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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Portability for GPU-accelerated molecular docking applications for cloud and HPC: can portable compiler directives provide performance across all platforms?

High-throughput structure-based screening of drug-like molecules has become a common tool in biomedical research. Recently, acceleration with graphics processing units (GPUs) has provided a large performance boost for molecular docking programs. Both cloud and high-performance computing (HPC) resources have been used for large screens with molecular docking programs; while NVIDIA GPUs have dominated cloud and HPC resources, new vendors such as AMD and Intel are now entering the field, creating the problem of software portability across different GPUs. Ideally, software productivity could be maximized with portable programming models that are able to maintain high performance across architectures. While in many cases compiler directives have been used as an easy way to offload parallel regions of a CPU-based program to a GPU accelerator, they may also be an attractive programming model for providing portability across different GPU vendors, in which case the porting process may proceed in the reverse direction: from low-level, architecture-specific code to higher-level directive-based abstractions. MiniMDock is a new mini-application (miniapp) designed to capture the essential computational kernels found in molecular docking calculations, such as are used in phar-maceutical drug discovery efforts, in order to test different solutions for porting across GPU architectures. Here we extend MiniMDock to GPU offloading with OpenMP directives, and compare to performance of kernels using CUDA and HIP on NVIDIA and AMD GPUs, respectively, as well as across different compilers, exploring performance bottlenecks. We document this reverse-porting process, from highly optimized device code to a higher-level version using directives, compare code structure, and describe barriers that were overcome in this effort.

Thavappiragasam, Mathialakan↗
DOE OSTI2024

Automated Calibration of Parallel and Distributed Computing Simulators: A Case Study

Many parallel and distributed computing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or “calibrated,” based on ground-truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributed computing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable tradeoffs between simulation accuracy and simulation speed.

Mc donald, Jesse↗
DOE OSTI2020

Spontaneous and field-induced crystallographic reorientation of metal electrodeposits at battery anodes

The propensity of metal anodes of contemporary interest (e.g., Li, Al, Na, and Zn) to form non-planar, dendritic morphologies during battery charging is a fundamental barrier to achievement of full reversibility. We experimentally investigate the origins of dendritic electrodeposition of Zn, Cu, and Li in a three-electrode electrochemical cell bounded at one end by a rotating disc electrode. We find that the classical picture of ion depletion–induced growth of dendrites is valid in dilute electrolytes but is essentially irrelevant in the concentrated (≥1 M) electrolytes typically used in rechargeable batteries. Using Zn as an example, we find that ion depletion at the mass transport limit may be overcome by spontaneous reorientation of Zn crystallites from orientations parallel to the electrode surface to dominantly homeotropic orientations, which appear to facilitate contact with cations outside the depletion layer. This chemotaxis-like process causes obvious texturing and increases the porosity of metal electrodeposits.

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

Memory access system

A method and system for accessing a memory for a data processing system. The method comprises sending a read request for a plurality of locations in the memory to read the plurality of locations in parallel based on an upper bound for reading the memory. The upper bound for a number of locations is based on a group of constraints for the memory. The method receives a summed value of a plurality of memory values in the plurality of locations in the memory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems

Active learning algorithms, integrating machine learning, quantum computing and optics simulation in an iterative loop, offer a promising approach to optimizing metamaterials. However, these algorithms can face difficulties in optimizing highly complex structures due to computational limitations. High-performance computing (HPC) and quantum computing (QC) integrated systems can address these issues by enabling parallel computing. In this study, we develop an active learning algorithm working on HPC-QC integrated systems. We evaluate the performance of optimization processes within active learning (i.e., training a machine learning model, problem-solving with quantum computing, and evaluating optical properties through wave-optics simulation) for highly complex metamaterial cases. Our results showcase that utilizing multiple cores on the integrated system can significantly reduce computational time, thereby enhancing the efficiency of optimization processes. Therefore, we expect that leveraging HPC-QC integrated systems helps effectively tackle large-scale optimization challenges in general.

