Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Scientific Computing.”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

The 1987 RIACS annual report

The Research Institute for Advanced Computer Science (RIACS) was established at the NASA Ames Research Center in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 64 universities with graduate programs in the aerospace sciences, under several Cooperative Agreements with NASA. RIACS's goal is to provide preeminent leadership in basic and applied computer science research as partners in support of NASA's goals and missions. In pursuit of this goal, RIACS contributes to several of the grand challenges in science and engineering facing NASA: flying an airplane inside a computer; determining the chemical properties of materials under hostile conditions in the atmospheres of earth and the planets; sending intelligent machines on unmanned space missions; creating a one-world network that makes all scientific resources, including those in space, accessible to all the world's scientists; providing intelligent computational support to all stages of the process of scientific investigation from problem formulation to results dissemination; and developing accurate global models for climatic behavior throughout the world. In working with these challenges, we seek novel architectures, and novel ways to use them, that exploit the potential of parallel and distributed computation and make possible new functions that are beyond the current reach of computing machines. The investigation includes pattern computers as well as the more familiar numeric and symbolic computers, and it includes networked systems of resources distributed around the world. We believe that successful computer science research is interdisciplinary: it is driven by (and drives) important problems in other disciplines. We believe that research should be guided by a clear long-term vision with planned milestones. And we believe that our environment must foster and exploit innovation. Our activities and accomplishments for the calendar year 1987 and our plans for 1988 are reported.

Source record↗
DOE OSTI2017

TeaLeaf: A Mini-Application to Enable Design-Space Explorations for Iterative Sparse Linear Solvers

Iterative sparse linear solvers are an important class of algorithm in high performance computing, and form a crucial component of many scientific codes. As intra and inter node parallelism continues to increase rapidly, the design of new, scalable solvers which can target next generation architectures becomes increasingly important. In this work we present TeaLeaf, a recent mini-app constructed to explore design space choices for highly scalable solvers. We then use TeaLeaf to compare the standard CG algorithm with a Chebyshev Polynomially Preconditioned Conjugate Gradient (CPPCG) iterative sparse linear solver. CPPCG is a communication-avoiding algorithm, requiring less global communication than previous approaches. TeaLeaf includes support for many-core processors, such as GPUs and Xeon Phi, and we include strong-scaling results across a range of world-leading Petascale supercomputers, including Titan and Piz Daint.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Cooperative Education

Los Alamos National Laboratory (LANL) is a multidisciplinary national laboratory that conducts research and development in national security, engineering, materials science, computational modeling, and advanced manufacturing. The laboratory develops innovative technologies to address complex scientific and engineering challenges. This project focuses on the development and evaluation of high-performance absorbing structures through computational design, simulation, and engineering analysis. Absorbing structures are used in applications where damage mitigation, structural protection, and material efficiency are critical performance requirements. The increasing demand for lightweight, high-strength, and highly efficient structural systems has created a need for improved design methodologies capable of maximizing absorption while minimizing weight and material usage. The project utilizes advanced engineering software, including 3D CAD software and FEA, to generate and optimize structural concepts. Computational simulations are performed to evaluate structural behavior under loading conditions, while mathematical analyses are conducted using Python-based tools as well as established analytical equations from material and structural mechanics. The project benefits LANL by supporting the development of advanced design methodologies and improving the understanding of material and structural performance. During the internship term, a significant portion of the design development, simulation, and data analysis activities will be completed. Success of the project depends on collaboration among engineering mentors and technical staff members. Work will be conducted at Los Alamos National Laboratory using laboratory computing resources and engineering software.

