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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Recent developments in the PySCF program package

PySCF is a Python-based general-purpose electronic structure platform that supports first-principles simulations of molecules and solids as well as accelerates the development of new methodology and complex computational workflows. Here, we explain the design and philosophy behind PySCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PySCF as a development environment. We then summarize the capabilities of PySCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PySCF across the domains of quantum chemistry, materials science, machine learning, and quantum information science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

First-principles calculations of defects and electron–phonon interactions: Seminal contributions of Audrius Alkauskas to the understanding of recombination processes

First-principles calculations of defects and electron–phonon interactions play a critical role in the design and optimization of materials for electronic and optoelectronic devices. The late Audrius Alkauskas made seminal contributions to developing rigorous first-principles methodologies for the computation of defects and electron–phonon interactions, especially in the context of understanding the fundamental mechanisms of carrier recombination in semiconductors. Alkauskas was also a pioneer in the field of quantum defects, helping to build a first-principles understanding of the prototype nitrogen-vacancy center in diamond, as well as identifying novel defects. Here, we describe the important contributions made by Alkauskas and his collaborators and outline fruitful research directions that Alkauskas would have been keen to pursue. Audrius Alkauskas’ scientific achievements and insights highlighted in this article will inspire and guide future developments and advances in the field.

36 MATERIALS SCIENCE↗
DOE OSTI2024

HELIUM LEAK TEST MODELING OF A SPENT NUCLEAR FUEL CANISTER

The U.S. Department of Energy (DOE) is considering the development of one or more federal consolidated interim storage facilities (CISFs) to be used to store commercial spent nuclear fuel (SNF) at locations in the U.S. One of the first technical challenges of a CISF is performing an inspection of SNF canisters upon their receipt to confirm they can be placed into the CISF’s licensed storage configuration. The canister receipt inspection is critical to CISF site operations. The test is conceived as being a helium (He) leak check, intended to confirm that the confinement boundary of a SNF canister is intact. SNF canisters are filled with He when they are sealed, so detection of a He leak indicates that a through-wall flaw has occurred in the canister confinement boundary. Other measurements are planned to occur upon canister receipt in addition to the He leak check such as krypton-85 measurements, which would indicate confinement breaches of one or more fuel rods in addition to a breach of the SNF canister. However, the He leak check has been identified as one of such high importance and has such significant technical challenges that a full-scale demonstration is needed to confirm the He leak test’s viability and to assist in planning relative to its operational requirements. A modeling methodology for simulating the He detection test was developed to help inform the test plan and the design of the test vessels. To develop the modeling methodology a detailed computational fluid dynamics (CFD) benchmark model was constructed to compare against leak rate test data from a transportation package for radioactive material. This report is focused on modeling efforts to simulate the benchmark leak test.

Suffield, Sarah R.↗
DOE OSTI2014

SU-E-T-517: Analytic Formalism to Compute in Real Time Dose Distributions Delivered by HDR Units

Purpose: Develop an analytical algorithm to compute the dose delivered by Ir-192 dwell positions with high accuracy using the 3-dimensional (3D) dose distribution of an HDR source. Using our analytical function, the dose delivered by an HDR unit as treatment progresses can be determined using the actual delivered temporal and positional data of each individual dwell. Consequently, true delivered dose can be computed when each catheter becomes active. We hypothesize that the knowledge of such analytical formulation will allow developing HDR systems with a real time treatment evaluation tool to avoid mistreatments. Methods: In our analytic formulation, the dose is computed by using the full anisotropic function data of the TG 43 formalism with 3D ellipsoidal function. The discrepancy between the planned dose and the delivered dose is computed using an analytic perturbation method over the initial dose distribution. This methodology speeds up the computation because only changes in dose discrepancies originated by spatial and temporal deviations are computed. A dose difference map at the point of interest is obtained from these functions and this difference can be shown during treatment in real time to examine the treatment accuracy. Results: We determine the analytical solution and a perturbation function for the 3 translational 3 rotational, and 1D temporal errors in source distributions. The analytic formulation is a sequence of simple equations that can be processed in any modern computer in few seconds. Because computations are based in an analytical solution, small deviations of the dose when sub-millimeter positional changes occur can be detected. Conclusions: We formulated an analytical method to compute 4D dose distributions and dose differences based on an analytical solution and perturbations to the original dose. This method is highly accurate and can be.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors

Hierarchical models with gamma hyperpriors provide a flexible, sparse-promoting framework to bridge L 1 and L 2 regularizations in Bayesian formulations to inverse problems. Despite the Bayesian motivation for these models, existing methodologies are limited to maximum a posteriori estimation. The potential to perform uncertainty quantification has not yet been realized. This paper introduces a variational iterative alternating scheme for hierarchical inverse problems with gamma hyperpriors. The proposed variational inference approach yields accurate reconstruction, provides meaningful uncertainty quantification, and is easy to implement. In addition, it lends itself naturally to conduct model selection for the choice of hyperparameters. Here, we illustrate the performance of our methodology in several computed examples, including a deconvolution problem and sparse identification of dynamical systems from time series data.

Bayesian shrinkage↗
DOE OSTI2016

Economic Analysis of Complex Nuclear Fuel Cycles with NE-COST

The purpose of this work is to present a new methodology, and associated computational tools, developed within the U.S. Department of Energy (U.S. DOE) Fuel Cycle Option Campaign to quantify the economic performance of complex nuclear fuel cycles. The levelized electricity cost at the busbar is generally chosen to quantify and compare the economic performance of different baseload generating technologies, including of nuclear: it is the cost of electricity which renders the risk-adjusted discounted net present value of the investment cash flow equal to zero. The work presented here is focused on the calculation of the levelized cost of electricity of fuel cycles at mass balance equilibrium, which is termed LCAE (Levelized Cost of Electricity at Equilibrium). To alleviate the computational issues associated with the calculation of the LCAE for complex fuel cycles, a novel approach has been developed, which has been called the “island approach” because of its logical structure: a generic complex fuel cycle is subdivided into subsets of fuel cycle facilities, called islands, each containing one and only one type of reactor or blanket and an arbitrary number of fuel cycle facilities. A nuclear economic software tool, NE-COST, written in the commercial programming software MATLAB®, has been developed to calculate the LCAE of complex fuel cycles with the “island” computational approach. NE-COST has also been developed with the capability to handle uncertainty: the input parameters (both unit costs and fuel cycle characteristics) can have uncertainty distributions associated with them, and the output can be computed in terms of probability density functions of the LCAE. In this paper NE-COST will be used to quantify, as examples, the economic performance of (1) current Light Water Reactors (LWR) once-through systems; (2) continuous plutonium recycling in Fast Reactors (FR) with driver and blanket; (3) Recycling of plutonium bred in FR into LWR. For each fuel cycle, the contributions to the total LCAE of the main cost components will be identified.

Fuel Cycles↗
DOE OSTI2025

Machine learning approaches for intentional materials engineering

In this article, the development of nanoporous metals and metallic composites through dealloying processes presents significant opportunities in materials engineering. However, designing multicomponent precursor alloys and establishing corresponding processing methods that yield predictable compositions and nanostructures remain a complex challenge. This article explores how machine learning (ML)-augmented computational and experimental methodologies can tackle these challenges by predicting precursor alloy compositions, final nanoporous structures, and mechanical properties, while integrating ML-enabled autonomous experimentation for material design and quantification. We highlight recent advancements in applying ML to nanostructured materials design via dealloying and discuss how techniques from other nanomaterial designs can be adapted for improved control over morphological and compositional outcomes in nanoporous and nanocomposite materials. Furthermore, we explore the role of ML in autonomous synchrotron x-ray experimentation, enabling real-time feedback between modeling and experimental setups. ML-driven approaches to microstructure characterization and mechanical property prediction are also examined, with a focus on modeling and advanced imaging techniques such as three-dimensional nanotomography. Finally, this article outlines future directions for ML-enhanced materials science, emphasizing the exploration of high-dimensional parameter spaces and the incorporation of materials kinetics into processing and property evaluation, ultimately advancing the design of nanoporous structures and materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Micropulse Lidar Cloud Mask (MPLCMASK) Value-Added Product for the Fast-Switching Polarized Micropulse Lidar Technical Report