Kim, Seongmin↗
DOE OSTI2024

Wireless Frequency‐Multiplexed Acoustic Array‐Based Acoustofluidics

Abstract Acoustofluidics has shown great potential in enabling on‐chip technologies for driving liquid flows and manipulating particles and cells for engineering, chemical, and biomedical applications. To introduce on‐demand liquid sample processing and micro/nano‐object manipulation functions to wearable and embeddable electronics, wireless acoustofluidic chips are highly desired. This paper presents wireless acoustofluidic chips to generate acoustic waves carrying sufficient energy and achieve key acoustofluidic functions, including arranging particles and cells, generating fluid streaming, and enriching in‐droplet particles. To enable these functions, the wireless acoustofluidic chips leverage mechanisms, including inductive coupling‐based wireless power transfer (WPT), frequency multiplexing‐based control of multiple acoustic waves, and the resultant acoustic radiation and drag forces. For validation, the wirelessly generated acoustic waves are measured using laser vibrometry when different materials (e.g., bone, tissue, and hand) are inserted between the WPT transmitter and receiver. Moreover, the wireless acoustofluidic chips successfully arrange nanoparticles into different patterns, align cells into parallel pearl chains, generate streaming, and enrich in‐droplet microparticles. This research is anticipated to facilitate the development of embeddable wireless on‐chip flow generators, wearable sensors with liquid sample processing functions, and implantable devices with flow generation and acoustic stimulation abilities for engineering, veterinary, and biomedical applications.

Li, Jiali↗
DOE OSTI2023

The Need for Nb3Sn Coated Cu Cavities for Future Accelerators

Based on current efforts in the U.S. on the novel concept of parallel-feed RF accelerator structures, and in the U.S. and abroad in producing Nb3Sn films on either Cu or bronze, we rec-ommend that the Particle Physics community foster R&D in Superconducting Nb3Sn coated Cu RF Cavities instead of costly bulk Nb. The paper includes methods to process the coated cavi-ties at temperatures consistent with Cu retaining its shape. A devoted global effort in develop-ing Cu cavity structures coated with Nb3Sn would make the ILC or Higgs factories more afforda-ble and more likely to be built. Not only do parallel-feed RF structures enable both higher ac-celerating gradients and higher efficiencies, but they would be applicable to both Cu and Nb3Sn coated Cu cells. Increased effort on these two techniques would synergize expenditures to-wards progress, which will converge on the choice of technology for the RF of an ILC or any fu-ture accelerator. The current methods of Nb3Sn coatings on Cu or bronze can be geared also towards standard cavity cells. In conclusion, the use of distributed coupling structure topology within improved performance parameters together with Nb3Sn coating technology can lead to a paradigm shift for superconducting linacs, with higher gradient, higher temperature of opera-tion, and reduced overall costs for any future collider.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Cyclotron breaking: a mechanism for parallel ion cyclotron waves to heat the fast solar wind

The Parker Solar Probe mission has observed near-continuous power in parallel ion cyclotron waves (PICWs) in the young, fast solar wind. These waves are unlikely to be directly produced by the turbulent cascade and are likely born of a local instability; yet, they are observed to both cool – and heat – the plasma. We propose that these observations can be self-consistently explained as the natural consequence of PICWs propagating in the inhomogeneous solar wind after they have been driven unstable. In this work, we argue that strong proton heating by a turbulent cascade of oblique ICWs will result in PICWs being driven unstable in a process known as quasi-linear focusing. Because the power in the turbulent cascade is concentrated at scales above the turbulent transition region, PICWs will be driven unstable within a range of wavenumbers parallel to the background magnetic field, 𝑘 ∥ , that is bounded from above by 𝑘$^{∗}_{∥P}$, corresponding to the start of the transition region. As unstable PICWs propagate away from the Sun to regions of lower proton density, their 𝑘 ∥ , multiplied by the proton inertial length 𝑑 p , increases. Eventually, 𝑘$^{∗}_{∥P}$ of the PICWs becomes larger than 𝑘$^{∗}_{∥P}$⁢𝑑 p and the waves damp, heating the solar wind. We call this effect ‘cyclotron breaking’, in analogy with ocean waves breaking on the shore. We then discuss the testable predictions of the theory, including a distinct heating signature in which PICWs cool fast protons and heat slow protons at any given heliocentric distance 𝑟. Finally, we conjecture that cyclotron breaking can lead to net heating by PICWs if the power emitted as PICWs decreases sufficiently rapidly with 𝑟 that local emission of PICWs is overwhelmed by the local damping of PICWs generated closer to the Sun.

