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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 OSTI2026

Production of muonic kaon atoms at high-energy colliders

We develop a framework for the formation of exotic muonic kaon atoms ($K\mu$) in semileptonic $D^{0}$ decays, using the effective weak Hamiltonian, a helicity-based treatment of the leptonic current, and a nonrelativistic bound-state projection. The resulting branching ratio, $\mathrm{BR}(D^{0}\!\to(K\mu )\nu_{\mu})=2.29\times10^{-10}$, is implemented in a ROOT-based code to estimate yields at RHIC, LHC, and STCF. We show quantitatively that $K\mu$ atoms—also produced through coalescence in the quark--gluon plasma (QGP)—provide a sensitive probe of low-momentum primordial muons and early-time electromagnetic radiation, offering complementary constraints in an otherwise unexplored phase space for thermal dilepton and photon emission. Newly estimated dissociation cross sections in detector material indicate that secondary‑vertex reconstruction should be experimentally feasible, allowing clean experimental identification of the atoms. Projected yields from QGP coalescence in LHC and RHIC heavy-ion collisions, and from $D^{0}$ decays in LHC high-luminosity $p+p$ collisions indicate that the first observation of $K\mu$ atoms is within reach. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Long Term Per-Component Power and Thermal Measurements of the OLCF Summit System

As we move into the exascale era, the power and energy footprints of high-performance computing (HPC) systems have grown significantly larger. Due to the harsh power and thermal conditions the system, components are exposed to extreme operating conditions. Operation of such modern HPC systems requires deep insights into long term system behavior to maintain its efficiency as well as its longevity. To help the HPC community to gain such insights, we provide a dataset that records the long-term power and thermal behavior of the 200PF pre-exascale supercomputer at the Oak Ridge Leadership Computing Facility (OLCF), Summit. This system is an IBM AC922 based system that has 9,252 IBM Power9 CPUs and 27,756 Nvidia V100 GPUs and can consume up to 13MW power at peak. Heat removal is performed using medium temperature direct liquid cooling and rear-door heat exchanger based secondary cooling loop. Originally extracted from a high-resolution (1Hz) per-component (GPUs, CPUs) measurements from the system, we primarily provide a dataset that has 10-second and 1-minute mean power and thermal measurements selected from five month-long segments over the course of 2020 (January and August), 2021 (February and August), and 2022 (January). For convenience, we also provide various sub datasets randomly sampled from the time and space (hosts) of the cluster. Further details and example code for analysis can be found in the following GitHub repository: https://github.com/at-aaims/summit_power_and_thermal_data

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Data Locality Enhancement of Dynamic Simulations for Exascale Computing (Final Report)