Lidar backscattered signal is a useful tool for identifying vertical cloud structure in the atmosphere in optically thin clouds. Cloud boundaries derived from lidar signals are a necessary input for popular ARM data products, such as the Active Remote Sensing of Clouds (ARSCL) product. An operational cloud boundary algorithm (Wang and Sassen 2001) has been implemented for use with the ARM Micropulse Lidar (MPL) systems. In addition to retrieving cloud boundaries above 500 m, the value-added product (VAP) named Micropulse Lidar Cloud Mask (MPLCMASK) applies lidar-specific corrections (i.e., range-square, background, deadtime, and overlap) as described in Campbell et al. (2002) to the measured backscattered lidar. Depolarization ratio is computed using the methodology developed by Flynn et al. (2007) for polarization-capable MPL systems. The cloud boundaries output from MPLCMASK will be the primary lidar cloud mask for input to the ARSCL product and will be applied to all MPL systems, including historical data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Q-value (MeV/fission) Determination for the Advanced Test Reactor

This report documents a step-by-step procedure that can be used to calculate a Q-value for the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). The Q-value calculation here is an estimate of the total energy released per fission event in the ATR driver fuel in units of MeV/fission and is recognized as a reactor-specific Q-value for the ATR. Such a Q-value is needed INL ATR reactor physicists to normalize calculated fluxes and reaction rates to measured ATR total core and lobe powers. The calculated ATR Q-value and the procedure used to calculate this value are presented in a detailed manner so as to establish a credible basis, such that this single Q-value can be used consistently among all ATR reactor physicists. A Q-value is essentially the summation of all the kinetic energy from all radiation components released from a single fission event that ends up as heat in the reactor coolant. Estimation of a Q-value for a given reactor can theoretically be very complex because it is dependent on several variables, which include (1) the materials in the reactor core, (2) the neutron energy spectrum, and (3) the mix of actinide isotopes that are being simultaneously fissioned. Since these three variables can potentially change over time or with reactor burnup, this potentially makes the Q-value a time-dependent quantity. However, for ATR, we will invoke some simplifying assumptions, based on the unique features of the ATR core and supported by neutronic calculations, which will reduce the complexity of the problem and lead to a single time-independent Q-value. Of the three variables, the materials in the core have the greatest impact on the ATR Q-value. Since the materials in the core are most important, an array of components (materials) have been carefully selected to compose a typical ATR core configuration. It should be noted that the step-by-step procedure here for calculating a reactor-specific Q-value can be applied to any nuclear reactor. In a more general procedure, if one needed to include time-dependent spectral and actinide composition changes as a function of burnup for a specific reactor, this procedure can still be used, but at discrete time steps throughout the burnup. The result will be a set of discrete Q-values as a function of burnup. However, here we will apply the procedure to the ATR, as an example, and obtain a single Q-value that is valid for the ATR reactor as a function of burnup. In the evaluated nuclear data file (ENDF), Q-values for specific actinides are known for a reasonably high degree of accuracy, especially the major uranium and plutonium isotopes. The isotopic Q-values are composed of the kinetic energy of the fission fragments and the energies of the various radiation components emitted from the fission event. One complication is the fact that some of these energy components are dependent on the incident neutron energy (EINC) that induces the fission event (neutron energy spectrum dependence). For ATR, we will estimate an average neutron energy based on the neutron energy flux weighted by the fission cross sections and use this average neutron energy value in the ENDF formulas. This neutron energy dependence is one of two dependencies that make the final ATR Q-value a reactor-specific value. The second dependency, and the most important variable for ATR, involves the materials in the core and the de-excitation radiations produced from neutron radiative capture radiation; radiation energy that becomes heat in the reactor coolant. This important dependency is discussed in detail below. The third dependency, or the mix of fissioning actinides as a function of ATR burnup, is ignored because the bulk of the fissions over a cycle are always due to U-235 in the high-enriched ATR core. In a fission event, the fission energy is partitioned between the fission fragments and the radiation components that include neutrons, gamma rays, and beta particles. The main variable that makes the calculated Q-value reactor-specific is the amount of radiative capture energy produced in the core due to prompt and delayed neutron capture in the reactor core materials and the subsequent release of nuclear binding energy in the form of de-excitation gamma radiation energy in the reactor core. This energy component, neutron radiative capture energy, is not in the ENDF, because it depends on the reactor and its core materials. The radiative capture gamma energy release can be significant, or approximately 3–12 MeV/fission depending on the materials in the reactor core (fuel materials, cladding, structural components, coolant, targets, reflector, etc.). For the ATR, the energy released from radiative capture gamma radiation is calculated to be near the middle of the 3–12 MeV range. This makes the radiative capture radiation component an important and significant contribution to the total ATR Q-value. The ATR has unique reactor features that will allow us to simplify the complex procedure for determining an ATR reactor-specific Q-value. For example, the ATR is a thermal reactor and contains high-enriched fuel (93.15 wt% U-235). Consequently, the bulk of the fissions in the ATR core from beginning-of-cycle (BOC) to end-of-cycle are from U-235. Even though U-238 makes up approximately 7% of the uranium atoms in the fuel, the number of fissions relative to U-235 is insignificant, since U-238 is fertile and ATR is a thermal reactor. The same is true for U-234 and U-236 (both fertile). The ATR’s high-enriched uranium fuel also inhibits the buildup of large quantities of plutonium isotopes. So, even though there is some buildup of U-236, Np-237, and Pu-239 in the ATR driver fuel, the impact or contribution of these isotopes fissions on the ATR Q-value is minor compared to U-235. Nonetheless, we will include fission contributions from all the major uranium isotopes (U-234, U-235, U-236, and U-238) in the determination of an average isotopic Q-value that is used in the ATR Q-value determination. Although the ATR Q-value calculated here is based on BOC conditions (fresh fuel with no higher order actinide buildup), it is believed that the ATR Q-value is relatively constant with burnup or during any given ATR power cycle (14–60 days). The reason is that the vast majority of the core driver fuel fissions are always from U-235 during the cycle. Also, since we effectively assume instantaneous energy release for the fission product gammas and beta particles and delayed neutrons from a fission event, this corresponds to equilibrium conditions for all fission product concentrations in the ATR driver fuel too. The goal of this effort was to determine a specific numerical and core-average Q-value for the recoverable energy released by a single fission event (MeV/fission) for the ATR. This calculated value has the potential to help standardize and add credibility to not only the Monte Carlo tally normalization process used by many ATR core physics analysts, but also to the normalization process for all reactor physics calculation methods involving ATR analyses. Currently, the ATR core physics analysts use slightly different Q-values. Some values are known to be conservative for specific applications. However, sometimes a more accurate Q-value is needed for the calculation in which ATR test specimens require non-conservative and as-accurate-as-possible Q-values to estimate burnups, heat rates, neutron fluxes, nuclear reaction rates, etc., where corresponding measured post-irradiation examination data provides the ultimate validation for the calculated results. A detailed Q-value procedure was developed and is outlined in this document along with numerical values calculated specifically for the determination of the ATR Q-value. This report attempts to document in some detail the calculation methodology, nuclear data, computer code, computer model, code calculated results, hand calculations, assumptions, and logic that went into the determination of the ATR Q-value.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

