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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
DOE OSTI2026

Model Data Archive for Manuscript Titled "Evaluation of a Coupled Surface–Subsurface Hydrologic Model Using Dense Water‑Level Sensors in a Mixed Urban–Rural Watershed"

This archive provides scripts, input files, and datasets used for the implementation and evaluation of a fully coupled surface–subsurface hydrologic model in the Neches River Basin, southeast Texas. The study uses the Advanced Terrestrial Simulator (ATS) to simulate coupled surface–subsurface hydrologic processes over a mixed urban–rural watershed and evaluates model performance using a dense network of 136 in situ water-level sensors, nine U.S. Geological Survey (USGS) stream gauges, and SSEBop-derived evapotranspiration estimates during the period October 2014–June 2024. The workflow is implemented primarily in Python 3 using the Watershed Workflow package. The Jupyter notebooks can be executed using open-source software such as Anaconda JupyterLab or Visual Studio Code. Other data files include TXT, CSV, XML, SHP, TIF, NetCDF, HDF5, and ExodusII files, which can be processed using the provided Python scripts. ATS input files are provided in XML format and can be edited using any commonly used text editor. This archive contains: *Scripts and input files used to generate the ATS model setup, including watershed discretization, mesh generation, parameter mapping, and model configuration. *Jupyter notebooks used for preprocessing observational data, evaluating streamflow, water levels, and evapotranspiration, computing performance metrics, and generating the figures presented in the manuscript. *ATS simulation outputs and processed observational datasets, including OneRain and DD6 water-level sensors, USGS streamflow observations, GIS data, and supporting spatial datasets used throughout the study.

Dense water-level sensor network↗
DOE OSTI2023

MyCrunchGPT: A LLM Assisted Framework for Scientific Machine Learning

Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. Here, the objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Joint optimal scheduling for electric vehicle battery swapping-charging system based on wind farms

Insufficiencies in charging facilities limit the broad application of electric vehicles (EVs). In addition, EV can hardly represent a green option if its electricity primarily depends on fossil energy. Considering these two problems, this paper studies a battery swapping-charging system based on wind farms (hereinafter referred to as W-BSCS). In a W-BSCS, the wind farms not only supply electricity to the power grid but also cooperate with a centralized charge station (CCS), which can centrally charge EV batteries and then distribute them to multiple battery swapping stations (BSSs). The operational framework of the W-BSCS is analyzed, and some preprocessing technologies are developed to reduce complexity in modeling. Then, a joint optimal scheduling model involving a wind power generation plan, battery swapping demand, battery charging and discharging, and a vehicle routing problem (VRP) is established. Then a heuristic method based on the exhaustive search and the Genetic Algorithm is employed to solve the formulated NP-hard problem. Numerical results verify the effectiveness of the joint optimal scheduling model, and they also show that the W-BSCS has great potential to promote EVs and wind power.

17 WIND ENERGY↗
DOE OSTI2026

Phase Picking Beyond Local Distances: Where Waveform Filtering Still Matters for Deep Learning Models

Waveform filtering is a standard step in traditional seismic phase picking but often receives little attention in deep learning workflows, where models are typically trained on raw or minimally processed waveforms. Although this strategy performs well for local events, we show that performance can degrade substantially at regional distances. To address this limitation, we introduce two ways to incorporate multiband-filtered waveforms into deep learning phase pickers. The stacking approach concatenates filtered inputs along the channel dimension, while the branching approach processes each frequency band through a dedicated network branch before feature fusion. Both approaches can substantially improve performance across epicentral distances of 0° to 20°, but their effectiveness depends strongly on the selected frequency bands. Tests with multiple filter banks show that filter-bank design should be treated as part of model optimization rather than as a fixed preprocessing choice. Grad-CAM analysis of the branching model indicates that band importance varies among waveform samples and across training realizations, with only a weak overall preference for the 0.25 to 0.5 Hz band. These results show that no single filter band is consistently optimal and demonstrate that explicit feature engineering remains valuable for robust deep learning-based seismic phase picking.

