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At least 1,531 records · Page 85

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Fast and accurate simulations of calorimeter showers with normalizing flows

In this study, we introduce caloflow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce many-channel calorimeter showers with extremely high fidelity, providing a fresh alternative to computationally expensive geant4 simulations, as well as other state-of-the-art fast simulation frameworks based on generative adversarial networks (GANs) or variational autoencoders (VAEs). In addition to the usual histograms of physical features and images of calorimeter showers, we introduce a new metric for judging the quality of generative modeling: the performance of a classifier trained to differentiate real from generated images. We show that GAN-generated images can be identified by the classifier with nearly 100% accuracy, while images generated from caloflow are better able to fool the classifier. More broadly, normalizing flows offer several advantages compared to other state-of-the-art approaches (GANs and VAEs), including tractable likelihoods, stable and convergent training, and principled model selection. Normalizing flows also provide a bijective mapping between data and the latent space, which could have other applications beyond simulation, for example, to detector unfolding.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. Here, in this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Chapter 18: Learning and Tracking Ad Hoc Fiducial Markers in Spatial Augmented Reality

We describe a spatial augmented reality system with a tangible user interface used to control computer simulations of complex systems. In spatial augmented reality, the user’s physical space is augmented with projected imagery, blending real objects with projected information, and a tangible user interface enables users to manipulate physical objects as controllers for interactive visualizations. Our system learns ad hoc objects in the user’s environment as fiducial markers (i.e., objects that are visually recognized and tracked). When combined with simulation and visualization tools, these interfaces allow the user to control simulations or ensembles of simulations via physical objects using apt metaphors. While other research has leveraged the use of depth cameras, our system enables the use of standard cameras in readily available smartphones and webcams and has an implementation that runs completely in JavaScript in the web browser. We discuss the prerequisite object-recognition requirements for such tangible user interfaces and describe computer-vision and machine-learning algorithms meeting those requirements. We conclude by presenting example applications, which are also available online.

energy distribution↗

DOE OSTI2021

Demonstration of the Plant Fuel Reload Process Optimization for an Operating PWR

The United States (U.S.) nuclear industry is facing a strong challenge to maintain regulatory-required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor (LWR) nuclear power plants (NPPs) and can be achieved more economically by using a risk-informed ecosystem such as that being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program is promoting a wide range of research and development (R&D) activities with the goal to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering second license renewal. The RISA Pathway has two main goals: (1) the deployment of methodologies and technologies that enable better representation of safety margins and the factors that contribute to cost and safety; and (2) the development of advanced applications that enable cost-effective plant operation. This report summarizes the research outcomes in FY-2021, which the project progressed from the planning and methodology development phase to the early demonstration phase. The highlights of these activities are: (1) the development of a multi-objective optimization process using Genetic Algorithms (GAs); (2) the development and test of an approach for optimization process acceleration using artificial intelligence (AI) that significantly reduces the computational burden; (3) the demonstration of the fuel reload optimization framework for a generic pressurized water reactor (PWR); and (4) the demonstration of limiting design basis accident (DBA) scenarios for evaluation of the transition from deterministic to risk-informed approach for fuel reload optimization.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

RTDP: Streaming Readout Real-Time Development and Testing Platform

The Thomas Jefferson National Accelerator Facility (JLab) has created and is currently working on various tools to facilitate streaming readout (SRO) for upcoming experiments. These include reconstruction frameworks with support for Artificial Intelligence/Machine Learning, distributed High Throughput Computing (HTC), and heterogeneous computing which all contribute significantly to swift data processing and analysis. Designing SRO systems that combine such components for new experiments would benefit from a platform that would combine both simulation and execution components for simulation, testing, and validation before large investments are made. The Real-Time Development Platform (RTDP) is being developed as part of an LDRD funded project at JLab. RTDP aims to establish a seamless connection between algorithms, facilitating the seamless processing of data from SRO to analysis, as well as enabling the execution of these algorithms in various configurations on compute and data centers. Individual software components simulating specific hardware can be replaced with actual hardware when it is available.

