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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

An Efficient Deterministic Parallel Algorithm for Adaptive Multidimensional Numerical Integration on GPUs

Recent development in Graphics Processing Units (GPUs) has enabled a new possibility for highly efficient parallel computing in science and engineering. Their massively parallel architecture makes GPUs very effective for algorithms where processing of large blocks of data can be executed in parallel. Multidimensional integration has important applications in areas like computational physics, plasma physics, computational fluid dynamics, quantum chemistry, molecular dynamics and signal processing. The computationally intensive nature of multidimensional integration requires a high-performance implementation. In this study, we present an efficient deterministic parallel algorithm for adaptive multidimensional numerical integration on GPUs. Various optimization techniques are applied to maximize the utilization of the GPU. GPU-based implementation outperforms the best known sequential methods and achieves a speed-up of up to 100. It also shows good scalability with the increase in dimensionality.

Arumugam, Kamesh↗
DOE OSTI2017

Improvement of the prediction of surface ozone concentration over conterminous U.S. by a computationally efficient second-order Rosenbrock solver in CAM4-Chem

The global chemistry-climate model (CAM4-Chem) overestimates the surface ozone concentration over the conterminous U.S. (CONUS). Reasons for this positive bias include emission, meteorology, chemical mechanism, and solver. In this study, we explore the last possibility by examining the sensitivity to the numerical methods for solving the chemistry equations. A second-order Rosenbrock (ROS-2) solver is implemented in CAM4-Chem to examine its influence on the surface ozone concentration and the computational performance of the chemistry program. Results show that under the same time step size (1800 s), statistically significant reduction of positive bias is achieved by the ROS-2 solver. The improvement is as large as 5.2 ppb in Eastern U.S. during summer season. The ROS-2 solver is shown to reduce the positive bias in Europe and Asia as well, indicating the lower surface ozone concentration over the CONUS predicted by the ROS-2 solver is not a trade-off consequence with increasing the ozone concentration at other global regions. In addition, by refining the time step size to 180 s, the first-order implicit solver does not provide statistically significant improvement of surface ozone concentration. It reveals that the better prediction from the ROS-2 solver is not only due to its accuracy but also due to its suitability for stiff chemistry equations. As an added benefit, the computation cost of the ROS-2 solver is almost half of first-order implicit solver. The improved computational efficiency of the ROS-2 solver is due to the reuse of the Jacobian matrix and lower upper (LU) factorization during its multistage calculation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Systems Life Cycle and Its Relation with the Triple Helix

This chapter examines the life cycle of complex systems in light of the dynamic interconnections among the university, industry and government sectors. Each sector is motivated in its resource allocation by principles discussed elsewhere in this book and yet remains complementary es-tablishing enduring and fundamental relationships. Industry and Government depend upon an educated workforce; universities depend upon industry to spark the R&D which is needed and to sponsor some basic research and much applied research. Government depends upon industry to address operational needs and provide finished products while universities offer government (along with industry) problem solving and problem solving environments. The life cycle of complex systems in this chapter will be examined in this context, providing historical examples. Current examples will then be examined within this multi-dimensional context with respect to the phases of program and project life cycle management from requirements definition through retirement and closeout of systems. During the explanation of these examples, the advances in research techniques to collect, analyze, and process the data will be examined.

and Law↗
DOE OSTI2023

Structural insights into opposing actions of neurosteroids on GABA A receptors

γ-Aminobutyric acid type A (GABA A ) receptors mediate fast inhibitory signaling in the brain and are targets of numerous drugs and endogenous neurosteroids. A subset of neurosteroids are GABA A receptor positive allosteric modulators; one of these, allopregnanolone, is the only drug approved specifically for treating postpartum depression. There is a consensus emerging from structural, physiological and photolabeling studies as to where positive modulators bind, but how they potentiate GABA activation remains unclear. Other neurosteroids are negative modulators of GABA A receptors, but their binding sites remain debated. Here we present structures of a synaptic GABA A receptor bound to allopregnanolone and two inhibitory sulfated neurosteroids. Allopregnanolone binds at the receptor-bilayer interface, in the consensus potentiator site. In contrast, inhibitory neurosteroids bind in the pore. MD simulations and electrophysiology support a mechanism by which allopregnanolone potentiates channel activity and suggest the dominant mechanism for sulfated neurosteroid inhibition is through pore block.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Learning continuous models for continuous physics

Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach is that ML models are typically trained on discrete data, using ML methodologies that are not aware of underlying continuity properties. This results in models that often do not capture any underlying continuous dynamics—either of the system of interest, or indeed of any related system. To address this challenge, we develop a convergence test based on numerical analysis theory. Our test verifies whether a model has learned a function that accurately approximates an underlying continuous dynamics. Models that fail this test fail to capture relevant dynamics, rendering them of limited utility for many scientific prediction tasks; while models that pass this test enable both better interpolation and better extrapolation in multiple ways. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines

A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic models and then applied to ab initio computer simulations of the photodissociation of acetaldehyde, an important chemical system in atmospheric chemistry. Our method is centered around training Support Vector Machines (SVMs) to identify optimal separatrices that delineate the regions of phase space that lead to different chemical reaction products. In contrast to the more common “black box” type machine learning methodologies for analyzing chemical simulation data, this SVM-based methodology allows for mechanistic insight to be gleaned from further analysis of the positioning of the phase space points used to train the SVM with respect to the separatrices. For example, a pair of phase space points that are in close proximity to each other but on opposite sides of a separatrix may be situated on opposite sides of a transition state, while phase space points occurring early in a simulation that are distant from a separatrix are likely to belong to trajectories that are highly biased toward the product state associated with the basin of attraction to which they belong. In addition to inferring mechanistic details about multiple-pathway chemical reactions, our method can also be used to increase reactive trajectory sampling efficiency in molecular simulations via rejection sampling, with trajectories leading to undesired product states being identified and terminated early in the simulation rather than being carried to completion.

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

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗
DOE OSTI2021

A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys

Abstract The class of 9–12% Cr ferritic-martensitic alloys (FMA) and austenitic stainless steels have received considerable attention due to their numerous applications in high temperature power generation industries. To design high strength steels with prolonged service life requires a thorough understanding of the long-term properties, e.g., creep rupture strength, rupture life, etc., as a function of the chemical composition and processing parameters that govern the microstructural characteristics. In this article, the creep rupture strength of both 9–12% Cr FMA and austenitic stainless steel has been parameterized using curated experimental datasets with a gradient boosting machine. The trained model has been cross validated against unseen test data and achieved high predictive performance in terms of correlation coefficient ( $$R^{2} > 0.98 $$ R 2 > 0.98 for 9–12% Cr FMA and $$R^{2} > 0.95 $$ R 2 > 0.95 for austenitic stainless steel) thus bypassing the need for additional comprehensive tensile test campaigns or physical theoretical calculations. Furthermore, the feature importance has been computed using the Shapley value analysis to understand the complex interplay of different features.

36 MATERIALS SCIENCE↗
DOE OSTI2022

BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects. BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.

Array↗
DOE OSTI2017

Computationally Driven Two-Dimensional Materials Design: What Is Next?

Two-dimensional (2D) materials offer many key advantages to innovative applications, such as spintronics and quantum information processing. Theoretical computations have accelerated 2D materials design. In this issue of ACS Nano, Kumar et al. report that ferromagnetism can be achieved in functionalized nitride MXene based on first-principles calculations. Their computational results shed light on a potentially vast group of materials for the realization of 2D magnets. In this Perspective, we briefly summarize the promising properties of 2D materials and the role theory has played in predicting these properties. Additionally, we discuss challenges and opportunities to boost the power of computation for the prediction of the 'structure-property-process (synthesizability)' relationship of 2D materials.

2D materials↗
DOE OSTI2020

Hybrid Modeling in the Era of Smart Manufacturing

Smart manufacturing (SM) is a new paradigm that allows manufacturing to enter its fourth revolution by exploiting state-of-the art sensing, communication and computation as the Industrial Internet of Things (IioT). Via the use of high-performance computing and advanced modeling, SM aims to improve the flexibility and adaptability of manufacturing. This paper addresses this trend by reviewing the combined use of data-driven and knowledge-enabled hybrid models (HM), and discusses how such techniques seamlessly fit in the SM platform. Furthermore, a discussion of the new paradigms of HM enabled by the SM platform is given, highlighting their importance in future large-scale applications of the SM platform.

42 ENGINEERING↗