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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

High-Accuracy Module Emulators from Physically-Constrained AI Algorithms

Focal Area(s): How do we use AI tools to integrate observations, simulated data and physical and chemical fundamentals (Focal Area 3) into model components (Focal Area 2) that have high accuracy and stability and low computational burden to improve Earth System Predictability? Science Challenge: Earth system modeling of the hydrological cycle involves compute-intensive modules representing complex chemical and physical process. Recently, AI tools that are far less compute intensive have been developed that emulate these modules, but many of these efforts are not yet sufficiently accurate or even stable. We know a lot about the physics and chemistry of earth system processes. The Science Challenge is developing AI tools that not only incorporate observations and simulated data, but also incorporate the physics and chemistry of the process, while still maintaining the compute efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

MCNP6.3 Unstructured Mesh Verification: GodivR and CANDU Models

A geometric cell of the Monte Carlo N-Particle (MCNP)1 transport code is traditionally created by using Boolean operators on defined surfaces. This constructive solid geometry (CSG) capability has been available in the MCNP code since its beginning. However, a CSG model approach is limited when it comes to constructing a representative geometry for a complex model in its ability to capture a correct model representation. Starting with the version 6.0, the MCNP code has the ability of embedding an unstructured mesh (UM) model into a CSG cell to create a hybrid geometry [1]. The MCNP UM feature provides the flexibility of defining very complex geometries because computer aided design (CAD) and mesh generation software packages can be utilized to construct UM models for MCNP simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Modeling Workloads of a Linear Electromagnetic Code for Load Balancing Matrix Assembly

This report presents our work to model the workloads of a linear electromagnetic application based on the method of moments in the frequency domain to effectively load balance the matrix assembly. This application is particularly challenging to load balance due to its lack of persistent iterative behavior, its operation under tight memory constraint (where the matrix may fill 80% of memory on each node), and the algorithmic complexity of the computational method. This report describes the first step in our work to apply an inspector-executor approach for load balancing workloads where key parameters are exposed during the inspector phase and a pre-trained model is applied to predict relative task weights for the load balancer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Wilkins: HPC in situ workflows made easy

In situ approaches can accelerate the pace of scientific discoveries by allowing scientists to perform data analysis at simulation time. Current in situ workflow systems, however, face challenges in handling the growing complexity and diverse computational requirements of scientific tasks. In this work, we present Wilkins, an in situ workflow system that is designed for ease-of-use while providing scalable and efficient execution of workflow tasks. Wilkins provides a flexible workflow description interface, employs a high-performance data transport layer based on HDF5, and supports tasks with disparate data rates by providing a flow control mechanism. Wilkins seamlessly couples scientific tasks that already use HDF5, without requiring task code modifications. We demonstrate the above features using both synthetic benchmarks and two science use cases in materials science and cosmology.

HPC↗
DOE OSTI2023

JOBRUNNER

SF-23-071 Jobrunner is a command line tool to manage and deploy computing jobs, organize complex workloads, and enforce a directory based hierarchy to enable reuse of files and bash scripts within a project. Organization details of a directory tree are encoded in Jobfiles which serve as an index of files/scripts, and indicate their purpose when deploying or setting up a job. It is a flexible tool that allows users to design 16 their own directory structure, perserve their design, and maintain 17 consistency with increase in complexity of the project.

DHRUV, AKASH VIJAYKUMAR↗
DOE OSTI2024

MSD CoP Webinar: Applied Science for Decision-Making at the Water-Energy Nexus

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: The energy transition is a game-changer across multiple sectors, requiring researchers and practitioners to re-evaluate the evolving connections between climate, water, and power systems, as well as the associated co-management of resources and decision-making. In anticipation of new technologies, policies, business models, and other solutions, applied science for decision-making at the climate-water-energy nexus is needed to support transitions with the expected climate resilience, maintained and enhanced energy security, thriving economies, and equity considerations. Through panelists' applied research and operational experience, the webinar will provide lesson-learnt in how to increase the readiness of water-energy research and become actionable in the context of energy transitions. A number of themes will be discussed, including the connection between growing computational resources and complexity of models, and the implications on the intended users, the actual actionable products, the availability of data for validation of the models, uncertainty characterization and decision-making under uncertainty, and the communication of the novelty to decision-makers and the public. Presenters : David McCollum (Oak Ridge National Laboratory; Co-Chair), and Gokul Iyer (Pacific Northwest National Laboratory; Co-Chair), Curt Jawdy (Tennessee Valley Authority), Nathalie Voisin (Pacific Northwest National Laboratory), and Andrew D. Jones (Lawrence Berkeley National Laboratory) Moderator: Pat M. Reed (MSD CoP Facilitation Team) This webinar was held on: April 30, 2024 from 1-2 PM ET