Gyurjyan, Vardan↗

DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗

DOE OSTI2025

SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions

Recent work has established an alternative to traditional multi-layer perceptron neural networks in the form of Kolmogorov-Arnold Networks (KAN). The general KAN framework uses learnable activation functions on the edges of the computational graph followed by summation on nodes. The learnable edge activation functions in the original implementation are basis spline functions (B-Spline). Here, we present a model in which learnable grids of B-Spline activation functions are replaced by grids of re-weighted sine functions (SineKAN). We evaluate numerical performance of our model on a benchmark vision task. We show that our model can perform better than or comparable to B-Spline KAN models and an alternative KAN implementation based on periodic cosine and sine functions representing a Fourier Series. Further, we show that SineKAN has numerical accuracy that could scale comparably to dense neural networks (DNNs). Compared to the two baseline KAN models, SineKAN achieves a substantial speed increase at all hidden layer sizes, batch sizes, and depths. Current advantage of DNNs due to hardware and software optimizations are discussed along with theoretical scaling. Additionally, properties of SineKAN compared to other KAN implementations and current limitations are also discussed.

Reinhardt, Eric↗

NASA NTRS1987

Metalevel programming in robotics: Some issues

Computing in robotics has two important requirements: efficiency and flexibility. Algorithms for robot actions are implemented usually in procedural languages such as VAL and AL. But, since their excessive bindings create inflexible structures of computation, it is proposed that Logic Programming is a more suitable language for robot programming due to its non-determinism, declarative nature, and provision for metalevel programming. Logic Programming, however, results in inefficient computations. As a solution to this problem, researchers discuss a framework in which controls can be described to improve efficiency. They have divided controls into: (1) in-code and (2) metalevel and discussed them with reference to selection of rules and dataflow. Researchers illustrated the merit of Logic Programming by modelling the motion of a robot from one point to another avoiding obstacles.

Kumarn, A.↗

NASA NTRS1990

Probabilistic simulation of uncertainties in thermal structures

Development of probabilistic structural analysis methods for hot structures is a major activity at Lewis Research Center. It consists of five program elements: (1) probabilistic loads; (2) probabilistic finite element analysis; (3) probabilistic material behavior; (4) assessment of reliability and risk; and (5) probabilistic structural performance evaluation. Recent progress includes: (1) quantification of the effects of uncertainties for several variables on high pressure fuel turbopump (HPFT) blade temperature, pressure, and torque of the Space Shuttle Main Engine (SSME); (2) the evaluation of the cumulative distribution function for various structural response variables based on assumed uncertainties in primitive structural variables; (3) evaluation of the failure probability; (4) reliability and risk-cost assessment, and (5) an outline of an emerging approach for eventual hot structures certification. Collectively, the results demonstrate that the structural durability/reliability of hot structural components can be effectively evaluated in a formal probabilistic framework. In addition, the approach can be readily extended to computationally simulate certification of hot structures for aerospace environments.

Chamis, Christos C.↗

NASA NTRS1990

Probabilistic simulation of uncertainties in thermal structures

Development of probabilistic structural analysis methods for hot structures is a major activity at NASA-Lewis, and consists of five program elements: (1) probabilistic loads, (2) probabilistic finite element analysis, (3) probabilistic material behavior, (4) assessment of reliability and risk, and (5) probabilistic structural performance evaluation. Attention is given to quantification of the effects of uncertainties for several variables on High Pressure Fuel Turbopump blade temperature, pressure, and torque of the Space Shuttle Main Engine; the evaluation of the cumulative distribution function for various structural response variables based on assumed uncertainties in primitive structural variables; evaluation of the failure probability; reliability and risk-cost assessment; and an outline of an emerging approach for eventual hot structures certification. Collectively, the results demonstrate that the structural durability/reliability of hot structural components can be effectively evaluated in a formal probabilistic framework. In addition, the approach can be readily extended to computationally simulate certification of hot structures for aerospace environments.

