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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Scalable Space-Time Domain Decomposition Approach for Solving Large Scale Nonlinear Regularized Inverse Ill Posed Problems in 4D Variational Data Assimilation

We address the development of innovative algorithms designed to solve the strong-constraint Four Dimensional Variational Data Assimilation (4DVar DA) problems in large scale applications. We present a space-time decomposition approach which employs the whole domain decomposition, i.e. both along the spacial and temporal direction in the overlapping case, and the partitioning of both the solution and the operator. Starting from the global functional defined on the entire domain, we get to a sort of regularized local functionals on the set of sub domains providing the order reduction of both the predictive and the Data Assimilation models. The algorithm convergence is developed. Performance in terms of reduction of time complexity and algorithmic scalability is discussed on the Shallow Water Equations on the sphere. The number of state variables in the model, the number of observations in an assimilation cycle, as well as numerical parameters as the discretization step in time and in space domain are defined on the basis of discretization grid used by data available at repository Ocean Synthesis/Reanalysis Directory of Hamburg University.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Scalable multiscale modeling of platelets with 100 million particles

Here, we developed the core components of the AI-aided multiple time stepping algorithm for multiscale modeling of cell dynamics. This algorithm was implemented and analyzed on two supercomputer architectures with an application of simulating the aggregation of 250 platelets, or 102 million particles. To scale on these computers with complex memory and network architectures with GPUs, we devised a biomechanics-informed task mapping scheme to optimize load imbalance, communications, and memory utilization. Our simulations, scaling well up to 192 nodes on a Summit-like supercomputer with a peak speed of 11 petaflops, achieved a rate of 423 μs/day which is 500 times faster than the conventional algorithm using static time step and this has enabled studies of record size blood clots at record spatial–temporal resolutions. Additionally, we discovered the sensitive dependence of the scalability and execution time on the methods of decomposition, CPU–GPU coupling, and task mapping.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Train small, model big: Scalable physics simulators via reduced order modeling and domain decomposition

Numerous cutting-edge scientific technologies originate at the laboratory scale, but transitioning them to practical industry applications is a formidable challenge. Traditional pilot projects at intermediate scales are costly and time-consuming. An alternative, the pilot-scale model, relies on high-fidelity numerical simulations, but even these simulations can be computationally prohibitive at larger scales. To overcome these limitations, we propose a scalable, physics-constrained reduced order model (ROM) method. The ROM identifies critical physics modes from small-scale unit components, projecting governing equations onto these modes to create a reduced model that retains essential physics details. We also employ Discontinuous Galerkin Domain Decomposition (DG-DD) to apply ROM to unit components and interfaces, enabling the construction of large-scale global systems without data at such large scales. Here this method is demonstrated on the Poisson and Stokes flow equations, showing that it can solve equations about 15–40 times faster with only ~1% relative error. Furthermore, ROM takes one order of magnitude less memory than the full order model, enabling larger scale predictions at a given memory limitation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

A scalable design of experiments framework for optimal sensor placement

Here, we present a scalable design of an experiments framework for sensor placement in systems described by partial differential equations (PDEs). In particular, we aim to compute optimal sensor locations by minimizing the uncertainty of parameters estimated from Bayesian inverse problems and where the system. The resulting problem is a computationally intractable mixed-integer nonlinear program constrained by PDEs. We approach this problem with two heuristics used in compressed sensing and optimal control literature: a sparsity-inducing approach and a sum-up rounding approach. We also investigate metrics to guide the design of experiments (the total flow variance and the A-optimal design criterion) and analyze the effect of different noise structures (white and colored). Using an application in natural gas pipelines, we conclude that the sum-up rounding approach approach gives the best results and produces shrinking gaps with increasing mesh resolution. We also observe that convergence for the white noise measurement error case is slower than for the colored noise case. For A-optimal design, the solution is close to a uniform distribution of sensors along the pipeline while for the flow variance design the distribution is unstructured.

42 ENGINEERING↗
DOE OSTI2022

Rate-capability of the VMM3a front-end in the RD51 Scalable Readout System

The VMM3a is an Application Specific Integrated Circuit (ASIC), specifically developed for the readout of gaseous detectors. Originally developed within the ATLAS New Small Wheel (NSW) upgrade, it has been successfully integrated into the Scalable Readout System (SRS) of the RD51 collaboration. This allows, to use the VMM3a also in small laboratory set-ups and mid-scale experiments, which make use of Micro-Pattern Gaseous Detectors (MPGDs). As part of the integration of the VMM3a into the SRS, the readout and data transfer scheme was optimised to reach a high rate-capability of the entire readout system and profit from the VMM3a's high single-channel rate-capability of 3.6Mhits/s. The optimisation focused mainly on the handling of the data output stream of the VMM3a, but also on the development of a trigger-logic between the front-end cards and the DAQ computer. In this article, two firmware implementations of the non-ATLAS continuous readout mode are presented, as well as the implementation of the trigger-logic. Afterwards, a short overview on X-ray imaging results is presented, to illustrate the high rate-capability from an application point-of-view.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Scalable Synthesis of Defect Abundant Si Nanorods for High-Performance Li-Ion Battery Anodes