Energy↗
DOE OSTI2020

Analytic Modeling of a Deep Shielding Problem

Previous generations of scientists would make tremendous efforts to simplify non-tractable problems and generate simpler models that preserved the fundamental physics. This process involved applying assumptions and simplifications to reduce the complexity of the problem until it reached a solvable form. Each assumption and simplification was chosen and applied with the intent to preserve the essential physics of the problem, since, if the core physics of the problem were eliminated, the simplified model served no purpose. Moreover, if done correctly, solutions to the reduced model would serve as useful approximations to the original problem. In a sense, solving the simple models laid the ground-work for and provided insight into the more complex problem. Today, however, the affordability of high performance computing has essentially replaced the process for analyzing complex problems. Rather than \building up" a problem by understanding smaller, simpler models, a user generally relies on powerful computational tools to directly arrive at solutions to complex problems. As computational resources grow, users continue trying to simulate new, more complex, or more detailed problems, resulting in continual stress on both the code and computational resources. When these resources are limited, the user will have to make concessions by simplifying the problem while trying to preserve important details. In the context of MCNP, simplifications typically come as reductions in geometry, or by using variance reduction techniques. Both approaches can influence the physics of the problem, leading to potentially inaccurate or non-physical results. Errors can also be introduced as a result of faulty input into a computational tool: something as simple as transposing numbers in a tally input can result in incorrect answers.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes

In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They can transform the solving of general nonlinear PDEs into solving quadratic optimization problems with nonlinear, PDE-induced constraints. However, the complexity bottleneck lies in computing with dense kernel matrices obtained from pointwise evaluations of the covariance kernel, and its partial derivatives, a result of the PDE constraint and for which fast algorithms are scarce. The primary goal of this paper is to provide a near-linear complexity algorithm for working with such kernel matrices. We present a sparse Cholesky factorization algorithm for these matrices based on the near-sparsity of the Cholesky factor under a novel ordering of pointwise and derivative measurements. The near-sparsity is rigorously justified by directly connecting the factor to GP regression and exponential decay of basis functions in numerical homogenization. We then employ the Vecchia approximation of GPs, which is optimal in the Kullback-Leibler divergence, to compute the approximate factor. This enables us to compute ϵ-approximate inverse Cholesky factors of the kernel matrices with complexity O(N log d (N/ϵ)) in space and O(N log 2d (N/ϵ)) in time. We integrate sparse Cholesky factorizations into optimization algorithms to obtain fast solvers of the nonlinear PDE. We numerically illustrate our algorithm’s near-linear space/time complexity for a broad class of nonlinear PDEs such as the nonlinear elliptic, Burgers, and Monge-Ampère equations. In summary, we provide a fast, scalable, and accurate method for solving general PDEs with GPs and kernel methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Circuit complexity near critical points

Here, we consider the Bose–Hubbard model in two and three spatial dimensions and numerically compute the quantum circuit complexity of the ground state in the Mott insulator and superfluid phases using a mean field approximation with additional quadratic fluctuations. After mapping to a qubit system, the result is given by the complexity associated with a Bogoliubov transformation applied to the reference state taken to be the mean field ground state. In particular, the complexity has peaks at the O(2) critical points where the system can be described by a relativistic quantum field theory. Given that we use a Gaussian approximation, near criticality the numerical results agree with a free field theory calculation. To go beyond the Gaussian approximation we use general scaling arguments that imply that, as we approach the critical point t → t c , there is a non-analytic behavior in the complexity c 2 (t) of the form |c 2 (t) – c 2 (t c )| ~ |t – t c | νd , up to possible logarithmic corrections. Here d is the number of spatial dimensions and ν is the usual critical exponent for the correlation length ξ ~ |t – t c | –ν . As a check, for d = 2 this agrees with the numerical computation if we use the Gaussian critical exponent $v=\frac{1}{2}$. Finally, using AdS/CFT methods, we study higher dimensional examples and confirm this scaling argument with non-Gaussian exponent ν for strongly interacting theories that have a gravity dual.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Online Learning of Effective Turbine Wind Speed in Wind Farms

To develop better wind farm controllers that can meet more complex objectives, methods of modeling the wind turbine wakes at low computational expense are needed. Gaussian process (GP) regression offers a computationally inexpensive framework for learning complex functions from noisy measurements with very few datapoints. In this work, an online learning approach is presented to learn the rotor-averaged wind velocity at downstream wind turbines with GPs, using the available datastream of wind field measurements and wind turbine control set-points. This framework can readily be integrated into model-based controls methods because the model a) is updated online at low computational expense, b) assumes a mathematically favorable Gaussian form, and c) explicitly quantifies the stochastic nature of the wake field so that the trade-off between exploration and exploitation, and the uncertainty in the prediction, can be utilized. We show that a GP-learned model can match true values with errors within 0.5% on average, with as few as 5 training data points.