The development of modern processors exhibits two trends that complicate the optimizations of modern software. The first is the increasing sensitivity of processors' throughput to irregularities in computation. With more processors produced through a massive integration of simple cores, future systems will increasingly favor regular data-level parallel computations, but deviate from the needs of applications with complex patterns. Some evidences are already shown on Graphic Processing Units (GPU): Irregular data accesses (e.g., indirect references A[D[i]]) and conditional branches are limiting many GPU applications' performance at a level an order of magnitude lower than the peak of GPU. The second hardware trend is the growing gap between memory bandwidth and the aggregate speed—that is, the sum of all cores' computing power—of a Chip Multiprocessor (CMP). Despite the capped growth of the peak CPU speed, the aggregate speed of a CMP keeps increasing as more cores get into a single chip. It is expected that by 2018, node concurrency in an exascale system will increase by hundreds of times, whereas, memory bandwidth will expand by only 10 to 20 times. Consequently, data movement and storage is expected to consume more than 70% of the total system power. Bridging this gap is difficult; the complexities of modern CMP memory hierarchy make it even harder: Data cache becomes shared among computing units, and the sharing is often non-uniform—whether two computing units share a cache depends on their proximity and the level of the cache. On the recent IBM Power7 architecture, for instance, four hardware contexts (or SMT threads) in a core share the entire memory hierarchy, all cores in one chip share an on-chip L3 cache, and cores across chips share L3 and main memory through off-chip connections. These two trends complicate the translation of computing power into performance, especially for a program with either intensive data accesses or complex patterns in data accesses or control flow paths. Unfortunately, both attributes present and will persist in a class of important applications. For instance, many scientific simulations deal with a large volume of data. And meanwhile, as most real-world processes are non-uniform and evolving (e.g., the evolution of a galaxy or the process of a drug injection), both the computations and data accesses of these programs tend to be irregular and dynamically changing. Currently, the lack of support to these applications on modern CMP severely limits their performance. On GPU, as our recent study shows and other studies echo, performance enhancement of a factor of integers is possible when memory accesses or control flows are streamlined for a set of GPU applications. On multicore CPU, our studies show that traditional locality enhancement, for being oblivious to the new features of multicore memory hierarchy, may even cause large slowdown to data-intensive dynamic applications. The severity of the issues is expected to worsen as the two hardware trends continue. Some recent studies try to match software with the trends, but in a limited scope or manner. For irregularities on GPU, most studies focus on irregularities analyzable through static analysis (e.g., data accesses in regular loops). Dynamic irregularities are harder to address because the needed analysis and transformations typically have to happen at run time. Some other research resorts to hardware extensions, an actual adoption of which is unclear for the entailed space cost and complexity. For data locality, recent years have seen some exploitations of the new memory hierarchy on multicore for performance, but most of them are on process or thread scheduling, rather than program transformations. Our recent study reveals that program-level transformations may magnify the scheduling benefits by a factor of seven, concluding that program-level transformation should play a central role for data locality enhancement on modern CMP. But research in this direction has been sparse, and most have focused on data layout or cache performance modeling, rather than program transformations to match with the new memory hierarchy features. Overall, it is still an open question how to bridge the gap between dynamic computations and the two prominent properties of modern processors. The goal of this project is to develop a set of techniques and software tools to enhance the matching between memory accesses in dynamic