SEARCH · Search NASA

Results for “data driven operations”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,009 records · Page 56

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Local limits of detection for anthropogenic aerosol-cloud interactions

Ship tracks are quasi-linear cloud patterns produced from the interaction of ship emissions with low boundary layer clouds. They are visible throughout the diurnal cycle in satellite images from space-borne assets like the Advanced Baseline Imagers (ABI) aboard the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES-R). However, complex atmospheric dynamics often make it difficult to identify and characterize the formation and evolution of tracks. Ship tracks have the potential to increase a cloud's albedo and reduce the impact of global warming. Thus, it is important to study these patterns to better understand the complex atmospheric interactions between aerosols and clouds to improve our climate models, and examine the efficacy of climate interventions, such as marine cloud brightening. Over the course of this 3-year project, we have developed novel data-driven techniques that advance our ability to assess the effects of ship emissions on marine environments and the risks of future marine cloud brightening efforts. The three main innovative technical contributions we will document here are a method to track aerosol injections using optical flow, a stochastic simulation model for track formations and an automated detection algorithm for efficient identification of ship tracks in large datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

ML based control systems for nuclear physics experiments

The Experimental Physics Software and Computing Infrastructure (EPSCI) group at Jefferson Lab is leading the use of machine learning (ML) to enhance control systems in nuclear physics experiments. Collaborating closely with domain experts and data scientists, we have developed an ML-based control system that uses a Gaussian process to dynamically adjust the high voltage of the GlueX Central Drift Chamber. This results in stable detector performance by adapting to environmental changes, thereby reducing the offline calibration effort. Furthermore, we are developing ML-driven systems for optimizing the polarization of photon beams and polarized cryotargets. These systems will maintain the optimal microwave frequency in cryogenic targets and make real-time adjustments to diamond radiators for polarized photon sources, tasks traditionally handled by human operators. By automating these functions, we aim to optimize the polarization, reduce downtime, and minimize human error. This talk will highlight the development of reliable ML-based control systems and the policies to ensure they are both effective and trustworthy.

Jeske, Torri↗

DOE OSTI2016

Life Extension of Nuclear Plant Facilities through Monitoring and Detailed Analysis - 16175

The UK has accumulated a substantial legacy of radioactive waste from various civil nuclear and defence programmes. Presently, however most of the UK radioactive waste is generated from nine power stations which generate approximately one sixth of the of the country's electricity. Irradiated fuel is removed from these reactors and safe management of the radioactive solid and liquid effluents is required. The safe management of these effluents is essential. Sellafield, in West Cumbria UK, contains two reprocessing plants, one designed to process nuclear fuel from Magnox reactors (CO{sub 2} cooled with Uranium bar fuel) and the other to process nuclear fuel from Advanced Gas cooled Reactors and Pressurized Water Reactors. The reprocessing stage enables extraction of fissile material for recycling. This leaves a volume of dilute but high-level waste. The high-level radioactive liquid waste is concentrated to reduce its volume and then incorporated into borosilicate glass, using a process called vitrification. The glass is then poured into stainless steel containers and placed in long term storage. This required concentration of HLW liquor is achieved using a suite of aging evaporators. All evaporators are of similar design constructed from corrosion resistant stainless steel having dimensions height ∼11 m, internal diameter 3.05 m and capable of holding 13.7 m{sup 3} of liquor. The basic principle of operation is to heat the dilute liquor under partial vacuum, at constant volume, until the target concentration is achieved. Heating of the liquor is achieved using steam heated coils within the evaporator, and steam in the jacket which surrounds the lower half of the evaporator vessel. The evaporator is then brought to atmospheric pressure and the concentrated liquor is ejected whilst hot. The evaporator is then cooled with water cooled coils and a water filled jacket. New dilute liquor is then added for a new batch. The process is then repeated. As the evaporators reach the end of their original design life accurate prediction of the current condition of nuclear plant facilities is essential to safely justify continued plant operation by ensuring structural integrity. This poses a significant challenge as many aging UK nuclear facilities were not inherently built with inspection in mind. The potential life limiting components of the HA evaporators at Sellafield are the surfaces that contact the product liquor at elevated temperatures i.e. the steam heated surfaces of the coils within the evaporator vessel and the vessel base and wall surrounded by the steam jacket. Each heating component is critical to the operational life of the evaporator - the removal from service of a coil heating component significantly reduces the operational flexibility and remnant life of this plant. Over the last decade a variety of cutting-edge expertise and methodologies have been utilised to accurately measure and then predict the current condition of the plant and also to predict its remnant life. This paper provides an overview of the development of several techniques deployed, namely non-destructive testing/inspection, in-house software development and finite element modelling. These methods have been validated by lab-scale or full-scale experiments along with historical plant data. These techniques have been evolved and refined for over a decade, providing vital insights into the physical behaviour of this complex reprocessing system. This enables not only accurate assessment of the current condition of the plant equipment but also allows scientifically underpinned predictions to be made of how it will behave into the future. This is critical for Sellafield as this knowledge feeds into their safety case to the Office of Nuclear Regulation to justify continued operation. It is concluded that this integrated approach provides a robust analysis tool which has driven change and optimisation of plant procedures ensuring continued safe operation whilst extending the life of the facility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

