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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Design of an Integrated-System FARAD Thruster

Pulsed inductive plasma accelerators are spacecraft propulsion devices in which energy is stored in a capacitor and then discharged through an inductive coil. The device is electrodeless, inducing a current s heet in a plasma located near the face of the coil. The propellant is accelerated and expelled at a high exhaust velocity (order of 10 km/s) through the interaction of the plasma current and the induced magne tic field, The Faraday Accelerator with RF-Assisted Discharge (FARAD) thruster is a type of pulsed inductive plasma accelerator in which t he plasma is preionized by a mechanism separate from that used to for m the current sheet and accelerate the gas. Employing a separate preionization mechanism allows for the formation of an inductive current s heet at much lower discharge energies and voltages than those used in previous pulsed inductive accelerators like the Pulsed Inductive Thr uster (PIT). In this paper, we present the design of a benchtop FARAD thruster with all the subsystems (mass injection, preionization, and acceleration) integrated into a single unit. Design of the thruster follows the guidelines and similarity performance parameters presented elsewhere. The system is designed to use the ringing, RF-frequency s ignal produced by a discharging Vector Inversion Generator (VIG) to p reionize the gas. The acceleration stage operates on the order of 100 J/pulse and can be driven by several different pulsed powertrains. These include a simple capacitor coupled to the system, a Bernardes and Merryman configuration, and a pulsecompression circuit that takes a temporally broad, low current pulse and transforms it into a short, h igh current pulse. A set of applied magnetic field coils are integrated into the system to guide the preionized propellant as it spreads ov er the face of the inductive acceleration coil. The coils are operate d in a pulsed mode, and the thruster can be operated without using the coils to determine if there is a performance improvement gain realiz ed when an applied field is present.

Polzin, K.A.↗
DOE OSTI2025

Introduction: Neuromorphic Materials

The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.

Circuits↗
NASA NTRS2021

Climate Change Impacts and Adaptation for Dryland Farming Systems in Zimbabwe: A Stakeholder-Driven Integrated Multi-Model Assessment

Decision makers need accurate information to address climate variability and change and accelerate transformation to sustainability. A stakeholder-driven, science-based multi-model approach has been developed and used by the Agricultural Model Intercomparison and Improvement Project (AgMIP) to generate actionable information for adaptation planning processes. For a range of mid-century climate projections—likely to be hotter, drier, and more variable—contrasting future socio-economic scenarios (Representative Agricultural Pathways, RAPs) were co-developed with stakeholders to portray a sustainable development scenario and a rapid economic growth pathway. The unique characteristic of this application is the integration of a multi-modeling approach with stakeholder engagement to co-develop scenarios and adaptation strategies. Distribution of outcomes were simulated with climate, crop, livestock, and economic impact assessment models for smallholder crop livestock farmers in a typical dryland agro-ecological zone in Zimbabwe, characterized by low and erratic rainfall and nutrient depleted soils. Results showed that in Nkayi District, Western Zimbabwe, climate change would threaten most of the farms, and, in particular, those with large cattle herds due to feed shortages. Adaptation strategies that showed the most promise included diversification using legume production, soil fertility improvement, and investment in conducive market environments. The switch to more legumes in the farming systems reduced the vulnerability of the very poor as well as the more resourced farmers. Overall, the sustainable development scenario consistently addressed institutional failures and motivated productivity-enhancing, environmentally sound technologies and inclusive development approaches. This yielded more favorable outcomes than investment in quick economic wins from commercializing agriculture.

Climate change adaptation↗
DOE OSTI2022

A hybrid nodal-staggered pseudo-spectral electromagnetic particle-in-cell method with finite-order centering

