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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mixing Cell Model: A One-Dimensional Numerical Model for Assessment of Water Flow and Contaminant Transport in the Unsaturated Zone

This report describes the Mixing Cell Model code, a one dimensional model for water flow and solute transport in the unsaturated zone under steady state or transient flow conditions. The model is based on the principles and assumptions underlying mixing-cell model formulations. The unsaturated zone is discretized into a series of independent mixing cells. Each cell may have unique hydrologic, lithologic, and sorptive properties. Ordinary differential equations describe the material (water and solute) balance within each cell. Water-flow equations are derived from the continuity equation, assuming that unit gradient conditions exist at all times in each cell. Pressure gradients are considered implicitly through model discretization. Unsaturated hydraulic conductivity and moisture contents are determined by the material specific moisture-characteristic curves. Solute-transport processes include explicit treatment of advective processes, first order chain decay, and linear sorption reactions. Dispersion is addressed through implicit and explicit dispersion. Implicit dispersion is an inherent feature of all mixing-cell models and originates from the formulation of the problem in terms of mass balance around fully mixed volume elements. Expressions are provided that relate implicit dispersion to the physical dispersion of the system. Two FORTRAN codes were developed to solve the water flow and solute-transport equations: (1) the Mixing Cell Model for Flow (MCMF) solves transient water-flow problems and (2) the Mixing Cell Model for Transport (MCMT) solves the solute-transport problem. The transient water-flow problem is typically solved first by estimating the water flux through each cell in the model domain as a function of time using the MCMF code. These data are stored in either ASCII or binary files that are later read by the solute transport code (MCMT). Code output includes solute pore water concentrations, water and solute inventories in each cell and at each specified output time, and water and solute fluxes through each cell and specified output time. Computer run times for coupled transient water flow and solute transport were typically several seconds on a 2 GHz Intel Pentium IV desktop computer. The model was benchmarked against analytical solutions and finite element approximations to the partial differential equations (PDE) describing unsaturated flow and transport. Differences between the maximum solute flux estimated by the mixing cell model and the PDE models were typically less than two percent. This revision includes an option for a fixed concentration lower boundary condition for diffusive fluxes for versions 020321 and later.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Santa Barbara Desalination Digital Twin Technical Report

A digital twin is a computer model that has sufficient breadth and fidelity to enable diagnostic and predictive analyses for outputs of interest. We have constructed a digital twin of the SB desal plant using the ProteusLib software developed for NAWI under task 4.2.2, and specifically the ProteusLib models of pumps, filters, mixers, splitters, and reverse osmosis units and a seawater property model. The steps needed to configure this digital twin with the real conditions of the SB desal plant are: (1) Data reconciliation, which is gathering the data and plant connectivity and ensure that all relevant values are present and in the correct units and validating the data using material and energy balances, and correct for errors, gaps, or deviations; (2) Parameter estimation, which is matching model parameters with data from previous step, filling missing parameters with initial estimates, then iteratively refining estimated values. Once we completed the digital twin, we used the optimization capabilities of ProteusLib to compute outputs of interest over a chosen range of inputs. The primary output of interest to the owners of the plant is its specific energy consumption (kWh/acre-foot). The remainder of this report will provide more detail on the data reconciliation, parameter estimation, and analyses we performed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Detector Characterization for Accurate Monte Carlo Simulation and Neutron Energy-spectrum Unfolding

This report details the characterization of a deuterated scintillation detector for use in neutron spectroscopy via the spectrum unfolding technique. The active volume of the detector consists of a 3-in. diameter by 3 in. depth right circular cylinder that contains 337 cm3 of liquid scintillator EJ-315, deuterated benzene with a D:H ratio of 141:1. A deuterated scintillator is desirable for unfolding fast neutron energy spectra because the backscattering of neutrons from deuterium produces peaks in the pulse height response corresponding to the incident neutron energy. Spectrum unfolding requires the detector’s response to be accurately known for any input neutron energy. The characterization in this report supports using a simulated response matrix calibrated by the measured light output and convolved with the measured resolution to produce a response matrix without bringing the detector to a neutron beam facility for characterization. In particular, characterization measurements were performed that included calibration of the detector’s light output to recoil electrons from gamma sources, calibration of the detector’s resolution to recoil electrons, and calibration of the detector’s light output to recoil protons from a time-tagged neutron source and a monoenergetic 14.1 MeV neutron source.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

