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DOE OSTI2023

CASTLE: Conflict Analysis Strategy Testing Laboratory Environment v.1.0.0

SAND2024-01743O The Conflict Analysis Strategy Testing Laboratory Environment (CASTLE) is a software framework that enables and simplifies building a novel, turn-based strategy game in which it can define its own rules, maps, pieces, and interactions. The software is for novice to experienced programmers with some knowledge of Unity3D, a tool used in game production. CASTLE includes a library of common game mechanics used for strategic wargames and traditional board games, such as cards, tokens, dice, and grid maps. It follows design principles popularized by the video game industry and uses singletons for managing portions of the code. CASTLE builds on Unity's component-based design and can respond to engine events during execution. Among the numerous user-friendly features: Build games quickly and cost-effectively Network in real-time and apply data to new games developed on the framework Host multiple participants online Connect rule- or machine learning-based agents to a CASTLE game to serve as opponents or to simulate games Collect data collection from players and in-game behaviors Create a survey to gather demographics or opinions from players Store data locally or save it to an external database through Representational State Transfer (REST) functions CASTLE, which was prototyped using Microsoft Azure, is also designed for easily distributing online games using popular cloud services. The multiplayer functionality includes an agent interface, allowing developers to construct AI players that can substitute for humans in any of the games. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fabian, Nathan↗

DOE OSTI2014

PERIODIC AND APERIODIC VARIABILITY IN THE MOLECULAR CLOUD ρ OPHIUCHUS

Presented are the results of a near-IR photometric survey of 1678 stars in the direction of the ρ Ophiuchus (ρ Oph) star forming region using data from the 2MASS Calibration Database. For each target in this sample, up to 1584 individual J-, H-, and K{sub s} -band photometric measurements with a cadence of ∼1 day are obtained over three observing seasons spanning ∼2.5 yr; it is the most intensive survey of stars in this region to date. This survey identifies 101 variable stars with ΔK{sub s} -band amplitudes from 0.044 to 2.31 mag and Δ(J – K{sub s} ) color amplitudes ranging from 0.053 to 1.47 mag. Of the 72 young ρ Oph star cluster members included in this survey, 79% are variable; in addition, 22 variable stars are identified as candidate members. Based on the temporal behavior of the K{sub s} time-series, the variability is distinguished as either periodic, long time-scale or irregular. This temporal behavior coupled with the behavior of stellar colors is used to assign a dominant variability mechanism. A new period-searching algorithm finds periodic signals in 32 variable stars with periods between 0.49 to 92 days. The chief mechanism driving the periodic variability for 18 stars is rotational modulation of cool starspots while 3 periodically vary due to accretion-induced hot spots. The time-series for six variable stars contains discrete periodic ''eclipse-like'' features with periods ranging from 3 to 8 days. These features may be asymmetries in the circumstellar disk, potentially sustained or driven by a proto-planet at or near the co-rotation radius. Aperiodic, long time-scale variations in stellar flux are identified in the time-series for 31 variable stars with time-scales ranging from 64 to 790 days. The chief mechanism driving long time-scale variability is variable extinction or mass accretion rates. The majority of the variable stars (40) exhibit sporadic, aperiodic variability over no discernable time-scale. No chief variability mechanism could be identified for these variable stars.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Disk and circumsolar radiances in the presence of ice clouds

The impact of ice clouds on solar disk and circumsolar radiances is investigated using a Monte Carlo radiative transfer model. The monochromatic direct and diffuse radiances are simulated at angles of 0 to 8° from the center of the sun. Input data for the model are derived from measurements conducted during the 2010 Small Particles in Cirrus (SPARTICUS) campaign together with state-of-the-art databases of optical properties of ice crystals and aerosols. For selected cases, the simulated radiances are compared with ground-based radiance measurements obtained by the Sun and Aureole Measurements (SAM) instrument. First, the sensitivity of the radiances to the ice cloud properties and aerosol optical thickness is addressed. The angular dependence of the disk and circumsolar radiances is found to be most sensitive to assumptions about ice crystal roughness (or, more generally, non-ideal features of ice crystals) and size distribution, with ice crystal habit playing a somewhat smaller role. Second, in comparisons with SAM data, the ice cloud optical thickness is adjusted for each case so that the simulated radiances agree closely (i.e., within 3 %) with the measured disk radiances. Circumsolar radiances at angles larger than ≈ 3° are systematically underestimated when assuming smooth ice crystals, whereas the agreement with the measurements is better when rough ice crystals are assumed. In conclusion, our results suggest that it may well be possible to infer the particle roughness directly from ground-based SAM measurements. In addition, the results show the necessity of correcting the ground-based measurements of direct radiation for the presence of diffuse radiation in the instrument's field of view, in particular in the presence of ice clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Progress on the FabrIc for Frontier Experiments project at Fermilab