Electromagnetic particle-in-cell (PIC) codes are widely used to perform computer simulations of a variety of physical systems, including fusion plasmas, astrophysical plasmas, plasma wakefield particle accelerators, and secondary photon sources driven by ultra-intense lasers. In a PIC code, Maxwell's equations are solved on a grid with a numerical method of choice. This article focuses on pseudo-spectral analytical time-domain (PSATD) algorithms and presents a novel hybrid PSATD PIC scheme that combines the respective advantages of standard nodal and staggered methods. The novelty of the hybrid scheme consists in using finite-order centering of grid quantities between nodal and staggered grids, in order to combine the solution of Maxwell's equations on a staggered grid with the deposition of charges and currents and the gathering of electromagnetic forces on a nodal grid. The correctness and performance of the novel hybrid scheme are assessed by means of numerical tests that employ different classes of PSATD equations in a variety of physical scenarios, ranging from the modeling of electron-positron pair creation in vacuum to the simulation of laser-driven and particle beam-driven plasma wakefield acceleration. It is shown that the novel hybrid scheme offers significant numerical and computational advantages, compared to purely nodal or staggered methods, for all the test cases presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2003

Long Duration Richtmyer-Meshkov Instability Experiments

This experimental study investigates the Richtmyer-Meshkov (RM) instability of an interface between incompressible, miscible liquids with an initial 2-D sinusoidal perturbation. The experiments are conducted in NASA Glenn Research Center's 2.2 Second Drop Tower. The experimental rig is isolated from aerodynamic drag by a surrounding drag shield. The rig falls 7 1/2 inches relative to the drag shield during the 79 ft fall of the system. An internal spring-driven sled impacting a clay ball provides the impulsive acceleration while the package is at the top of the drop tower, with the package timed to release just after the impulsive acceleration is complete. The instability evolves for 2.2 seconds until the package impacts an air bag at the bottom of the drop tower. The increased duration of these experiments provides for more than twice the observation time of the RM instability in the non-linear regime that will allow for better experimental comparison with asymptotic theories of perturbation amplitude and velocity.

Niederhaus, Charles↗
DOE OSTI2026

Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the Spallation Neutron Source

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory accelerates proton beams, which are directed toward a mercury target to generate the world’s most intense neutron beams via spallation. The target system consists of several interconnected subsystems and accounts for a major share of the facility’s overall downtime. Early detection of anomalies in the target system response can thus provide the possibility of taking corrective actions to reduce downtime. Accelerator facilities have largely focused on the beam side for data-driven fault prognostics. On the target side, SNS relies on operational shift technicians (OSTs), who respond to alarms and manually flag anomalies onto the System Tracking and Reliability (STAR) platform. This paper presents one of the first studies of using machine learning (ML) to automate anomaly detection in the target system. The study focused on the mercury process system as the first use case and employed reconstruction-based anomaly detection on minutely sampled time series signals. The pipeline was integrated into the STAR platform to autonomously rank and flag anomalies every week. The STAR platform provides a user interface for the OSTs to evaluate the flagged anomalies, thereby incorporating human feedback.

Anomaly detection↗
NASA NTRS2016

SMART NAS Test Bed Overview

These slides presents an overview of SMART NAS Test Bed. The test bed is envisioned to be connected to operational systems and to allow a new concept and technology to be evaluated in its realistic environment. Its role as an accelerator of concepts and technologies development, its use-case-driven development approach, and its state are presented.

national airspace system↗
DOE OSTI2016

Parallel electric fields are inefficient drivers of energetic electrons in magnetic reconnection

We present two-dimensional kinetic simulations, with a broad range of initial guide fields, which isolate the role of parallel electric fields (E- ∥ ) in energetic electron production during collisionless magnetic reconnection. In the strong guide field regime, E- ∥ drives essentially all of the electron energy gains, yet fails to generate an energetic component. Here, we suggest that this is due to the weak energy scaling of particle acceleration from E- ∥ compared to that of a Fermi-type mechanism responsible for energetic electron production in the weak guide-field regime. Lastly, this result has important implications for energetic electron production in astrophysical systems and reconnection-driven dissipation in turbulence.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Managing a Real-Time Embedded Linux Platform with Buildroot

Developers of real-time embedded software often need to build the operating system, kernel, tools and supporting applications from source to work with the differences in their hardware configuration. The first attempts to introduce Linux-based real-time embedded systems into the Fermilab accelerator controls system used this approach but it was found to be time-consuming, difficult to maintain and difficult to adapt to different hardware configurations. Buildroot is an open source build system with a menu-driven configuration tool (similar to the Linux kernel build system) that automates this process. A customized Buildroot [1] system has been developed for use in the Fermilab accelerator controls system that includes several hardware configuration profiles (including Intel, ARM and PowerPC) and packages for Fermilab support software. A bootable image file is produced containing the Linux kernel, shell and supporting software suite that varies from 3 to 20 megabytes large – ideal for network booting. The result is a platform that is easier to maintain and deploy in diverse hardware configurations

