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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Interplay between the non-resonant streaming instability and self-generated pressure anisotropies

The non-thermal particles escaping from collision-less shocks into the surrounding medium can trigger a non-resonant streaming instability that converts parts of their drift kinetic energy into large amplitude magnetic field perturbations, and promote the confinement and acceleration of high energy cosmic rays. We present simulations of the instability using an hybrid-Particle-in-Cell approach including Monte Carlo collisions, and demonstrate that the development of the non-resonant mode is associated with important ion pressure anisotropies in the background plasma. Depending on the initial conditions, the anisotropies may act on the instability by lowering its growth and trigger secondary micro-instabilities. Introducing collisions with neutrals yield a strong reduction of the magnetic field amplification as predicted by linear fluid theory. In contrast, Coulomb collisions in fully ionized plasmas are found to mitigate the self-generated pressure anisotropies and promote the growth of the magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction

Here, this work introduces an online greedy method for constructing quadratic manifolds from streaming data, designed to enable in situ analysis of numerical simulation data on the Petabyte scale. Unlike traditional batch methods, which require all data to be available upfront and take multiple passes over the data, the proposed online greedy method incrementally updates quadratic manifolds in one pass as data points are received, eliminating the need for expensive disk input/output operations as well as storing and loading data points once they have been processed. A range of numerical examples demonstrate that the online greedy method learns accurate quadratic manifold embeddings while being capable of processing data that far exceed common disk input/output capabilities and volumes as well as main-memory sizes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Species Separation and Hydrogen Streaming upon Shock Release from Polystyrene under Inertial Confinement Fusion Conditions

Shock release from inertial confinement fusion (ICF) shells poses a great challenge to single-fluid hydrodynamic equations, especially for describing materials composed of different ion species. This has been evidenced by a recent experiment [Haberberger et al., Phys. Rev. Lett. 123, 235001 (2019)], in which low-density plasmas ( 10 19 to 10 20 cm – 3 ) are measured to move far ahead of what radiation-hydrodynamic simulations predict. To understand such experimental observations, in this work we have performed large-scale nonequilibrium molecular-dynamics simulations of shock release in polystyrene (CH) at experimental conditions. These simulations revealed that upon shock releasing from the back surface of a CH foil, hydrogen can stream out of the bulk of the foil due to its mass being lighter than carbon. This released hydrogen, exhibiting a much broader velocity distribution than carbon, forms low-density plasmas moving in nearly constant velocities ahead of the in-flight shell, which is in quantitative agreement with the experimental measurements. Such kinetic effect of species separation is currently missing in single-fluid radiation-hydrodynamics codes for ICF simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Optimizing Grid-interactive Efficient Building Designs with Stacked Value Streams

Grid-interactive efficient buildings (GEBs) are those characterized by the combination of energy efficiency and demand flexibility with smart technologies and communications to not only deliver greater affordability and comfort to buildings, but also help utilities manage grid operations and lower system costs. This paper presents an innovative techno-economic assessment framework to effectively examine different GEB design options, explore various use cases, define technically achievable benefits, and thereby assist in informed decision-making. In particular, building load flexibility, thermal storage, and battery energy storage are considered. Advanced optimal dispatch problem is formulated to maximize the stacked value streams from multiple, competing use cases, subject to the physical capabilities and operational flexibility associated with different designs and configurations. Comprehensive case studies were performed for a real-world building to evaluate the cost-effectiveness of different GEB designs and offer in-depth insights. It was found that the proposed assessment method could effectively capture the costs and benefits linked to each GEB design option. Furthermore, the study revealed that outage mitigation and demand response are the two most significant sources of benefits for GEBs.

Ma, Xu↗
DOE OSTI2021

Platform Agnostic Streaming Data Application Performance Models

The mapping of computational needs onto execution resources is, by and large, a manual task, and users are frequently guided simply by intuition and past experiences. We present a queueing theory based performance model for streaming data applications that takes steps towards a better understanding of resource mapping decisions, thereby assisting application developers to make good mapping choices. The performance model (and associated cost model) are agnostic to the specific properties of the compute resource and application, simply characterizing them by their achievable data throughput. We illustrate the model with a pair of applications, one chosen from the field of computational biology and the second is a classic machine learning problem.