(U) SENSMG: First-Order Sensitivities of Neutron Reaction Rates, Reaction-Rate Ratios, Leakage, k eff , α , and Subcritical Multiplication Using PARTISN

SENSMG is a tool for computing first-order sensitivities of neutron reaction rates, reaction-rate ratios, leakage, k eff , α, and subcritical multiplication using the PARTISN multigroup discrete-ordinates code. SENSMG computes sensitivities to all of the transport cross sections and data (total, fission, a nu, chi, and all scattering moments), two edit cross sections (absorption and capture), and the density for every nuclide and energy group. It also computes sensitivities to the mass density for every material and derivatives with respect to all interface locations and outer boundaries. It computes sensitivities to user specified reactions whose cross sections are available in a user-supplied NJOY output file. The tool can be used for one-dimensional spherical and slab (r) and two-dimensional cylindrical (r-z) geometries. The tool can be used for fixed-source and eigenvalue problems. For most responses, the tool implements Generalized Perturbation Theory (GPT) as discussed by Williams and Stacey. The tool is thus limited to computing sensitivities only for GPT-allowable responses. For subcritical multiplication, the tool implements sensitivities derived by O’Brien and Clark. SENSMG has a similar role as the old SWANLAKE (Ref. 8), FORSS (Ref. 9), and SENSIT (Ref. 10) codes. It has capabilities similar to those of SUSD3D (Refs. 11 and 12), which also uses PARTISN. Section II of this report describes the theory behind adjoint-based sensitivities, gives the equations that SENSMG solves, and defines the sensitivities that are output. Section III describes the user interface, including the input file and command line options. Section IV describes the output. Section V gives some notes about the coding that may be of interest. Section VI presents some sample problems and discusses verification, which is ongoing. Section VII lists needs and ideas for future work. Appendix A lists most of the input files whose results are presented in Sec. VI. Appendix B provides some useful details on one of the cross-section libraries that SENSMG supports.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Preliminary Pilot-scale Study and Techno-economic Analysis of Chemical Heat Pump with Conventional Nuclear Reactors

The energy economy is continually evolving, particularly in terms of primary energy sources, their conversions to useful forms such as electricity and heat, and their utilization in different sectors, in response to socio-political factors. Because nuclear source is clean and non-carbon-emitting energy source, it is crucial to consider its role in the evolution of the energy economy. A pilot-scale study was conducted for Chemical Heat Pump (ChHP) system at three different scales (25-, 100-, and 1000-kW thermal outputs) using steady state thermal model. To establish the viability of selling heat rather than electricity alone, techno-economic analysis was undertaken for advanced Small Modular Reactor (SMR), and SMR paired with varied thermal output ChHP systems was referred to as the nuclear hybrid energy system (NHES) in this study. Using the U.S. Energy Information Administration statistics, pricing for electricity and natural gas were anticipated for U.S. regions (California, Northwest, Midwest, Southwest, New England, and PJM). Based on thermal output from ChHP, advanced SMR with 100 MWth and four alternative NHES scenarios were explored, namely 50-, 10-, 5-, and 1-MWth. Net present value, payback period, discounted cash flow return, and levelized cost of energy were evaluated for all scenarios. Based on the economic analysis, selling heat to high-temperature industrial processes is more profitable compared to selling electricity only. Higher carbon taxes showed significant improvement in economic parameters for NHESs. Providing heat to high-temperature industries could be beneficial, helping to reduce the greenhouse gas emissions by slashing the fossil fuel consumption.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A Decision Theoretic Approach To Optimizing Machine Learning Decisions with Prediction Uncertainty

While the use of machine learning (ML) classifiers is widespread, their output is often not part of any follow-on decision-making process. To illustrate, consider the scenario where we have developed and trained an ML classifier to find malicious URL links. In this scenario, network administrators must decide whether to allow a computer user to visit a particular website, or to instead block access because the site is deemed malicious. It would be very beneficial if decisions such as these could be made automatically using a trained ML classifier. Unfortunately, due to a variety of reasons discussed herein, the output from these classifiers can be uncertain, rendering downstream decisions difficult. Herein, we provide a framework for: (1) quantifying and propagating uncertainty in ML classifiers; (2) formally linking ML outputs with the decision-making process; and (3) making optimal decisions for classification under uncertainty with single or multiple objectives.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High-Resolution Wind Resource Data Set of the Greater Puerto Rico Region

