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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

The dynamic conformational landscape of the protein methyltransferase SETD8

Elucidating the conformational heterogeneity of proteins is essential for understanding protein function and developing exogenous ligands. With the rapid development of experimental and computational methods, it is of great interest to integrate these approaches to illuminate the conformational landscapes of target proteins. SETD8 is a protein lysine methyltransferase (PKMT), which functions in vivo via the methylation of histone and nonhistone targets. Utilizing covalent inhibitors and depleting native ligands to trap hidden conformational states, we obtained diverse X-ray structures of SETD8. These structures were used to seed distributed atomistic molecular dynamics simulations that generated a total of six milliseconds of trajectory data. Markov state models, built via an automated machine learning approach and corroborated experimentally, reveal how slow conformational motions and conformational states are relevant to catalysis. These findings provide molecular insight on enzymatic catalysis and allosteric mechanisms of a PKMT via its detailed conformational landscape.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1989

Atmospheric correction of high resolution land surface images

Algorithms to correct for atmospheric-scattering effects in high-spatial resolution land-surface images require the ability to perform rapid and accurate computations of the top-of-atmosphere diffuse radiance field for arbitrarily general surface reflectance distributions (which may be both heterogeneous and non-Lambertian) and atmospheric models. Using three-dimensional radiative transfer (3DRT) theory algorithms are being developed. The methodology used to perform the 3DRT calculations is described. It is shown how these calculations are used to perform atmospheric corrections, and the sensitivity of the retrieved surface reflectances to atmospheric structural parameters is illustrated.

Diner, D. J.↗
NASA NTRS1994

The chemistry of bromine in the stratosphere: Influence of a new rate constant for the reaction BrO + HO2

The impact of new laboratory data for the reaction BrO + HO2 yields HOBr + O2 in the depletion of global stratospheric ozone has been estimated using a one-dimensional photochemical model taking into account the heterogeneous reaction on sulphate aerosols which converts N2O5 into HNO3. Assuring an aerosol loading 2 times as large as the 'background' and a reaction probability of 0.1 for the above heterogeneous reaction, the 6 fold increase in the measured rate constant for the reaction of BrO with HO2 increases the computed depletion of global ozone produced by 20 ppt of total bromine from 2.01 percent to 2.36 percent. The use of the higher rate constant increases the HOBr mixing ratio and makes the bromine partitioning and the ozone depletion very sensitive to the branching ratio of the potential channel forming HBr in the BrO + HO2 reaction.

Pirre, Michel↗
NASA NTRS1998

Developing Information Power Grid Based Algorithms and Software

This was an exploratory study to enhance our understanding of problems involved in developing large scale applications in a heterogeneous distributed environment. It is likely that the large scale applications of the future will be built by coupling specialized computational modules together. For example, efforts now exist to couple ocean and atmospheric prediction codes to simulate a more complete climate system. These two applications differ in many respects. They have different grids, the data is in different unit systems and the algorithms for inte,-rating in time are different. In addition the code for each application is likely to have been developed on different architectures and tend to have poor performance when run on an architecture for which the code was not designed, if it runs at all. Architectural differences may also induce differences in data representation which effect precision and convergence criteria as well as data transfer issues. In order to couple such dissimilar codes some form of translation must be present. This translation should be able to handle interpolation from one grid to another as well as construction of the correct data field in the correct units from available data. Even if a code is to be developed from scratch, a modular approach will likely be followed in that standard scientific packages will be used to do the more mundane tasks such as linear algebra or Fourier transform operations. This approach allows the developers to concentrate on their science rather than becoming experts in linear algebra or signal processing. Problems associated with this development approach include difficulties associated with data extraction and translation from one module to another, module performance on different nodal architectures, and others. In addition to these data and software issues there exists operational issues such as platform stability and resource management.

Dongarra, Jack↗
NASA NTRS2001

XML-Based Visual Specification of Multidisciplinary Applications

The advancements in the Internet and Web technologies have fueled a growing interest in developing a web-based distributed computing environment. We have designed and developed Arcade, a web-based environment for designing, executing, monitoring, and controlling distributed heterogeneous applications, which is easy to use and access, portable, and provides support through all phases of the application development and execution. A major focus of the environment is the specification of heterogeneous, multidisciplinary applications. In this paper we focus on the visual and script-based specification interface of Arcade. The web/browser-based visual interface is designed to be intuitive to use and can also be used for visual monitoring during execution. The script specification is based on XML to: (1) make it portable across different frameworks, and (2) make the development of our tools easier by using the existing freely available XML parsers and editors. There is a one-to-one correspondence between the visual and script-based interfaces allowing users to go back and forth between the two. To support this we have developed translators that translate a script-based specification to a visual-based specification, and vice-versa. These translators are integrated with our tools and are transparent to users.