58 GEOSCIENCES↗
DOE OSTI2017

SAM Process Heat Model Development and Validation: Liquid-HTF Trough and Direct Steam Generation Linear Focus Systems

Two new solar industrial process heat (SIPH) models have been added to the System Advisor Model (SAM) energy simulation tool. The two process heat models were: i) liquid-heat transfer fluid (HTF) parabolic troughs; and ii) direct steam generation (DSG) linear-focus collectors. The DSG model can simulate either DSG parabolic troughs (PT) or DSG Linear Fresnel (LF) fields, based on the optical characteristics supplied. These models help to meet the need for independent, validated tools that reliably simulate the effects of adding a solar field to provide heat for potential IPH applications. During the pre-validation of the liquid-HTF model, operational data was provided and preprocessed by SPF and evaluated at ZAE Bayern. The beta liquid-HTF model provided excellent agreement between the simulations and the operational data for the solar thermal yield generation on a yearly and monthly basis (4.4% difference). The final liquid-HTF process heat model has been further optimized with test users such as ZAE Bayern and SPF. Key improvements in the SIPH models to better model SIPH fields include the use of smaller piping systems and heat capacities, a single collector field, and a higher time-step resolution than for electricity generation (down to 10 minutes). Both the final liquid-HTF and DSG models were successfully validated against operational data for respective solar fields, and showed less than 5% deviation to actual results. A brief application where the liquid-HTF process heat model is used for a brewery in California and the value of reliably simulating a potential SIPH solar field are highlighted.

14 SOLAR ENERGY↗
DOE OSTI2017

PANSOPHY, a JLAB SRF engineering data management system, supporting data collection, retrieval and analysis utilized by LCLS-II

Pansophy is an Engineering Data Management System that provides a comprehensive solution for managing information in the production and testing of cryomodules. It is especially suited to supporting the Data & Quality Management Systems for large projects like LCLS-II. With extensive amounts of data collected for an individual project, data retrieval to facilitate feedback and enhancement of production and processing activities is a high priority. The priority shares importance with the needs of managing the project, including production status, NCR, and Quality Management reports. Recent Pansophy enhancements have been to Data and Quality management reports and statistical analysis. Such enhancements include a database driven menu system, extended MSWord macro and preprocessing of travelers, and an extensive reporting system. The reporting system allows managers and group leaders to quickly respond to the needs of the project in areas of cavity and cryomodule production, data collection, NCR, Quality Management and schedule. Extensions include integration with the SRF inventory system PRIMeS, allowing traceability from receiving of manufactured parts to final cryomodule product.

Bookwalter, Valerie D.↗
DOE OSTI2016

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models feature diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Life-cycle analysis of energy use, greenhouse gas emissions, and water consumption in the 2016 MYPP algal biofuel scenarios

The Department of Energy (DOE) Bioenergy Technologies Office (BETO) Multi-year Program Plan (MYPP) describes the bioenergy objectives pursued by BETO, the strategies for achieving those objectives, the current state of technology (SOT), and a number of design cases that explore cost and operational performance required to advance the SOT towards middle and long term goals (MYPP, 2016). Two options for converting algae to biofuel intermediates were considered in the MYPP, namely algal biofuel production via lipid extraction and algal biofuel production by thermal processing. The first option, lipid extraction, is represented by the Combined Algae Processing (CAP) pathway in which algae are hydrolyzed in a weak acid pretreatment step. The treated slurry is fermented for ethanol production from sugars. The fermentation stillage contains most of the lipids from the original biomass, which are recovered through wet solvent extraction. The process residuals after lipid extraction, which contain much of the original mass of amino acids and proteins, are directed to anaerobic digestion (AD) for biogas production and recycle of N and P nutrients. The second option, thermal processing, comprises direct hydrothermal liquefaction (HTL) of the wet biomass, separation of aqueous, gas, and oil phases, and treatment of the aqueous phase with catalytic hydrothermal gasification (CHG) to produce biogas and to recover N and P nutrients. The present report describes a life cycle analysis of energy use and greenhouse gas (GHG) emissions of the CAP and HTL options for the three scenarios just described. Water use is also reported. Water use during algal biofuel production comes from evaporation during cultivation, discharge to bleed streams to control pond salinity (“blowdown”), and from use during preprocessing and upgrading. For scenarios considered to date, most water use was from evaporation and, secondarily, from bleed streams. Other use was relatively small at the level of fidelity being modeled now.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Plasma Science and Innovation Center at Washington, Wisconsin, and Utah State: Final Scientific Report for the University of Wisconsin-Madison