Chamis, C. C.↗

NASA NTRS1995

Nonlinear Fluid Computations in a Distributed Environment

The performance of a loosely and tightly-coupled workstation cluster is compared against a conventional vector supercomputer for the solution the Reynolds- averaged Navier-Stokes equations. The application geometries include a transonic airfoil, a tiltrotor wing/fuselage, and a wing/body/empennage/nacelle transport. Decomposition is of the manager-worker type, with solution of one grid zone per worker process coupled using the PVM message passing library. Task allocation is determined by grid size and processor speed, subject to available memory penalties. Each fluid zone is computed using an implicit diagonal scheme in an overset mesh framework, while relative body motion is accomplished using an additional worker process to re-establish grid communication.

Atwood, Christopher A.↗

NASA NTRS1994

Cluster Computation of Flight Reynolds Number Flows

The performance of a workstation cluster used for the solution of the Reynolds-averaged Navier-Stokes equations is compared with a conventional vector supercomputer architecture. The application simulation of the steady flowfield about a transonic transport was computed using an implicit diagonal scheme in an overset mesh framework. Static load balancing was used, while coarse grain decomposition was achieved by solution of a grid zone per processor. Price/performance ratios are estimated for several scenarios in which such clusters may be utilized.

Atwood, Christopher A.↗

NASA NTRS1998

Towards a Theory for Integration of Mathematical Verification and Empirical Testing

From the viewpoint of a project manager responsible for the V&V (verification and validation) of a software system, mathematical verification techniques provide a possibly useful orthogonal dimension to otherwise standard empirical testing. However, the value they add to an empirical testing regime both in terms of coverage and in fault detection has been difficult to quantify. Furthermore, potential cost savings from replacing testing with mathematical verification techniques cannot be realized until the tradeoffs and synergies can be formulated. Integration of formal verification with empirical testing is also difficult because the idealized view of mathematical verification providing a correctness proof with total coverage is unrealistic and does not reflect the limitations imposed by computational complexity of mathematical techniques. This paper first describes a framework based on software reliability and formalized fault models for a theory of software design fault detection - and hence the utility of various tools for debugging. It then describes a utility model for integrating mathematical and empirical techniques with respect to fault detection and coverage analysis. It then considers the optimal combination of black-box testing, white-box (structural) testing, and formal methods in V&V of a software system. Using case studies from NASA software systems, it then demonstrates how this utility model can be used in practice.

Lowry, Michael↗

NASA NTRS2003

Computational Aspects of Data Assimilation and the ESMF

The scientific challenge of developing advanced data assimilation applications is a daunting task. Independently developed components may have incompatible interfaces or may be written in different computer languages. The high-performance computer (HPC) platforms required by numerically intensive Earth system applications are complex, varied, rapidly evolving and multi-part systems themselves. Since the market for high-end platforms is relatively small, there is little robust middleware available to buffer the modeler from the difficulties of HPC programming. To complicate matters further, the collaborations required to develop large Earth system applications often span initiatives, institutions and agencies, involve geoscience, software engineering, and computer science communities, and cross national borders.The Earth System Modeling Framework (ESMF) project is a concerted response to these challenges. Its goal is to increase software reuse, interoperability, ease of use and performance in Earth system models through the use of a common software framework, developed in an open manner by leaders in the modeling community. The ESMF addresses the technical and to some extent the cultural - aspects of Earth system modeling, laying the groundwork for addressing the more difficult scientific aspects, such as the physical compatibility of components, in the future. In this talk we will discuss the general philosophy and architecture of the ESMF, focussing on those capabilities useful for developing advanced data assimilation applications.

daSilva, A.↗

NASA NTRS2018

Application of Lattice Boltzmann and Navier-Stokes Methods to NASA's Wall Mounted Hump

