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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

A Fast Implementation of the ISOCLUS Algorithm

Unsupervised clustering is a fundamental building block in numerous image processing applications. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute the coordinates of a set of cluster centers in d-space, such that those centers minimize the mean squared distance from each data point to its nearest center. This clustering algorithm is similar to another well-known clustering method, called k-means. One significant feature of ISOCLUS over k-means is that the actual number of clusters reported might be fewer or more than the number supplied as part of the input. The algorithm uses different heuristics to determine whether to merge lor split clusters. As ISOCLUS can run very slowly, particularly on large data sets, there has been a growing .interest in the remote sensing community in computing it efficiently. We have developed a faster implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm of Kanungo, et al. They showed that, by using a kd-tree data structure for storing the data, it is possible to reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm, and we show that it is possible to achieve essentially the same results as ISOCLUS on large data sets, but with significantly lower running times. This adaptation involves computing a number of cluster statistics that are needed for ISOCLUS but not for k-means. Both the k-means and ISOCLUS algorithms are based on iterative schemes, in which nearest neighbors are calculated until some convergence criterion is satisfied. Each iteration requires that the nearest center for each data point be computed. Naively, this requires O(kn) time, where k denotes the current number of centers. Traditional techniques for accelerating nearest neighbor searching involve storing the k centers in a data structure. However, because of the iterative nature of the algorithm, this data structure would need to be rebuilt with each new iteration. Our approach is to store the data points in a kd-tree data structure. The assignment of points to nearest neighbors is carried out by a filtering process, which successively eliminates centers that can not possibly be the nearest neighbor for a given region of space. This algorithm is significantly faster, because large groups of data points can be assigned to their nearest center in a single operation. Preliminary results on a number of real Landsat datasets show that our revised ISOCLUS-like scheme runs about twice as fast.

Memarsadeghi, Nargess↗
NASA NTRS1983

Acceleration environment of payloads while being handled by the Shuttle Remote Manipulator System

Described in this paper is the method used in the Draper Remote Manipulator System (RMS) Simulation to compute linear accelerations at the point on the SPAS01 payload where its accelerometers are mounted. Simulated accelerometer output for representative on-orbit activities is presented. The objectives of post-flight analysis of SPAS01 data are discussed. Finally, the point is made that designers of acceleration-dependent payloads may have an interest in the capability of simulating the acceleration environment of payloads while under the control of the overall Payload Deployment and retrieval System (PDRS) that includes the Orbiter and its attitude control system as well as the Remote Manipulator Arm.

Turnbull, J. F.↗
DOE OSTI2025

APOLLO: a facility-scale differentiable virtual accelerator at Fermilab FAST/IOTA

As the design complexity of modern accelerators grows, there is more interest in using advanced simulations that have fast execution time or yield additional insights like gradients. The FAST/IOTA facility has been working on implementing and experimentally validating an end-to-end digital twin that is both fast and gradient-aware, allowing for rapid prototyping of new software and experiments with minimal beam time costs. Our framework integrates physics and ML codes for linac and ring simulation through a set of generic interfaces between surrogate and physics-based sections. To reproduce device inputs and outputs, system state is exposed as a deterministic event loop in a specialized discrete event simulator architecture. Because Fermilab is undergoing control system transition, several APIs were implemented as final user interfaces - a fully asynchronous EPICS soft IOC, a gRPC-based Data Pool Manager (DPM), and legacy ACNET protocols. We discuss implementation details as well as challenges handling live data assimilation and future plans to extend modelling to main complex proton accelerators like PIPII and Booster.

Kuklev, Nikita [Fermilab]↗
DOE OSTI2022

VWC-BERT: Scaling Vulnerability–Weakness–Exploit Mapping on Modern AI Accelerators