Diamond, J.↗
DOE OSTI2017

Simulation Exploration through Immersive Parallel Planes

We present a visualization-driven simulation system that tightly couples systems dynamics simulations with an immersive virtual environment to allow analysts to rapidly develop and test hypotheses in a high-dimensional parameter space. To accomplish this, we generalize the two-dimensional parallel-coordinates statistical graphic as an immersive 'parallel-planes' visualization for multivariate time series emitted by simulations running in parallel with the visualization. In contrast to traditional parallel coordinate's mapping the multivariate dimensions onto coordinate axes represented by a series of parallel lines, we map pairs of the multivariate dimensions onto a series of parallel rectangles. As in the case of parallel coordinates, each individual observation in the dataset is mapped to a polyline whose vertices coincide with its coordinate values. Regions of the rectangles can be 'brushed' to highlight and select observations of interest: a 'slider' control allows the user to filter the observations by their time coordinate. In an immersive virtual environment, users interact with the parallel planes using a joystick that can select regions on the planes, manipulate selection, and filter time. The brushing and selection actions are used to both explore existing data as well as to launch additional simulations corresponding to the visually selected portions of the input parameter space. As soon as the new simulations complete, their resulting observations are displayed in the virtual environment. This tight feedback loop between simulation and immersive analytics accelerates users' realization of insights about the simulation and its output.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Design and Testing of a Small Inductive Pulsed Plasma Thruster

The design and testing of a small inductive pulsed plasma thruster (IPPT) is described. The device was built as a test-bed for the pulsed gas-valves and solid-state switches required for a thruster of this kind, and was designed to be modular to facilitate modification. The thruster in its present configuration consists of a multi-turn, spiral-wound acceleration coil (270 millimeters outer diameter, 100 millimeters inner diameter) driven by a 10 microfarad capacitor and switched with a high-voltage thyristor, a propellant delivery system including a fast pulsed gas-valve, and a glow-discharge pre-ionizer circuit. The acceleration coil circuit may be operated at voltages up to 4 kilovolts (the thyristor limit is 4.5 kilovolts) and the thruster operated at cyclic-rates up to 30 Herz. Initial testing of the thruster, both bench-top and in-vacuum, has been performed. Cyclic operation of the complete device was demonstrated (at 2 Herz), and a number of valuable insights pertaining to the design of these devices have been gained.

Martin, Adam K.↗
NASA NTRS1994

Fluid System Angular Momentum and Moment Fluctuations Driven by Gravity Gradient or Gravity Jitter in Microgravity

The dynamical behaviour of fluids affected by the asymmetric gravity gradient acceleration and gravity jitter acceleration, in particular the effect of surface tension on partially-filled rotating fluids with special example applicable to a full-scale Gravity Probe-B Spacecraft dewar tank have been investigated. Results of slosh wave excitation along liquid-vapor interface induced by gravity gradient acceleration indicated that one-up one-down and one-down one-up oscillations of two bubbles in the cross-section of doughnut profiles in the vertical r-z plane of rotating dewar, and an eccentric contour of bubble rotating around the axis of dewar in horizontal r-Theta plane. Results of slosh wave excitation along liquid-vapor interface induced by gravity jitter acceleration indicated equivalent to time-dependent oscillatory forces which push bubble in the combined bubble motion of down-and-up and leftward-and-rightward as the bubble is rotating with respect to rotating dewar axis. Fluctuations of angular momentum, fluid moment and bubble mass center caused by slosh wave excitations driven by gravity gradient acceleration or gravity jitter acceleration are also investigated.