The University of Wisconsin-Madison component of the Plasma Science and Innovation Center (PSI Center) contributed to modeling capabilities and algorithmic efficiency of the Non-Ideal Magnetohydrodynamics with Rotation (NIMROD) Code, which is widely used to model macroscopic dynamics of magnetically confined plasma. It also contributed to the understanding of direct-current (DC) injection of electrical current for initiating and sustaining plasma in three spherical torus experiments: the Helicity Injected Torus-II (HIT-II), the Pegasus Toroidal Experiment, and the National Spherical Torus Experiment (NSTX). The effort was funded through the PSI Center's cooperative agreement with the University of Washington and Utah State University over the period of March 1, 2005 - August 31, 2016. In addition to the computational and physics accomplishments, the Wisconsin effort contributed to the professional education of four graduate students and two postdoctoral research associates. The modeling for HIT-II and Pegasus was directly supported by the cooperative agreement, and contributions to the NSTX modeling were in support of work by Dr. Bickford Hooper, who was funded through a separate grant. Our primary contribution to model development is the implementation of detailed closure relations for collisional plasma. Postdoctoral associate Adam Bayliss implemented the temperature-dependent effects of Braginskii's parallel collisional ion viscosity. As a graduate student, John O'Bryan added runtime options for Braginskii's models and Ji's K2 models of thermal conduction with magnetization effects and thermal equilibration. As a postdoctoral associate, O'Bryan added the magnetization effects for ion viscosity. Another area of model development completed through the PSI-Center is the implementation of Chodura's phenomenological resistivity model. Finally, we investigated and tested linear electron parallel viscosity, leveraged by support from the Center for Extended Magnetohydrodynamic Modeling (CEMM). Work on algorithmic efficiency improved NIMROD's element-based computations. We reordered arrays and eliminated a level of looping for computations over the data points that are used for numerical integration over elements. Moreover, the reordering allows fewer and larger communication calls when using distributed-memory parallel computation, thereby avoiding a data starvation problem that limited parallel scaling over NIMROD's Fourier components for the periodic coordinate. Together with improved parallel preconditioning, work that was supported by CEMM, these developments allowed NIMROD's first scaling to over 10,000 processor cores. Another algorithm improvement supported by the PSI Center is nonlinear numerical diffusivities for implicit advection. We also developed the Stitch code to enhance the flexibility of NIMROD's preprocessing. Our simulations of HIT-II considered conditions with and without fluctuation-induced amplification of poloidal flux, but our validation efforts focused on conditions without amplification. A significant finding is that NIMROD reproduces the dependence of net plasma current as the imposed poloidal flux is varied. The modeling of Pegasus startup from localized DC injectors predicted that development of a tokamak-like configuration occurs through a sequence of current-filament merger events. Comparison of experimentally measured and numerically computed cross-power spectra enhance confidence in NIMROD's simulation of magnetic fluctuations; however, energy confinement remains an open area for further research. Our contributions to the NSTX study include adaptation of the helicity-injection boundary conditions from the HIT-II simulations and support for linear analysis and computation of 3D current-driven instabilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

FY17 Status Report on NEAMS Neutronics Activities

Under the U.S. DOE NEAMS program, the high-fidelity neutronics code system has been developed to support the multiphysics modeling and simulation capability named SHARP. The neutronics code system includes the high-fidelity neutronics code PROTEUS, the cross section library and preprocessing tools, the multigroup cross section generation code MC2-3, the in-house meshing generation tool, the perturbation and sensitivity analysis code PERSENT, and post-processing tools. The main objectives of the NEAMS neutronics activities in FY17 are to continue development of an advanced nodal solver in PROTEUS for use in nuclear reactor design and analysis projects, implement a simplified sub-channel based thermal-hydraulic (T/H) capability into PROTEUS to efficiently compute the thermal feedback, improve the performance of PROTEUS-MOCEX using numerical acceleration and code optimization, improve the cross section generation tools including MC2-3, and continue to perform verification and validation tests for PROTEUS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Final LDRD Report: Using Linkography of Cyber Attack Patterns to Inform Honeytoken Placement

The war to establish cyber supremacy continues, and the literature is crowded with strictly technical cyber security measures. We present the results of a three year LDRD project using Linkography, a methodology new to the field of cyber security, we establish the foundation necessary to track and profile the microbehavior of humans attacking cyber systems. We also propose ways to leverage this understanding to influence and deceive these attackers. We studied the science of linkography, applied it to the cyber security domain, implemented a software package to manage linkographs, generated the preprocessing blocks necessary to ingest raw data, produced machine learning models, created ontology refinement algorithms and prototyped a web application for researchers and practitioners to apply linkography. Machine learning produced some of our key results: We trained and validated multinomial classifiers with a real world data set and predicted the attacker's next category of action with 86 to 98% accuracy; dimension reduction techniques indicated that the linkography-based features were among the most powerful. We also discovered ontology refinement algorithms that advanced the state of the art in linkography in general and cyber security in particular. We conclude that linkography is a viable tool for cyber security; we look forward to expanding our work to other data sources and using our prediction results to enable adversary deception techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Building and Running TDAAPS Models: WRF Postdictions