42 ENGINEERING↗
DOE OSTI2023

Panorama 360 (Final Report)

This is the final technical report for the DOE-funded Panorama 360 project. Panorama 360 provided a resource for the collection, analysis, and sharing of performance data about end-to-end scientific workflows executing on DOE facilities. The work focused on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: 1. A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); 2. A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; 3. A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and 4. Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Data Cards for Standardized Metadata Across DOE-Aligned Data Initiatives: Toward Transparent, Interoperable, and Governed Dataset Documentation

As data-intensive research, advanced computing, and artificial intelligence become increasingly central to scientific and operational workflows, the need for consistent, transparent, and machine-actionable documentation has grown correspondingly. Multiple DOE-aligned communities—including Office of Science, Genesis Mission, American Science Cloud (AmSC), National Nuclear Security Administration (NNSA) stewardship and governance, and related cross-laboratory collaborations—have independently developed metadata practices to support discovery, access, reuse, repository deposit, and compliance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Higher-order LaSDI: Reduced order modeling with multiple time derivatives

Solving complex partial differential equations (PDEs) is essential across scientific disciplines but often requires numerical models that can be prohibitively expensive in time-sensitive applications. Reduced-order models (ROMs) address this challenge by exploiting low-dimensional structure to create fast approximations. The Latent Space Dynamics Identification (LaSDI) framework has demonstrated success in learning ROMs for parameterized PDE families, but remains limited to first-order systems. Here, in this paper, we propose Higher-Order LaSDI (HLaSDI), which extends the LaSDI framework to PDEs with arbitrary order of time derivatives. This generalization significantly expands the applicability of LaSDI-based methods to systems previously outside their scope, including hyperbolic PDEs. We demonstrate HLaSDI’s accuracy and efficiency on several linear and nonlinear benchmark problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Multiscale Computation: Needs and Opportunities for BER Science

The Environmental Molecular Sciences Laboratory (EMSL), a scientific user facility managed by Pacific Northwest National Laboratory for the U.S. Department of Energy, Office of Biological and Environmental Research (BER), conducted a one-day workshop on August 26, 2014 on the topic of “Multiscale Computation: Needs and Opportunities for BER Science.” Twenty invited participants, from various computational disciplines within the BER program research areas, were charged with the following objectives: Identify BER-relevant models and their potential cross-scale linkages that could be exploited to better connect molecular-scale research to BER research at larger scales, and Identify critical science directions that will motivate EMSL decisions regarding future computational (hardware and software) architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Arrow of time and its reversal on the IBM quantum computer

Uncovering the origin of the “arrow of time” remains a fundamental scientific challenge. Within the framework of statistical physics, this problem was inextricably associated with the second Law of thermodynamics, which declares that entropy growth proceeds from the system’s entanglement with the environment. this poses a question of whether it is possible to develop protocols for circumventing the irreversibility of time and if so to practically implement these protocols. Here we show that, while in nature the complex conjugation needed for time reversal may appear exponentially improbable, one can design a quantum algorithm that includes complex conjugation and thus reverses a given quantum state. Using this algorithm on an IBM quantum computer enables us to experimentally demonstrate a backward time dynamics for an electron scattered on a two-level impurity.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Applications of artificial intelligence to scientific research

Artificial intelligence (AI) is a growing field which is just beginning to make an impact on disciplines other than computer science. While a number of military and commercial applications were undertaken in recent years, few attempts were made to apply AI techniques to basic scientific research. There is no inherent reason for the discrepancy. The characteristics of the problem, rather than its domain, determines whether or not it is suitable for an AI approach. Expert system, intelligent tutoring systems, and learning programs are examples of theoretical topics which can be applied to certain areas of scientific research. Further research and experimentation should eventurally make it possible for computers to act as intelligent assistants to scientists.

Prince, Mary Ellen↗
DOE OSTI2018

Exploring the Optimal Platform Configuration for Power-Constrained HPC Workflows

In high-performance computing (HPC) workflows, data analytics is typically utilized to gain insights from scientific simulations. Approaching the era of exascale, online analysis is gaining popularity due to the savings of I/O to persistent storage. As computing capability keeps growing, power consumption is becoming critical to HPC facilities. Enforcing power limits is emerging as a practical trend for power-constrained HPC facilities. However, it remains unclear how to choose the appropriate power limits for various HPC workflows and how to distribute the power limit of a workflow between simulation and analysis. In addition, given a power limit, it is unclear what the optimal scales and power capping levels are for various workflows, especially when taking reliability into account. In order to resolve these issues in power-constrained HPC, in this paper, we propose a reliability-aware model to determine the aforementioned platform configurations for HPC workflows. We also validate our model and present model-driven studies for a wide range of real-system scenarios. Our study reveals interesting insights about how platform configuration affects the performance and energy efficiency of HPC workflows under power constraints.

