Search NASA⌕ Search

SEARCH · Search NASA

Results for “computational challenges”

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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Generalization of NESTLE into a Multi-Energy N-Group Formulation

The NESTLE code is a few-group, diffusion-based core simulation code that employs the Nodal Expansion Method (NEM). NESTLE has the capability to solve both steady-state and transient problems, using either Cartesian or hexagonal geometry, and to apply thermal-hydraulic feedback including single and two-phase flow. A modern version of the NESTLE nodal-diffusion simulator is currently maintained and developed at the University of Tennessee. The original version of the code was limited to only two or four neutron energy groups, therefore, this article describes recent work carried out to expand NESTLE into a general multi-group solver. Generalization of a fixed group finite-difference diffusion theory into arbitrary N groups is a well-known problem [3], however, this paper describes the intricacies associated with the generalization of the Nodal Expansion Method, and the methodical and computational challenges encountered and managed along the way.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Graph Neural Network-based Tracking as a Service

Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelerate the inference time. Additionally, the large input graph size demands a large device memory for efficient computation, a requirement not met by all computing facilities used for particle physics experiments, particularly those lacking advanced GPUs. Furthermore, deploying the GNN-based track-finding algorithm in a production environment requires the installation of all dependent software packages, exclusively utilized by this algorithm. These computing challenges must be addressed for the successful implementation of GNN-based track-finding algorithm into production settings. In response, we introduce a ``GNN-based tracking as a service'' approach, incorporating a custom backend within the NVIDIA Triton inference server to facilitate GNN-based tracking. This paper presents the performance of this approach using the Perlmutter supercomputer at NERSC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Thermal feedback coupling in a transient Monte Carlo high-order/low-order scheme

Transient simulations of nuclear systems face the computational challenge of resolving both space and time during reactivity changes. A common strategy for tackling this issue is to split the neutron flux into shape and amplitude functions. This split can be solved with high- order/low-order methods. While this multi-fidelity approach has traditionally been reserved for deterministic methods, it is also possible to implement in Monte Carlo as an efficient alternative to Dynamic Monte Carlo. This work implements the frequency transform method with thermal feedback in high-order/low-order Monte Carlo by blending static coupling methods such as single-batch Monte Carlo, with a simple thermal-fluids calculation. While previous work focused solely on prescribed transients, the addition of time-dependent thermal-fluids allows transients to be self-propagating. Tests were run in a fluids-initiated transient to showcase the basic functionalities of this methodology. Preliminary results behave as expected, paving the way for studying more sophisticated thermal-fluids coupling methods. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

ZEUS: An Efficient GPU Optimization Method Integrating PSO, BFGS, and Automatic Differentiation

We introduce a novel, efficient computational method, ZEUS, for numerical optimization, and provide an open-source implementation. It has four key ingredients: (1) particle swarm optimization (PSO), (2) the use of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, (3) automatic differentiation (AD), and (4) GPUs. Our approach addresses the computational challenges inherent in high-dimensional, non-convex optimization problems. In the first phase of the algorithm, we get a potentially good set of starting points using PSO. Thereafter, we run BFGS independently in parallel from these starting points. BFGS is one of the best-performing algorithms for numerical optimization. However, it requires the gradient of the function being optimized. ZEUS integrates automatic differentiation into BFGS thus avoiding the need for the user to calculate derivatives explicitly. The use of GPUs allows ZEUS to speed up the calculations substantially. We carry out systematic studies to explore the trade-offs between the number of PSO iterations taken, starting points, and BFGS iteration depth. We show that a handful of iterations of PSO can improve global convergence when combined with BFGS. We also present performance studies using common test functions. The source code can be found at https://github.com/fnal-numerics/global-optimizer-gpu.

Soos, Dominik [Old Dominion U.]↗
DOE OSTI2026

Integrated Transmission-Distribution Multi-Period Switching for Wildfire Risk Mitigation: Improving Speed and Scalability with Distributed Optimization: Preprint

With increasingly severe wildfire conditions driven by climate change, utilities must manage the risk of wildfire ignitions from electric power lines. During "public safety power shutoff'" events, utilities de-energize power lines to reduce wildfire ignition risk, which may result in load shedding. Distributed energy resources provide flexibility that can help support the system to reduce load shedding when lines are de-energized. We investigate a coordinated transmission-distribution optimization problem that balances wildfire risk mitigation and load shedding. We model distribution systems that include battery energy storage systems which may support loads when transmission lines are de-energized. This multi-period integrated transmission-distribution optimal switching problem jointly optimizes line switching decisions, the generators' setpoints, load shedding, and the batteries' states of charge, resulting in significant computational challenges. To improve scalability, we decompose the problem over both space and time and apply a distributed optimization algorithm. Using a large-scale synthetic California test case with realistic distribution models and real wildfire risk data, we show that distributed optimization can solve large-scale multi-period switching problems that are otherwise intractable for centralized solvers. We also discuss challenges and future directions for improving the distributed algorithm's convergence performance as the number of time periods increases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2003