The FabrIc for Frontier Experiments (FIFE) project is an ambitious, major-impact initiative within the Fermilab Scientific Computing Division designed to lead the computing model for Fermilab experiments. FIFE is a collaborative effort between experimenters and computing professionals to design and develop integrated computing models for experiments of varying needs and infrastructure. The major focus of the FIFE project is the development, deployment, and integration of Open Science Grid solutions for high throughput computing, data management, database access and collaboration within experiment. To accomplish this goal, FIFE has developed workflows that utilize Open Science Grid sites along with dedicated and commercial cloud resources. The FIFE project has made significant progress integrating into experiment computing operations several services including new job submission services, software and reference data distribution through CVMFS repositories, flexible data transfer client, and access to opportunistic resources on the Open Science Grid. Hence, the progress with current experiments and plans for expansion with additional projects will be discussed. FIFE has taken a leading role in the definition of the computing model for Fermilab experiments, aided in the design of computing for experiments beyond Fermilab, and will continue to define the future direction of high throughput computing for future physics experiments worldwide

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Site Scientist for the North Slope of Alaska Site

Under this grant our team contributed scientific support to the Department of Energy Atmospheric Radiation Program’s (DOE-ARM) Infrastructure team to maintain high quality research data at the DOE-ARM North Slope of Alaska with special emphasis on the radars. Under our guidance two major field campaigns focusing on mixed-phase Arctic clouds were conducted that greatly increased the community’s understanding of the many processes working together to control the evolution of single-layer cloud mixed-phase clouds. A series of modeling and observational studies revealed that the longevity of the radiatively important liquid phase is strongly dependent on how the ice phase develops in mixed-phase clouds. A new ice microphysics parameterization was developed to capture better the natural evolution of ice particle growth in evolving environments. An ice particle scattering database was developed for all ARM radar frequencies. This database was used in a radar simulator (Doppler spectrum and polarimetric variables) to aid in the interpretation of the advanced ARM radars. At the conclusion of this project our team was poised to develop a complete radar simulator consistent with the new microphysical parameterization, taking advantage of parameterization’s advanced characterization of the ice shape and ice density.

47 OTHER INSTRUMENTATION↗

DOE OSTI2015

Radiological data consolidation and 3D modeling of a building - 15473

A portion of a building at an industrial facility that conducted monazite sands-processing operations in the 1950's under contract to the Atomic Energy Commission (AEC) became contaminated. Subsequently the U.S. Department of Energy designated the facility for remedial action under the Formerly Used Sites Remedial Action Program (FUSRAP). Remedial investigation, remedial actions and post-remediation surveys have been conducted in portions of the building. Radiological data for the interior of the building were compiled, evaluated and presented in a three dimensional (3-D) model (the model) created by AMEC Foster Wheeler in order to identify areas that meet the Record of Decision (ROD) cleanup criteria, and identity areas that may require remedial action or may require additional investigation due to data gaps. The data from different phases of investigation and remediation cover different portions of the building and in some cases overlap. In addition, the data were collected over a period of more than ten years, for different objectives, and thus the instruments and methods for establishing efficiency were not necessarily consistent. The data consolidation process included determining the relative quality of data points to include in the model and the development of rationale associated with assigning a quality level (i.e. ranking) to each data point. The rationale for ranking data quality included, as the primary determining factors, chronological order of the data (most recent was ranked as more reliable), certainty of the sample locations within the building, and analytical data quality. A 3-D model of the building was created using a laser point cloud scan which was then converted into a 3-D design model. Sample locations were then entered into the model and linked with the analytical database to allow display of the radiological results in the model. The visual model allows both strategic and tactical decisions to be made, depending on the level of detail examined. The evaluation process allowed maximum use of the historical data set, rather than requiring older data to be disregarded, and provided at a minimum a qualitative sense of data quality/confidence in the data. The (authors)process also highlights the importance of collecting high quality data from the very beginning with a consistent locational coordinate system, and the need for a good visualization model in order to assess contaminant locations during remedial action or post-remediation surveys.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

Design and implementation of wearable devices in machine tools monitoring system - 279