simulations and the prominent features of modern and future CMP, alleviating the memory performance issues for petascale and exascale computing. This report summarizes the discoveries and products produced throughout this project. It includes free launch, a new software approach to overcoming the shortcomings of both methods; coherence-free multiview, an approach that allows multiple views of a single data object to co-exist on GPU memory during a GPU kernel execution; algorithmic optimizations to data analytics problems, especially those that involve lots of distance calculations. Supporting dynamic parallelism is important for GPU to benefit a broad range of applications. There are currently two fundamental ways for programs to exploit dynamic parallelism on GPU: a software-based approach with software-managed worklists, and a hardware-based approach through dynamic subkernel launches. Neither is satisfactory. The former is complicated to program and is often subject to some load imbalance; the latter suffers large runtime overhead. In this work, we propose free launch, a new software approach to overcoming the shortcomings of both methods. It allows programmers to use subkernel launches to express dynamic parallelism. It employs a novel compiler-based code transformation named subkernel launch removal to replace the subkernel launches with the reuse of parent threads. Coupled with an adaptive task assignment mechanism, the transformation reassigns the tasks in the subkernels to the parent threads with a good load balance. The technique requires no hardware extensions, immediately deployable on existing GPUs. It keeps the programming convenience of the subkernel launch-based approach while avoiding its large runtime overhead. Meanwhile, its superior load balancing makes it outperform manual worklist-based techniques by 3X on average. The work was published at the 48th Annual IEEE/ACM International Symposium on Microarchitecture (Micro'2015). A Graphic Processing Unit (GPU) system is typically equipped with many types of memory (e.g., global, constant, texture, shared, cache). Data placement determines what data are placed on which type of memory, essential for GPU memory performance. Prior optimizations of data placement always require a single view of a data object on memory, which limits the optimization effectiveness. In this work, we propose coherence-free multiview, an approach that allows multiple views of a single data object to co-exist on GPU memory during a GPU kernel execution. We demonstrate that under certain conditions, the multiple views can remain incoherent while facilitating enhanced data placement. We present a theorem and some compiler support to ensure the soundness of the usage of coherence-free multiview. We further develop reference-discerning data placement, a new way to enhance data placements on GPU. It enables more flexible data placements by using coherence-free multiview to leverage the slack in coherence requirement of some GPU programs. Experiments on three types of GPU systems show that, with less than 200KB space cost, the new data placement technique can provide a 1.6X average (up to 4.27X) speedup. In addition, we have examined algorithmic optimizations to data analytics problems, especially those that involve lots of distance calculations. Computing distances among data points is an essential part of many important algorithms in data analytics, graph analysis, and other domains. In each of these domains, developers have spent significant manual effort optimizing algorithms, often through novel applications of the triangle equality, in order to minimize the number of distance computations in the algorithms. In this work, we observe that many algorithms across these domains can be generalized as an instance of a generic distance-related abstraction. Based on this abstraction, we derive seven principles for correctly applying the triangular inequality to optimize distance-related algorithms. Guided by the findings, we develop Triangular OPtimizer (TOP), the first software framework that is able to automatically produce optimized algorithms that either matches or outperforms manually designed algorithms for solving distance-related problems. TOP achieves up to 237x speedups and 2.5X on average. The work has been published at the 32nd International Conference on Machine Learning (ICML'15) and the 41st International Conference on Very Large Data Bases (VLDB'15).