In February 2022, the U.S. Department of Energy and six national laboratories launched the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100). PR100 aims to provide a comprehensive analysis of possible pathways for Puerto Rico's energy future, with a goal of 100% renewable energy by 2050. As a part of the renewable energy potential assessment in this project, we developed 20 years (2001-2020) of data using a numerical weather prediction (NWP) model for onshore and offshore wind resource assessment for the Puerto Rico. The research steps in developing the long-term wind resource data sets based on the NWP model were: 1. Model wind resource based on the Weather Research and Forecasting (WRF) model. 2. Develop WRF model configurations for Puerto Rico. 3. Test WRF with 11 different physics parameterizations for planetary boundary layer (PBL). 4. Assess WRF output from the different PBL schemes against observations. 5. Select a final model configuration which can produce the modeled wind speed with sufficient accuracy. 6. Produce 20 years wind resource data sets for Puerto Rico region. In the first stage of our framework for developing wind resource data, we developed a WRF model configuration using two nested domains (9 km and 3 km) to cover Puerto Rico and U.S. Virgin Islands and downscale the ERA5 reanalysis data (0.25 degrees x 0.25 degrees; hourly interval) to a 3-km domain. For the second stage, we implemented one-year simulations focused on using 11 different PBL physics parameterizations to find a combination of WRF physics parameterizations that could provide accurately modeled wind speed for Puerto Rico. We also analyzed the sensitivity of the modeled wind speed to PBL schemes for onshore and offshore locations. The WRF output resulting from the 11 WRF experiments using different PBL parameterizations were evaluated against observations obtained from the National Data Buoy Center (NDBC) as well as at hub height for a location for which measurements were available. A final model setup selected through the validation with observational data was used to produce 20 years of data with 3-km spatial and 5-minute temporal resolution. The WRF model output was post-processed to include wind profiles and basic atmospheric variables in a format that can be easily used for downstream modeling. The 20 years of wind resource data will be made available through NREL and support the estimation of wind energy development costs for the PR100 study.

17 WIND ENERGY↗
DOE OSTI2022

GDSA - Modeling and Integration – ORNL: PFLOTRAN modeling

This report summarizes work done at Oak Ridge National Laboratory (ORNL) related to post-closure criticality consequence analysis of dual-purpose canister (DPC) direct disposal in unsaturated alluvium. PFLOTRAN software was used to model the behavior of a breached DPC with low water infiltration rates and unsaturated conditions in alluvial deposits such that a low-power criticality event occurs with a focus on thermal hydrological constraints on criticality timing and power output. This summary describes initial exploration of those thermal hydrological processes and of the long-term average power output from a criticality event that could be sustained without driving water out of the DPC package and thereby terminating the criticality event. The objectives are (1) to identify, for different groundwater infiltration rates, the time at which the DPC fills with water sufficiently to potentially initiate criticality; (2) to bound the power output that could be produced by a criticality event that would thermally drive water out of the package at various percolation rates; and (3) provide an estimate of how the size of a package breach affects the water infiltration and therefore the possibility timing of criticality.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Standoff Detection of Chemical Plumes Using Swept-ECQCL Remote Detection Technology: Results from EMRTC Complex Terrain Dual Tracer Experiment

