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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 OSTI2022

Viability of S3 Object Storage for the ASC Program at Sandia

Recent efforts at Sandia such as DataSEA are creating search engines that enable analysts to query the institution’s massive archive of simulation and experiment data. The benefit of this work is that analysts will be able to retrieve all historical information about a system component that the institution has amassed over the years and make better-informed decisions in current work. As DataSEA gains momentum, it faces multiple technical challenges relating to capacity storage. From a raw capacity perspective, data producers will rapidly overwhelm the system with massive amounts of data. From an accessibility perspective, analysts will expect to be able to retrieve any portion of the bulk data, from any system on the enterprise network. Sandia’s Institutional Computing is mitigating storage problems at the enterprise level by procuring new capacity storage systems that can be accessed from anywhere on the enterprise network. These systems use the simple storage service, or S3, API for data transfers. While S3 uses objects instead of files, users can access it from their desktops or Sandia’s high-performance computing (HPC) platforms. S3 is particularly well suited for bulk storage in DataSEA, as datasets can be decomposed into object that can be referenced and retrieved individually, as needed by an analyst. In this report we describe our experiences working with S3 storage and provide information about how developers can leverage Sandia’s current systems. We present performance results from two sets of experiments. First, we measure S3 throughput when exchanging data between four different HPC platforms and two different enterprise S3 storage systems on the Sandia Restricted Network (SRN). Second, we measure the performance of S3 when communicating with a custom-built Ceph storage system that was constructed from HPC components. Overall, while S3 storage is significantly slower than traditional HPC storage, it provides significant accessibility benefits that will be valuable for archiving and exploiting historical data. There are multiple opportunities that arise from this work, including enhancing DataSEA to leverage S3 for bulk storage and adding native S3 support to Sandia’s IOSS library.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Mobiliti v1.0

Mobiliti is a software platform designed to emulate the dynamics of a regional transportation road network. It is built on open-source software that provides parallel discrete-event simulation. The software is transformative in the area of transportation network simulation because of the geospatial scale and fidelity of the network model and the computational time it takes to model a full day of travel demand. For example, it runs a simulation of the entire San Francisco Bay Area, with a network model of ~1M links and a population that completes ~19M trips in ~5 minutes. This scale of simulation has not been attempted with existing simulation models due to the complexity of the model and the computational time it would take to complete. The intent of the software is to create a digital twin capability for cities to evaluate consequences of infrastructure or policy changes on road network dynamics.

Macfarlane, Jane↗
DOE OSTI2022

Nonconvex regularization for sparse neural networks

Convex ℓ 1 regularization using an infinite dictionary of neurons has been suggested for constructing neural networks with desired approximation guarantees, but can be affected by an arbitrary amount of over-parametrization. This can lead to a loss of sparsity and result in networks with too many active neurons for the given data, in particular if the number of data samples is large. As a remedy, in this paper, a nonconvex regularization method is investigated in the context of shallow ReLU networks: We prove that in contrast to the convex approach, any resulting (locally optimal) network is finite even in the presence of infinite data (i.e., if the data distribution is known and the limiting case of infinite samples is considered). Moreover, here we show that approximation guarantees and existing bounds on the network size for finite data are maintained.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The role of stereochemistry in combustion processes

Stereochemical effects significantly influence chemical processes, yet it is not well understood if they are a leading source of uncertainty in combustion modeling. Stereochemistry influences a combustion model (i) at the earliest stage of its construction when mapping the reaction network, (ii) in the computation of individual thermochemical and rate parameters, and (iii) in the prediction of combustion observables. The present work reviews the importance of enumerating stereochemical species and reactions at each of these steps. Further, it analyzes the separate influence of several types of stereochemistry, including geometric, optical, and fleeting transition state diastereomers. Three reaction networks serve to examine which stages of low-temperature oxidation are most affected by stereochemistry, including the first and second oxidation of n-butane, the third oxidation of n-pentane, and the early stages of pyrolysis of 1- and 2-pentene. The 149 reactions in the n-butane mechanism are expanded to 183 reactions when accounting for diastereomerism. Each of these 183 reactions is parameterized with ab initio kinetics computations to determine that, for the n-butane mechanism, the median factor of diastereomeric deviation is 3.5 at 360 K for rate constants and as high as 1.6 for mechanism reactivity, in terms of ignition delay times, as opposed to a mechanism without stereochemical expansion.