Faber, Clayton↗
DOE OSTI2022

Resource selection and species interactions between native and non‐native fishes in a simulated stream system

Abstract Effective fishery management necessitates understanding of resource partitioning by fishes that inhabit complex systems composed of biotic and abiotic features. Evaluations of non‐native species introductions have continually demonstrated adverse effects associated with abundance and distribution of native fishes. Therefore, understanding resource selection and interactions between native and non‐native species is important for recovery efforts. Habitat use by two native fish species (largescale sucker Catostomus macrocheilus [Girard] and mountain whitefish Prosopium williamsoni [Girard]) and one non‐native fish species (pumpkinseed Lepomis gibbosus [Linnaeus]) of the Kootenai River, Idaho, were evaluated in a laboratory stream system. Trials were conducted in allopatry and in sympatry with and without the presence of wood to describe habitat selection in the context of on‐going habitat rehabilitation efforts. Interactions were evident between native largescale sucker and non‐native pumpkinseed concerning use of a woody structure and current velocity. Mountain whitefish used low‐velocity habitats and selected locations that were further from wood when in sympatry with pumpkinseed. Our research suggests that habitat use of native, large‐river fishes may be influenced by the presence of a non‐native species, and that considering such interactions is critical when designing and implementing habitat rehabilitation efforts in river ecosystems.

Branigan, Philip R.↗
DOE OSTI2022

Parametric Modeling and Economic Analysis of a 2MW th 3-Stream sCO 2 Heat Exchanger

Here, this paper presents the design and cost optimization of a novel 2MW th 3-stream sCO2 plate-fin heat exchanger. This heat exchanger design is unique in that it uses reduced metal oxide particle-to-sCO2 heat exchanger for cost-effective energy storage applications. The design uses low velocity, laminar air as the re-oxidizing reactant to transfer the heat of the re-oxidizing reaction to a sCO2 power loop. The design of the heat exchanger is based on a 2-D, 3-fluid plate/fin heat transfer model. The model parameterizes the size, shape, and number of passages of the heat exchanger to calculate the temperature profile, pressure drop, and fluid velocities of all three fluids. Global heat exchanger parameters such as the effectiveness and total heat transferred to the sCO2 are then calculated for overall performance. Due to the value and increased use of sCO2 heat exchangers in power cycles, a cost model of the system based on the unique high temperature/high pressure operating conditions was created using quotes from reference projects and market analysis. These quoted air-to-sCO2 heat exchangers are then processed using multiple weighting factors pertinent to heat exchanger design, including heat exchanger type, maximum temperature, differential pressures, fluids, duty, and more. These factors are then used in an exponential function in order to generate a parameterized cost curve. The design and cost of the heat exchanger are then optimized using the SMPSO genetic algorithm in Python. The optimization objectives for the system are to maximize the overall system effectiveness, including an air recuperator for preheating, and to minimize unit costs. Additional constraints are added to the system for the sCO2 and air pressure drops, air velocity to reduce particle entrainment, and the length and volume of the heat exchanger.

Cost Model↗
DOE OSTI2023

High-temperature low-humidity proton exchange membrane with “stream-reservoir” ionic channels for high-power-density fuel cells

The perfluorosulfonic acid (PFSA) proton exchange membrane (PEM) is the key component for hydrogen fuel cells (FCs). We used in situ synchrotron scattering to investigate the PEM morphology evolution and found a “stream-reservoir” morphology, which enables efficient proton transport. The short-side-chain (SSC) PFSA PEM is fabricated under the guidance of morphology optimization, which delivered a proton conductivity of 193 milliSiemens per centimeter [95% relativity humidity (RH)] and 40 milliSiemens per centimeter (40% RH) at 80°C. The improved glass transition temperature, water permeability, and mechanical strength enable high-temperature low-humidity FC applications. Performance improvement by 82.3% at 110°C and 25% RH is obtained for SSC-PFSA PEM FCs compared to Nafion polymer PEM devices. The insights in chain conformation, packing mechanism, crystallization, and phase separation of PFSAs build up the structure-property relationship. In addition, SSC-PFSA PEM is ideal for high-temperature low-humidity FCs that are needed urgently for high-power-density and heavy-duty applications.