The world's fourth industrial revolution has finally opened after the term Industry 4.0 was proposed by Germany at the Hannover Industrial Fair in 2011, which combined industrial production with an unmanned plant to perform automated production. With the rise of Internet of Things (IoT), cloud computing, and wearing computerized devices has brought more convenience to mankind with the immediate accessibility of the Internet and technology. For example, the development of mobile devices and network technology has inspired the rapid development of wearable devices on mobile devices. Thus, to enhance the current prospects of Taiwan's unmanned machinery factory's monitoring and maintenance capabilities, this paper studies the use of Google glass Machine Tools Monitoring system, GMTM system, to be implemented in Taiwan's machinery factories. GMTM system, via the Internet, monitors sensor tools to collect tool work data to be transferred to cloud computing for analysis, processing and show the actual working conditions of each machine tool. Furthermore, the proposed system uses the monitoring data in the database to inform the factory supervisors the working status of each machine to determine whether there is a need for machine tool maintenance. This system combines real-time monitoring of material with the presentation of Google Glass wearable devices and iBeacon interior space positioning technology. GMTM system can improve the quality of machine tool production and productivity as well as enhance Taiwan's industrial competitiveness. (authors)

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Classification of Cloud Particle Imagery from Aircraft Platforms Using Convolutional Neural Networks

Abstract A vast amount of ice crystal imagery exists from a variety of field campaign initiatives that can be utilized for cloud microphysical research. Here, nine convolutional neural networks are used to classify particles into nine regimes on over 10 million images from the Cloud Particle Imager probe, including liquid and frozen states and particles with evidence of riming. A transfer learning approach proves that the Visual Geometry Group (VGG-16) network best classifies imagery with respect to multiple performance metrics. Classification accuracies on a validation dataset reach 97% and surpass traditional automated classification. Furthermore, after initial model training and preprocessing, 10 000 images can be classified in approximately 35 s using 20 central processing unit cores and two graphics processing units, which reaches real-time classification capabilities. Statistical analysis of the classified images indicates that a large portion (57%) of the dataset is unusable, meaning the images are too blurry or represent indistinguishable small fragments. In addition, 19% of the dataset is classified as liquid drops. After removal of fragments, blurry images, and cloud drops, 38% of the remaining ice particles are largely intersecting the image border (≥10% cutoff) and therefore are considered unusable because of the inability to properly classify and dimensionalize. After this filtering, an unprecedented database of 1 560 364 images across all campaigns is available for parameter extraction and bulk statistics on specific particle types in a wide variety of storm systems, which can act to improve the current state of microphysical parameterizations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

The National Climate Database (NCDB): An Unbiased 100-Year Dataset for PV Modeling

In this study, we develop a statistical technique to downscale the future projection of solar irradiance for photovoltaics (PV) energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the downscaling method, a summary of the 100-year dataset from 2001-2100, and future extension of this research.

bias correction↗

DOE OSTI2016

The FIFE Project at Fermilab

The FabrIc for Frontier Experiments (FIFE) project is an initiative within the Fermilab Scientific Computing Division designed to steer the computing model for non-LHC Fermilab experiments across multiple physics areas. FIFE is a collaborative effort between experimenters and computing professionals to design and develop integrated computing models for experiments of varying size, needs, and infrastructure. The major focus of the FIFE project is the development, deployment, and integration of solutions for high throughput computing, data management, database access and collaboration management within an experiment. To accomplish this goal, FIFE has developed workflows that utilize Open Science Grid compute sites along with dedicated and commercial cloud resources. The FIFE project has made significant progress integrating into experiment computing operations several services including a common job submission service, software and reference data distribution through CVMFS repositories, flexible and robust data transfer clients, and access to opportunistic resources on the Open Science Grid. The progress with current experiments and plans for expansion with additional projects will be discussed. FIFE has taken the leading role in defining the computing model for Fermilab experiments, aided in the design of experiments beyond those hosted at Fermilab, and will continue to define the future direction of high throughput computing for future physics experiments worldwide.