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

Parallelized telecom quantum networking with an ytterbium-171 atom array

The integration of quantum computers and sensors into a quantum network enables new capabilities in quantum information science. Most networks with atom-like qubits operate at visible or near-ultraviolet wavelengths and require conversion to the telecom band for long-distance communication, which reduces efficiency and potentially introduces noise. In this article we report high-fidelity entanglement between ytterbium-171 atoms and optical photons generated directly in the telecommunication band, where fibre loss is low. The nuclear spin of the atom is entangled with a single photon in the time-bin basis, yielding a high atom-measurement-corrected atom–photon Bell state fidelity. This can be further improved by addressing photon measurement errors. By imaging the atom array onto an optical fibre array, we also implement a parallelized networking protocol that can increase the remote entanglement rate proportionately with the number of channels. We also preserve coherence on a memory qubit during operations on communication qubits. These results support the integration of atomic systems into scalable quantum networks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Automatically parallelizing batch inference on deep neural networks using Fiats and Fortran 2023 `do concurrent`

This paper introduces novel programming strategies that leverage features of the Fortran 2023 standard of the International Standards Organization (ISO) to automatically parallelize computations on deep neural networks. The paper focuses on the interplay of object-oriented, parallel, and functional programming paradigms in the Fiats deep learning library. We demonstrate how several infrequently used language features play a role in enabling efficient, parallel execution. Specifically, the ability to explicitly declare that a procedure is pure facilitates inference in the context of the language’s loop-parallelism construct `do concurrent`. Also, explicitly prohibiting the overriding of a parent type’s type-bound procedures eliminates the need for dynamic dispatch in performance-critical code. Finally, this paper uses batch inference calculations on a neural network surrogate for atmospheric aerosol dynamics to demonstrate that LLVM Flang compiler’s automatic parallelization of `do concurrent` achieves roughly the same performance and scalability as achieved by OpenMP compiler directives. We also demonstrate that double-precision inference costs 37–72% longer runtime than default-real precision with most values in the range 57-60%.

Rouson, Damian↗
DOE OSTI2021

Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters

Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.

42 ENGINEERING↗
DOE OSTI2023

Quantitative relationships between film morphology, charge carrier dynamics, and photovoltaic performance in bulk-heterojunction binary vs. ternary acceptor blends

Addressing pertinent and perplexing questions regarding why nonfullerene acceptors (NFAs) promote higher power conversion efficiencies (PCEs) than traditional fullerenes and how photoactive bulk heterojunction (BHJ) film morphology, charge photogeneration, and recombination dynamics dictate solar cell performance have stimulated many studies of polymer solar cells (PSCs), yet quantitative relationships remain limited. Better understanding in these areas offers the potential to advance materials design and device engineering, afford higher PCEs, and ultimate commercialization. Here we probe quantitative relationships between BHJ film morphology, charge carrier dynamics, and photovoltaic performance in model binary and ternary blend systems having a wide bandgap donor polymer, a fullerene, and a promising NFA. We show that optimal PC 71 BM incorporation in a PBDB-TF:ITIC-Th binary system matrix retains the original π-face-on orientation, ITIC-Th crystallinity and BHJ film crystallite dimensions, and reduces film upper surface ITIC-Th segregation. Such morphology changes together simultaneously increase hole (μ h ) and electron (μ e ) mobilities, facilitate light-activated ITIC-Th to PC 71 BM domain electron delocalization, reduce free charge carrier (FC) bimolecular recombination (BR) within PBDB-TF:ITIC-Th mixed regions, and increase FC extraction pathways viaPBDB-TF:PC 71 BM pairs. The interplay of these effects yields significantly enhanced inverted cell short-circuit current density (J SC ), fill factor (FF), and PCE. Unexpectedly, we also find that excessive PC 71 BM in the PBDB-TF:ITIC-Th binary system alters the PBDB-TF orientation to π-edge-on, increases large scale PC 71 BM-rich aggregations and BHJ upper surface PC 71 BM composition. Further, these morphology changes increase parasitic decay processes such as intersystem crossing from photoexcited PC 71 BM, compromising the J SC , FF, and PCE metrics. ITIC-Th X-ray diffraction reveals a unique sidechain-dominated molecular network with previously unknown sidechain-end group stacking, rationalizing the STEM and GIWAXS results, photophysics, and the high μ e . DFT computation reveals charge transfer networks within ITIC-Th crystallites, supporting excited-state electron delocalization from ITIC-Th to PC 71 BM. This structure–property understanding leads to a newly reported NFA blend with PCE near 17%.