As part of the Complex Terrain Dual Tracer Experiments performed in support of the LYNM program, a swept-wavelength external cavity quantum cascade laser (swept-ECQCL) remote detection system was deployed to measure chemical plume propagation with high temporal resolution. The swept-ECQCL remote detection technology was initially developed at PNNL, and is now under further development at Opticslah, LLC under SBIR funding from DOE (DOE SBIR Phase I Project DE-SC0019855, Remote Detection Technologies, Program Manager Chris Ramos). The swept-ECQCL technology provides a single-frequency infrared laser source which is rapidly scanned over a large wavelength range to measure the infrared absorption of gases, liquids, or solids. Analysis of the measured infrared absorption spectrum is used to detect, identify, and quantify the chemical species which are present. While most previous laser-based techniques used small wavelength tuning ranges to measure only a few isolated spectral lines of a few species, the swept-ECQCL provides the large tuning range needed to measure multiple species simultaneously, and to detect broad absorption features from large molecules or condensed phase materials. In addition, the swept-ECQCL systems have high enough spectral resolution and sensitivity to measure narrow lines of small molecules and determine isotope ratios. The remote detection system for gas plumes is based on the swept-ECQCL technology, and consists of a swept-ECQCL source, infrared detector, control electronics, and beam transmitting and alignment optics. The remote detection system directs the infrared output of the swept-ECQCL to a target retro reflector typically located at ~10-10,000 m distance. The swept-ECQCL wavelength is varied continuously over a large wavelength range (>1000 nm) at rates up to 1000 Hz. The swept ECQCL light reflected from the remote target is collected and focused onto an infrared photodetector. By measuring the detector signal continuously as the swept-ECQCL wavelength is varied, a time-series of absorption spectra are measured for the gases along the beam line of-sight. Based on analysis and fitting of the measured absorption spectra, the time-dependent chemical concentrations of species are determined. Multiple species and mixtures can be detected simultaneously, for all species with absorption features above the sensor noise floor. The sensor output is thus a continuous, high-speed record of identified chemicals along with their concentrations and isotope ratios of interest. The high-speed operation enables low-noise operation in conditions of atmospheric turbulence and allows tracking of fluctuating concentrations in transient chemical plumes as needed for reliable remote detection applications. All components operate with minimal cooling requirements and no cryogens are needed. In addition, the output infrared light is invisible with an intensity below the maximum permissible exposure (MPE) threshold of 100 mW/cm2 for these infrared wavelengths and thus eye safe. The measurement concept for plume detection using the swept-ECQCL remote detection system is as follows. The ECQCL beam is directed toward a remote retro-reflector, which reflects the beam back to the ECQCL system where it is focused onto an infrared photodetector. The line-of-sight from the ECQCL system to the retro-reflector defines the measurement path. The detected infrared signal is recorded continuously and detects the absorption of molecular species along the measurement path. Before a plume release, the system measures absorption from atmospheric constituents, which in this LWIR band consist primarily of lines from H 2 O and CO 2 , with additional weaker absorption from N 2 O, NH 3 , and O 3 possibly contributing. The system then measures any changes in absorption due to the tracer compounds when they enter the measurement path. The shape of absorption features is used to distinguish and identify the different tracer compounds, and the strength of the absorption is used to determine the path-integrated concentration – also called the column density.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Refining Processing Engines from SAPHIRE: Initialization of Fault Tree/Event Tree Solver

SAPHIRE has been extensively employed for over 35 years to model risk-important systems and quantify risk models. As a well-established and thoroughly documented Probabilistic Risk Assessment (PRA) tool, SAPHIRE has continuously tracked computational trends and received regular updates. Despite its ongoing evolution, there remains a need for further enhancements, particularly in dealing with the quantification of exceptionally large models. These improvements could take the form of algorithmic advancements, harnessing the power of parallel computing, and exploring the potential benefits of cloud computing solutions. Considering these aspirations, the notion of a remote solve option was introduced and subsequently evolved into a dedicated project within the SAPHIRE development team. A significant outcome of this initiative is SAPHSOLVE, an engine extracted from the SageRisk API designed specifically for remote solving capabilities. The ongoing project is nearing its culmination, marked by a series of discoveries that have brought undocumented aspects to light. Among these revelations is the intricacy of the input and output format for the SAPHSOLVE engine. This document serves the crucial purpose of meticulously delineating the precise formats for both input and output, as they form an indispensable foundation. The importance of documenting these formats cannot be overstated, as it is a pivotal step in facilitating rigorous testing and comparison. Whether it involves scrutinizing SAPHSOLVE results against those of the internal integrated solver or other external solvers, the ability to construct models or transform existing ones into a compatible SAPHSOLVE format is imperative. Chapter 1 offers a succinct introduction to both SAPHIRE and SAPHSOLVE, followed by Chapter 2 which outlines the roadmap for enhancing SAPHSOLVE. The core of this report is Chapter 3, which intricately elucidates the intricacies of the input and output file formats. To provide a tangible illustration of these formats, a rudimentary example has been compiled and is available in Appendix. SAPHSOLVE represents a novel external solving mechanism developed by the SAPHIRE team, although it has not yet reached the full spectrum of capabilities possessed by SAPHIRE's internal solver. However, the SAPHIRE team has set a comprehensive course for incorporating the functionalities of SAPHSOLVE. A comprehensive outlook on the future of SAPHSOLVE is expounded upon in Chapter 4.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Theory Manual for the Reaction Summary Module- SUMMAR