42 ENGINEERING↗
DOE OSTI2017

Methodological approach to the sensitivity analysis of failure effects in modern digital I and C systems - 136

Modern I and C systems of nuclear power plants increasingly use digital equipment, software-based applications for I and C functions and different types and topologies of communication networks as well. The structure, operation and communication of digital safety-related I and C systems are strongly influenced by the implemented automatic fault detection and fault treatment procedures as barriers against failure propagation. Thus, these I and C systems can change their operating states automatically during the operation time, e.g. due to the detection and correction of certain failure types. Reasonably because of these dynamic properties of digital I and C systems the use of classical fault tree and event tree methods solely for the reliability analysis of modern I and C systems is less suitable. These methods basically apply static models and do not consider the temporal changes of the operating state of the I and C system or equipment appropriately. In this paper, a recently developed approach for the sensitivity analysis of digital I and C systems will be introduced that allows to investigate the dynamic behavior of such systems in cases of internal faults and to quantify the effects on the system reliability. The development and testing of the analysis methodology is carried out using models of generic I and C systems with different typical architectures of stepwise increased complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗
DOE OSTI2017

OECD/NEA EGMPEBV Activities in Multi-Physics Verification and Validation

The Expert Group on Multi-Physics Experimental Data, Benchmarks and Validation (EGMPEBV) was established in 2014 by the Nuclear Energy Agency (NEA) at the Organization for Economic Co-operation and Development (OECD) to establish processes for certification of experimental data and development of benchmark models for validation of Multi-Physics (MP) Modeling and Simulation (M and S) computational systems. The aim of the group is to provide member countries with guidelines and recommendations for validating and improving their novel multi-physics simulations, and with an access to key experimental data. To be used to their full potential, these novel tools will require a more complex suite of validation experiments due to the multiple energy, spatial and temporal scales, as well as the number of physical phenomena being simulated. They will also require accurate measurements of all terms, including coupling terms representing the feedback effects, in situations where the appropriate experimental techniques and facilities might not exist today. However, the ability to conduct validation experiments has for certain applications progressed at a slower pace than for computational methods, or in some cases it has significantly regressed (through shutdown of facilities, or through retirement of experts). In addition, as modelling capabilities are reaching deeper levels in single physics phenomena and in coupled behaviors, appropriate experimental techniques may not yet exist. Validation of multiple physics models requires a wide variety of experimental data, which emphasizes the importance of maximizing the use of historically accumulated data to avoid the significant cost of performing similar experiments today. Moreover, the pedigree and quality of this data as it pertains to modern day simulation methods may be insufficient for rigorous validation of high fidelity modeling and simulation tools. Thus, these data can often exhibit limitations including a lack of measurement accuracy or a lack of knowledge of the measurement uncertainty in the experimental techniques; an absence of measurements for some critical parameters; an obscuring of relationships between individual physics phenomena owing to the output data resolution; or simply an inability to allow for a detailed reinterpretation of the experiments based on the documented information. The organization of the EGMPEBV relies on three Task Forces, focused on 1) Experimental Data Qualification and Benchmark Evaluation; 2) Methods and Standards, and 3) Specific Applications. Task Force 1 is focused on providing better and more accurate experimental datasets, which support validation of high-fidelity multi-physics modelling and simulation (M and S) tools. At the same time it has to consider the incompleteness of past experimental data sets (in terms of data, documentation, or uncertainties) and with the limited number of available multi-physics experimental facilities. Task Force 2 is designed to develop validation methods and guidelines, and uncertainty qualification for the new family of multi-physics, multi-scale codes in the context of emerging demands such as longer fuel cycles and power uprate. Task Force 3 is focused on applications of validation experiments and has the objective to organize multi-physics M and S benchmarks involving validation experiments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Indicator-directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems

Modern high-performance and warehouse computing centers show strong interest in minimizing system power consumption while satisfying customers’ quality of service (QoS). Dynamic voltage and frequency scaling (DVFS) is effective for achieving this goal. Nevertheless, automating the process online and making it transparent to users must address three major challenges: (1) Complexity — today’s hardware components (e.g., CPUs, GPUs, memory, network, etc.) can be configured in several or dozens of frequency/voltage states for satisfying divergent system demands. Given their combination and the emergence of heterogeneity, searching the optimal configuration in the design space online can be timing consuming. (2) QoS guarantee — user-defined objectives such as power constraint and performance target must be monitored, predicted and ensured at the best effort. (3) Adaptability — various known and unknown workloads run on systems. Workloads characteristics should be quickly determined and configurations dynamically adjusted in accord with workloads and QoS. In this work, we focus on applications exhibiting an interesting feature – iterative or periodic, which is common among conventional HPC and emerging machine learning workloads. We propose an online dynamic power-performance (ODPP) management framework to dynamically adjust GPU DVFS configurations to meet performance and power objectives and constraints, without any code annotation or intrusion. Particularly, ODPP extracts the performance and power indicators for applications from their resources utilization profiles in a short episode. It further automatically constructs an accurate model that infers from the indicators how the application's performance and power vary with GPU core and memory frequencies. Aided with the model, for both seen and unseen applications, ODPP can quickly determine the most appropriate DVFS configuration for their execution. We evaluate ODPP on an NVIDIA GPU using multiple exascale computing (ECP) and deep learning applications.

Zou, Pengfei↗
DOE OSTI2025

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Nuclear Structure Studies for Schiff Moment Enhancement

One of the major open questions in modern physics is the origin of the matter-antimatter asymmetry observed in the universe. While the Standard Model contains sources of charge-parity (CP) violation, they appear insufficient to explain the observed dominance of matter over antimatter. As a result, searches for additional sources of CP violation are an important area of contemporary nuclear and particle physics. Atomic electric dipole moments (EDMs) provide sensitive probes of CP-violating interactions. In heavy nuclei, the observable atomic EDM can be enhanced through the presence of a nuclear Schiff moment, which arises from CP-violating nuclear forces and nuclear structure effects. The review paper Nuclear Schiff Moments and CP Violation by Jonathan Engel provides an overview of the theoretical framework connecting CP violation, nuclear structure, and Schiff moments. The long-term goal of this project is to contribute to the identification and characterization of nuclei that may exhibit enhanced Schiff moments. Such nuclei are often associated with strong octupole correlations or low-energy parity-doublet structures that can amplify CP-violating effects. This report summarizes progress made during the initial phase of the project, including a survey of candidate isotopes and preparation for nuclear-structure calculations using the HFODD code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Reactivity Feedback Modeling in SAM