Computational Aeroelasticity: Success, Progress, Challenge

The formal term Computational Aeroelasticity (CAE) has only been recently adopted to describe aeroelastic analysis methods coupling high-level computational fluid dynamics codes with structural dynamics techniques. However, the general field of aeroelastic computations has enjoyed a rich history of development and application since the first hand-calculations performed in the mid 1930 s. This paper portrays a much broader definition of Computational Aeroelasticity; one that encompasses all levels of aeroelastic computation from the simplest linear aerodynamic modeling to the highest levels of viscous unsteady aerodynamics, from the most basic linear beam structural models to state-of-the-art Finite Element Model (FEM) structural analysis. This paper is not written as a comprehensive history of CAE, but rather serves to review the development and application of aeroelastic analysis methods. It describes techniques and example applications that are viewed as relatively mature and accepted, the "successes" of CAE. Cases where CAE has been successfully applied to unique or emerging problems, but the resulting techniques have proven to be one-of-a-kind analyses or areas where the techniques have yet to evolve into a routinely applied methodology are covered as "progress" in CAE. Finally the true value of this paper is rooted in the description of problems where CAE falls short in its ability to provide relevant tools for industry, the so-called "challenges" to CAE.

Schuster, David M.↗
DOE OSTI2013

Grand Challenges of Advanced Computing for Energy Innovation Report from the Workshop Held July 31-August 2, 2012

On July 31-August 2 of 2012, the U.S. Department of Energy (DOE) held a workshop entitled Grand Challenges of Advanced Computing for Energy Innovation. This workshop built on three earlier workshops that clearly identified the potential for the Department and its national laboratories to enable energy innovation. The specific goal of the workshop was to identify the key challenges that the nation must overcome to apply the full benefit of taxpayer-funded advanced computing technologies to U.S. energy innovation in the ways that the country produces, moves, stores, and uses energy. Perhaps more importantly, the workshop also developed a set of recommendations to help the Department overcome those challenges. These recommendations provide an action plan for what the Department can do in the coming years to improve the nation’s energy future.

Larzelere, Alex R.↗
DOE OSTI2016

Cone Beam Computed Tomography: The Challenges and Strategies in Its Application for Dose Accumulation

Online image guidance using cone beam computed tomography (CBCT) has greatly improved the geometric precision of radiotherapy. Changes in anatomy are common during a course of fractionated treatment, resulting in dose deviation from the planned distribution. There is increased interest in performing dose accumulation to compute the actual delivered dose and to adapt the treatment when necessary. This can be achieved by delineating the volume of interest and by generating “dose of the day” through dose computation on the CBCT. However, the image quality and the accuracy of the CT number of CBCT are deemed to be inferior to fan beam CT, which increases the uncertainty associated in this process. A review of literature was conducted to assess the reliability of and to examine strategies for overcoming the challenges in using CBCT for volume delineation and dose computation. The review demonstrates that the uncertainty varies across body sites, and different strategies have been recommended to generate comparable results to images from CT simulators. This facilitates a better understanding of the potential and the limitation of using CBCT for dose accumulation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Quantum Computing and Visualization Research Challenges and Opportunities

Here, quantum computing (QC) has experienced rapid growth in recent years with the advent of robust programming environments, readily accessible software simulators and cloud-based QC hardware platforms, and growing interest in learning how to design useful methods that leverage this emerging technology for practical applications. From the perspective of the field of visualization, this article examines research challenges and opportunities along the path from initial feasibility to practical use of QC platforms applied to meaningful problems.

Data visualization↗
DOE OSTI2011

Climate Change Modeling:Computational Opportunities and Challenges

High- delity climate models are the workhorses of modern climate change sciences. In thisarticle, the authors focus on several computational issues associated with climate changemodeling, covering simulation methodologies, temporal and spatial modeling restrictions,the role of high-end computing, as well as the importance of data-driven regional climateimpact modeling.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

Multidisciplinary computational aerosciences

As the challenges of single disciplinary computational physics are met, such as computational fluid dynamics, computational structural mechanics, computational propulsion, computational aeroacoustics, computational electromagnetics, etc., scientists have begun investigating the combination of these single disciplines into what is being called multidisciplinary computational aerosciences (MCAS). The combination of several disciplines not only offers simulation realism but also formidable computational challenges. The solution of such problems will require computers orders of magnitude larger than those currently available. Such computer power can only be supplied by massively parallel machines because of the current speed-of-light limitation of conventional serial systems. Even with such machines, MCAS problems will require hundreds of hours for their solution. To efficiently utilize such a machine, research is required in three areas that include parallel architectures, systems software, and applications software. The main emphasis of this paper is the applications software element. Examples that demonstrate application software for multidisciplinary problems currently being solved at NASA Ames Research Center are presented. Pacing items for MCAS are discussed such as solution methodology, physical modeling, computer power, and multidisciplinary validation experiments.