This document serves to guide a researcher through the process of running the Weather Research and Forecasting (WRF) model and incorporating observations into coarse resolution reanalysis products to model atmospheric conditions at high (50 m) resolution. This documentation is specific to WRF and the WRF Preprocessing System (WPS) version 3.8.1 and the Objective Analysis (OBSGRID) code released on April 8, 2016. Output from WRF serves as an input into the Time-Domain Atmospheric Acoustic Propagation Suite (TDAAPS) which performs staggered-grid finite difference modeling of the acoustic velocity pressure system to produce Green's functions through these atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Chemical Characterization and Conversion Assessment of BurCell® System Treated MSW Materials

Cornerstone Resources has developed a proprietary advanced preprocessing technology, the BurCell® System, for production of a high quality organic biomass feedstock from numerous waste resources. While suitable for a variety of feedstock, the focus of development for the BurCell® System has been thus far Municipal Solid Waste (MSW). In MSW, the system technology is able to capture non-recyclable waste paper, cardboard, and organic materials, homogenize these materials and allow for separation of the non-organic fraction. The resulting homogenized organic material typically represents in excess of 50% of the raw MSW feed. The recently released Billion Ton Study now includes MSW as a feedstock resource showing it to be one of the lowest cost and most widely distributed biomass resources available; however, acknowledging the concerns with the potential physical and chemical variability of an MSW based feedstock stream. The focus on MSW has also been driven by MSW being a resource with a clear, well understood and mature supply-chain. Treating MSW in the BurCell® System will make that source of biomass substantially more accessible. To further promote the use of this MSW derived biomass and beneficially exploit the use of the BurCell® System for processing of biomass for Bioenergy production facilities, more detailed lab data is now required

09 BIOMASS FUELS↗
DOE OSTI2019

Conversion of Coal Wastes and Coal Plus Other Opportunity Fuel Mixtures, Such as Coal Plus Municipal Solids, to Produce Electricity and/or Liquid Fuels

For the foreseeable future, the Nation’s energy demand will continue to come largely from indigenous fossil energy resources and hydrocarbon fuels. Specifically waste coal plus other opportunity fuels need to be utilized as a feedstock to eliminate a waste stream and optimized to maximize energy yield and cleanly minimize pollutant emissions. Modular equipment is needed for installation in coal preparation locations, military installations, and research park facilities. Thermal decomposition of coal, biomass, and mixed solid waste (MSW) have been shown to reduce a waste stream while providing liquid and gaseous fuel products with low pollutant emissions. However, thermal decomposition processes have several challenges associated with preprocessing biomass and MSW, feeding and conveying material into the reactor, and post processing of syngas to remove undesirable products while upgrading to increase yield of high value products. Increasing total energy output from power plants by utilizing biomass and mixed wastes ultimately lowers energy costs, harmful environmental impacts of emissions, and has significant impacts on the energy security and sustainability of the U.S. economy. Entrained flow gasifiers (EFGs) produce very little CH4 and a wide range of H2/CO ratios (0.5–2.0) but are limited in their ability to effectively feed large biomass or MSW particles into the reactor. A solution to the feeding problems is to use pyrolysis torrefaction (PT) for conversion of waste materials to create a feedstock similar to coal and capable of grinding down and mixing with the coal feed. Mainstream experimentally explored reaction pathways for the conversion of coal waste plus municipal solids (CWPMS) and demonstrated a bench-scale reactor used for torrefaction of the municipal solid waste (MSW) to create a coal-like feed for blending with the coal waste. The material was processed in a pilot-scale EFG to determine product yield and emissions with CWPMS fuels, as well as the ash content and slagging properties of these feedstocks. The PT-EFG system was experimentally validated, where emissions and yield data were used to develop a high-fidelity process model and economic analysis. The following was accomplished in the Phase I: Determined optimal torrefaction process conditions for MSW and biomass through TGA; maximized heating value of fuel while maximizing final mass of torrefied product Demonstrated torrefaction of MSW and biomass feedstocks at 334°C, pulverized to uniform 74 µm particle size, and processed into coal-like feedstocks Demonstrated 8 lb/h pilot-scale EFG using MSW and biomass blends with waste coal; demonstrated 100% biomass in EFG Determined major effects of MSW torrefaction and co-gasifying high-ash coal waste-MSW blends have on pilot-scale EFG Demonstrated blending low-ash, low-sulfur torrefied biomass with high-ash waste bituminous and anthracite refuse coals generates a higher quality syngas compared to syngas from high-ash waste coals Demonstrated a high carbon conversion (> 99.5%) and syngas with 29.47% H2 content, and 0% CH4 content; greater water–gas shift occurs in EFG than predicted by equilibrium ASPEN Plus model Blended lower-ash, high-alkali biomass MSW to lower slag viscosities of the high-ash coal refuse sufficiently for better slag flow compared to feeding the high-ash coal Used low-sulfur biomass to greatly reduce the EFG syngas H2S concentrations and produce non-leachable slag and quench water with very little organic and trace metal contamination Developed a process model and TEA showing the modular PT EFG is cost competitive to existing power plants with the advantage of using low- or negative-value feedstocks such as waste coal stockpiles and non-landfilled MSW; providing a large incentive to implement an economical PT-EFG process