System Analysis Module (SAM) is being developed at Argonne National Laboratory as a modern system-level modeling and simulation tool for advanced non-light water reactor safety analyses. It utilizes the object-oriented application framework MOOSE to leverage the modern software environment and advanced numerical methods. The capabilities of SAM are being extended to enable the transient modeling, analysis, and design of various advanced nuclear reactor systems. This report presents the development of Point-Kinetics modeling and reactivity feedback mechanisms in SAM. It also presents a new capability for predicting the thermal expansion in various structural components by coupling SAM with an external thermomechanics module (Tensor Mechanics) from the MOOSE framework. The analysis of the transient behavior of a nuclear reactor requires the coupled simulation of reactor kinetics and thermal-hydraulics of the reactor core, especially for those unprotected transients where the reactor scram system may not function properly. The point kinetics model has been widely used for reactor safety analysis due to its simplicity to capture the transient behavior of the reactor. Various reactivity feedback models have been developed and integrated with the Point-Kinetics module, including fuel axial expansion, core radial expansion, fuel Doppler, and coolant density reactivity. The reactivity feedback models in SAM are similar to the respective models used in SAS4A/SASSYS-1. This report first presents the brief theory of the Point-Kinetics module and reactivity feedback models. A number of verification tests have been performed where the code simulations are compared to the analytical model results. The reactivity feedback due to the thermal deformation, such as the fuel axial expansion and core radial expansion, is important for SFR transient analysis. Simplified thermal expansion models for the fuel pin and reactor constrain system (e.g. grid plate) are developed and verified in SAM. Additionally, a coupling interface is developed to couple SAM with external thermomechanical analysis modules for more accurate predictions of the thermal expansion of different components during the transients. The current coupling interface has been tested with the Tensor Mechanics module from MOOSE. These point kinetics and reactivity feedback modeling capabilities have also been demonstrated by simulating the early stage of the unprotected loss-of-flow (ULOF) accident in the Advanced Burner Test Reactor (ABTR). Both the stand-alone SAM and coupled SAM and Tensor Mechanics simulations are performed. It is confirmed that the major physics phenomena in the heat transport system of the ABTR reactor are captured by SAM, and the point kinetics model, reactivity feedback models, and the coupling schemes are working well as expected.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Cyber-Physical System Implementation for Manufacturing With Analytics in the Cloud Layer

Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Development and application of FPGA-based logic controller - 357

The control systems of nuclear power plants contain many components which are becoming obsolete at an increasing rate. Nowadays maintenance for analog-based protection systems may be difficult as analog parts become obsolete or hard to obtain. Various studies have been conducted to address this control system hardware obsolescence issue. Those control systems of nuclear power plants have been replaced with modern digital control systems such as the programmable logic controller (PLC) and the distributed control system (DCS). The central processing unit (CPU)- based platforms such as the PLC and the DCS require an operating system (OS) and application software, and thus they might result in a common-cause failure when a problem occurs in the operating system or application software. The field programmable gate array (FPGA) is highlighted as an alternative to the conventional protection or control systems. This paper presents the development of an FPGA-based logic controller (FLC) and a diverse protection system (DPS) with the FLC platform. Protection functions for the DPS were implemented in FPGAs without any CPU or OS. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2016

Development of Multi-Physics Dynamics Models for High-Frequency Large-Amplitude Structural Response Simulation