Microsized nanostructured silicon–carbon composite is a promising anode material for high energy Li-ion batteries. However, large-scale synthesis of high-performance nano-Si materials at a low cost still remains a significant challenge. Here we report a scalable low cost method to synthesize Al/Na-doped and defect-abundant Si nanorods that have excellent electrochemical performance with high first-cycle Coulombic efficiency (90%). The unique Si nanorods are synthesized by acid etching the refined and rapidly solidified eutectic Al–Si ingot. To maintain the high electronic conductivity, a thin layer of carbon is then coated on the Si nanorods by carbonization of self-polymerized polydopamine (PDA) at 800 °C. The carbon coated Si nanorods (Si@C) electrode at 0.9 mg cm –2 loading (corresponding to area-specific-capacity of ~2.0 mAh cm –2 ) exhibits a reversible capacity of ~2200 mAh g –1 at 100 mA g –1 current, and maintains ~700 mAh g –1 over 1000 cycles at 1000 mA g –1 with a capacity decay rate of 0.02% per cycle. High Coulombic efficiencies of 87% in the first cycle and ~99.7% after 5 cycles are achieved due to the formation of an artificial Al 2 O 3 solid electrolyte interphase (SEI) on the Si surface, and the low surface area (31 m 2 g –1 ), which has never been reported before for nano-Si anodes. The excellent electrochemical performance results from the massive defects (twins, stacking faults, dislocations) and Al/Na doping in Si nanorods induced by rapid solidification and Na salt modifications; this greatly enhances the robustness of Si from the volume changes and alleviates the mechanical stress/strain of the Si nanorods during the lithium insertion/extraction process. Introducing massive defects and Al/Na doping in eutectic Si nanorods for Li-ion battery anodes is unexplored territory. We venture this uncharted territory to commercialize this nanostructured Si anode for the next generation of Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Blade-Coated Electron Transport Layers to Enable Scalable Perovskite Photovoltaics

With substantial improvements in efficiency of organic-inorganic halide perovskite solar cells (PSCs), attention has shifted to scale and commercial viability. While deposition of the perovskite absorber layer has been demonstrated using high throughput blade coating and even roll-to-roll printing, associated transport layers are often deposited using energy- and cost-intensive methods such as thermal evaporation. Here, we present an ambient process to blade coat mixed fullerene and bathocuproine (BCP) electron transport layers (ETLs) in inverted PSCs for low-cost, high throughput, scalable devices demonstrating efficiencies matching evaporated methods and >15% for device areas of 1 cm2.

bathocuproine↗
DOE OSTI2019

Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators

We present an adaptive grid matrix-free operator approximation scheme based on a "product-convolution" interpolation of convolution operators. This scheme is appropriate for operators that are locally translation-invariant, even if these operators are high rank or full rank. Such operators arise in Schur complement methods for solving partial differential equations (PDEs), as Hessians in PDE-constrained optimization and inverse problems, as integral operators, as covariance operators, and as Dirichlet-to-Neumann maps. Constructing the approximation requires computing the impulse responses of the operator to point sources centered on nodes in an adaptively refined grid of sample points. A randomized a posteriori error estimator drives the adaptivity. Once constructed, the approximation can be efficiently applied to vectors using the fast Fourier transform. The approximation can be efficiently converted to hierarchical matrix (H-matrix) format, then inverted or factorized using scalable H-matrix arithmetic. The quality of the approximation degrades gracefully as fewer sample points are used, allowing cheap lower quality approximations to be used as preconditioners. Furthermore, this yields an automated method to construct preconditioners for locally translation-invariant Schur complements. We directly address issues related to boundaries and prove that our scheme eliminates boundary artifacts. We test the scheme on a spatially varying blurring kernel, on the nonlocal component of an interface Schur complement for the Poisson operator, and on the data misfit Hessian for an advection dominated advection-diffusion inverse problem. Numerical results show that the scheme outperforms existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Consistent Second Moment Methods with Scalable Linear Solvers for Radiation Transport

Second moment methods (SMMs) are developed that are consistent with the discontinuous Galerkin spatial discretization of the discrete ordinates (or S\(_N\)) transport equations. The low-order (LO) diffusion system of equations is discretized with fully consistent P\(_1\), local discontinuous Galerkin (LDG), and interior penalty (IP) methods. A discrete residual approach is used to derive SMM correction terms that make each of the LO systems consistent with the high-order discretization. We show that the consistent methods are more accurate and have better solution quality than independently discretized LO systems, that they preserve the diffusion limit, and that the LDG and IP consistent SMMs can be scalably solved in parallel on a challenging, multimaterial benchmark problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Improving Uintah's Scalability Through the Use of Portable Kokkos-Based Data Parallel Tasks