plasma heating↗
DOE OSTI2024

xesn: Echo state networks powered by Xarray and Dask

Xesn is a Python package that allows scientists to easily design Echo State Networks (ESNs) for forecasting problems. ESNs are a Recurrent Neural Network architecture introduced by Jaeger (2001) that are part of a class of techniques termed Reservoir Computing. One defining characteristic of these techniques is that all internal weights are determined by a handful of global, scalar parameters, thereby avoiding problems during backpropagation and reducing training time significantly. Because this architecture is conceptually simple, many scientists implement ESNs from scratch, leading to questions about computational performance. Xesn offers a straightforward, standard implementation of ESNs that operates efficiently on CPU and GPU hardware. The package leverages optimization tools to automate the parameter selection process, so that scientists can reduce the time finding a good architecture and focus on using ESNs for their domain application. Importantly, the package flexibly handles forecasting tasks for out-of-core, multi-dimensional datasets, eliminating the need to write parallel programming code. Xesn was initially developed to handle the problem of forecasting weather dynamics, and so it integrates naturally with Python packages that have become familiar to weather and climate scientists such as Xarray (Hoyer & Hamman, 2017). However, the software is ultimately general enough to be utilized in other domains where ESNs have been useful, such as in signal processing (Jaeger & Haas, 2004).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Scalable implementation of polynomial filtering for density functional theory calculation in PARSEC

In this work, we present an efficient implementation of polynomial filtering methods in PARSEC, a real-space pseudopotential based Kohn–Sham density functional theory solver. The implementation described here improves upon a Chebyshev-filtered subspace iteration algorithm used in the previous version of PARSEC. We present a hybrid polynomial filtering scheme that combines Chebyshev-filtered subspace iteration and a spectrum slicing method that partitions the spectrum into several spectral slices and uses bandpass-filtered subspace iteration to compute approximate eigenpairs within each interior slice simultaneously. We describe a procedure to partition a spectrum and construct polynomial filters. We also discuss a number of practical issues such as the use of appropriate data layouts for carrying out the computation on a two-dimensional process grid and how to achieve good load balance by allocating an appropriate number of process groups to each spectral slice. Numerical examples are presented to demonstrate the effectiveness of the hybrid polynomial filtering method as well as the superior parallel scalability of spectrum slicing in comparison to that of Chebyshev-filtered subspace iteration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A remarkable CHF of 345W/cm 2 is achieved in a wicked-microchannel using HFE-7100

Boiling heat transfer of dielectric fluids is a promising cooling technique for thermal management of microelectronic systems. However, the critical heat flux achievable is generally low, because of the poor thermophysical properties of these fluids. Here, to address this dilemma, we propose a new cooling concept to substantially enhance global liquid supply during phase-change process using enhanced capillary-driven force. Additionally, dedicated vapor pathways are designed among a bank of micro-pillars to facilitate vapor removal. In this work, new wicks comprised of silicon micro-pinfin arrays are explored to significantly enhance the flow boiling heat transfer performance. To examine the functionalities of this wick, experiments on HFE-7100 were carried out with mass velocities varying from 247 to 3,465 kg/m 2 s. To explore the enhancement mechanisms and to analyze the capillary-assisted flow boiling process, visualization studies were conducted. The results indicate that sustainable evaporation induced by wick microstructures and efficient liquid supply are the enhancement mechanisms compared to parallel microchannels with solid walls. It is found that the overall heat transfer coefficient is substantially increased up to 75%. Remarkably, a high critical heat flux (CHF) of approximately 345 W/cm 2 is recorded at G = 3,465 kg/m 2 s at coolant inlet temperature of ~20 °C. Equally importantly, this noticeable enhancement of CHF value is associated with drastically decreased pressure drops compared to microchannels decorated with μ-pinfin fences.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-order multirate explicit time-stepping schemes for the baroclinic-barotropic split dynamics in primitive equations

In order to treat the multiple time scales of ocean dynamics in an efficient manner, the baroclinic-barotropic splitting technique has been widely used for solving the primitive equations for ocean modeling. Based on the framework of strong stability-preserving Runge-Kutta approach, we propose two high-order multirate explicit time-stepping schemes (SSPRK2-SE and SSPRK3-SE) for the resulting split system in this paper. The proposed schemes allow for a large time step to be used for the three-dimensional baroclinic (slow) mode and a small time step for the two-dimensional barotropic (fast) mode, in which each of the two mode solves just need to satisfy their respective CFL conditions for numerical stability. Specifically, at each time step, the baroclinic velocity is first computed by advancing the baroclinic mode and fluid thickness of the system with the large time-step and the assistance of some intermediate approximations of the barotropicmode obtained by substepping with the small time step; then the barotropic velocity is corrected by using the small time step to re-advance the barotropic mode un-der an improved barotropic forcing produced by interpolation of the forcing terms from the preceding baroclinic mode solves; lastly, the fluid thickness is updated by coupling the baroclinic and barotropic velocities. Additionally, numerical inconsistencies on the discretized sea surface height caused by the mode splitting are relieved via a reconciliation process with carefully calculated flux deficits. Here, two benchmark tests from the “MPAS-Ocean” platform are carried out to numerically demonstrate the performance and parallel scalability of the proposed SSPRK-SE schemes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Degradation by Kinking in Layered Cathode Materials