The post-processing module of DIF3D called SUMMAR was created to produce commonly used reactor engineering outputs. It is a optional output option for DIF3D and provides alternative summarizes of power, flux, and neutron balance over the user specified regions and areas. It can also provide isotope-wise microscopic and macroscopic reaction rates along with the effective one-group microscopic cross sections needed for solving the Bateman equations. This report serves as both the theory manual, user guide, and software verification of the SUMMAR module. The methodology and equations for all SUMMAR calculated parameters are presented here and discussed. Then the verification work, based upon simple DIF3D models, is presented to validate the derived equations. The SUMMAR inputs and outputs are detailed and examples are given to demonstrate it.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Using Parameter Sweep in WaterTAP to Analyze New Water Treatment Technologies

We describe a powerful and generalized parameter sweep tool in this report that was originally developed to analyze the performance of existing and novel water treatment models being developed in WaterTAP. Since WaterTAP is built upon IDAES and Pyomo, the parameter sweep tool can be used to systematically explore and debug the behavior of most Pyomo and IDAES numerical models. In order to enable meaningful analyses, the parameter sweep tool has been designed with the following features: 1) Model flexibility: The parameter sweep tool does not enforce any restrictions on the types of models that can be used with it. As long as a Pyomo model can be solved and the parameter is active and mutable, the tool only needs functions that describe how to run the model, the sweep parameters, and the output quantities of interest. 2) Flexible sampling: The parameter sweep tool has inbuilt functions to generate samples from a random distribution or a multidimensional Euclidean space. Furthermore, the users have to ability to supply samples generated from a tool of their choice. 3) Multiple sweep types: A user can choose from one of 3 types of parameter sweeps depending on their needs. 4) Detailed outputs: Outputs generated by the parameter sweep tool can be stored in detailed H5 file or user-friendly CSV files for post processing. 5) Parallel computing: The parameter sweep supports shared and distributed memory parallel computing to enable the use of high performance computers (HPC) for large-scale analyses. 6) Modular: The parameter sweep tool is self-contained and can easily be integrated within an outer-loop analysis or as desired by the user. 7) Ease of use: The tool is well documented and a simple sweep can be easily executed by following the online documentation in a few lines of code. We demonstrate the use of the parameter sweep tool on a simple water treatment system from the WaterTAP repository and show its parallel scaling performance on an Apple laptop and NREL's Eagle HPC. The parameter sweep tool is actively being used with models currently being developed within WaterTAP and we expect its use to grow beyond it to other IDAES and Pyomo models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Application of Partial Least Squares Approaches to Pyroprocessing ER Data

Multivariate approaches show promise for application to process monitoring for safeguards of pyroprocessing. Past MPACT work explored the application of Principal Component Analysis (PCA) to detect off-normal conditions in pyroprocessing electrorefiner (ER) data from in the Hot Fuel Examination Facility (HFEF) at Idaho National Laboratory (INL) known as the Scalable Pyrochemical Recycling testbed (SPyRe) ER. PCA, however, does not consider the output variables. In FY24, multivariate analysis was extended from PCA to Partial Least Squares (PLS) analysis. PLS maximizes the variance between both the input signals and output variables. In the case of this work, PLS was applied in two different manners: Predictive PLS and Discriminant PLS. Predictive PLS maximizes the covariance between the process variables of the ER and the measured U concentration from in-situ voltammetry. Discriminant PLS maximizes the covariance between the process variables and a set of training process “states” such as known off-normal conditions. By projecting into the latent variable space in PLS, the process variables can be regressed onto the outputs and predictions can be made for new data sets. In this work, by applying predictive PLS, a penalized non-linear PLS approach was able to make predictions of concentration based on test and training data and detect when operations were off-normal. However, the predictive PLS does not classify the signals to which off-normal operations are attributable. Discriminant PLS can be used to classify off-normal operations but is inadequate to properly classify specific off-normal classes like power supply faults when the Discriminant PLS model is only specifically trained to detect that off-normal class. When all faults are trained against the observation data, all three operational classes are accurately classified and distinguished. Thus, future application of latent variable techniques should not select any given method, but should use a mixture of PCA, Predictive PLS, and Discriminant PLS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

A Hot‐Swappable, Fault‐Tolerant, Modular Power Converter System for Solar Photovoltaic Plants

