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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Computational homogenization at extreme scales

Multi-scale simulations at extreme scales in terms of both physical length scales and computational resources are presented. In this letter, we introduce a hierarchically parallel computational homogenization solver that employs hundreds of thousands of computing cores and resolves in material length scales (from to ). Simulations of this kind are important in understanding the multi-scale essence of many natural and synthetically made materials. Thus, we present a simulation consisting of 53.8 Billion finite elements with 28.1 Billion nonlinear equations that is solved on 393,216 computing cores (786,432 threads). The excellent parallel performance of the computational homogenization solver is demonstrated by a strong scaling test from 4,096 to 262,144 cores. A fully coupled multi-scale damage simulation shows a complex crack profile at the micro-scale and the macroscopic crack tunneling phenomenon. Such large and predictive simulations are an important step towards Virtual Materials Testing and can aid in development of new material formulations with extreme properties. Furthermore, the high computational efficiency of our computational homogenization solver holds great promise for utilizing the next generation of exascale parallel computing platforms that are expected to accelerate computations through orders of magnitude increase in parallelism rather than speed of each processor.

Engineering↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1978

Specialized computer architectures for computational aerodynamics

In recent years, computational fluid dynamics has made significant progress in modelling aerodynamic phenomena. Currently, one of the major barriers to future development lies in the compute-intensive nature of the numerical formulations and the relative high cost of performing these computations on commercially available general purpose computers, a cost high with respect to dollar expenditure and/or elapsed time. Today's computing technology will support a program designed to create specialized computing facilities to be dedicated to the important problems of computational aerodynamics. One of the still unresolved questions is the organization of the computing components in such a facility. The characteristics of fluid dynamic problems which will have significant impact on the choice of computer architecture for a specialized facility are reviewed.

Stevenson, D. K.↗
NASA NTRS1993

System for simultaneously loading program to master computer memory devices and corresponding slave computer memory devices

A bus programmable slave module card for use in a computer control system is disclosed which comprises a master computer and one or more slave computer modules interfacing by means of a bus. Each slave module includes its own microprocessor, memory, and control program for acting as a single loop controller. The slave card includes a plurality of memory means (S1, S2...) corresponding to a like plurality of memory devices (C1, C2...) in the master computer, for each slave memory means its own communication lines connectable through the bus with memory communication lines of an associated memory device in the master computer, and a one-way electronic door which is switchable to either a closed condition or a one-way open condition. With the door closed, communication lines between master computer memory (C1, C2...) and slave memory (S1, S2...) are blocked. In the one-way open condition invention, the memory communication lines or each slave memory means (S1, S2...) connect with the memory communication lines of its associated memory device (C1, C2...) in the master computer, and the memory devices (C1, C2...) of the master computer and slave card are electrically parallel such that information seen by the master's memory is also seen by the slave's memory. The slave card is also connectable to a switch for electronically removing the slave microprocessor from the system. With the master computer and the slave card in programming mode relationship, and the slave microprocessor electronically removed from the system, loading a program in the memory devices (C1, C2...) of the master accomplishes a parallel loading into the memory devices (S1, S2...) of the slave.

Hall, William A.↗
NASA NTRS2000

Impact of Parallel Computing on Large Scale Aeroelastic Computations

Aeroelasticity is computationally one of the most intensive fields in aerospace engineering. Though over the last three decades the computational speed of supercomputers have substantially increased, they are still inadequate for large scale aeroelastic computations using high fidelity flow and structural equations. In addition to reaching a saturation in computational speed because of changes in economics, computer manufactures are stopping the manufacturing of mainframe type supercomputers. This has led computational aeroelasticians to face the gigantic task of finding alternate approaches for fulfilling their needs. The alternate path to over come speed and availability limitations of mainframe type supercomputers is to use parallel computers. During this decade several different architectures have evolved. In FY92 the US Government started the High Performance Computing and Communication (HPCC) program. As a participant in this program NASA developed several parallel computational tools for aeroelastic applications. This talk describes the impact of those application tools on high fidelity based multidisciplinary analysis.

Guruswamy, Guru P.↗
DOE OSTI2019

Nanosystems, Edge Computing, and the Next Generation Computing Systems

It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Perspective on Quantum Computing Applications in Quantum Chemistry Using 25-100 Logical Qubits

The intersection of quantum computing and quantum chemistry represents a promising frontier for achieving quantum utility in domains of both scientific and societal relevance. Owing to the exponential growth of classical resource requirements for simulating quantum systems, quantum chemistry has long been recognized as a natural candidate for quantum computation. This perspective focuses on identifying scientifically meaningful use cases where early fault-tolerant quantum computers, which are considered to be equipped with approximately 25-100 logical qubits, could deliver tangible impact. While recent advances in classical computing have pushed the boundaries of tractable simulations to unprecedented scales, this logical-qubit regime represents the first window where quantum devices can pursue qualitatively distinct strategies, such as polynomial-scaling phase estimation, direct simulation of quantum dynamics, and active-space embedding, that remain challenging for classical solvers, such as multireference charge-transfer and conical-intersection states central to photochemistry and materials design. We highlight near-term opportunities in algorithm and software design, discuss representative chemical problems suited for quantum acceleration, and propose strategic roadmaps and collaborative pathways for advancing practical quantum utility in quantum chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