Defending cybersystems needs accurate mapping of software and hardware vulnerabilities to generalized descriptions of weaknesses, and weaknesses to exploits. These mappings enable cyber defenders to build plans for effective defense and assessment of potential risks to a cybersystem. With close to 170k vulnerabilities, manual mapping is not a feasible option. However, automated mapping is challenging due to limited training data, computational intractability, and limitations in computational natural language processing. Tools based on breakthroughs in Transformer-based language models have been demonstrated to classify vulnerabilities with high accuracy. We make three key contributions in this paper: (1) We present a new framework, \VWCBERT, that augments the Transformer-based hierarchical multi-class classification framework of Das et al. (\textsc{V2W-BERT}) with the ability to map weaknesses to exploits. (2) We implement \VWCBERT~ on modern AI accelerator platforms using two data parallel techniques for the pre-training phase and demonstrate nearly linear speedups across NVIDIA and Graphcore accelerator platforms. We observe nearly linear speedups for up to 16 V100 and 8 A100 GPUs, and about 3.4$\times$ speedup for A100 relative to V100 GPUs. We also observe excellent speedups on Graphcore, with $5.7\times$ speedup on 128 IPUs relative to 16 IPUs. Enabled by scaling, we also demonstrate higher accuracy using a larger language model, RoBERTa-Large. We show up to 87\% accuracy for strict and up to 98\% accuracy for relaxed classification. (3) We develop a novel parallel link manager for the link prediction phase and demonstrate up to 21$\times$ speedup with 16 V100 GPUs relative to one V100 GPU, and thus reducing the runtime from 2.5 hours to 10 minutes. We believe that generalizability and scalability of \VWCBERT~ will benefit both the theoretical development and practical deployment of novel cyberdefense solutions and vulnerability classification.

Das, Siddhartha Shankar↗
NASA NTRS2003

Radiation Risk Projections for Space Travel

Space travelers are exposed to solar and galactic cosmic rays comprised of protons and heavy ions moving with velocities close to the speed of light. Cosmic ray heavy ions are known to produce more severe types of biomolecular damage in comparison to terrestrial forms of radiation, however the relationship between such damage and disease has not been fully elucidated. On Earth, we are protected from cosmic rays by atmospheric and magnetic shielding, and only the remnants of cosmic rays in the form of ground level muons and other secondary radiations are present. Because human epidemiology data is lacking for cosmic rays, risk projection must rely on theoretical understanding and data from experimental models exposed to space radiation using charged particle accelerators to simulate space radiation. Although the risks of cancer and other late effects from cosmic rays are currently believed to present a severe challenge to space travel, this challenge is centered on our lack of confidence in risk projections methodologies. We review biophysics and radiobiology data on the effects of the cosmic ray heavy ions, and the current methods used to project radiation risks . Cancer risk projections are described as a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Risk projections for space travel are described using Monte-Carlo sampling from subjective error di stributions that represent the lack of knowledge in each factor that contributes to the projection model in order to quantify the overall uncertainty in risk projections. This analysis is applied to space mi ssion scenarios including lunar colony, deep space outpost, and a Mars mission. Results suggest that the number of days in space where cancer mortality risks can be assured at a 95% confidence level to be below the maximum acceptable risk for radi ation workers on Earth or the International Space Station is only on the order of 100-200 days. Approaches to reduce these unceI1ainties and mitigate risks are described.

Cucinotta, Francis↗
DOE OSTI2025

A GPU‐Accelerated Generative Adversarial Model for Causal Inference

We develop a GPU-accelerated machine learning generative adversarial model designed to facilitate causal inferences from observational data. Our model's theoretical framework is conceptualized in a manner that is amenable to being operable and scalable for high-performance computing platforms. We leverage GPU acceleration to develop a parallel evolutionary algorithm to achieve large-scale parallel computation of the model within a now widely accessible computing platform. This capability both enhances computational speedup and efficiency and also extends the use of the model to a broader range of substantive research domains while maintaining the underlying theoretical properties of the model.

GPU↗
DOE OSTI2026

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows

In modern science, the growing complexity of large-scale scientific projects has led to an increasing reliance on cross-facility scientific workflows, where resources and expertise from multiple institutions and geographic locations are leveraged to accelerate scientific discovery. These workflows often require transmitting huge amounts of scientific data through wide-area networks. Although high-speed networks like ESnet and transfer services such as Globus have improved data mobility, several challenges remain. The sheer volume of data can overwhelm network bandwidth, widely used transport protocols such as TCP suffer from inefficiencies due to retransmissions triggered by packet loss, and existing fault-tolerance mechanisms like erasure coding introduce substantial overhead. In this paper, we propose Janus, a resilient and adaptable data transmission approach designed for cross-facility scientific workflows. Unlike traditional TCP-based methods, Janus leverages UDP, integrates erasure coding for fault tolerance, and combines it with error-bounded lossy compression to reduce overhead. This novel design allows users to balance data transmission time and accuracy, optimizing transfer performance based on specific scientific requirements. Additionally, Janus dynamically adjusts erasure coding parameters in response to real-time network conditions, ensuring efficient data transfers even in fluctuating environments. We develop optimization models for determining ideal configurations and implement adaptive data transfer protocols to enhance reliability. Through extensive simulations and real-network experiments, we demonstrate that Janus significantly improves transfer efficiency while maintaining data fidelity.