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↗

DOE OSTI2025

Block-Structured Operator Inference for Coupled Multiphysics Model Reduction

This work presents a block-structured formulation of Operator Inference as a way to learn structured reduced-order models for multiphysics systems. The approach specifies the governing equation structure for each physics component and the structure of the coupling terms. Once the multiphysics structure is specified, the reduced-order model is learned from snapshot data following the nonintrusive Operator Inference methodology. In addition to preserving physical system structure, which in turn permits preservation of system properties such as stability and second-order structure, the block-structured approach has the advantages of reducing the overall dimensionality of the learning problem and admitting tailored regularization for each physics component. The numerical advantages of the block-structured formulation over a monolithic Operator Inference formulation are demonstrated for aeroelastic analysis, which couples aerodynamic and structural models. For the benchmark test case of the AGARD 445.6 wing, block-structured Operator Inference provides an average 20% online prediction speedup over monolithic Operator Inference across subsonic and supersonic flow conditions in both the stable and fluttering parameter regimes while preserving the accuracy achieved with monolithic Operator Inference.

42 ENGINEERING↗

DOE OSTI2019

The laser shock station in the dynamic compression sector. I

The Laser Shock Station in the Dynamic Compression Sector [Advanced Photon Source (APS), Argonne National Laboratory] links a laser-driven shock compression platform with high energy x-ray pulses from the APS to achieve in-situ, time-resolved x-ray measurements (diffraction and imaging) in materials subjected to well-characterized, high stress, short duration planar shock waves. Additionally, the combination of this station and the other DCS experimental stations provides a unique and versatile experimental facility to study condensed state phenomena by undertaking real-time, multi-scale measurements in materials subjected to a wide range of shock amplitudes (to above ~350 GPa) and time-durations (~10 ns to 1 μs). The Laser Shock Station uses a 100 joule, 5-17 ns, 351 nm frequency tripled Nd:glass laser with programmable pulse shaping and focal profile smoothing for maximum precision. The laser is capable of a shot every 30 minutes. The interaction chamber contains multiple ports for simultaneous diagnostics, a sample holder that allows sequential exposure for 14 samples without breaking vacuum, the capability to vary the angle between the x-ray beam and laser beam by 135°, and the ability to translate to select one of two types of x-ray beams. X-ray data can be taken with a temporal resolution of ~90 psec. Highly reproducible, planar shocks can be propagated in samples to undertake precise and well-characterized experiments. In a series of ten shots, the absolute variation in the time the shock breaks out at the back of the sample is less than 500ps and the variation in peak particle velocity at the sample/window interface is 4.3%. Lastly, this paper describes the entire DCS Laser Shock Station, including sample fabrication and diagnostics, as well as representative experimental results from shock compressed tantalum to demonstrate the excellent quality of x-ray diffraction data in solid tantalum and to establish the stress for tantalum melting.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Feasibility study of a high spatial and time resolution beam emission spectroscopy diagnostic for localized density fluctuation measurements in Lithium Tokamak eXperiment-β (LTX-β)

Trapped electron mode (TEM) is the main source of turbulence predicted for the unique operation regime of a flat temperature profile under low-recycling conditions in the LTX-β tokamak, while ion temperature gradient driven turbulence may also occur with gas fueling from the edge. To investigate mainly TEM scale density fluctuations, a high spatial and time resolution 2D beam emission spectroscopy (BES) diagnostic is being developed. Apart from spatially localized density turbulence measurement, BES can provide turbulence flow and flow shear dynamics. This BES system will be realized using an avalanche photodiode-based camera and narrow band interference filter. The system can acquire data at 2 MHz. Simulations with the Simulation of Spectra (SOS) code indicate that a high signal to noise ratio can be achieved with the proposed system. This will enable sampling the density fluctuations at this high time resolution. Finally, the design considerations and system optimization using the SOS code are presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Characterization and Modeling of Amorphous Metallic Cores