PANTHER. Pattern ANalytics To support High-performance Exploitation and Reasoning.

Sandia has approached the analysis of big datasets with an integrated methodology that uses computer science, image processing, and human factors to exploit critical patterns and relationships in large datasets despite the variety and rapidity of information. The work is part of a three-year LDRD Grand Challenge called PANTHER (Pattern ANalytics To support High-performance Exploitation and Reasoning). To maximize data analysis capability, Sandia pursued scientific advances across three key technical domains: (1) geospatial-temporal feature extraction via image segmentation and classification; (2) geospatial-temporal analysis capabilities tailored to identify and process new signatures more efficiently; and (3) domain- relevant models of human perception and cognition informing the design of analytic systems. Our integrated results include advances in geographical information systems (GIS) in which we discover activity patterns in noisy, spatial-temporal datasets using geospatial-temporal semantic graphs. We employed computational geometry and machine learning to allow us to extract and predict spatial-temporal patterns and outliers from large aircraft and maritime trajectory datasets. We automatically extracted static and ephemeral features from real, noisy synthetic aperture radar imagery for ingestion into a geospatial-temporal semantic graph. We worked with analysts and investigated analytic workflows to (1) determine how experiential knowledge evolves and is deployed in high-demand, high-throughput visual search workflows, and (2) better understand visual search performance and attention. Through PANTHER, Sandia's fundamental rethinking of key aspects of geospatial data analysis permits the extraction of much richer information from large amounts of data. The project results enable analysts to examine mountains of historical and current data that would otherwise go untouched, while also gaining meaningful, measurable, and defensible insights into overlooked relationships and patterns. The capability is directly relevant to the nation's nonproliferation remote-sensing activities and has broad national security applications for military and intelligence- gathering organizations.