Al-Theneyan, Ahmed↗
DOE OSTI2012

Experimental Investigation of the Kinetics of a Ruthenium-Catalyzed Deconstruction of Lignin Model Compounds

Lignin is a major component of plant cell walls that is typically underutilized in selective conversion strategies for renewable fuels and chemicals. The mechanisms by which thermal and catalytic treatments deconstruct lignin remain elusive. In this work, we have investigated the kinetics of the catalytic deconstruction of lignin model compounds using a ruthenium catalyst reported by Bergman et al. in 2010. A series of investigations using NMR spectroscopy and GC/MS will be presented. In addition, we propose detailed mechanisms for lignin depolymerization with ruthenium catalysts. We have also used DFT to compute transition state geometries of catalytic cycle intermediates and compare the values determined computationally with those observed experimentally. Ultimately, our objective is to apply our methods to the design of new homogeneous and heterogeneous catalysts for the effective deconstruction and valorization of lignin.

09 BIOMASS FUELS↗
DOE OSTI2016

Engineering Platinum Alloy Electrocatalysts in Nanoscale for PEMFC Application

Fuel cells are expected to be a key next-generation energy source used for vehicles and homes, offering high energy conversion efficiency and minimal pollutant emissions. However, due to large overpotentials on anode and cathode, the efficiency is still much lower than theoretically predicted. During the past decades, considerable efforts have been made to investigate synergy effect of platinum alloyed with base metals. But, engineering the alloy particles in nanoscale has been a challenge. Most important challenges in developing nanostructured materials are the abilities to control size, monodispersity, microcomposition, and even morphology or self-assembly capability, so called Nanomaterials-by-Design, which requires interdisciplinary collaborations among computational modeling, chemical synthesis, nanoscale characterization as well as manufacturing processing. Electrocatalysts, particularly fuel cell catalysts, are dramatically different from heterogeneous catalysts because the surface area in micropores cannot be electrochemically controlled on the same time scale as more transport accessible surfaces. Therefore, electrocatalytic architectures need minimal microporous surface area while maximizing surfaces accessible through mesopores or macropores, and to "pin" the most active, highest performance physicochemical state of the materials even when exposed to thermodynamic forces, which would otherwise drive restructuring, crystallization, or densification of the nanoscale materials. In this presentation, results of engineering nanoscale platinum alloy particles down to 2 ~ 4 nm will be discussed. Based on nature of alloyed base metals, various synthesis technologies have been studied and developed to achieve capabilities of controlling particle size and particle microcomposition, namely, core-shell synthesis, microemulsion technique, thermal decomposition process, surface organometallic chemical method, etc. The results show that by careful engineering the particle size and microcomposition in nanoscale, it is able to achieve superior electrocatalytic activities comparing with traditional preparative methods. Examples to be discussed are high surface area carbon supported Pt, PtM binary, and PtMN ternary alloys, their synthesis processes, characterizations and electrocatalytic activities towards molecular oxygen reduction.

Alloy↗
DOE OSTI2024

Emerging Jets Search, Triton Server Deployment, and Track Quality Development: Machine Learning Applications in High Energy Physics

Machine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called “emerging jets” is performed, using graph neural networks to greatly increase sensitivity to the signal’s signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory’s computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users’ analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment’s real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle’s “track quality” in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part I: design principles and rapid down-selection

Abstract Refractory metal-based multi-principal element alloys (MPEAs) are compelling materials for high-temperature (1000–2000 K) structural applications. However, only a minuscule fraction of their vast and heterogeneous compositional design space has been explored, leaving many potentially interesting alloys undiscovered. In this two-part work, a large region of the 11-element Al-Cr-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr design space is computationally explored to identify refractory MPEAs with simultaneously high yield strength or specific yield strength and body-centered cubic (BCC) solid solution stability. In Part I , two case studies illuminate key factors and considerations in the yield strength versus phase stability tradeoff, provide guidelines for narrowing the expansive design space, and identify many candidates predicted to be stronger than refractory MPEAs reported to date, with BCC phase stability. Our findings indicate that medium entropy ternary alloys can outperform alloys with more elements and highlight the importance of exploring regions away from the equiatomic center of composition space.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Computational micromechanics model based failure criteria for chopped carbon fiber sheet molding compound composites