Los Alamos is planning a new compact multi-pulsed electron linear induction accelerator (LIA) for flash radiography of explosively driven hydrodynamic experiments, the Advanced Radiography Induction Accelerator (ARIA). In order to operate in multi-pulse mode without the need to reset the inductor cores between each pulse, the design incorporates amorphous metallic glass cores. Due to their high magnetic saturation value, these provide enough volt-seconds for sequential pulsing before reaching saturation. Although metallic glass cores are used extensively, their behavior depends greatly on the particular formulation and on the specific application. For this reason, we have undertaken an effort to better characterize the cores under consideration and use this data to develop models that can be used in EM simulations. We present the characterization of two types of cores being consid- ered: Metglas 2605CO and 2605HB1M cores. We include detailed measurements of BH curves at various magneti- zation rates, measurements of multiple pulses on the same core, and initial modeling efforts.

43 PARTICLE ACCELERATORS↗

DOE OSTI2017

Electric actuator modification on a turbine driven feed pump at Calvert Cliffs nuclear power plant - 296

Feedwater systems are a leading contributor to unit SCRAMs as indicated by the 2015 INPO data, where 9 of the 51 SCRAMs recorded were a result of feedwater issues. Digital feedwater control systems and enhanced feed pump control modifications can greatly improve performance and control of the plant feedwater systems and help drive down the potential for reactivity management events and unit SCRAMs. Westinghouse has a strong background in the implementation of digital feedwater control systems, and in planning for a recently completed feedwater control system upgrade at Calvert Cliffs, the Westinghouse team worked with Calvert Cliffs to design a solution to a number of common operational issues. Primarily, improving feedwater control over the entire operating range, eliminating single points of vulnerability (SPV) in the feedwater/feed pump systems and eliminating operational issues with the feed pump steam admission valve hydraulic control oil system. A robust first-of-a-kind design and implementation program was undertaken to split the existing hydraulic valve gear actuator with separate electric actuators. Westinghouse worked with Calvert Cliffs and Emerson Process Management (EPM) to install and commission General Electric (GE) feed pump turbines with separate electric actuators for the high-pressure (HP) and low-pressure (LP) steam admission valves. This successful mechanical and control modification was a first for both the nuclear and non-nuclear power generation industries. Westinghouse is working with a number of other utilities to implement similar modifications for PWR and BWR feed pump applications. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Cross section measurements at LANSCE for defense, science and applications

The Los Alamos Neutron Science Center (LANSCE) has three neutron sources that are used for nuclear science measurements. These sources are driven by an 800 MeV proton linear accelerator and cover an energy range from sub-thermal to hundreds of MeV. Research at the facilities is performed under the auspices of a US DOE user program under which research proposals are rated for merit by a program advisory committee and are scheduled based on merit and availability of beam time. A wide variety of instruments is operated at the neutron flight paths at LANSCE including neutron detector arrays, gamma-ray detector arrays, fission fragment detectors, and charged particle detectors. These instruments provide nuclear data for multiple uses that range from increasing knowledge in fundamental science to satisfying data needs for diverse applications such as nuclear energy, global security, and industrial applications. In addition, highlights of recent research related to cross sections measurements are presented, and future research initiatives are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Future Physics Programme of BESIII

There has recently been a dramatic renewal of interest in hadron spectroscopy and charm physics. This renaissance has been driven in part by the discovery of a plethora of charmonium-like XYZ states at BESIII and B factories, and the observation of an intriguing proton-antiproton threshold enhancement and the possibly related X(1835) meson state at BESIII, as well as the threshold measurements of charm mesons and charm baryons. We present a detailed survey of the important topics in tau-charm physics and hadron physics that can be further explored at BESIII during the remaining operation period of BEPCII. This survey will help in the optimization of the data-taking plan over the coming years, and provides physics motivation for the possible upgrade of BEPCII to higher luminosity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Data Science in Chemical Engineering: Applications to Molecular Science

Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science—the application tools for molecular discovery and property optimization at the atomic scale. Here, we summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

SAS4A/SASSYS-1 Validation with EBR-II Tests Performed During the SHRT Testing Program