The University of Utah’s Carbon Capture Multidisciplinary Simulation Center (CCMSC) is using the Uintah Computational Framework to predict performance of a 1000 MWe ultra-supercritical clean coal boiler. The center aims to utilize the Intel Xeon Phi-based DOE systems, Theta and Aurora, through the Aurora Early Science Program by using the Kokkos C++ library to enable node-level performance portability. This paper describes infrastructure advancements and portability improvements made possible by the integration of Kokkos within Uintah. This integration marks a step towards consolidating Uintah’s MPI+PThreads and MPI+CUDA hybrid parallelism approaches into a single MPI+Kokkos approach. Scalability results are presented that compare serial and data parallel task execution models for a challenging radiative heat transfer calculation, central to the center’s predictive boiler simulations. Here, these results demonstrate both good strong-scaling characteristics to 256 Knights Landing (KNL) processors on the NSF Stampede system, and show the KNL-based calculation to compete with prior GPU-based results for the same calculation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

ISAVS: Interactive Scalable Analysis and Visualization System

Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability. We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. Here, we demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A scalable framework for the global offline community land model ensemble simulation

Current earth system models have a large range of uncertainty, owing to differences in the simulation of feedbacks and insufficient information to constrain model parameters. Parameter disturbance experiment provides a straightforward method to quantify the variation (uncertainty) outputs caused by model inputs. Owing to the software complexity and computational intensity of earth system models, a large-scale simulation framework is needed to support ensemble simulation required by parameter disturbance experiment. This paper presents a parallel framework for the community land model ensemble simulation. After a software structure review of the community land model simulation, a single factor parameter disturbance experiment of a reference computational experiment design is used to demonstrate the software design principles, computational characteristics of individual application, parallel ensemble simulation implementation, as well as the weak scalability of this simulation framework on a high-end computer. Lastly, the paper discusses some preliminary diagnostic analysis results of the single factor parameter disturbance experiments. The framework design considerations and implementation details described in this paper can be beneficial to many other research programmes involving large scale, legacy modelling system.

58 GEOSCIENCES↗
DOE OSTI2013

FINAL REPORT: Scalable Methods for Electronic Excitations and Optical Responses of Nanostructures: Mathematics to Algorithms to Observables

Work in nanoscience has increased substantially in recent years owing to its potential technological applications and to fundamental scientific interest. A driving force for this activity is to capitalize on new phenomena that occurs at the nanoscale. For example, the physical confinement of electronic states, i.e., quantum confinement, can dramatically alter the electronic and optical properties of matter. A prime example of this occurs for the optical properties of nanoscale crystals such as those composed of elemental silicon. Silicon in the bulk state is optically inactive due to the small size of the optical gap, which can only be accessed by indirect transitions. However, at the nanoscale, this material becomes optically active. The size of the optical gap is increased by confinement and the conservation of crystal momentum ceases to hold, resulting in the viability of indirect transitions. Our work associated with this grant has focused on developing new scalable algorithms for describing the electronic and optical properties of matter at the nanoscale such as nano structures of silicon and related semiconductor properties.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Center for Scalable Application Development Software (Final Report)

The Center for Scalable Application Development Software (CScADS) was established as a part- nership between Rice University, Argonne National Laboratory, University of California Berkeley, University of Tennessee – Knoxville, and University of Wisconsin – Madison. CScADS pursued an integrated set of activities with the aim of increasing the productivity of DOE computational scientists by catalyzing the development of systems software, libraries, compilers, and tools for leadership computing platforms. Principal Center activities were workshops to engage the research community in the challenges of leadership computing, research and development of open-source software, and work with computational scientists to help them develop codes for leadership computing platforms. This final report summarizes CScADS activities at Rice University in these areas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Computational Strategies for Scalable Genomics Analysis

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Tip-Based Nanofabrication for Scalable Manufacturing

Tip-based nanofabrication (TBN) is a family of emerging nanofabrication techniques that use a nanometer scale tip to fabricate nanostructures. Here in this review, we first introduce the history of the TBN and the technology development. We then briefly review various TBN techniques that use different physical or chemical mechanisms to fabricate features and discuss some of the state-of-the-art techniques. Subsequently, we focus on those TBN methods that have demonstrated potential to scale up the manufacturing throughput. Finally, we discuss several research directions that are essential for making TBN a scalable nano-manufacturing technology.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Visible Scalable Terrain (ViSTa) format

Visible Scalable Terrain is a format for the production, interchange, and display of 3D terrain data that is specifically suited to stereo vision based robotic applications.

ViSTa Visible Scalable Terrain robotic application↗