Layered cathode materials are commonly used in lithium and sodium ion batteries, but they are prone to degradation under electrochemical cycling during battery operation. Here we report a new type of degradation mechanism through the electrochemically induced mechanical buckling and delamination cracking of intercalation layers in a P2 Na 0.7 -Ni 0.3 Mn 0.6 Co 0.1 O 2 (Na-NMC) cathode material. Kinks form in the delaminated layers due to severe local bending, and each kink consists of a vertical array of dislocations, resulting from an easy slip between transition metal oxide layers. In situ mechanical compression experiments directly reveal the kink formation due to strong mechanical anisotropy parallel and perpendicular to the intercalation layers in single-crystal Na-NMC. In situ electrochemical experiments indicate that kinks form during the desodiation process. Our results unveil a new mechanism of electrochemically induced mechanical degradation stemming from weak interlayer bonding in layered cathode materials. In conclusion, this work has broad implications for the mitigation of degradation associated with irreversible interlayer slip in layered cathode materials.

25 ENERGY STORAGE↗
DOE OSTI2020

Effect of magnetic field ripple on parallel electron transport during microwave plasma heating in the Proto-MPEX linear plasma device

The Prototype Material Plasma Exposure eXperiment (Proto-MPEX) is a linear plasma device located at Oak Ridge National Laboratory to develop the plasma source concept for the Materials Plasma Exposure eXperiment (MPEX). Recent experiments have demonstrated the heating of electrons in a high-density deuterium plasma (n e ~ 5 × 10 19 m -3 ) via electron Bernstein waves and source plasma production via helicon waves in this linear configuration. Moreover, experimental observations suggest that the magnetic ripple adversely affects the parallel transport of the heated electrons toward the target where material samples are to be exposed to the plasma. In order to understand the transport process during microwave application, a test-particle Monte-Carlo (MC) code has been developed that incorporates the effects of Coulomb collisions, magnetic mirror adiabatic trapping and electron cyclotron interaction via a quasilinear RF heating operator. MC calculations indicate that the absence of 2nd turning points along the trajectory of cyclotron heated electrons significantly reduces adiabatic trapping and maximizes power transport to the Target surface. Thermalization of cyclotron heating electrons is analyzed with the MC code and it is found that for conditions relevant to ProtoMPEX, a significant fraction of the absorbed heating power is coupled to the Target surface via ballistic fast electrons; however, under certain conditions, up to 60% of the absorbed RF power can be dissipated via thermalization of the fast electrons on the background plasma. Results are compared with experimental measurements. Finally, the implications of the results are discussed in the context of the upcoming MPEX device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

An Algorithmic and Software Pipeline for Very Large Scale Scientific Data Compression with Error Guarantees

Efficient data compression is becoming increasingly critical for storing scientific data because many scientific applications produce vast amounts of data. This paper presents an end-to-end algorithmic and software pipeline for data compression that guarantees both error bounds on primary data (PD) and derived data, known as Quantities of Interest (QoI).We demonstrate the effectiveness of the pipeline by compressing fusion data generated by a large-scale fusion code, XGC, which produces tens of petabytes of data in a single day. We demonstrate that the compression is conducted by setting aside computational resources known as staging nodes, and does not impact the simulation performance. For efficient parallel I/O, the pipeline uses ADIOS2, which many codes such as XGC already use for their parallel I/O. We show that our approach can compress the data by two orders of magnitude while guaranteeing high accuracy on both the PD and the QoIs. Further, the amount of resources required by compression is a few percent of the resources required by simulation while ensuring that the compression time for each stage is less than the corresponding simulation time.This pipeline consists of three main steps. The first step decomposes the data using domain decomposition into small subdomains. Each subdomain is then compressed independently to achieve a high level of parallelism. The second step uses existing techniques that guarantee error bounds on the primary data for each subdomain. The third step uses a post-processing optimization technique based on Lagrange multipliers to reduce the QoI errors for data corresponding to each subdomain. The Lagrange multipliers generated can be further quantized or truncated to increase the compression level. All of the above characteristics of our approach make it highly practical to apply on-the-fly compression while guaranteeing errors on QoIs that are critical to the scientists.