Box, D.↗

DOE OSTI2026

An Infrared Database of n/k Optical Constants for Calculating Aerosol Spectra for the PICARD Program

The objective of the PICARD Program is to develop fieldable sensing platforms for the rapid chemical identification of aerosol particles in plumes. Standoff detection involves interrogating the aerosol cloud from a distance using optical methods and probing the signal returned from direct backscattering from the aerosol particles or transmitted through the plume after reflection from a retroreflector or surface of opportunity (SOO). The identification of chemical species, however, in aerosols is complicated by their complex compositions and morphologies, chemical interferants, and non-uniform particle sizes. To advance standoff detection of aerosols, modelling the infrared transmittance, reflectance and scattering spectra of aerosolized liquid and solid chemical compounds is required, and then testing that model experimentally via laboratory and field experiments. To perform accurate modeling, the infrared optical constants (n/k), i.e., the complex refractive index, of the compounds of interest are required. Thus, PNNL was tasked to provide the optical constants for a set of analytes relevant to the PICARD program. This report describes the experimental techniques used to derive the optical constants of both liquid and solid compounds using established “gold-standard” protocols, how the experimental data are processed to produce the wavenumber dependent optical constant vectors, and how the data are used in the aerosol absorption spectra modeling.

complex refractive index↗

DOE OSTI2016

THE YOUNG STELLAR POPULATION OF LYNDS 1340. AN INFRARED VIEW

We present results of an infrared study of the molecular cloud Lynds 1340, forming three groups of low- and intermediate-mass stars. Our goals are to identify and characterize the young stellar population of the cloud, study the relationships between the properties of the cloud and the emergent stellar groups, and integrate L1340 into the picture of the star-forming activity of our Galactic environment. We selected candidate young stellar objects (YSOs) from the Spitzer and WISE databases using various published color criteria and classified them based on the slope of the spectral energy distribution (SED). We identified 170 Class II, 27 flat SED, and 45 Class 0/I sources. High angular resolution near-infrared observations of the RNO 7 cluster, embedded in L1340, revealed eight new young stars of near-infrared excess. The surface density distribution of YSOs shows three groups, associated with the three major molecular clumps of L1340, each consisting of ≲100 members, including both pre-main-sequence stars and embedded protostars. New Herbig–Haro objects were identified in the Spitzer images. Our results demonstrate that L1340 is a prolific star-forming region of our Galactic environment in which several specific properties of the intermediate-mass mode of star formation can be studied in detail.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Physics-guided machine learning for improved accuracy of the National Solar Radiation Database

The National Solar Radiation Database (NSRDB) provides high-resolution spatiotemporal solar irradiance data for the entire globe. The NSRDB uses a two-step Physical Solar Model (PSM) to compute the effects of clouds and other atmospheric variables on the solar radiation reaching the surface of the Earth. Physical and optical cloud properties are fundamental inputs to the PSM and are derived from the National Oceanic and Atmospheric Administration's Geostationary Operational Environmental Satellites. This paper describes recent improvements to the NSRDB driven by physics-guided machine learning methods for cloud property retrieval. The impacts of these new methods on the NSRDB irradiance data are validated using an extensive set of ground measurement sites, showing significant improvement for all sites. We report on average, the mean absolute percentage error for global horizontal irradiance and direct normal irradiance show reductions of 2.16 and 3.95 percentage points respectively for all daylight conditions, 5.92 and 17.39 percentage points respectively for cloudy conditions, and 9.00 and 22.59 percentage points respectively for gap-filled cloudy conditions. These new methods will help improve the quality and accuracy of the irradiance and cloud data in the NSRDB.

14 SOLAR ENERGY↗

DOE OSTI2019

WLCG Web Proxy Auto Discovery for Dynamically Created Web Proxies

The WLCG Web Proxy Auto Discovery (WPAD) service provides a convenient mechanism for jobs running anywhere on the WLCG to dynamically discover web proxy cache servers that are nearby. The web proxy caches are general purpose for a number of different http applications, but different applications have different usage characteristics and not all proxy caches are engineered to work with the heaviest loads. For this reason, the initial sources of information for WLCG WPAD were the static configurations that ATLAS and CMS maintain for the Conditions data that they read through the Frontier Distributed Database system, which is the most demanding popular WLCG application for web proxy caches. That works well for use at traditional statically defined WLCG sites, but now that usage of commercial clouds is increasing, there is also a need for web proxy caches to dynamically register themselves as they are created. A package called Shoal had already been created to manage dynamically created web proxy caches. This paper describes the integration of the Shoal package into the WLCG WPAD system, such that both statically and dynamically created web proxy caches can be located from a single source. It also describes other improvements to the WLCG WPAD system since the last CHEP publication.