An analytic approach is demonstrated to reveal potential pyroshock-driven dynamic effects causing power losses in the Thermo-Electric (TE) module bars of the Mars Science Laboratory (MSL) Multi-Mission Radioisotope Thermoelectric Generator (MMRTG). This study utilizes high-fidelity finite element analysis with SIERRA/PRESTO codes to estimate wave propagation effects due to large-amplitude suddenly-applied pyro shock loads in the MMRTG. A high fidelity model of the TE module bar was created with approximately 30 million degrees-of-freedom (DOF). First, a quasi-static preload was applied on top of the TE module bar, then transient tri-axial acceleration inputs were simultaneously applied on the preloaded module. The applied input acceleration signals were measured during MMRTG shock qualification tests performed at the Jet Propulsion Laboratory. An explicit finite element solver in the SIERRA/PRESTO computational environment, along with a 3000 processor parallel super -computing framework at NASA AMES, was used for the simulation. The simulation results were investigated both qualitatively and quantitatively. The predicted shock wave propagation results provide detailed structural responses throughout the TE module bar, and key insights into the dynamic response (i.e., loads, displacements, accelerations) of critical internal spring/piston compression systems, TE materials, and internal component interfaces in the MMRTG TE module bar. They also provide confidence on the viability of this high-fidelity modeling scheme to accurately predict shock wave propagation patterns within complex structures. This analytic approach is envisioned for modeling shock sensitive hardware susceptible to intense shock environments positioned near shock separation devices in modern space vehicles and systems.

Shock Waves↗
DOE OSTI2023

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

artificial intelligence, machine learning↗
DOE OSTI2024

Machine Learning for Predictive Performance Analysis in Charged Particle Beam Tools

Imaging methods driven by probes, electrons, and ions have played a dominant role in modern science and engineering. Opportunities for machine vision and AI that focus on consumer problems like driving and feature recognition, are now presenting themselves for automating aspects of the scientific processes. This proposal aims to enable and drive discovery in ultra-low energy implantation by taking advantage of faster processing, flexible control and detection methods, and architecture-agnostic workflows that will result in higher efficiency and shorter scientific development cycles. Custom microscope control, collection and analysis hardware will provide a framework for conducting novel in situ experiments revealing unprecedented insight into surface dynamics at the nanoscale. Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low energy ion implants can be achieved by either lowering the column voltage, or decelerating ions using bias – while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found that is due to lattice enrichment with high stopping power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way to much lower implantation energies, while maintaining high spatial resolution.

47 OTHER INSTRUMENTATION↗
NASA NTRS2012

Practical Applications of Cosmic Ray Science: Spacecraft, Aircraft, Ground-Based Computation and Control Systems, and Human Health and Safety

Three twentieth century technological developments, 1) high altitude commercial and military aircraft; 2) manned and unmanned spacecraft; and 3) increasingly complex and sensitive solid state micro-electronics systems, have driven an ongoing evolution of basic cosmic ray science into a set of practical engineering tools needed to design, test, and verify the safety and reliability of modern complex technological systems. The effects of primary cosmic ray particles and secondary particle showers produced by nuclear reactions with the atmosphere, can determine the design and verification processes (as well as the total dollar cost) for manned and unmanned spacecraft avionics systems. Similar considerations apply to commercial and military aircraft operating at high latitudes and altitudes near the atmospheric Pfotzer maximum. Even ground based computational and controls systems can be negatively affected by secondary particle showers at the Earth s surface, especially if the net target area of the sensitive electronic system components is large. Finally, accumulation of both primary cosmic ray and secondary cosmic ray induced particle shower radiation dose is an important health and safety consideration for commercial or military air crews operating at high altitude/latitude and is also one of the most important factors presently limiting manned space flight operations beyond low-Earth orbit (LEO). In this paper we review the discovery of cosmic ray effects on the performance and reliability of microelectronic systems as well as human health and the development of the engineering and health science tools used to evaluate and mitigate cosmic ray effects in ground-based atmospheric flight, and space flight environments. Ground test methods applied to microelectronic components and systems are used in combinations with radiation transport and reaction codes to predict the performance of microelectronic systems in their operating environments. Similar radiation transport codes are used to evaluate possible human health effects of cosmic ray exposure, however, the health effects are based on worst-case analysis and extrapolation of a very limited human exposure data base combined with some limited experimental animal data. Finally, the limitations on human space operations beyond low-Earth orbit imposed by long term exposure to galactic cosmic rays are discussed.