Tang, Kun↗
DOE OSTI2022

Using photoelectron spectroscopy to measure resonant inelastic X-ray scattering: a computational investigation

Resonant inelastic X-ray scattering (RIXS) has become an important scientific tool. Nonetheless, conventional high-resolution (few hundred meV or less) RIXS measurements, especially in the soft X-ray range, require low-throughput grating spectrometers, which limits measurement accuracy. Here, the performance of a different method for measuring RIXS, i.e. photoelectron spectrometry for analysis of X-rays (PAX), is computationally investigated. This method transforms the X-ray measurement problem of RIXS to an electron measurement problem, enabling use of high-throughput, compact electron spectrometers. X-rays to be measured are incident on a converter material and the energy distribution of the resultant photoelectrons, the PAX spectrum, is measured with an electron spectrometer. A deconvolution algorithm for analysis of such PAX data is proposed. It is shown that the deconvolution algorithm works well on data recorded with ~0.5 eV resolution. Additional simulations show the potential of PAX for estimation of RIXS features with smaller widths. For simulations using the 3d levels of Ag as a converter material, and with 10 5 simulated detected electrons, it is estimated that features with a few hundred meV width can be accurately estimated in a model RIXS spectrum. For simulations using a sharp Fermi edge to encode RIXS spectra, it is estimated that one can accurately distinguish 100 meV FWHM peaks separated by 45 meV with 10 5 simulated detected electrons that were photoemitted from within 0.4 eV of the Fermi level.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Visualization at exascale: Making it all work with VTK-m

The VTK-m software library enables scientific visualization on exascale-class supercomputers. Exascale machines are particularly challenging for software development in part because they use GPU accelerators to provide the vast majority of their computational throughput. Algorithmic designs for GPUs and GPU-centric computing often deviate from those that worked well on previous generations of high-performance computers that relied on traditional CPUs. Fortunately, VTK-m provides scientific visualization algorithms for GPUs and other accelerators. VTK-m also provides a framework that simplifies the implementation of new algorithms and adds a porting layer to work across multiple processor types. This paper describes the main challenges encountered when making scientific visualization available at exascale. Here, we document the surprises and obstacles faced when moving from pre-exascale platforms to the final exascale designs and the performance on those systems including scaling studies on Frontier, an exascale machine with over 37,000 AMD GPUs. We also report on the integration of VTK-m with other exascale software technologies. Finally, we show how VTK-m helps scientific discovery for applications such as fusion and particle acceleration that leverage an exascale supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Software-Defined Network for End-to-end Networked Science at the Exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work we presen here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Software-defined network for end-to-end networked science at the exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end- to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Tigres Workflow Library: Supporting Scientific Pipelines on HPC Systems

The growth in scientific data volumes has resulted in the need for new tools that enable users to operate on and analyze data on large-scale resources. In the last decade, a number of scientific workflow tools have emerged. These tools often target distributed environments, and often need expert help to compose and execute the workflows. Data-intensive workflows are often ad-hoc, they involve an iterative development process that includes users composing and testing their workflows on desktops, and scaling up to larger systems. In this paper, we present the design and implementation of Tigres, a workflow library that supports the iterative workflow development cycle of data-intensive workflows. Tigres provides an application programming interface to a set of programming templates i.e., sequence, parallel, split, merge, that can be used to compose and execute computational and data pipelines. We discuss the results of our evaluation of scientific and synthetic workflows showing Tigres performs with minimal template overheads (mean of 13 seconds over all experiments). We also discuss various factors (e.g., I/O performance, execution mechanisms) that affect the performance of scientific workflows on HPC systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Exascale Was Not Inevitable; Neither Is What Comes Next

The long, steady advance of supercomputing capabilities historically makes it tempting to assume that similar future improvements are inevitable. In fact, the successful path to exascale systems was far from certain, requiring a shift to accelerator-based designs, an associated end-to-end rethinking of traditional computational approaches, and a fundamental change in how scientists collaborate and interact. Here in this article, we share lessons from the U.S. Department of Energy Exascale Computing Project along with perspectives on how to design and implement scientific applications in the exascale and post-exascale eras.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Deploy production sliding mesh capability with linear solver benchmarking.