Esaulov, Vladislav [Georgia State University, Atla↗
DOE OSTI2023

Using National-Scale Data to Inform Coal Power Plant Redevelopment and Coal Community Revitalization Planning

In the United States, individual coal plants have high variation in their construction and operation. Coal plants commonly have multiple units, with different commissioning ages, operational position, and maintenance conditions – even multiple fuels. These units may be in changing states of utilization, with variation in productivity depending on economic contexts. Coal plants themselves may be temporarily offline, mothballed, retired, or decommissioned due to owner/operator portfolios or market conditions, and the current posture of the plant may be difficult to ascertain without local news sources (Tarekegne et al. 2021). Anticipated dates of plant closures are prospectively represented to regulators and to the Energy Information Administration (EIA), but may also experience uncertainty due to the engineering, environmental, economic and social complexity of closing a very large power plant. As noted in this report, closure of the first unit to the last within a single plant may span more than 20 years. Yet tracking and predicting the position of our nation’s coal fleets on a national scale is deeply important. In developing a case for federal datasets and an approach to building and curating these data, this report identified that data must have high fidelity at the plant-level to be meaningful in aggregate. Plant-scale detail is also a match for its application: national priorities in federal investment range from economic revitalization for coal plant communities to maintaining reliable electric grid operations. This document is organized into a statistical review of coal plant retirements, past and prospective, in the United States; trends in the relationship between closures and existing federal investment programs for community revitalization; and potential methods for accelerating site redevelopment through advanced geospatial analysis based on federal data.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Computationally efficient evaluation of optimum homogeneous charge compression ignition operating range with accelerated multizone engine cycle simulation

The very intensive calculations necessary to define a performance map requiring evaluation of over a hundred individual operating points can be efficiently conducted with accelerated multizone for engine cycle simulation, leading to a definition of regions of acceptable and optimum homogeneous charge compression ignition operation. Accelerated multizone for engine cycle simulation has the virtue of enabling accurate evaluation of many operating conditions based on thermal stratification data from a single fluid mechanics run at motored conditions. This is possible because thermal stratification is more sensitive to engine geometry than to operating conditions. In this article, accuracy of accelerated multizone for engine cycle simulation is demonstrated by comparison with experimental data for iso-octane homogeneous charge compression ignition operation over a broad range of lean equivalence ratios (0.14–0.28). The validated accelerated multizone for engine cycle simulation model is then applied to generating a performance map for an engine controlled by appropriately adjusting equivalence ratio and internal exhaust gas recirculation. Regions of acceptable and optimum combustion are identified. It is finally demonstrated that while indicated mean effective pressure remains low for optimum homogeneous charge compression ignition operation (1–4 bar), this is sufficient for a large fraction of typical driving in light-duty vehicles. Much driving including idle can therefore be done in homogeneous charge compression ignition mode at high efficiency and low (essentially zero) NOx and particulate matter emissions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

In the studies of materials, experimental measurements often serve as the reference to verify physics theory and modeling; while theory and modeling provide a fundamental understanding of the physics and principles behind. However, the interactions and cross validation between them have long been a challenge even to-date. Not only that inferring a physics model from experimental data is itself a difficult inverse problem, another major challenge is the orders-of-magnitude longer wall-clock time required to carry out high-fidelity computer modeling to match the timescale of experiments. We envisage that by combining high performance computing, data science, and edge computing technology, the current predicament can be alleviated, and a new paradigm of data-driven physics research will open up. For example, we can accelerate computer simulations by first performing the large-scale modeling on high performance computers and train a machine-learned surrogate model. This computationally inexpensive surrogate model can then be transferred to the computing units residing closely to the experimental facilities to perform high-fidelity simulations at a much higher throughout. The model will also be more amenable to analyzing and validating experimental observations in comparable time scales at a much lower computational cost. Further integration of these accelerated computer simulations with an outer machine learning loop can also inform and direct future experiments, while making the inverse problem of physics model inference more tractable. We will demonstrate a proof-of-concept by using a quantum Monte Carlo application, Dynamical Cluster Approximation (DCA++), to machine-learn a surrogate model and accelerate the study of quantum correlated materials.