Dykstra, Dave↗

DOE OSTI2014

A theoretical study on the interstellar synthesis of H{sub 2}NCS{sup +} and HNCSH{sup +} cations

HNCS and NCSH molecules, recently discovered in the interstellar medium, are likely formed via the dissociative recombination of H{sub 2}NCS{sup +} or HNCSH{sup +} isomeric ions. Interstellar synthesis of the latter is discussed on theoretical grounds. The analysis of relevant potential energy surfaces suggests a key role for chemical processes in which CSH{sup +} or HCS{sup +} cations (most likely formed in CS+H{sub 3}{sup +} collisions) react with NH{sub 2} or NH{sub 3}. The astrochemical kinetic database (kida.uva.2011), appended with 7 sulfur-bearing molecules and 48 corresponding reactions, has been applied to model the evolution of HNCS, NCSH, and their cationic precursors in a quiescent molecular cloud. Based on the model and on spectroscopic predictions, for an object like TMC-1, we expect the total intensity of H{sub 2}NCS{sup +} microwave lines to be comparable to that observed for HSCN. Theoretically derived molecular parameters, of interest for radio spectroscopy, are given for the most stable cations sharing the H{sub 2}NCS{sup +} stoichiometry.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

The role of major gas-rich mergers on the evolution of galaxies from the blue cloud to the red sequence

With the aim of exploring the fast evolutionary path from the blue cloud of star-forming galaxies to the red sequence of quiescent galaxies in the local universe, we select a local advanced merging infrared luminous and ultraluminous galaxy (adv-merger (U)LIRGs) sample and perform careful dust extinction corrections to investigate their positions in the star formation rate–M {sub *}, u − r, and NUV − r color–mass diagrams. The sample consists of 89 (U)LIRGs at the late merger stage, obtained from cross-correlating the Infrared Astronomical Satellite Point Source Catalog Redshift Survey and 1 Jy ULIRGs samples with the Sloan Digital Sky Survey DR7 database. Our results show that 74%±5% of adv-merger (U)LIRGs are localized above the 1σ line of the local star-forming galaxy main sequence. We also find that all adv-merger (U)LIRGs are more massive than and as blue as the blue cloud galaxies after corrections for Galactic and internal dust extinctions, with 95%±2% and 81%±4% of them outside the blue cloud on the u − r and NUV − r color–mass diagrams, respectively. These results, combined with the short timescale for exhausting the molecular gas reservoir in adv-merger (U)LIRGs (3×10{sup 7} to 3×10{sup 8} years), imply that the adv-merger (U)LIRGs are likely at the starting point of the fast evolutionary track previously proposed by several groups. While the number density of adv-merger (U)LIRGs is only ∼0.1% of the blue cloud star-forming galaxies in the local universe, this evolutionary track may play a more important role at high redshift.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

Fast All-Sky Radiation Model for Solar Applications (FARMS): A Brief Overview of Mechanisms, Performance, and Applications: Preprint

Solar radiation can be computed using radiative transfer models, such as the Rapid Radiation Transfer Model (RRTM) and its general circulation model applications, and used for various energy applications. Due to the complexity of computing radiation fields in aerosol and cloudy atmospheres, simulating solar radiation can be extremely time-consuming, but many approximations--e.g., the two-stream approach and the delta-M truncation scheme--can be utilized. To provide a new fast option for computing solar radiation, we developed the Fast All-sky Radiation Model for Solar applications (FARMS) by parameterizing the simulated diffuse horizontal irradiance and direct normal irradiance for cloudy conditions from the RRTM runs using a 16-stream discrete ordinates radiative transfer method. The solar irradiance at the surface was simulated by combining the cloud irradiance parameterizations with a fast clear-sky model, REST2. To understand the accuracy and efficiency of the newly developed fast model, we analyzed FARMS runs using cloud optical and microphysical properties retrieved using GOES data from 2009-2012. The global horizontal irradiance for cloudy conditions was simulated using FARMS and RRTM for global circulation modeling with a two-stream approximation and compared to measurements taken from the U.S. Department of Energy's Atmospheric Radiation Measurement Climate Research Facility Southern Great Plains site. Our results indicate that the accuracy of FARMS is comparable to or better than the two-stream approach; however, FARMS is approximately 400 times more efficient because it does not explicitly solve the radiative transfer equation for each individual cloud condition. Radiative transfer model runs are computationally expensive, but this model is promising for broad applications in solar resource assessment and forecasting. It is currently being used in the National Solar Radiation Database, which is publicly available from the National Renewable Energy Laboratory at http://nsrdb.nrel.gov.

14 SOLAR ENERGY↗