The performance metrics of the state-of-the-art commercial solar inverters, such as system cost, operation and maintenance (O&M) cost, service life, reliability, maintainability, and power density are much lower than the target metrics needed to achieve SunShot’s 2030 levelized cost of energy (LCOE) goals. To overcome the shortcomings of the existing solar inverters, this project proposed a novel Hot-Swappable, Fault-Tolerant, Modular Power Converter (HSFT-MPC) concept for solar photovoltaic (PV) plants and proved the concept through the design, fabrication, and laboratory test validation of a single-phase HSFT-MPC prototype. The HSFT-MPC has the following distinct advantages over the state-of-the-art: 1) elimination of harmonic/ electromagnetic interference (EMI) filter in the inverter stage due to the novel topology, 2) lower system cost and higher power density due to the modular design, elimination of harmonic/EMI filter, and lower cooling requirement, 3) higher efficiency due to lower switching frequencies, 4) higher reliability and longer (50 years) service life due to simpler cooling and fault tolerance capability, 5) easier installation, lower O&M cost, and improved maintainability due to the modular design and hot-swappable power electronic building blocks (PEBBs), and 6) improved manufacturability due to the modular design. This project developed a single-phase HSFT-MPC prototype with 25kW nominal output power, 2.4kV, 60Hz nominal AC output, lower than 5% AC output voltage total harmonic distortion, over 5 kW/L inverter power density, and 99.4% inverter peak efficiency, being tolerant to failure of single and multiple PEBBs, and capable of hot swapping of the failed PEBB(s). The HSFT-MPC enables uninterruptable operation of the solar PV plant when failure of single or multiple PEBBs or PV modules occurs. Compared with the existing solar inverters in the market, the HSFT-MPC is expected to reduce the inverter failure-caused downtime and energy losses of solar PV plants by more than 60% and 50%, respectively. Project findings have been presented at major conferences in the field and published in peer-reviewed papers, which added new knowledge to the field of power electronics for solar PV systems. A minicourse on Solar PV Systems was developed for outreach activities. The minicourse will help attract young individuals to the renewable energy profession which has a significant talent shortage. The HSFT-MPC is expected to overcome all of the shortcomings of the state-of-the-art solar inverters in terms of cost, efficiency, service life, reliability, maintainability, and manufacturability targets needed to achieve SunShot’s 2030 LCOE goals. Therefore, the proposed HSFT-MPC concept has the great potential to disrupt the current solar inverter market. This project created a pathway towards industry adoption of the HSFT-MPC to help achieve 50-year service life solar PV systems. Since the solar PV plants using the HSFT-MPC will feature with higher reliability, longer service life, and easier maintenance, they are particularly useful for the rural areas with underserved populations that demand reliable and affordable clean electricity. The outcomes of the project have the strong potential to address national needs in the field of renewable energy to reduce CO 2 emissions from the electricity sector, reduce imports of energy from foreign sources, and improve energy security, efficiency, and sustainability. Since electricity is used in almost all of society’s sectors, the outcomes of the project will benefit various sectors of society and economy.

14 SOLAR ENERGY↗
DOE OSTI2024

A Roadmap for a Lightning Modeling Grand Challenge

This document is a roadmap for building an interconnected model of the physical processes that produce a lightning discharge, and its observable optical and radio signals. We call this a Lightning Modeling Grand Challenge, recognizing that significant effort and coordination of human and financial resources is required to realize the capability. The roadmap serves to outline the coordination of resources necessary to enable stitching together existing knowledge and model components to make a lightning prediction, and to test these predictions with observations. Such a capability does not currently exist. The roadmap is motivated not only by a spirit of scientific inquiry, but by practical challenges faced by US Federal and societal stakeholders. Advancements in lightning observations have outpaced our tests of integrated understanding, leaving many stakeholders unsure how to design their missions to properly detect and discriminate lightning, and unsure how to apply the sometimes-disagreeing lightning signals from diverse instruments. The time is right to connect existing theories and models to support stakeholders in understanding the signals they observe, for needs as diverse as climate monitoring, national security, weather forecasting, public safety, and protection of natural and built environments. The roadmap’s two main technical sections describe the components of a linked physical model, followed by a description of models of lightning signals and sensors that are driven by outputs from the physical model. The goal is to predict the time-varying physical properties of lightning that are self-consistent with the thunderstorm’s structure and dynamics. These lightning signals then propagate through the storm, with realistic dispersion and attenuation, to receivers on the ground or in space. At a high level, the model begins with weather (cloud) model output, including explicit prediction of the electrification of cloud particles. The cloud’s electrical structure drives a model of lightning physics, from initiation, through channel development, and discharges along those channels. Key lightning parameters, such as the temperature and currents in the channel, and their space and time distribution, are then used to produce optical and electromagnetic signal sources that propagate to modeled receivers. This architecture therefore generates a dataset suitable for comparison to existing and envisioned observing systems. The need for additional measurements and field campaigns to support model development is described. In each model sub-component, inputs, outputs, uncertainties, evaluation methods, and next steps are summarized, interleaved with references to the scientific literature. Identifying boundaries between the model sub-components aids in segmenting an integrated, complex model into practical work packages and system sub-components, allowing a diverse team to contribute and maintain the system. We estimate that at least five years of effort and a $\$$10M initial investment is necessary to make a significant step forward. Mechanisms to facilitate community coordination, including annual workshops and open-source code repositories, are described.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Learning Optimal Aerodynamic Designs