14 SOLAR ENERGY↗
DOE OSTI2021

Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. As a result, a case study of several real-world distribution systems is presented to validate the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimizing power system restoration with damaged communications

Utility procedures for power system blackstart and restoration typically assume that energization decisions can be reliably communicated across the grid. In reality, the communications and control network would likely also be affected in power outages, such as those caused by extreme weather events or cyber-attacks. This paper studies the effect of damage to the power system communications and control infrastructure on restoration operations following a blackout. We model the communications infrastructure as a graph, overlaying the power grid, and imposing the requirement that every energized element in the power grid be observable from a control center. We expand on a specialized branch-and-bound algorithm from the literature to optimize the restoration process and devise an initialization heuristic and a rounding heuristic to improve solution speed. We perform numerical experiments on synthetic systems for Illinois and Texas with outages based on a solar flare or hurricane. We compare the results of our specialized branch-and-bound algorithm to the results from (i) the initialization heuristic alone, (ii) a variation of this heuristic that we use as a baseline, and (iii) the restoration optimization for the power system without communications constraints. Here, we find that damage to the communications infrastructure significantly increases the time required to re-energize the grid. Moreover, by simultaneously optimizing communications repairs and grid energization decisions, we are able to re-energize the grid significantly faster than if communications repairs and energization decisions were made independently or with partial coordination, motivating improvements to current industry practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Resilience of the slow component in timescale separated synchronized oscillators

Physiological networks are usually made of a large number of biological oscillators evolving on a multitude of different timescales. Phase oscillators are particularly useful in the modelling of the synchronization dynamics of such systems. If the coupling is strong enough compared to the heterogeneity of the internal parameters, synchronized states might emerge where phase oscillators start to behave coherently. Here, we focus on the case where synchronized oscillators are divided into a fast and a slow component so that the two subsets evolve on separated timescales. We assess the resilience of the slow component by, first, reducing the dynamics of the fast one using Mori-Zwanzig formalism. Second, we evaluate the variance of the phase deviations when the oscillators in the two components are subject to noise with possibly distinct correlation times. From the general expression for the variance, we consider specific network structures and show how the noise transmission between the fast and slow components is affected. Interestingly, we find that oscillators that are among the most robust when there is only a single timescale, might become the most vulnerable when the system undergoes a timescale separation. We also find that layered networks seem to be insensitive to such timescale separations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Cost function dependent barren plateaus in shallow parametrized quantum circuits

Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized quantum circuit V(θ) to minimize a cost function C. While VQAs may enable practical applications of noisy quantum computers, they are nevertheless heuristic methods with unproven scaling. Here, we rigorously prove two results, assuming V(θ) is an alternating layered ansatz composed of blocks forming local 2-designs. Our first result states that defining C in terms of global observables leads to exponentially vanishing gradients (i.e., barren plateaus) even when V(θ) is shallow. Hence, several VQAs in the literature must revise their proposed costs. On the other hand, our second result states that defining C with local observables leads to at worst a polynomially vanishing gradient, so long as the depth of V(θ) is Ο(logn). Our results establish a connection between locality and trainability. We illustrate these ideas with large-scale simulations, up to 100 qubits, of a quantum autoencoder implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantum networks with neutral atom processing nodes

Abstract Quantum networks providing shared entanglement over a mesh of quantum nodes will revolutionize the field of quantum information science by offering novel applications in quantum computation, enhanced precision in networks of sensors and clocks, and efficient quantum communication over large distances. Recent experimental progress with individual neutral atoms demonstrates a high potential for implementing the crucial components of such networks. We highlight latest developments and near-term prospects on how arrays of individually controlled neutral atoms are suited for both efficient remote entanglement generation and large-scale quantum information processing, thereby providing the necessary features for sharing high-fidelity and error-corrected multi-qubit entangled states between the nodes. We describe both the functionality requirements and several examples for advanced, large-scale quantum networks composed of neutral atom processing nodes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Proxy Applications for Converged Workloads: DMC LDRD Initiative

