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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Petascale Application of a Coupled CPU-GPU Algorithm for Simulation and Analysis of Multiphase Flow Solutions in Porous Medium Systems, In: 2014 IEEE 28th International Parallel and Distributed Processing Symposium

Large-scale simulation can provide a wide range of information needed to develop and validate theoretical models for multiphase flow in porous medium systems. In this paper, we consider a coupled solution in which a multiphase flow simulator is coupled to an analysis approach used to extract the interfacial geometries as the flow evolves. This has been implemented using MPI to target heterogeneous nodes equipped with GPUs. The GPUs evolve the multiphase flow solution using the lattice Boltzmann method while the CPUs compute up scaled measures of the morphology and topology of the phase distributions and their rate of evolution. Our approach is demonstrated to scale to 4,096 GPUs and 65,536 CPU cores to achieve a maximum performance of 244,754 million-lattice-node updates per second (MLUPS) in double precision execution on Titan. In turn, this approach increases the size of systems that can be considered by an order of magnitude compared with previous work and enables detailed in situ tracking of averaged flow quantities at temporal resolutions that were previously impossible. Furthermore, it virtually eliminates the need for post-processing and intensive I/O and mitigates the potential loss of data associated with node failures.

Computer Science↗
DOE OSTI2026

Energy–Performance Trade-offs in Privacy-Preserving Federated Learning on SmartNIC-Enabled HPC Systems

Federated learning (FL) is increasingly deployed on accelerator-rich high-performance computing (HPC) systems, yet the system-level energy cost of privacy-aware FL remains poorly understood, particularly across heterogeneous networking and server-placement options. We present a measurement-driven study of energy–performance trade-offs for FL on GH200-class nodes across three deployment configurations: CPU-Ethernet, CPU-InfiniBand (RDMA-capable), and a DPU-hosted FL server over InfiniBand using a BlueField-3 SmartNIC/DPU. Using NVIDIA FLARE (NVFLARE), we align node-level power telemetry with per-round timing extracted from NVFLARE logs to quantify time-to-solution (TTS), energy-to-solution (ETS), energy-delay product (EDP), and synchronization behavior for three transformer models (ALBERT, DistilBERT, BERT), trained with and without differential privacy (DP). We find that interconnect choice is the dominant driver of runtime and energy: host-managed InfiniBand consistently reduces communication overhead versus Ethernet, yielding lower TTS/ETS/EDP. In contrast, in our NVFLARE deployment, placing the FL server on the DPU does not consistently match CPU-InfiniBand performance and can be slower—especially for larger models—highlighting that server placement alone is not sufficient to guarantee end-to-end gains. Finally, under our fixed-round protocol, DP increases per-round cost and runtime variance; ETS increases largely in proportion to TTS because average node power remains relatively stable across configurations.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
DOE OSTI2015

Algorithms for propagating uncertainty across heterogeneous domains

We address an important research area in stochastic multi-scale modeling, namely the propagation of uncertainty across heterogeneous domains characterized by partially correlated processes with vastly different correlation lengths. This class of problems arise very often when computing stochastic PDEs and particle models with stochastic/stochastic domain interaction but also with stochastic/deterministic coupling. The domains may be fully embedded, adjacent or partially overlapping. The fundamental open question we address is the construction of proper transmission boundary conditions that preserve global statistical properties of the solution across different subdomains. Often, the codes that model different parts of the domains are black-box and hence a domain decomposition technique is required. No rigorous theory or even effective empirical algorithms have yet been developed for this purpose, although interfaces defined in terms of functionals of random fields (e.g., multi-point cumulants) can overcome the computationally prohibitive problem of preserving sample-path continuity across domains. The key idea of the different methods we propose relies on combining local reduced-order representations of random fields with multi-level domain decomposition. Specifically, we propose two new algorithms: The first one enforces the continuity of the conditional mean and variance of the solution across adjacent subdomains by using Schwarz iterations. The second algorithm is based on PDE-constrained multi-objective optimization, and it allows us to set more general interface conditions. The effectiveness of these new algorithms is demonstrated in numerical examples involving elliptic problems with random diffusion coefficients, stochastically advected scalar fields, and nonlinear advection-reaction problems with random reaction rates.

Cho, Heyrim↗
DOE OSTI2013

Predictive modeling of reactive wetting and metal joining.

The performance, reproducibility and reliability of metal joints are complex functions of the detailed history of physical processes involved in their creation. Prediction and control of these processes constitutes an intrinsically challenging multi-physics problem involving heating and melting a metal alloy and reactive wetting. Understanding this process requires coupling strong molecularscale chemistry at the interface with microscopic (diffusion) and macroscopic mass transport (flow) inside the liquid followed by subsequent cooling and solidification of the new metal mixture. The final joint displays compositional heterogeneity and its resulting microstructure largely determines the success or failure of the entire component. At present there exists no computational tool at Sandia that can predict the formation and success of a braze joint, as current capabilities lack the ability to capture surface/interface reactions and their effect on interface properties. This situation precludes us from implementing a proactive strategy to deal with joining problems. Here, we describe what is needed to arrive at a predictive modeling and simulation capability for multicomponent metals with complicated phase diagrams for melting and solidification, incorporating dissolutive and composition-dependent wetting.

van Swol, Frank B.↗
DOE OSTI2016

Modeling the Spatio-Temporal Variability in Subsurface Thermal Regimes Across a Low-Relief Polygonal Tundra Landscape: Modeling Archive

Vast carbon stocks stored in permafrost soils of Arctic tundra are under risk of release to atmosphere under warming climate. Ice--wedge polygons in the low-gradient polygonal tundra create a complex mosaic of microtopographic features. The microtopography plays a critical role in regulating the fine scale variability in thermal and hydrological regimes in the polygonal tundra landscape underlain by continuous permafrost. Modeling of thermal regimes of this sensitive ecosystem is essential for understanding the landscape behaviour under current as well as changing climate. We present here an end-to-end effort for high resolution numerical modeling of thermal hydrology at real-world field sites, utilizing the best available data to characterize and parameterize the models. We develop approaches to model the thermal hydrology of polygonal tundra and apply them at four study sites at Barrow, Alaska spanning across low to transitional to high-centered polygon and representative of broad polygonal tundra landscape. A multi--phase subsurface thermal hydrology model (PFLOTRAN) was developed and applied to study the thermal regimes at four sites. Using high resolution LiDAR DEM, microtopographic features of the landscape were characterized and represented in the high resolution model mesh. Best available soil data from field observations and literature was utilized to represent the complex heterogeneous subsurface in the numerical model. This data collection provides the complete set of input files, forcing data sets and computational meshes for simulations using PFLOTRAN for four sites at Barrow Environmental Observatory. It also documents the complete computational workflow for this modeling study to allow verification, reproducibility and follow up studies. This dataset includes two zipped files and one .pdf file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
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↗
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↗