Li, Ying Wai↗
NASA NTRS1971

The effects of random accelerations on estimation accuracy with applications to the Mariner 1969 relativity experiment

A simple point of view is presented for establishing the relationship between a general modeling error and estimation accuracy. Tentative models are given for the random, nongravitational forces thought likely to affect the Mariner 6 and 7 spacecraft. The navigation data from these spacecraft were used to determine the relativity parameter Gamma. The effect that random accelerations have on the classical least squares filter, which assumes the accelerations do not exist, is calculated, and it is demonstrated that their presence seriously affects the estimates, particularly when an extended data arc is employed. Finally, some preliminary exploration using sequential-type filters, which attempt to model and thus ameliorate the effects of these same accelerations, is reported.

Curkendall, D. W.↗
DOE OSTI2021

Accelerated Creep Testing of Inconel 718 Using the Stepped Isostress Method (SSM)

This paper demonstrates the stepped isostress method (SSM); an accelerated creep test (ACT) for the rapid assessment of metallic materials. The SSM test is based on the time-temperature-stress superposition principle (TTSSP) where temperature and/or stress are step increased to accelerate the time-to-rupture. The SSM test has proven successful for the ACT of polymers and polymeric composites but has yet to be proven for metallic materials. In this study, new test matrix design rules for the SSM of metals are established based on deformation mechanism, time-temperature transformation, and time-temperature precipitation maps. A test matrix of SSM and conventional creep tests (CCTs) are executed for alloy Inconel 718 at 750°C (1382°F) with stress levels ranging from 100 to 350 MPa. Validation CCT data is gathered from the Japan National Institute of Material Science (NIMS). Material constants for the Sine-hyperbolic (Sinh) constitutive model are calibrated using the SSM data and employed to predict the conventional creep response. Here, when blindly compared to the CCT data, the SSM calibrated Sinh model can accurately predict the conventional creep response across logarithmic decades and thus accelerate the capture of conventional creep data. Fractography indicates ductile fracture by transgranular microvoid coalescence (TMVC) with the same fracture mode observed in both SSM and CCT specimens. Creep cavitation is indicated by the population, smoothness, and size of microvoids.

36 MATERIALS SCIENCE↗
NASA NTRS1992

STS-40 orbital acceleration research experiment flight results during a typical sleep period

The Orbital Acceleration Research Experiment (OARE), an electrostatic accelerometer package with complete on-orbit calibration capabilities, was flown for the first time aboard the Space Shuttle on STS-40. This is also the first time an accelerometer package with nano-g sensitivity and a calibration facility has flown aboard the Space Shuttle. The instrument is designed to measure and record the Space Shuttle aerodynamic acceleration environment from the free molecule flow regime through the rarified flow transition into the hypersonic continuum regime. Because of its sensitivity, the OARE instrument defects aerodynamic behavior of the Space Shuttle while in low-earth orbit. A 2-hour orbital time period on day seven of the mission, when the crew was asleep and other spacecraft activities were at a minimum, was examined. During the flight, a 'trimmed-mean' filter was used to produce high quality, low frequency data which was successfully stored aboard the Space Shuttle in the OARE data storage system. Initial review of the data indicated that, although the expected precision was achieved, some equipment problems occurred resulting in uncertain accuracy. An acceleration model which includes aerodynamic, gravity-gradient, and rotational effects was constructed and compared with flight data. Examination of the model with the flight data shows the instrument to be sensitive to all major expected low frequency acceleration phenomena; however, some erratic instrument bias behavior persists in two axes. In these axes, the OARE data can be made to match a comprehensive atmospheric-aerodynamic model by making bias adjustments and slight linear corrections for drift. The other axis does not exhibit these difficulties and gives good agreement with the acceleration model.