Modern scientific applications are complicated and require coordination of several components. Proxy application driven software-hardware co-design plays a vital role in driving innovation among the developments of applications, software infrastructure and hardware architecture. Proxy applications are self-contained and simplified codes that are intended to model the performance-critical computations within applications. Applications executing on modern High Performance Computing (HPC) systems are susceptible to network congestion, insufficient memory bandwidth within and across compute nodes, and inadvertent loss of performance due to bugs and unoptimized programming models. Modern numerical simulations and machine learning models play a critical role in studying physical phenomenon under myriad uncertainties. Such applications often exhibit irregular computation and memory accesses at specific regions of the application code, which can contribute to various performance bottlenecks at scale. To mitigate such issues and prepare the next generation hardware for a variety of computation and data movement contingencies, a well-known practice is to consider "proxy" applications as representative motifs for various classes of scientific applications. While there is disagreement in the HPC community on the mechanisms of construction of the proxy applications, there is a strong consensus on their positive impact in co-design. Proxy Applications for Converged Workloads (PACER) is about facilitating software-hardware co-design through proxy applications with the goal of improving the performance of converged science workflows on heterogeneous systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modeling Distributed Computing Infrastructures for HEP Applications

Predicting the performance of various infrastructure design options in complex federated infrastructures with computing sites distributed over a wide area network that support a plethora of users and workflows, such as the Worldwide LHC Computing Grid (WLCG), is not trivial. Due to the complexity and size of these infrastructures, it is not feasible to deploy experimental test-beds at large scales merely for the purpose of comparing and evaluating alternate designs. An alternative is to study the behaviours of these systems using simulation. This approach has been used successfully in the past to identify efficient and practical infrastructure designs for High Energy Physics (HEP). A prominent example is the Monarc simulation framework, which was used to study the initial structure of the WLCG. New simulation capabilities are needed to simulate large-scale heterogeneous computing systems with complex networks, data access and caching patterns. A modern tool to simulate HEP workloads that execute on distributed computing infrastructures based on the SimGrid and WRENCH simulation frameworks is outlined. Studies of its accuracy and scalability are presented using HEP as a case-study. Hypothetical adjustments to prevailing computing architectures in HEP are studied providing insights into the dynamics of a part of the WLCG and candidates for improvements.

Horzela, Maximilian↗
DOE OSTI2023

Modular chip-integrated photonic control of artificial atoms in diamond waveguides

A central goal in creating long-distance quantum networks and distributed quantum computing is the development of interconnected and individually controlled qubit nodes. Atom-like emitters in diamond have emerged as a leading system for optically networked quantum memories, motivating the development of visible-spectrum, multi-channel photonic integrated circuit (PIC) systems for scalable atom control. However, it has remained an open challenge to realize optical programmability with a qubit layer that can achieve high optical detection probability over many optical channels. Here, we address this problem by introducing a modular architecture of piezoelectrically actuated atom-control PICs (APICs) and artificial atoms embedded in diamond nanostructures designed for high-efficiency free-space collection. The high-speed four-channel APIC is based on a splitting tree mesh with triple-phase shifter Mach–Zehnder interferometers. This design simultaneously achieves optically broadband operation at visible wavelengths, high-fidelity switching (>40dB) at low voltages, submicrosecond modulation timescales (>30MHz), and minimal channel-to-channel crosstalk for repeatable optical pulse carving. Via a reconfigurable free-space interconnect, we use the APIC to address single silicon vacancy color centers in individual diamond waveguides with inverse tapered couplers, achieving efficient single photon detection probabilities (∼15%) and second-order autocorrelation measurements g (2) (0)<0.14 for all channels. The modularity of this distributed APIC–quantum memory system simplifies the quantum control problem, potentially enabling further scaling to thousands of channels.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Sensitivity Analysis of Genome-Scale Metabolic Flux Prediction

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a Transcription Factor (TF)-gene regulatory network (TRN), which is modeled via a Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. Here we have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

59 BASIC BIOLOGICAL SCIENCES↗