Gaussian process↗
DOE OSTI2020

Methods and algorithms for computer synthesis of holographic elements to obtain a complex impulse response of optical information processing systems based on modern spatial light modulators

The possibilities of designing optical devices for data processing and imaging based on the manipulation of coherent light beams by means of spatial light modulators (SLMs) are investigated. A review of commercially available SLMs is presented and the limitations of their complex modulation characteristics are analysed. The main problem of using present-day SLMs is the lack of the ability to modulate directly all states within a unit circle in the complex plane. In this regard, the characteristics of current methods for the synthesis of holographic elements are described that implement a given complex impulse response of the optical system and are optimal for using SLMs with purely amplitude, purely phase, and hybrid amplitude – phase modulation. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A spectroscopic, structural, and computational study of Ag–oxo interactions in Ag + /UO 2 2+ complexes

For this work, twelve novel Ag + /UO 2 2+ heterometallic complexes have been prepared and characterized via structural, spectroscopic, and computational methods to probe the effects of Ag–oxo interactions on bonding and photophysical properties of the uranyl cation. Structural characterization reveals Ag–oxo interaction distances ranging from 2.475(3) Å to 4.287(4) Å. These interactions were probed using luminescence and Raman spectroscopy which displayed little effect on the luminescence intensity and the energy of the Raman active U═O symmetric stretch peak as compared to previously reported Pb–oxo interactions. Computational efforts via density functional theory-based natural bond orbital analysis revealed that the highest stabilization energy associated with the Ag–oxo interaction had a value of only 11.03 kcal mol –1 and that all other energy values fell at 7.05 kcal mol –1 or below indicating weaker interactions relative to those previously reported for Pb 2+ /UO 2 2+ heterometallic compounds. In contrast, quantum theory of atoms in molecules analysis of bond critical point electron density values indicated higher electron density in Ag–oxo interactions as compared to Pb–oxo interactions which suggests more covalent character with the Ag + . Overall, this data indicates that Ag + has a less significant effect on UO 2 2+ bonding and photophysical properties as compared to other Pb 2+ , likely due to the high polarizability of the cation.

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

Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems

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

Kim, Seongmin↗
DOE OSTI2020

The SONATA data format for efficient description of large-scale network models

Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Fast Time-Stepping Strategy for Dynamical Systems Equipped with a Surrogate Model

Simulation of complex dynamical systems arising in many applications is computationally challenging due to their size and complexity. Model order reduction, machine learning, and other types of surrogate modeling techniques offer cheaper and simpler ways to describe the dynamics of these systems but are inexact and introduce additional approximation errors. In order to overcome the computational difficulties of the full complex models, on one hand, and the limitations of surrogate models, on the other, this work proposes a new accelerated time-stepping strategy that combines information from both. This approach is based on the multirate infinitesimal general-structure additive Runge--Kutta framework. The inexpensive surrogate model is integrated with a small time step to guide the solution trajectory, and the full model is treated with a large time step to occasionally correct for the surrogate model error and ensure convergence. Here, we provide a theoretical error analysis, and several numerical experiments, to show that this approach can be significantly more efficient than using only the full or only the surrogate model for the integration.

Surrogate models↗
DOE OSTI2024

Nonlinear elasticity with the Shifted Boundary Method

Here, we propose a new unfitted/immersed computational framework for nonlinear solid mechanics, which bypasses the complexities associated with the generation of CAD representations and subsequent body-fitted meshing. This approach allows to speed up the cycle of design and analysis in complex geometry and requires relatively simple computer graphics representations of the surface geometries to be simulated, such as the Standard Tessellation Language (STL format). Complex data structures and integration on cut elements are avoided by means of an approximate boundary representation and a modification (shifting) of the boundary conditions to maintain optimal accuracy. An extensive set of computational experiments in two and three dimensions is included.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The ECP SICM project: Managing complex memory hierarchies for exascale applications

The Exascale Computing Project (ECP)’s Simplified Interface to Complex Memories (SICM) effort focuses on developing universal interfaces for discovering, managing, and sharing data across complex memory hierarchies. These facilitate the exploitation of emerging memory technologies and support precise control over their various trade-offs such as high-bandwidth versus low-latency, persistent versus ephemeral, high-capacity versus low-capacity, and near-CPU versus near-GPU. SICM comprises three interrelated components: a low-level interface, a high-level interface, and a persistent-heap interface. The low-level SICM interface is intended for system and run-time developers as well as expert application developers who prefer full control of the memory objects used within their application. The high-level SICM interface builds upon the low-level interface, employing application-level profiling and analysis to optimize data management for complex memory hierarchies. The persistent-heap interface provides applications with a persistent memory allocator that can allocate custom C++ data structures in both block-storage and byte-addressable persistent memories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Quantum graph learning and algorithms applied in quantum computer sciences and image classification

Graph and network theory play a fundamental role in quantum computer sciences, including quantum information and computation. Random graphs and complex network theory are pivotal in predicting novel quantum phenomena, where entangled links are represented by edges. Quantum algorithms have been developed to enhance solutions for various network problems, giving rise to quantum graph computing and quantum graph learning (QGL). Here, in this review, we explore graph theory and graph learning methods as powerful tools for quantum computers to generate efficient solutions to problems beyond the reach of classical systems. We delve into the development of quantum complex network theory and its applications in quantum computation, materials discovery, and research. We also discuss quantum machine learning (QML) methodologies for effective image classification using qubits, quantum gates, and quantum circuits. Additionally, the paper addresses the challenges of QGL and algorithms, emphasizing the steps needed to develop flexible QGL solvers. This review presents a comprehensive overview of the fields of QGL and QML, highlights recent advancements, and identifies opportunities for future research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