Lattice Boltzmann (LB) based Large Eddy Simulation (LES), Reynolds-averaged Navier-Stokes (RANS) as well as hybrid RANS/LES methods within the Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework are applied to NASA's wall-mounted hump. Computational results are compared with experiments performed by Greenblatt et al. A detailed comparison between the accuracy and resolution requirements of the two approaches for turbulence resolving simulations, as well as the suitability of different grid paradigms (body-fitted curvilinear and block structured Cartesian) are presented. This test case is part of NASA's Revolutionary Computational Aerosciences (RCA) sub-project which addresses the technical challenge of predicting flow separation and reattachment accurately. Improvements in predictive accuracy by as much as 90% are demonstrated using LB as well as hybrid RANS/LES approaches compared to state-of-the-art steady state RANS simulations.

Lattice Boltzmann↗

NASA NTRSDate not supplied

NASA’s Advance Information Systems Technology (AIST) Program

NASA’s Earth Science Technology Office (ESTO) develops early-stage technologies to enhance scientific understanding. ESTO’s technologies can be reliably and confidently applied to a broad range of science measurements and missions, as well as facilitate practical applications to benefit society at large. As the lead technology office within the Earth Science Division of the NASA Science Mission Directorate, ESTO is focused on the technological challenges inherent to space-based investigations of our planet's dynamic, interrelated systems. Through flexible, science-driven technology strategies and a competitive selection process, ESTO-funded technologies have supported numerous Earth science missions as well as commercial applications. From next generation sensors and instruments to communication and information systems, ESTO technologies enable many NASA missions and data services. The Advanced Information Systems Technology (AIST) Program is one focal area of ESTO. AIST innovates information system technologies that enable the development of new observing systems as well as agile science investigations through data analytics and artificial intelligence tools and algorithms. AIST utilizes an end-to-end development approach with the goal of infusing mature technologies into future missions, measurements, and analysis. One thrust of AIST is data exploitation and analysis using an analytic center framework to make observational data and model output more accessible and usable to scientists conducting specific investigations as well as to extract higher-level science content and information from the data. The framework accelerates scientific discovery by harmonizing the data, tools, and computational resources. NASA’s upcoming Surface, Biology and Geology (SBG) mission is one such mission whose data architecture will leverage many of the technologies developed through the AIST program. This talk will highlight current and future program development goals.

Laura Rogers↗

NASA NTRSDate not supplied

Evaluation of User Experience of Self-Scheduling Software for Astronauts: Defining a Satisfaction Baseline

As NASA turns its sights to deep-space exploration, a greater focus on sup-porting crew autonomy has led to the development of Playbook, a self-scheduling software tool. Evaluating the user satisfaction of Playbook is essential in ensuring its usability for critical spaceflight operations. Satisfaction of an interface is often quantified with attitude surveys, such as the User Experience Questionnaire (UEQ). This paper demonstrates an application of the UEQ in comparing the user experience of Playbook interface de-signs for displaying graphical data. We lay the foundation for future user experience comparisons by defining a satisfaction baseline, which is crucial as more features are integrated into Playbook’s interface. This work ex-tends a validated user experience framework into a spaceflight domain, allowing optimization of human-computer interaction as future operational tools are developed.

user experience↗

DOE OSTI2023

DER Cybersecurity R&D

The National Renewable Energy Laboratory (NREL) conducted more than 30 assessments for utilities across the United States with a cybersecurity assessment tool based on the U.S. Department of Energy (DOE) Cybersecurity Capability Maturity Model (C2M2) and the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) and focused on business process. With funding from the DOE Office of Renewable Energy and Energy Efficiency Federal Energy Management Program, NREL modified the current cyber governance assessment tool to include an assessment process specifically for distributed energy resources (DERs). The Distributed Energy Resources Cybersecurity Framework (DER-CF) was developed to help federal agencies mitigate gaps in their cybersecurity posture for distributed energy systems.

cybersecurity valuation↗