Atwell, William↗
DOE OSTI2019

HALA: Handy Accelerated Linear Algebra

Accelerated linear algebra libraries often come with C-style of interfaces or even older Fortran77 standards, which makes it difficult to use within generic template programming environment of modern C++. For example, the BLAS and derived standards such as cuBLAS do not accept overloads and a different functions have to be called for each input type, thus it is very challenging to write a single template that handles multiple precision modes and works well with different C++ containers. HALA offers a series of templates that wrap around the BLAS/cuBLAS and other similar methods, automatically infer the relevant types and call the appropriate back-end. The templates work out-of-the-box with all C++ standard vector-like containers and can be easily extended (with template specializations) to handle user-provided containers. The HALA API is also overloaded to handle both GPU and CPU cases with a single "engine" class, thus a single high-level algorithms can utilize both CPU or GPU backends, i.e., the HALA API is unified and handles the variations between cuBLAS and BLAS. In addition to BLAS and cuBLAS, wrapper templates are provided for subset of LAPACK, cuSparse, and Cholmod methods. Work is in progress to complete as many of these standards as possible. HALA also includes a module for low-level extended register vectorization for complex number arithmetic and several simple solvers that build on the basic linear algebra capabilities. A single unified C++ API will benefit fast prototyping and easily writing code that is portable across GPU/CPU platforms as well as precision types and user containers. Note that HALA does not provide any accelerated algorithms, it simply serves as a "handy" front-end to other standards.

Stoyanov, MiroslavK.↗
NASA NTRS1996

Hypercars: The next industrial revolution

The auto industry -- one-seventh of the GNP, and the highest expression of the Iron Age -- is about to trigger the biggest transformation in industrial structure since the microchip. Ultralight cars molded from net-shape advanced composites can be several-fold lighter than present steel cars, yet safer, sportier, and more comfortable, durable, and beautiful. Modern hybrid-electric drives boost efficiency approximately 1.3-1.5x in heavy steel cars, but approximately 5-20x in ultralight, very slippery plafforms. Synergistically combined into ultralight-hybrid 'hypercars,' these elements can yield state-of-the-shelf family cars that average 150-300+ mi/gal -- twice that with state-of-the-art technologies -- yet can also be superior in all other respects, probably including cost: carbon-fiber monocoques can actually be cheaper to mass-produce that steel unibodies. Designing cars more like aircraft and less like tanks requires not only an approximately 400-500 kg curb mass and very low air and road drag, but also an aerospace philosophy of engineering integration. Mass, cost, and complexity turn out to compound with heavy hybrids but to decompound with ultralight hybrids, owing partly to radical simplification. Excellent aerodynamics, preferable including advanced techniques for passive boundary-layer control, will be the key to successful design integration. Transforming automaking is a competitive and environmental imperative, could form the nucleus of a green industrial Renaissance, and would enhance national security by, among other things, saving as much oil as OPEC now extracts. However, this transformation faces serious cultural barriers. For example, hypercars will be more like computers with wheels than like cars with chips -- they'll have an order of magnitude more code than today's cars -- but Detroit is not a software culture. Just the transition from stamped and welded steel to integrated and adhesive-joined synthetics is difficult enough. Nonetheless, hypercars are rapidly heading to market in the late 1990s, because approximately 25 current and intending automakers are eager to capture their potentially decisive competitive advantages -- including order-of-magnitude reductions in product cycle time, tooling cost, assembly effort, and parts count. Hypercars will succeed, and may well sweep the market, not because of mandates or subsidies, but because of manufacturers' quest for competitive advantage and customers' desire for better, smarter cars.

Lovins, Amory B.↗