A Bayesian approach for parameter estimation and prediction using a computationally intensive model

Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model eta(theta), where theta denotes the uncertain, best input setting. Hence the statistical model is of the form y = eta(theta) + c, where epsilon accounts for measurement, and possibly other, error sources. When nonlinearity is present in eta(center dot), the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model eta(center dot). This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. We also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory.

Higdon, Dave↗
DOE OSTI2015

Next Generation Workload Management System For Big Data on Heterogeneous Distributed Computing

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS and ALICE are the largest collaborations ever assembled in the sciences and are at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, both experiments rely on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System (WMS) for managing the workflow for all data processing on hundreds of data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. The scale is demonstrated by the following numbers: PanDA manages O(10 2 ) sites, O(10 5 ) cores, O(10 8 ) jobs per year, O(10 3 ) users, and ATLAS data volume is O(10 17 ) bytes. In 2013 we started an ambitious program to expand PanDA to all available computing resources, including opportunistic use of commercial and academic clouds and Leadership Computing Facilities (LCF). The project titled 'Next Generation Workload Management and Analysis System for Big Data' (BigPanDA) is funded by DOE ASCR and HEP. Extending PanDA to clouds and LCF presents new challenges in managing heterogeneity and supporting workflow. The BigPanDA project is underway to setup and tailor PanDA at the Oak Ridge Leadership Computing Facility (OLCF) and at the National Research Center "Kurchatov Institute" together with ALICE distributed computing and ORNL computing professionals. Our approach to integration of HPC platforms at the OLCF and elsewhere is to reuse, as much as possible, existing components of the PanDA system. Finally, we will present our current accomplishments with running the PanDA WMS at OLCF and other supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Computing Free Energies with Fluctuation Relations on Quantum Computers

As a central thermodynamic property, free energy enables the calculation of virtually any equilibrium property of a physical system, allowing for the construction of phase diagrams and predictions about transport, chemical reactions, and biological processes. Thus, methods for efficiently computing free energies, which in general is a difficult problem, are of great interest to broad areas of physics and the natural sciences. The majority of techniques for computing free energies target classical systems, leaving the computation of free energies in quantum systems less explored. Recently developed fluctuation relations enable the computation of free energy differences in quantum systems from an ensemble of dynamic simulations. While performing such simulations is exponentially hard on classical computers, quantum computers can efficiently simulate the dynamics of quantum systems. Here, we present an algorithm utilizing a fluctuation relation known as the Jarzynski equality to approximate free energy differences of quantum systems on a quantum computer. In this work we discuss under which conditions our approximation becomes exact, and under which conditions it serves as a strict upper bound. Furthermore, we successfully demonstrate a proof of concept of our algorithm using the transverse field Ising model on a real quantum processor. As quantum hardware continues to improve, we anticipate that our algorithm will enable computation of free energy differences for a wide range of quantum systems useful across the natural sciences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Pacing a data transfer operation between compute nodes on a parallel computer

Methods, systems, and products are disclosed for pacing a data transfer between compute nodes on a parallel computer that include: transferring, by an origin compute node, a chunk of an application message to a target compute node; sending, by the origin compute node, a pacing request to a target direct memory access (`DMA`) engine on the target compute node using a remote get DMA operation; determining, by the origin compute node, whether a pacing response to the pacing request has been received from the target DMA engine; and transferring, by the origin compute node, a next chunk of the application message if the pacing response to the pacing request has been received from the target DMA engine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Scheduling applications for execution on a plurality of compute nodes of a parallel computer to manage temperature of the nodes during execution

Methods, apparatus, and products are disclosed for scheduling applications for execution on a plurality of compute nodes of a parallel computer to manage temperature of the plurality of compute nodes during execution that include: identifying one or more applications for execution on the plurality of compute nodes; creating a plurality of physically discontiguous node partitions in dependence upon temperature characteristics for the compute nodes and a physical topology for the compute nodes, each discontiguous node partition specifying a collection of physically adjacent compute nodes; and assigning, for each application, that application to one or more of the discontiguous node partitions for execution on the compute nodes specified by the assigned discontiguous node partitions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

An atomic orbital based real-time time-dependent density functional theory for computing electronic circular dichroism band spectra

One of the challenges of interpreting electronic circular dichroism (ECD) band spectra is that different states may have different rotatory strength signs, determined by their absolute configuration. If the states are closely spaced and opposite in sign, observed transitions may be washed out by nearby states, unlike absorption spectra where transitions are always positive additive. To accurately compute ECD bands, it is necessary to compute a large number of excited states, which may be prohibitively costly if one uses the linear-response time-dependent density functional theory (TDDFT) framework. Here we implement a real-time, atomic-orbital based TDDFT method for computing the entire ECD spectrum simultaneously. The method is advantageous for large systems with a high density of states. In contrast to previous implementations based on real-space grids, the method is variational, independent of nuclear orientation, and does not rely on pseudopotential approximations, making it suitable for computation of chiroptical properties well into the X-ray regime.

