data structured in the following way: - Ni_ground_state.cfg --> atomic configuration for the pure nickel - Pt_ground_state.cfg --> atomic configuration for the pure platinum - Pt#_filtered --> folders containing atomic configurations for #at% concentration of platinum. The folder contains 100 atomic configurations, each saved in a subfolder - Each subfolder named config* is associated with a specific atomic configuration. Each of these subfolders contains files with .cfg format, corresponding to outputs for each atomic configuration The total number of atomic configurations contained in atoms108.zip is 66,132. This dataset is an extension to the dataset ORNL_AISD_NiPt [5] that has been previously released with crystal structures of 256 atoms, 864 atoms, and 2,048 atoms, with the same methodology for data collection. References [1] https://www.lammps.org/ [2] https://openkim.org/ [3] https://openkim.org/id/MEAM_LAMMPS_KimSeolJi_2017_PtNi__MO_020840179467_001 [4] El-Gendy, Ahmed A. and Hampel, Silke and Büchner, Bernd and Klingeler, Rüdiger, Tuneable magnetic properties of carbon-shielded NiPt-nanoalloys, RSC Adv., volume 6, issue 57, pages 52427-52433, 2016, The Royal Society of Chemistry, doi:10.1039/C6RA05910D [5] M. Karabin, M. Lupo Pasini, and M. Eisenbach. ORNL_AISD_NiPt. United States: N. p., 2023. Web. doi:10.13139/OLCF/1958172.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nationwide HVAC Energy-Saving Potential Quantification for Office Buildings with Occupant-Centric Controls in Various Climates

The occupant-centric control (OCC) is receiving an increasing attention due to its ability to reduce building heating ventilation and air-conditioning (HVAC) system energy consumptions while not affecting the occupant thermal comfort. This paper aims to investigate and quantify the nationwide energy-saving potential of implementing the occupant-centric HVAC controls in typical office buildings using a whole building simulation software EnergyPlus. First, the medium office and large office from the Department of Energy (DOE) Commercial Prototype Building Models (CBPM) were enhanced to have detailed layouts and dynamic occupancy schedules. Then, a comprehensive simulation plan was created by incorporating the multiple zone-level and system-level occupant-centric building HVAC controls recommended by the updated ASHRAE Standard 90.1 – 2019 and ASHRAE Guideline 36 – 2018. Three control scenarios with different occupancy sensing methods were identified in this simulation plan. A nation-wide parametric analysis which includes two building types, three occupancy sensing scenarios, two building code versions, and 16 U.S. climate zones was carried out. The simulation results of the key control variables and HVAC energy consumption suggest that generally, both the occupancy presence sensor and occupant counting sensor could achieve energy savings for the office buildings in majority of the scenarios. However, compared with the occupancy presence sensor, which could support both the temperature setpoint reset and operational breathing zone airflow rate reset for the unoccupied zones, the occupant counting sensor only brings a marginal benefit. Besides, a higher HVAC energy-saving ratio could be achieved in the heating-dominated zone, since the energy reduction brought with the minimum outdoor airflow rate reset is stronger in the heating mode.

Pang, Zhihong↗
DOE OSTI2021

Controlling quantum many-body dynamics in driven Rydberg atom arrays

Dynamic stabilization of an array Large-scale systems comprising one-dimensional chains and two-dimensional arrays of excited atoms held in a programmable optical lattice are a powerful platform with which to simulate emergent phenomena. Bluvstein et al. built an array of up to 200 Rydberg atoms and subjected the system to periodic excitation. Under such driven excitation, they found that the array of atoms stabilized, freezing periodically into what looked like time crystals. Understanding and controlling the dynamic interactions in quantum many-body systems lies at the heart of contemporary condensed matter physics and the exotic phenomena that can occur. Science , this issue p. 1355

Science & Technology - Other Topics↗