58 GEOSCIENCES↗
DOE OSTI2018

Theoretical Studies of Elementary Hydrocarbon Species and Their Reactions

The research program supported by this DOE grant carried out both methodological development and computational applications of first-principles theoretical chemistry based on quantum mechanical wavefunctions, as directed toward understanding and harnessing the fundamental chemical physics of combustion. To build and refine the world’s database of thermochemistry, spectroscopy, and chemical kinetics, predictive and definitive computational methods are needed that push the envelope of modern electronic structure theory. The application of such methods has been made to gain comprehensive knowledge of the paradigmatic reaction networks by which the n- and i-propyl, t-butyl, and n-butyl radicals are oxidized by O 2 . Numerous ROO and QOOH intermediates in these R + O 2 reaction systems have been characterized along with the interconnecting isomerization transition states and the barriers leading to fragmentation. Other combustion-related intermediates have also been studied, including methylsulfinyl radical, cyclobutylidene, and radicals derived from acetaldehyde and vinyl alcohol. Theoretical advances have been achieved and made available to the scientific community by implementation into PSI4, an open-source electronic structure computer package emphasizing automation, advanced libraries, and interoperability. We have pursued the development of universal explicitly correlated methods applicable to general electronic wavefunctions, as well as a framework that allows multideterminant reference functions to be expressed as a single determinant from quasiparticle operators. Finally, a rigorous analytical tool for correlated wavefunctions has been created to elucidate dispersion interactions, which play essential roles in many areas of chemistry, but whose effects are often masked and enigmatic. Our research decomposes and analyzes the coupled-cluster electron correlation energy in molecular systems as a function of interelectronic distance. Concepts are emerging that can be used to explain the influence of dispersion on the thermochemistry of large hydrocarbons, including fuels important to combustion technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Space-Time Reduced-Order Modeling for Uncertainty Quantification

This work focuses on the space-time reduced-order modeling (ROM) method for solving large-scale uncertainty quantification (UQ) problems with multiple random coefficients. In contrast with the traditional space ROM approach, which performs dimension reduction in the spatial dimension, the space-time ROM approach performs dimension reduction on both the spatial and temporal domains, and thus enables accurate approximate solutions at a low cost. We incorporate the space-time ROM strategy with various classical stochastic UQ propagation methods such as stochastic Galerkin and Monte Carlo. Numerical results demonstrate that our methodology has significant computational advantages compared to state-of-the-art ROM approaches. By testing the approximation errors, we show that there is no obvious loss of simulation accuracy for space-time ROM given its high computational efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Exact signed distance fields using parallel Fast Sweeping Method

Signed distance fields are often used in multiphysics simulations to track material interfaces. We present a simple methodology based on the fast sweeping method to generate the exact signed distance from triangular meshes and linear paths on Cartesian grids. The methodology propagates the closest primitive to the boundary to the rest of the domain following the characteristics. A local upwind criterion is used to decide between the new and existing closest primitive at each grid point while capturing the correct sign of the global function. The methodology has optimal computational complexity and runs efficiently in distributed-memory architectures. We include 2D and 3D test cases along with a resolution study up to 0.512 trillion zones and 1,000 computer cores. The solution strategy can also be applied to other types of meshes or collections of primitives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Integrating biological knowledge for mechanistic inference in the host-associated microbiome

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Separating physical impacts from natural variability using piggybacking technique