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

Evaluation of Agricultural Equipment Manufacturing for a Bio-Based Economy

This report provides the analysis results of agricultural and preprocessing equipment and manufacturing requirements to support the mobilization of the Billion Ton projections utilizing the conventional supply chain logistics. This report discusses the number of required agricultural machinery and their market values, the drivers and barriers of the transition in agricultural equipment, the potential economic impacts to the US associated with expansion of these equipment manufacturing and the factors that impact the transition in agricultural machinery to support the growth of a large-scale biofuel and bio-products industry in short and long terms. Short and long terms refer to the early development and mature development phases of the cellulosic biofuels industry in the United States.

09 BIOMASS FUELS↗
DOE OSTI2017

Cornerstone Resources Feedstock Characterization and Conversion Assessment of Treated MSW Materials (CRADA Closeout Final Report)

Cornerstone Resources has developed a proprietary advanced preprocessing technology, the BurCell® System, for production of a high quality organic biomass feedstock from numerous waste resources. While suitable for a variety of feedstock, the focus of development for the BurCell® System has been thus far municipal solid waste (MSW). In MSW, the system technology is able to capture non-recyclable waste paper, cardboard, and organic materials, homogenize these materials and allow for separation of the non-organic fraction. The resulting homogenized organic material typically represents in excess of 50% of the raw MSW feed. The recently released Billion Ton Study now includes MSW as a feedstock resource showing it to be one of the lowest cost and most widely distributed biomass resources available; however, acknowledging the concerns with the potential physical and chemical variability of an MSW based feedstock stream. The focus on MSW has also been driven by MSW being a resource with a clear, well understood and mature supply-chain. Treating MSW in the BurCell® System will make that source of biomass substantially more accessible. To further promote the use of this MSW derived biomass and beneficially exploit the use of the BurCell® System for processing of biomass for Bioenergy production facilities, more detailed lab data is now required.

09 BIOMASS FUELS↗
DOE OSTI2020

Development of Prognostic Models Using Plant Asset Data

The recent growth of machine learning and artificial intelligence technologies provides opportunities for leveraging data-driven algorithms to address the problems of diagnostics and prognostics in the nuclear power industry. The use of machine learning and other statistical methods as prognostic models is of particular interest in the nuclear industry to accurately predict future equipment or plant state given a set of measurements. Such predictive capability will enable predictive assessment of component condition and remaining life and allow for condition-based predictive maintenance. The resulting optimization of maintenance scheduling and reduction in unnecessary maintenance activities will lower overall maintenance costs and improve the economics of nuclear power. This report discusses the various aspects of data processing and model development that are likely to influence the performance of prognostic models. Data from a boiling-water reactor was used to evaluate several prognostic models to identify key considerations for developing such models to predict data-driven plant state and equipment degradation condition. Preliminary results indicate the need for data sets that are relevant to the problem at hand and contain signatures that may be correlated to the prediction problem. Assuming such data exist, development of prognostic models using data-driven methods requires an understanding of the various sources of influence on the prediction accuracy (such as the model architecture, data preprocessing approaches, and potentially external factors influencing the equipment or plant system under assessment). Ongoing research is evaluating these factors in greater detail and examining techniques for calculating prediction uncertainty bounds.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Building and Running TDAAPS Models: WRF Predictions

This document serves to guide a researcher through the process of predicting atmospheric conditions in a region of interest utilizing the Weather Research and Forecasting (WRF) model. This documentation is specific to WRF and WRF Preprocessing System (WPS) version 3.8.1. WRF is an atmospheric prediction system designed for meteorological research and numerical atmospheric prediction. In WRF, simulations may be generated utilizing real data or idealized atmospheric conditions. Output from WRF serves as input into the Time-Domain Atmospheric Acoustic Propagation Suite (TDAAPS) which performs staggered-grid finite difference modeling of the acoustic velocity pressure system to produce Green's functions through these atmospheric models.

54 ENVIRONMENTAL SCIENCES↗