25 ENERGY STORAGE↗
DOE OSTI2022

Streaming readout for next generation electron scattering experiments

Current and future experiments at the high-intensity frontier are expected to produce an enormous amount of data that needs to be collected and stored for offline analysis. Thanks to the continuous progress in computing and networking technology, it is now possible to replace the standard ‘triggered’ data acquisition systems with a new, simplified and outperforming scheme. ‘Streaming readout’ (SRO) DAQ aims to replace the hardware-based trigger with a much more powerful and flexible software-based one, that considers the whole detector information for efficient real-time data tagging and selection. Considering the crucial role of DAQ in an experiment, validation with on-field tests is required to demonstrate SRO performance. In this paper, we report results of the on-beam validation of the Jefferson Lab SRO framework. In this work, we exposed different detectors (PbWO-based electromagnetic calorimeters and a plastic scintillator hodoscope) to the Hall-D electron-positron secondary beam and to the Hall-B production electron beam, with increasingly complex experimental conditions. By comparing the data collected with the SRO system against the traditional DAQ, we demonstrate that the SRO performs as expected. Furthermore, we provide evidence of its superiority in implementing sophisticated AI-supported algorithms for real-time data analysis and reconstruction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data

We consider a continuous-valued simulation optimization (SO) problem, where a simulator is built to optimize an expected performance measure of a real-world system while parameters of the simulator are estimated from streaming data collected periodically from the system. At each period, a new batch of data is combined with the cumulative data and the parameters are re-estimated with higher precision. The system requires the decision variable to be selected in all periods. Therefore, it is sensible for the decision-maker to update the decision variable at each period by solving a more precise SO problem with the updated parameter estimate to reduce the performance loss with respect to the target system. We define this decision-making process as the multi-period SO problem and introduce a multi-period stochastic approximation (SA) framework that generates a sequence of solutions. Two algorithms are proposed: Re-start SA (ReSA) reinitializes the stepsize sequence in each period, whereas Warm-start SA (WaSA) carefully tunes the stepsizes, taking both fewer and shorter gradient-descent steps in later periods as parameter estimates become increasingly more precise. We show that under suitable strong convexity and regularity conditions, ReSA and WaSA achieve the best possible convergence rate in expected sub-optimality either when an unbiased or a simultaneous perturbation gradient estimator is employed, while WaSA accrues significantly lower computational cost as the number of periods increases. In addition, we present the regularized ReSA, which obviates the need to know the strong convexity constant and achieves the same convergence rate at the expense of additional computation.

Computer Science↗
DOE OSTI2025

IRIS-MASH: Efficient Multi-device Asynchronous Multi-Stream Heterogeneous Computing

In the rapidly evolving field of high-performance computing (HPC), effectively leveraging heterogeneous devices through asynchronous task programming is paramount. This paper presents a robust asynchronous task programming model tailored for a multi-device, multi-stream execution environment that incorporates a diverse array of heterogeneous computing units, including GPUs from various vendors and other accelerators. Current state-of-the-art task programming models provide methodologies to support asynchronous task executions, but they typically handle homogeneous devices using native programming languages, while support for heterogeneous devices is limited to frameworks like OpenCL. This gap presents significant challenges in abstracting heterogeneous devices to harness their true asynchronous capabilities effectively using their native programming languages. By implementing asynchronous task execution, our model significantly boosts the performance of tiled algorithm task graphs through overlapping data transfers with computation and enabling the simultaneous execution of multiple kernels. We integrate this approach into a heterogeneous Intelligent Runtime System (IRIS) and assess its performance using a suite of tiled algorithm benchmarks from the heterogeneous math kernels library (MatRIS) based on IRIS. Experimental results demonstrate a performance improvement ranging from 1.6 × to 2 × over IRIS without asynchronous support, and a notable 22% performance enhancement compared to established runtime systems such as StarPU and PaRSEC. This approach significantly improves computation efficiency of HPC workflows and provides a solid base for future exploration and development in the area of asynchronous task programming in heterogeneous systems.