Chopped carbon fiber sheet molding compound has a great potential in lightweight automotive, marine, and aerospace applications. One of the most challenging tasks is to predict the failure strength of the material due to its anisotropy and heterogeneity, as well as complex stress states in real-world working conditions. In this work, a novel constitutive model of carbon fiber chip is proposed to capture the pre- and post-failure behaviors under different loading modes. On this basis, we propose a new computational micromechanics model, which is calibrated and validated by uniaxial tensile, compressive, and in-plane shear experiments. Furthermore, a set of microstructures representative volume element (RVE) models under complex loading conditions are reconstructed to understand the relationship between the microstructure characteristics and the failure envelopes. Finally, several modified versions of classical failure criteria are proposed for anisotropic materials with consideration of the fiber orientation tensor. The modified Tsai-Wu failure criterion, which shows the best accuracy among all failure criteria, is highlighted in the comparative study.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Super-Grid Modeling of the Elastic Wave Equation in Semi-Bounded Domains

Abstract We develop a super-grid modeling technique for solving the elastic wave equation in semi-bounded two- and three-dimensional spatial domains. In this method, waves are slowed down and dissipated in sponge layers near the far-field boundaries. Mathematically, this is equivalent to a coordinate mapping that transforms a very large physical domain to a significantly smaller computational domain, where the elastic wave equation is solved numerically on a regular grid. To damp out waves that become poorly resolved because of the coordinate mapping, a high order artificial dissipation operator is added in layers near the boundaries of the computational domain. We prove by energy estimates that the super-grid modeling leads to a stable numerical method with decreasing energy, which is valid for heterogeneous material properties and a free surface boundary condition on one side of the domain. Our spatial discretization is based on a fourth order accurate finite difference method, which satisfies the principle of summation by parts. We show that the discrete energy estimate holds also when a centered finite difference stencil is combined with homogeneous Dirichlet conditions at several ghost points outside of the far-field boundaries. Therefore, the coefficients in the finite difference stencils need only be boundary modified near the free surface. This allows for improved computational efficiency and significant simplifications of the implementation of the proposed method in multi-dimensional domains. Numerical experiments in three space dimensions show that the modeling error from truncating the domain can be made very small by choosing a sufficiently wide super-grid damping layer. The numerical accuracy is first evaluated against analytical solutions of Lamb’s problem, where fourth order accuracy is observed with a sixth order artificial dissipation. We then use successive grid refinements to study the numerical accuracy in the more complicated motion due to a point moment tensor source in a regularized layered material.

58 GEOSCIENCES↗
DOE OSTI2025

Fair Concurrent Training of Multiple Models in Federated Learning

Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL applications may increasingly require multiple FL tasks to be trained simultaneously, sharing clients’ computing resources, which we call Multiple-Model Federated Learning (MMFL). Current MMFL algorithms use naïve average-based client-task allocation schemes that often lead to unfair performance when FL tasks have heterogeneous difficulty levels, as the more difficult tasks may need more client participation to train effectively. Furthermore, in the MMFL setting, we face a further challenge that some clients may prefer training specific tasks to others, and may not even be willing to train other tasks, e.g., due to high computational costs, which may exacerbate unfairness in training outcomes across tasks. We address both challenges by firstly designing FedFairMMFL, a difficulty-aware algorithm that dynamically allocates clients to tasks in each training round, based on the tasks’ current performance levels. We provide guarantees on the resulting task fairness and FedFairMMFL’s convergence rate. We then propose novel auction designs that incentivizes clients to train multiple tasks, so as to fairly distribute clients’ training efforts across the tasks, and extend our convergence guarantees to this setting. Here, we finally evaluate our algorithm with multiple sets of learning tasks on real world datasets, showing that our algorithm improves fairness by improving the final model accuracy and convergence speed of the worst performing tasks, while maintaining the average accuracy across tasks.