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

Energy Efficiency Scaling for 2 Decades (EES2) Roadmap for Computing

In response to the looming crisis in global energy consumption required for advanced computing applications, the United States Department of Energy (DOE) Advanced Materials and Manufacturing Technology Office (AMMTO) is leading a multi-organizational effort to define a roadmap for energy efficiency scaling for two decades (EES2) with the aim to reduce energy use in all aspects of computation by more than a factor of 1000 in two decades. By July of 2024, over 60 organizations representing industry, academia, and the national laboratories have pledged to work in various aspects of research and development to enable energy efficiency in computing including in the development of the EES2 roadmap, with an initial public release in 2024 as the first phase of an ongoing commitment to energy-efficient and sustainable computation.

Kaarsberg, Tina [U.S. Department of Energy (DOE)]↗
DOE OSTI2014

Vision 20/20: Automation and advanced computing in clinical radiation oncology

This Vision 20/20 paper considers what computational advances are likely to be implemented in clinical radiation oncology in the coming years and how the adoption of these changes might alter the practice of radiotherapy. Four main areas of likely advancement are explored: cloud computing, aggregate data analyses, parallel computation, and automation. As these developments promise both new opportunities and new risks to clinicians and patients alike, the potential benefits are weighed against the hazards associated with each advance, with special considerations regarding patient safety under new computational platforms and methodologies. While the concerns of patient safety are legitimate, the authors contend that progress toward next-generation clinical informatics systems will bring about extremely valuable developments in quality improvement initiatives, clinical efficiency, outcomes analyses, data sharing, and adaptive radiotherapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Materials challenges and opportunities for quantum computing hardware

Combatting noise on the platform The potential of quantum computers to solve problems that are intractable for classical computers has driven advances in hardware fabrication. In practice, the main challenge in realizing quantum computers is that general, many-particle quantum states are highly sensitive to noise, which inevitably causes errors in quantum algorithms. Some noise sources are inherent to the current materials platforms. de Leon et al. review some of the materials challenges for five platforms for quantum computers and propose directions for their solution. Science , this issue p. eabb2823

Science & Technology - Other Topics↗
DOE OSTI2023

A computational fluid dynamics model to estimate local quantities in firebrand char oxidation

Firebrand burning is a complex phenomenon that is influenced by several parameters which are difficult to fully explore experimentally. Computational fluid dynamics models capable of predicting local quantities are essential for accurate prediction of char oxidation in firebrands. This article presents a computational fluid dynamics model to estimate firebrand mass loss, diameter change, and surface temperature during char oxidation. The model was validated using previously conducted wind tunnel experiments. These experiments were conducted for firebrands of two different aspect ratios, which were arranged in three different configurations (single, horizontal array, and vertical array), and for four different wind speeds (0.5, 1, 1.5, and 2 m/s). The computational fluid dynamics results were compared with a previous 1 D model. In all the test cases, the computational fluid dynamics model predicted the physical phenomena with significantly improved accuracy compared to a 1 D model. The char oxidation model presented in this article can be coupled with other models to study firebrand generation and trajectory, biomass pyrolysis, fluidized bed reactors, and coal combustion.

Engineering↗
DOE OSTI2018

Fault Diagnosis of Hybrid Computing Systems Using Chaotic-Map Method

Computing systems are becoming increasingly complex with nodes consisting of a combination of multi-core central processing units (CPUs), many integrated core (MIC) and graphics processing unit (GPU) accelerators. These computing units and their interconnections are subject to different classes of hardware and software faults, which should be detected to support mitigation measures. We present the chaotic-map method that uses the exponential divergence and wide Fourier properties of the trajectories, combined with memory allocations and assignments to diagnose component-level faults in these hybrid computing systems. We propose lightweight codes that utilize highly parallel chaotic-map computations tailored to isolate faults in arithmetic units, memory elements and interconnects. The diagnosis module on a node utilizes pthreads to place chaotic-map threads on CPU and MIC cores, and CUDA C and OpenCL kernels on GPU blocks. We present experimental diagnosis results on five multi-core CPUs; one MIC; and, seven GPUs with typical diagnosis run-times under a minute.

Rao, Nageswara↗