Miniskar, Narasinga Rao [ORNL] (ORCID:000000018259↗
DOE OSTI2024

nng_stream

This project uses the NanoMsg next-generation library to receive data, temporarily store in an in-memory buffer, and then send it out. The buffer handles a high-bandwidth stream from multiple producers (sources) to multiple consumers (sinks).

Rogers, DavidM [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

Autonomous Anomaly Detection For Continuous Streams

The code implements the Isolation Forest (IFML) algorithm within the digital twin (DT) of the AGN-201 nuclear reactor. The DT captures real-time operational data including control rod positions, reactor power, and temperature. The IFML model isolates anomalies by detecting patterns that deviate from expected operational behavior. The algorithm recursively partitions the data and assigns anomaly scores based on the isolation of rare and different events. By tuning parameters specific to the reactor’s operational data, the IFML identifies deviations such as unauthorized material insertions or reactor reactivity shifts. The system streams data using LabView and integrates with the DeepLynx data warehouse for anomaly processing.

Trevino, Eduardo↗
DOE OSTI2026

Detector Interface for Streaming, Control, and Open-source integration (DISCO) v1.0.0

This suite consists of a multi-package ecosystem featuring detector emulators, EPICS areaDetector drivers, and remote server frameworks designed for the Advanced Light Source (ALS). Engineered for high-bandwidth devices—including VFCCD, Timepix3, Timepix4, and related pixel detectors—the software simulates hardware, wraps vendor SDKs into remote-callable servers, and integrates with open-source control systems. Key Capabilities: Distributed SDK Architecture: Server packages wrap hardware-specific SDKs, allowing areaDetector drivers to execute remote framework calls. This isolates proprietary libraries from the EPICS IOC, enhancing stability and enabling distributed computing across beamline networks. Device Support: Custom drivers for VFCCD, the Timepix family, and similar sensors optimize the data path from hardware control to high-speed transport. Full-Stack Emulation: Sophisticated emulator packages allow end-to-end pipeline testing and software development without requiring physical hardware or beam time. Integrated Workflows: Supports high-bandwidth streaming for real-time analysis and robust, metadata-rich file-based workflows (e.g., HDF5/NeXus). By standardizing interfaces across heterogeneous hardware, this suite reduces technical debt. It provides the ALS with a scalable, open-source solution to manage massive data rates within a unified control environment.

Mahl, Johannes [Lawrence Berkeley National Laborat↗
DOE OSTI2022

Monitoring Noble Gases (Xe and Kr) and Aerosols (Cs and Rb) in a Molten Salt Reactor Surrogate Off-Gas Stream Using Laser-Induced Breakdown Spectroscopy (LIBS)

In this study with surrogate materials we show that laser-induced breakdown spectroscopy (LIBS) is a robust tool with promising capability toward monitoring gaseous (Xe and Kr) and aerosol (Cs and Rb) species in an off-gas stream from a molten salt reactor (MSR). MSRs will continually evolve fission products into the cover gas flowing across the reactor headspace. The cover gas entrains Xe and Kr gases, along with aerosol particles, before passing into an off-gas treatment system. Univariate models of Xe and Kr peaks showed a strong correlation to concentration indicated by their coefficients of determination of 0.983 and 0.997, respectively. Multivariate models were built for all four analytes using partial least squares regression coupled with preprocessing steps including normalization, trimming, and/or genetic algorithm derived filters. The models were evaluated by predicting the concentrations of the analytes in four validation samples, in which all calibration models were successfully validated at a confidence interval of 99.9%. Finally, pressure controllers were used to regulate the mass flow rate of Kr flowing into the measurement cell in sinusoidal and stepwise waveforms to test the real-time monitoring capabilities of the regression models. Both univariate and partial least squares Kr models were able to successfully quantify the gas concentration in the real-time evaluation. The root mean squared error of prediction (RMSEP) values for these real-time tests were calculated to be 0.051, 0.060, and 0.121 mol% demonstrating the measurement systems’ capability to perform online monitoring with acceptable accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