This project created a framework for efficient, accurate, and scalable deep neural network representations of design optimization problem solutions. The inputs to these DNN representations are the vector of design requirement parameters, the outputs are the optimal design variables, and the goal is to learn the map from inputs to outputs (i.e., inverse design). The team addressed the problem of the optimal shape design of aerodynamic lifting surfaces—in particular aircraft wings—using a Reynolds-Average Navier Stokes model to govern the CFD-based aerodynamic shape optimization. The inverse design map for such problems is very complex and high-dimensional, involving inputs and outputs on the order of 1000s. To approximate this inverse design map, the team developed algorithms to construct parsimonious DNN architectures, which automatically identify low-dimensional manifolds in which design requirements affect optimal shape parameters, and trained these architectures with multifidelity optimization methods. The resulting methodology accurately and automatically designs optimal aerodynamic lifting surfaces with very high accuracy (99%) at interactive speeds, of the order of milliseconds, resulting in factors of one million or more speedup relative to CFD-based design optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Collaborative: in situ visual analytics technologies for extreme scale combustion simulations

This project aims to drastically enhance the usability of in situ analysis and visualization for extreme-scale scientific simulations. Current exascale computing capabilities promise to offer greater predictive ability of simulations and to further push the frontiers of science and technology. However, to validate the simulation output at extreme scale, examine the modeled phenomena, and discover previously unknowns from the output data, the output must be reduced or transformed in situ as it is being generated during the simulation such that the amount of data to examine and store is kept to a minimum. Such in situ approaches allow us to process and analyze the data and any embedded geometry to an extent that would be prohibitively expensive, if not impossible, to perform as a post hoc task. While in situ processing has been demonstrated to be a feasible and promising approach, its full potential has not yet been leveraged. In this project, we have developed comprehensive enhancements to in situ technology based on probability distributions in data. Our research focuses on jointly developing new ways of interacting with massive statistical samples while creatively utilizing new state-of-the-art computational resources to push the boundaries of in situ exploration. Moreover, we have developed new time-dependent techniques to enable previously unattainable capabilities in areas such as intelligent simulation steering and precise feature identification. We have experimentally studied our design and implementation at NERSC and OLCF, and are able to leverage existing in situ infrastructures whenever possible. While the exemplar in this project is combustion, many other fields for which turbulent transport is important, e.g., fusion, climate, astrophysics among others, encounter similar issues as simulations scale up to the exascale. This project shows its potential to generate high impact on DOE missions since the resulting technology promises to improve scientists’ ability to rapidly and correctly interpret and tune extreme-scale simulations, leading to new scientific understanding and advancements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

FECM/NETL Offshore CO 2 Saline Storage Cost Model Version 1 – QuickStart Guide

The purpose of this QuickStart is to assist users in operating the Office of Fossil Energy and Carbon Management/National Energy Technology Laboratory (FECM/NETL) Offshore Carbon Dioxide (CO2) Saline Storage Cost Model (CO2_S_COM_Offshore) Version 1. This manual outlines the major outputs, provides an overview of how the outputs are calculated, and provides a more detailed understanding of how a user can edit the inputs to affect outputs for the purpose of evaluating a storage project aimed for conducting operations in the Outer Continental Shelf (OCS) of the Gulf of America (GOA).

54 ENVIRONMENTAL SCIENCES↗