Kutler, Paul↗
DOE OSTI2021

Cold ion beam in a storage ring as a platform for large-scale quantum computers and simulators: Challenges and directions for research and development

The purpose of this paper is to evaluate the possibility of constructing a large-scale storage-ring-type ion-trap system capable of storing, cooling, and controlling a large number of ions as a platform for scalable quantum computing (QC) and quantum simulations. In such a trap, the ions form a crystalline beam moving along a circular path with a constant velocity determined by the frequency and intensity of the cooling lasers. In this paper, we consider a large leap forward in terms of the number of ions that serve as qubits in QC, from fewer than 100 available in state of the art linear ion-trap devices today to an order of 10 5 crystallized ions in the storage-ring setup. This new trap design unifies two different concepts: the storage rings of charged particles and the linear ion traps used for QC and mass spectrometry. In this paper, we use the language of particle accelerators to discuss the ion state and dynamics. We outline the differences between the above concepts, analyze challenges of the large ring with a revolving chain of ions, and propose goals for the research and development required to enable future quantum computers with 1000 times more qubits than available today. The challenge of creating such a large-scale quantum system while maintaining the necessary coherence of the qubits and the high fidelity of quantum logic operations is significant. Performing analog quantum simulations may be an achievable initial goal for such a device. Quantum calculations and simulations of complex quantum systems will move forward both the fundamental science and the applied research. Nuclear and particle physics, many-body quantum systems, lattice gauge theories, and nuclear structure calculations are just a few examples in which a large-scale quantum simulation system will become a very powerful tool to move forward our understanding of nature.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design and Challenges of Edge Computing ASICs on Front-End Electronics

In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted

47 OTHER INSTRUMENTATION↗
NASA NTRS2023

Computer Human Interface Challenges in Space Exploration

NASA’s plans to return humans to the Lunar surface require overcoming a variety of challenging technical and operational obstacles. In 2022, NASA formed the Extravehicular Activity (EVA) and Human Surface Mobility (HSM) Program (EHP) at the Johnson Space Center with responsibilities including development of space suits and surface mobility systems for Lunar missions. This program includes a Technology Development and Partnerships office chartered to identify high priority gaps in capabilities for Lunar surface mobility and to coordinate resources to close those gaps. This presentation details the EHP technology roadmap for “Informatics and Decision Support,” a subset of spacecraft avionics focused on effective and autonomous crew interaction with spaceflight systems. The gaps, grouped into displays, audio systems, and information technology infrastructure, are largely driven by the unique interaction requirements for human spacecraft and the severe radiation environments beyond low earth orbit. The roadmap identifies ongoing activities and paths to technology infusion into Lunar spacecraft. NASA is seeking input on the content and ideas for alternative paths to gap closure. Closing these gaps is important to successful human operations on the Lunar surface and vital to NASA’s long-term goal of human missions to Mars.

Spacecraft Displays↗
DOE OSTI2023

Modular machine learning-based elastoplasticity: Generalization in the context of limited data

The development of highly accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions and from the viewpoint of data availability, verification, and validation. Recently, data-driven modeling approaches have been proposed that aim to establish stress-evolution laws that avoid user-chosen functional forms by relying on machine learning representations and algorithms. However, these approaches not only require a significant amount of data but also need data that probes the full stress space with a variety of complex loading paths. Furthermore, they rarely enforce all necessary thermodynamic principles as hard constraints. Hence, they are in particular not suitable for low-data or limited-data regimes, where the first arises from the cost of obtaining the data and the latter from the experimental limitations of obtaining labeled data, which is commonly the case in engineering applications. In this work, we discuss a hybrid framework that can work on a variable amount of data by relying on the modularity of the elastoplasticity formulation where each component of the model can be chosen to be either a classical phenomenological or a data-driven model depending on the amount of available information and the complexity of the response. The method is tested on synthetic uniaxial data coming from simulations as well as cyclic experimental data for structural materials. The discovered material models are found to not only interpolate well but also allow for accurate extrapolation in a thermodynamically consistent manner far outside the domain of the training data. This ability to extrapolate from limited data was the main reason for the early and continued success of phenomenological models and the main shortcoming in machine learning-enabled constitutive modeling approaches. Training aspects and details of the implementation of these models into Finite Element simulations are discussed and analyzed.

42 ENGINEERING↗
DOE OSTI2019

Chapter 1: Looking Forward: The Promise and Challenge of Exascale Computing

The demand for HFM development and HPC resources for improved predictive modelling capability for wind energy will continue to grow as industry and government seek solutions to (1) mitigate risk and uncertainty of technology development at multimegawatt-turbine scales, (2) address new requirements driving renewable energy deployment and high -penetration scenarios, (3) understand the impacts of multiple gigawatt-scale plant deployments on microclimatology and grid architectures for both land-based and offshore installation, and (4) evolve the systems and processes needed for the dispatch of renewable energy generation on a national scale supporting a mix of power-generation technologies. When considering the cost of developing a new offshore multimegawatt commercial wind turbine product line or the financial risk of a large offshore wind plant deployment, predictive HPC and HFM simulations that incorporate integrated design, cost analysis, and O&M predictions will become an essential part of the early design and development process.

97 MATHEMATICS AND COMPUTING↗