Banerjee, Tania↗
DOE OSTI2022

Scalable Deep Learning-Based Microarchitecture Simulation on GPUs

Cycle-accurate microarchitecture simulators are essential tools for designers to architect, estimate, optimize, and manufacture new processors that meet specific design expectations. However, conventional simulators based on discrete-event methods often require an exceedingly long time-to-solution for the simulation of applications and architectures at full complexity and scale. Given the excitement around wielding the machine learning (ML) hammer to tackle various architecture problems, there have been attempts to employ ML to perform architecture simulations, such as Ithemal and SimNet. However, the direct application of existing ML approaches to architecture simulation may be even slower due to overwhelming memory traffic and stringent sequential computation logic. This work proposes the first graphics processing unit (GPU)-based microarchitecture simulator that fully unleashes the potential of GPUs to accelerate state-of-the-art ML-based simulators. First, considering the application traces are loaded from central processing unit (CPU) to GPU for simulation, we introduce various designs to reduce the data movement cost between CPUs and GPUs. Second, we propose a parallel simulation paradigm that partitions the application trace into sub-traces to simulate them in parallel with rigorous error analysis and effective error correction mechanisms. Combined, this scalable GPU-based simulator outperforms by orders of magnitude the traditional CPU-based simulators and the state-of-the-art ML-based simulators, i.e., SimNet and Ithemal.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High-Efficiency and Low-Carbon Energy Storage and Power Generation System for Electric Aviation

This report summarizes the work performed by University of California San Diego (UCSD) – Honeywell Aerospace (Honeywell) team for the U.S. Department of Energy/Advanced Research Projects Agency-Energy (DOE/ARPA-E) under Phase 1 (April 2021 – October 2023) project, Cooperative Agreement DE-AR0001347 entitled “High-Efficiency and Low-Carbon Energy Storage and Power Generation System for Electric Aviation”. The main objective of this project is to develop and demonstrate an energy storage and power generation (ESPG) system operating on bio liquid natural gas (LNG) for electric aviation applications. The ESPG system concept in this project is a fuel cell, battery, and gas turbine hybrid system that incorporates an innovative solid oxide fuel cell (SOFC) technology. This SOFC technology has two main novel elements: (i) a lightweight and compact stack architecture that consists of cells and cell modules in electrical parallel and series connections (the module design) and (ii) exceptionally high performance, direct methane thin-film cells on porous substrate made by sputtering deposition process. This fuel cell has the specific power and volumetric power density suitable for electric aviation applications. Based on the current status of the SOFC technology, the Phase 1 work focused on the following activities: (i) ESPG System Modeling – to design and optimize an aircraft SOFC-based ESPG system concept that met the performance, weight and cost targets; (ii) Cell Material Development and Scaleup – to demonstrate scalability of the sputtering process for manufacture of thin-film SOFC cells of practical sizes, confirm the exceptional performance of sputtered cells, improve cell stability and durability for operation with hydrogen and methane fuel, and develop a suitable electrically conducting porous substrate to replace the current non-conducting ceramic substrate; (iii) Stack Development – to design and manufacture stack components for the stack architecture, evaluate and select a suitable sealant, and build and operate multi-cell stacks to demonstrate stack operation, and (iv) Technology to Market – to develop business models and commercialization plans, conduct various market and technology analysis and estimate SOFC and ESPG system costs.

25 ENERGY STORAGE↗
DOE OSTI2022

EJFAT: Towards Intelligent Compute Destination Load Balancing

To handle increased data flow, Jefferson Lab (JLab) is partnering with ESnet for development of an AI/ML directed compute work Load Balancer (LB) of UDP streamed data. The LB is FPGA based featuring dynamically configurable, low latency and high throughput destination address switching. The LB provides integration of edge and core computing to support JLab experimental programs, the Electron-Ion Collider, as well as data centers of the future. In the ESnet/JLab FPGA Accelerated Transport (EJFAT) initiative, the function of the LB Data Plane (DP) is to redirect data streams to selectable (but unknown to sender) destination hosts based on current worload and within that host to destination ports as a function of sub- stream id. This effects hierarchical scaling, first across compute machines for processing over a series of events and second, across ports so different data source sub-streams may be assigned to different processors for further parallelization. The LB Control Plane (CP) programs the DP using compute farm telemetry to direct and balance workloads across a compute cluster as the operating conditions require. While Proportional/Integrative/Derivative (PID) controllers are often seen in similar applications, here we investigate the feasibility of a Reinforcement Learning (RL) based schedule manager running in the CP to provide dynamic updates to the DP scheduling policy.

Lawrence, David↗