Abstract. In a chaotic system, like moist convection, it is difficult to separate the impact of a physical process from effects of natural variability. This is because modifying even a small element of the system physics typically leads to a different system evolution and it is difficult to tell whether the difference comes from the physical impact or it merely represents a different flow realization. This paper discusses a relatively simple and computationally efficient modelling methodology that allows separation of the two. The methodology is referred to as the piggybacking approach. The idea is to use two sets of thermodynamic variables (the temperature, water vapor, and all aerosol, cloud, and precipitation variables) in a single cloud simulation. The two sets differ in a specific element of the physics, such as aerosol properties, microphysics parameterization, large-scale forcing, environmental profiles, etc. One thermodynamic set is coupled to the dynamics and drives the simulated flow, and the other set piggybacks the flow, that is, thermodynamic variables are carried by the flow but they do not affect it. By switching the two sets (i.e. the set driving the simulation becomes the piggybacking one, and vice versa), the impact on the cloud dynamics can be evaluated. This paper provides details of the method and reviews results of its application to such problems as the postulated deep convection invigoration in polluted environments, the impact of changes in environmental profiles (e.g., due to climate change) on convective dynamics, and the role of cloud-layer heterogeneities for shallow convective cloud field evolution. Prospects for applying piggybacking technique to other areas of atmospheric simulation (e.g., weather prediction or geoengineering) are also mentioned.

Grabowski, Wojciech W.↗
DOE OSTI2025

ChemComp: Compiling and Computing with Chemical Reaction Networks

The exponential growth in computing demands driven by scientific computing, data analytics, and artificial intelligence is pushing conventional CMOS-based high-performance computing systems to their physical and energy efficiency limits. As we approach the era of post-exascale computing, disruptive approaches are necessary to overcome these barriers and achieve substantial gains in energy efficiency. Analog and hybrid digital-analog computing systems have emerged as promising alternatives, offering the potential for orders-of-magnitude improvements in efficiency. Among these, biochemical computing stands out as a novel paradigm capable of leveraging the natural efficiency of chemical reactions, which have shown promise in solving optimization problems by converging to steady states. By scaling up reaction networks or reaction vessel sizes, biochemical systems present an opportunity to meet the high-performance demands of modern computing tasks. Despite their promise, significant theoretical and practical challenges remain, particularly in formulating and mapping computational problems to chemical reaction networks (CRNs) and designing viable biochemical computing devices. This paper addresses these challenges by introducing new ideas to ChemComp, a compilation and emulation framework for chemical computation. This work describes the mechanisms through which solutions to ordinary differential equations (ODEs) that can be represented as CRN systems can be achieved. Furthermore, we explain the design principles of an ODE dialect implemented as a multi-level intermediate representation (MLIR) compiler extension that will be coupled with existing infrastructure. We demonstrate the potential of our framework through a case study emulating a simplified chemical reservoir computing device. This work establishes foundational tools and methodologies necessary to harness the computational power of chemistry, paving the way for the development of energy-efficient, high-performance computing systems tailored to contemporary and future computational needs.

Bohm Agostini, Nicolas↗
DOE OSTI2016

Visualization of the Eastern Renewable Generation Integration Study: Preprint

The Eastern Renewable Generation Integration Study (ERGIS), explores the operational impacts of the wide spread adoption of wind and solar photovoltaics (PV) resources in the U.S. Eastern Interconnection and Quebec Interconnection (collectively, EI). In order to understand some of the economic and reliability challenges of managing hundreds of gigawatts of wind and PV generation, we developed state of the art tools, data, and models for simulating power system operations using hourly unit commitment and 5-minute economic dispatch over an entire year. Using NREL's high-performance computing capabilities and new methodologies to model operations, we found that the EI, as simulated with evolutionary change in 2026, could balance the variability and uncertainty of wind and PV at a 5-minute level under a variety of conditions. A large-scale display and a combination of multiple coordinated views and small multiples were used to visually analyze the four large highly multivariate scenarios with high spatial and temporal resolutions. state of the art tools, data, and models for simulating power system operations using hourly unit commitment and 5-minute economic dispatch over an entire year. Using NRELs high-performance computing capabilities and new methodologies to model operations, we found that the EI, as simulated with evolutionary change in 2026, could balance the variability and uncertainty of wind and PV at a 5-minute level under a variety of conditions. A large-scale display and a combination of multiple coordinated views and small multiples were used to visually analyze the four large highly multivariate scenarios with high spatial and temporal resolutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