Blanchard, R. C.↗
NASA NTRS1972

Response of a seat-passenger system to impulsive loading.

This paper presents a summary of a study of the dynamic response of an aircraft seat-passenger system to impulsive loading typical of aircraft crash situations. A brief description of the computer model SIMULA is presented, and selected data from 305 separate cases which have been studied are discussed. Maximum system forces, displacements, velocities, and accelerations are presented as functions of velocity change, aircraft deceleration, crash pulse shape, passenger weight, and seat belt slack. Data from both single and coupled parameter studies are included. A correlation of SIMULA results with experimentally obtained data is made.

Collins, J. A.↗
NASA NTRS2007

Smart Payload Development for High Data Rate Instrument Systems

This slide presentation reviews the development of smart payloads instruments systems with high data rates. On-board computation has become a bottleneck for advanced science instrument and engineering capabilities. In order to improve the computation capability on board, smart payloads have been proposed. A smart payload is a Localized instrument, that can offload the flight processor of extensive computing cycles, simplify the interfaces, and minimize the dependency of the instrument on the flight system. This has been proposed for the Mars mission, Mars Atmospheric Trace Molecule Spectroscopy (MATMOS). The design of this system is discussed; the features of the Virtex-4, are discussed, and the technical approach is reviewed. The proposed Hybrid Field Programmable Gate Array (FPGA) technology has been shown to deliver breakthrough performance by tightly coupling hardware and software. Smart Payload designs for instruments such as MATMOS can meet science data return requirements with more competitive use of available on-board resources and can provide algorithm acceleration in hardware leading to implementation of better (more advanced) algorithms in on-board systems for improved science data return

Virtex-4↗
DOE OSTI2026

An Autonomous MCP Bridge to Rucio: Enhancing Data Management Accessibility for High Energy Physics

The Rucio Data Management System [1] is an important tool used by High Energy Physics experiments, including those at Fermi National Accelerator Laboratory, to store and manage exabyte-scale scientific datasets. Despite its central role in coordinating data across globally distributed storage sites, Rucio's command line interface (CLI) presents a steep learning curve, and makes it difficult for scientists to navigate through. To solve this issue, a containerized Model Context Protocol (MCP) [2] server was built that connects Large Language Models directly to Rucio, allowing AI agents to handle data tasks by using simple, natural language rather than memorized terminal commands. The core engineering focus of this project was moving the server away from slow terminal commands that require text parsing and replacing them with a native Python Client API toolset and a planned REST API framework. Moving to the Python API handles data operations directly in memory, which helps clear up formatting errors, provides the AI with clean, structured JSON data and speeds up tool execution. To prove that the system actually works, a benchmarking pipeline was also built with various questions to test the AI across four different model configurations. The questions included finding data scopes, tracking down specific datasets, and checking replication rules. Through benchmarking, early runs showed that with raw terminal text, the model would get confused and stuck, whereas switching to the Python API to feed the AI clean, structured data yielded massive improvement. By creating an intelligent and autonomous bridge to a storage network, this project shows how AI can be implemented in scientific data management, which ultimately helps scientists at Fermilab spend less time sorting through data and more time focusing on their experiments and analysis.

Akella, Kashyap [William Rainey Harper Coll.]↗
DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗
NASA NTRS1990

A new limit on the time between the nucleosynthesis and the acceleration of cosmic rays in supernova remnants using the Co/Ni ratio

Using new cross section measurements of Ni into Co, data on the Co/Ni ratio in cosmic rays from the HEAO C spacecraft have been reinterpreted in terms of the time between nucleosynthesis and the acceleration of cosmic rays, delta t. The observed Co/Ni ratio is now consistent with interstellar fragmentation only, leading to a small or zero source abundance. In terms of the decay of e-process nucleosynthesis nuclides into Co after a supernova explosion, this permits an estimate of delta t = 4-30,000 yr for the time between nucleosynthesis and the acceleration of cosmic rays if supernovae are the direct progenitors of cosmic rays. These age limits are used in conjunction with models of the expansion of supernova remnants (SNRs), to estimate that cosmic rays are accelerated when the radius of these remnants is between 0.1 and 25 pc.

Webber, W. R.↗