Federated learning↗
DOE OSTI2023

Middleware for a Heterogeneous CAV Fleet

This paper introduces CAN to ROS, a model-based code generation tool used in development, testing, and deployment of a heterogeneous fleet of vehicles with robotic sensing in ROS. Code generation supports two main features: (1) self-configuration for deployment in a heterogeneous vehicle fleet, and (2) quick iteration for testing and development of reading vehicle sensors and robotic control. This tool features the ability to detect the vehicle it is in and regenerate and rebuild itself at runtime to provide the proper two-way bridge between ROS and the sensed on-board vehicle sensor network. Code generation relies on a per-model defined JSON to map a CAN database (DBC) to the desired ROS topic names and message types. The live ROS publishing of CAN messages allows for instant feedback, and the code regeneration allows for adjustments in DBC or vehicle JSON to iteratively hone in on new vehicle signals. Generated ROS nodes are written in C++ for runtime use in lightweight embedded computers. This has been tested in vehicles from three different Original Equipment Manufacturers (OEMs), and can be extended to support a wide array of vehicles. By using a unifying ROS specification, a heterogeneous set of vehicles can be unified into a fleet with abstracted model-specific details; this opens the door for developing cross-model software applications for vehicle control, connected vehicle applications, or fleet monitoring systems.

42 ENGINEERING↗
DOE OSTI2017

Modern gyrokinetic particle-in-cell simulation of fusion plasmas on top supercomputers

The Gyrokinetic Toroidal Code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5D Vlasov-Poisson equation featuring efficient utilization of modern parallel computer architectures at the petascale and beyond. Motivated by the goal of developing a modern code capable of dealing with the physics challenge of increasing problem size with sufficient resolution, new thread-level optimizations have been introduced as well as a key additional domain decomposition. GTC-P's multiple levels of parallelism, including inter-node 2D domain decomposition and particle decomposition, as well as intra-node shared memory partition and vectorization have enabled pushing the scalability of the PIC method to extreme computational scales. In this paper, we describe the methods developed to build a highly parallelized PIC code across a broad range of supercomputer designs. This particularly includes implementations on heterogeneous systems using NVIDIA GPU accelerators and Intel Xeon Phi (MIC) co-processors and performance comparisons with state-of-the-art homogeneous HPC systems such as Blue Gene/Q. New discovery science capabilities in the magnetic fusion energy application domain are enabled, including investigations of Ion-Temperature-Gradient (ITG) driven turbulence simulations with unprecedented spatial resolution and long temporal duration. Performance studies with realistic fusion experimental parameters are carried out on multiple supercomputing systems spanning a wide range of cache capacities, cache-sharing configurations, memory bandwidth, interconnects and network topologies. Furthermore, these performance comparisons using a realistic discovery-science-capable domain application code provide valuable insights on optimization techniques across one of the broadest sets of current high-end computing platforms worldwide.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Polysulfide-incompatible additive suppresses spatial reaction heterogeneity of Li-S batteries

Rational electrolyte engineering for practical pouch cells remains elusive because the correlation between the cathode/solid-electrolyte interphase layer and cell-level reaction behavior is poorly understood. Here, by combining multiscale characterization and computational modeling, we show that—counter to the conventional perception of polysulfide-incompatible additives—the spontaneous reaction of sparingly solvated polysulfides with Lewis acid additives (LAAs) can induce in situ formation of a homogeneous interphase on thick and tortuous S cathode. Multiscale synchrotron X-ray characterization consistently affirms that such interface design could effectively eliminate the notorious problems of polysulfide shuttle and lithium corrosion and, more importantly, provide an interconnected “ion transport highway” to alleviate the uneven ion transport within the tortuous S cathode. Hence, this design dramatically reduces the reaction heterogeneity of lithium-sulfur (Li-S) pouch cells under lean electrolyte conditions. Further, this work resolves controversy around the role of polysulfide-incompatible additives in high-energy Li-S pouch cells and highlights the importance of suppressing reaction heterogeneity for practical batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

COMET: A Domain-Specific Compilation of High-Performance Computational Chemistry

The computational power increases over the past decades have greatly enhanced the ability to simulate chemical reactions and understand ever more complex transformations. Tensor contractions are the fundamental computational building block of these simulations. These simulations have often been tied to one platform and restricted in generality by the interface provided to the user. The expanding prevalence of accelerators and researcher demands necessitate a more general approach which is not tied to specific hardware or requires contortion of algorithms to specific hardware platforms. In this paper we present COMET, a domain-specific programming language and compiler infrastructure for tensor contractions targeting heterogeneous accelerators. We present a system of progressive lowering through multiple layers of abstraction and optimization that achieves up to 1.98×speedup for 30 tensor contractions commonly used in computational chemistry and beyond.

Mutlu, Erdal↗