Hung, R. J.↗
DOE OSTI2024

Towards Sustainable Post-Exascale Leadership Computing

As computing systems approach the limits of traditional silicon technology, the diminishing returns in performance per watt present a significant barrier to sustaining growth in HPC. From a large-scale scientific supercomputing facility point of view, we propose a multifaceted strategy toward specialized hardware and architectures that are optimized for energy efficiency in specific applications. We also emphasize the need for integrating energy-aware practices across all levels of HPC, from system design and software development to operational policies. We discuss strategic opportunities such as the adoption of application-specific accelerators, the development of energy-efficient algorithms, and the implementation of data-driven operational analytics. Our goal is to develop a comprehensive roadmap ensuring that future leadership systems at OLCF can meet scientific demands while operating within stringent energy budgets, thereby supporting sustainable computing growth.

Shin, Woong↗
DOE OSTI2015

Near-Term Acceleration In The Rate of Temperature Change

Anthropogenically-driven climate changes, which are expected to impact human and natural systems, are often expressed in terms of global-mean temperature . The rate of climate change over multi-decadal scales is also important, with faster rates of change resulting in less time for human and natural systems to adapt . We find that current trends in greenhouse gas and aerosol emissions are now moving the Earth system into a regime in terms of multi-decadal rates of change that are unprecedented for at least the last 1000 years. The rate of global-mean temperature increase in the CMIP5 archive over 40-year periods increases to 0.25±0.05 (1σ) °C per decade by 2020, an average greater than peak rates of change during the previous 1-2 millennia. Regional rates of change in Europe, North America and the Arctic are higher than the global average. Research on the impacts of such near-term rates of change is urgently needed.

Smith, Steven J.↗
DOE OSTI2021

Uncertainty quantification for deep learning in particle accelerator applications

With the advent of increased computational resources and improved algorithms, machine learning-based models are being increasingly applied to complex problems in particle accelerators. However, such data-driven models may provide overly confident predictions with unknown errors and uncertainties. For reliable deployment of machine learning models in high-regret and safety-critical systems such as particle accelerators, estimates of prediction uncertainty are needed along with accurate point predictions. In this investigation, we evaluate Bayesian neural networks (BNN) as an approach that can provide accurate predictions along with reliably quantified uncertainties for particle accelerator problems, and compare their performance with bootstrapped ensembles of neural networks. We select three accelerator setups for this evaluation: a storage ring, a photoinjector, and a linac. The problems span different data volumes and dimensionalities (e.g., scalar predictions as well as image outputs). It is found that BNN provide accurate predictions of the mean along with reliable estimates of predictive uncertainty across the test cases. In this vein, BNN may offer an attractive alternative to deterministic deep learning tools to generate accurate predictions with quantified uncertainties in particle accelerator applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Semi-empirical quantum optics for mid-infrared molecular nanophotonics

Nanoscale infrared (IR) resonators with sub-diffraction limited mode volumes and open geometries have emerged as new platforms for implementing cavity quantum electrodynamics at room temperature. The use of IR nanoantennas and tip nanoprobes to study strong light–matter coupling of molecular vibrations with the vacuum field can be exploited for IR quantum control with nanometer spatial and femtosecond temporal resolution. In order to advance the development of molecule-based quantum nanophotonics in the mid-IR, we propose a generally applicable semi-empirical methodology based on quantum optics to describe light–matter interaction in systems driven by mid-IR femtosecond laser pulses. The theory is shown to reproduce recent experiments on the acceleration of the vibrational relaxation rate in infrared nanostructures. It also provides physical insights on the implementation of coherent phase rotations of the near-field using broadband nanotips. We then apply the quantum framework to develop general tip-design rules for the experimental manipulation of vibrational strong coupling and Fano interference effects in open infrared resonators. We finally propose the possibility of transferring the natural anharmonicity of molecular vibrational levels to the resonator near-field in the weak coupling regime to implement intensity-dependent phase shifts of the coupled system response with strong pulses and develop a vibrational chirping model to understand the effect. The semi-empirical quantum theory is equivalent to first-principles techniques based on Maxwell’s equations, but its lower computational cost suggests its use as a rapid design tool for the development of strongly coupled infrared nanophotonic hardware for applications ranging from quantum control of materials to quantum information processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Data-driven prediction of battery cycle life before capacity degradation

Accurately predicting the lifetime of complex, nonlinear systems such as lithium-ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine-learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems.

25 ENERGY STORAGE↗