The EBR-II sodium-cooled fast reactor supported a wide range of fast reactor research activities. The final fifteen years of operations at EBR-II were used for experiments and tests to demonstrate the importance of passive safety in liquid metal reactors and the capability of such systems to provide strong passive responses during off-normal conditions. The Shutdown Heat Removal Test program at EBR-II provided test data supporting the validation of computer codes for design, licensing, and operation of LMRs, among other objectives. The program included nearly sixty tests, most of which were protected and unprotected loss of flow and loss of heat sink tests run at various initial powers and flow rates. This report documents the results of modeling and simulation of tests from the Shutdown Heat Removal Test program to support validation of Argonne’s SAS4A/SASSYS-1 fast reactor safety analysis code. SHRT-17 was a protected loss of flow test where a loss of electrical power to all sodium coolant pumps was simulated to demonstrate the effectiveness of natural circulation cooling characteristics. SHRT-45R was an unprotected loss of flow test where the control rod scram function of the plant protection system was disabled to demonstrate the effectiveness of EBR-II’s passive reactivity feedbacks. BOP-301 and BOP-302R were unprotected loss of heat sink tests that demonstrated how reactivity feedback effects driven by changes to the core inlet temperature can shut down the fission process. This validation activity leverages modeling and simulation efforts that originated under an International Atomic Energy Agency Coordinated Research Project for benchmark analysis of the EBR-II Shutdown Heat Removal Tests. The SAS4A/SASSYS-1 models originally developed for the CRP have been modernized to utilize new code features and modeling practices. Differences between the predicted and measured test data were explored and the causes of these differences were identified as being due to modeling approximations, uncertainty in transient component behavior, and instrumentation uncertainties. Overall, it was concluded that for SHRT-17, SHRT-45R, BOP-301, and BOP-302R there was good agreement between the flow, power, and temperature predictions and the measured data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

Novel method for accurately estimating membrane transport properties and mass transfer coefficients in reverse osmosis

Here, we present a simple and robust method to simultaneously characterize the water and salt permeability (A, B) of reverse osmosis (RO) membranes and mass transfer coefficient (k) in membrane modules. The proposed methodology comprises a set of RO experiments performed at different operating pressures or stages. The measured water and salt fluxes in each stage are simultaneously fitted to the RO transport equations by performing a non-linear regression, using A, B, and k as regression parameters. We first perform a systematic accuracy analysis of the proposed method across the full operational range of RO. The assessment shows that the method accuracy is substantially higher than current methods and increases with number of experimental stages and driving forces. This assessment is used to inform the design of an experimental protocol that minimizes errors in estimated A, B, and k. We then evaluate two commercial RO membranes following the new protocol. For both membranes, A and B parameters decrease by 17% and 15% from the dilute solution to seawater concentrations, whereas the k parameter remains constant. Our study demonstrates that the proposed method, informed by data-driven experimental designs, provides a new approach for accurately characterizing transport phenomenon in membrane processes with feeds of less than 100 g/L total dissolved solids.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Predicting the chemical reactivity of organic materials using a machine-learning approach

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system. In configuring devices such as batteries or solar cells, not only the functionality of individual constituting materials such as electrodes or electrolyte but also an appropriate combination of materials which do not undergo unwanted side reactions is critical in ensuring their reliable performance in long-term operation. While the universal theory that can predict the general chemical reactivity between materials is long awaited and has been the subject of studies with a rich history, traditional ways proposed to date have been mostly based on simple electronic properties of materials such as electronegativity, ionization energy, electron affinity and hardness/softness, and could be applied to only a small group of materials. Moreover, prediction has often been far from accurate and has failed to offer general implications; thus it was practically inadequate as a selection criterion from a large material database, i.e. data-driven material discovery. Herein, we propose a new model for predicting the general reactivity and chemical compatibility among a large number of organic materials, realized by a machine-learning approach. As a showcase, we demonstrate that our new implemented model successfully reproduces previous experimental results reported on side-reactions occurring in lithium–oxygen electrochemical cells. Furthermore, the mapping of chemical stability among more than 90 available electrolyte solvents and the representative redox mediators is realized by this approach, presenting an important guideline in the development of stable electrolyte/redox mediator couples for lithium–oxygen batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

High sensitivity far infrared laser diagnostics for the C-2U advanced beam-driven field-reversed configuration plasmas

A high sensitivity multi-channel far infrared laser diagnostics with switchable interferometry and polarimetry operation modes for the advanced neutral beam-driven C-2U field-reversed configuration (FRC) plasmas is described. The interferometer achieved superior resolution of 1 × 10{sup 16} m{sup −2} at >1.5 MHz bandwidth, illustrated by measurement of small amplitude high frequency fluctuations. The polarimetry achieved 0.04° instrument resolution and 0.1° actual resolution in the challenging high density gradient environment with >0.5 MHz bandwidth, making it suitable for weak internal magnetic field measurements in the C-2U plasmas, where the maximum Faraday rotation angle is less than 1°. The polarimetry resolution data is analyzed, and high resolution Faraday rotation data in C-2U is presented together with direct evidences of field reversal in FRC magnetic structure obtained for the first time by a non-perturbative method.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