Wind applications require the ability to simulate rotating blades. To support this use-case, a novel design-order sliding mesh algorithm has been developed and deployed. The hybrid method combines the control volume finite element methodology (CVFEM) with concepts found within a discontinuous Galerkin (DG) finite element method (FEM) to manage a sliding mesh. The method has been demonstrated to be design-order for the tested polynomial basis (P=1 and P=2) and has been deployed to provide production simulation capability for a Vestas V27 (225 kW) wind turbine. Other stationary and canonical rotating ow simulations are also presented. As the majority of wind-energy applications are driving extensive usage of hybrid meshes, a foundational study that outlines near-wall numerical behavior for a variety of element topologies is presented. Results indicate that the proposed nonlinear stabilization operator (NSO) is an effective stabilization methodology to control Gibbs phenomena at large cell Peclet numbers. The study also provides practical mesh resolution guidelines for future analysis efforts. Application-driven performance and algorithmic improvements have been carried out to increase robustness of the scheme on hybrid production wind energy meshes. Specifically, the Kokkos-based Nalu Kernel construct outlined in the FY17/Q4 ExaWind milestone has been transitioned to the hybrid mesh regime. This code base is exercised within a full V27 production run. Simulation timings for parallel search and custom ghosting are presented. As the low-Mach application space requires implicit matrix solves, the cost of matrix reinitialization has been evaluated on a variety of production meshes. Results indicate that at low element counts, i.e., fewer than 100 million elements, matrix graph initialization and preconditioner setup times are small. However, as mesh sizes increase, e.g., 500 million elements, simulation time associated with \setup-up" costs can increase to nearly 50% of overall simulation time when using the full Tpetra solver stack and nearly 35% when using a mixed Tpetra- Hypre-based solver stack. The report also highlights the project achievement of surpassing the 1 billion element mesh scale for a production V27 hybrid mesh. A detailed timing breakdown is presented that again suggests work to be done in the setup events associated with the linear system. In order to mitigate these initialization costs, several application paths have been explored, all of which are designed to reduce the frequency of matrix reinitialization. Methods such as removing Jacobian entries on the dynamic matrix columns (in concert with increased inner equation iterations), and lagging of Jacobian entries have reduced setup times at the cost of numerical stability. Artificially increasing, or bloating, the matrix stencil to ensure that full Jacobians are included is developed with results suggesting that this methodology is useful in decreasing reinitialization events without loss of matrix contributions. With the above foundational advances in computational capability, the project is well positioned to begin scientific inquiry on a variety of wind-farm physics such as turbine/turbine wake interactions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling

Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems. However, the computational cost remains a major bottleneck in various scientific and engineering applications, which has motivated the development of reduced-order models (ROMs). Recently, machine-learning-based ROMs have gained significant popularity and are promising for addressing some limitations of traditional ROM methods, especially for advection dominated systems. In this chapter, we focus on a particular framework known as Latent Space Dynamics Identification (LaSDI), which transforms the high-fidelity data, governed by a PDE, to simpler and low-dimensional latent-space data, governed by ordinary differential equations (ODEs). These ODEs can be learned and subsequently interpolated to make ROM predictions. Each building block of LaSDI can be easily modulated depending on the application, which makes the LaSDI framework highly flexible. In particular, we present strategies to enforce the laws of thermodynamics into LaSDI models (tLaSDI), enhance robustness in the presence of noise through the weak form (WLaSDI), select high-fidelity training data efficiently through active learning (gLaSDI, GPLaSDI), and quantify the ROM prediction uncertainty through Gaussian processes (GPLaSDI). We demonstrate the performance of different LaSDI approaches on Burgers equation, a non-linear heat conduction problem, and a plasma physics problem, showing that LaSDI algorithms can achieve relative errors of less than a few percent and up to thousands of times speed-ups.

Computational Engineering, Finance, and Science (c↗