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NASA NTRSDate not supplied

A Simplified Model of VIPER Thermal Management System. Part I: Loop Heat Pipe

NASA’s Volatiles Investigating Polar Exploration Rover (VIPER) uses a combination of loop heat pipes (LHPs) and heaters as the primary hardware to modulate the temperature of the electronic boxes and payloads instruments. LHPs have been designed and instrumented to be capable of passive and active shutdown. The goal of the passive shutdown, using a thermal control valve (TCV) to limit the flow of the working fluid, is to preserve thermal energy and avoid unnecessary heat leaks during lunar night. The goal of the active shutdown on the other hand, is to forcefully increase the vehicle internal temperature to bakeout the payload instruments. Several numerical modeling tools are available to analyze LHP performance under the complex set of operational requirements and environments levied on the VIPER Thermal Management System (TMS). Yet, while those numerical modeling tools are capable of detailed performance analysis, they tend to take large computational resources and long computational time. In this paper, a model of a single LHP developed based on control volume approach is presented. By using the control volume approach, the complex geometry of the LHP, and the components attached to it, are reduced to thermal lump capacitances, thus reducing the computation effort. This paper also describes the effort to correlate the proposed LHP model using thermal vacuum (TVAC) test data.

Loop Heat Pipe↗
NASA NTRSDate not supplied

A Simplified Model of VIPER Thermal Management System. Part I: Loop Heat Pipe

NASA’s Volatiles Investigating Polar Exploration Rover (VIPER) uses a combination of loop heat pipes (LHPs) and heaters as the primary hardware to modulate the temperature of the electronic boxes and payloads instruments. LHPs have been designed and instrumented to be capable of passive and active shutdown. The goal of the passive shutdown, using a thermal control valve (TCV) to limit the flow of the working fluid, is to preserve thermal energy and avoid unnecessary heat leaks during lunar night. The goal of the active shutdown on the other hand, is to forcefully increase the vehicle internal temperature to bakeout the payload instruments. Several numerical modeling tools are available to analyze LHP performance under the complex set of operational requirements and environments levied on the VIPER Thermal Management System (TMS). Yet, while those numerical modeling tools are capable of detailed performance analysis, they tend to take large computational resources and long computational time. In this paper, a model of a single LHP developed based on control volume approach is presented. By using the control volume approach, the complex geometry of the LHP, and the components attached to it, are reduced to thermal lump capacitances, thus reducing the computation effort. This paper also describes the effort to correlate the proposed LHP model using thermal vacuum (TVAC) test data.

Loop Heat Pipe↗
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Model-based approach to investigate equipment-induced error in pressure-waveform derived hemodynamic measurements

Advanced hemodynamic monitoring systems have provided less invasive methods for estimating pressure-derived measurements such as pressure-derived cardiac output (CO) measurements. These devices apply algorithms to arterial pressure waveforms recorded via pressure recording components that transmit the pressure signal to a pressure monitor. While standards have been developed for pressure monitoring equipment, it is unclear how the equipment-induced error can affect secondary measurements from pressure waveforms. We propose an approach for modelling different components of a pressure monitoring system and use this model-based approach to investigate the effect of different pressure recording configurations on pressure-derived hemodynamic measurements. The proposed model-based approach is a three-step process. (1) Modelling the response of pressure recording components using bench tests; (2) verifying the identified models through nonparametric equivalence tests; and (3) assessing the effects of pressure recording components on pressure-derived measurements. To delineate the application of this approach, we performed a series of model-based analyses to quantify the combined effect of a wide range of tubing configurations with various damping ratios and natural frequencies and monitors with different bandwidths on pressure waveforms and CO measurements by six pulse contour algorithms. Model-based results show the error in pressure-derived CO measurements because of tubing configurations with different natural frequencies and damping ratios. Tubing configurations with low natural frequencies (<23 Hz) altered characteristics of pressure waveforms in a way that affected the CO measurement, some by as much as 20%. Our method can serve as a tool to quantify the performance of pressure recording systems with different dynamic properties. In conclusion, this approach can be applied to investigate the effects of physiologic signal recording configurations on various pressure-derived hemodynamic measurements.

42 ENGINEERING↗
NASA NTRSDate not supplied

Humans to Mars, but How Many? Using Imprint Human Performance Models to Inform Crew Size

HUMANS TO MARS, BUT HOW MANY? USING IMPRINT HUMAN PERFORMANCE MODELS TO INFORM CREW SIZE Missions to Mars will differ from previous human spaceflight missions in that the onboard crew of astronauts will be required to operate in an Earth-independent manner due to long communication delays. Without a systematic, repeatable process to determine the number and composition of crew necessary to successfully accomplish these missions, NASA increases the risk that crew sizes may be too small to meet primary mission objectives under nominal conditions and, more consequentially, that crewmembers may not have the expertise needed to successfully respond to unforeseen failures without the real-time expertise of the Mission Control Central (MCC) team NASA currently relies upon. The NASA Engineering and Safety Center (NESC) is developing a methodology for assessing the trade space of factors that affect the number of crew for future missions. This methodology includes the consideration of results from three human performance models developed using the Improved Performance Research and Integration Tool (IMPRINT) modeling platform as well as a custom-built model on expertise trained within the crew. The custom-built crew expertise model results will be presented in the training sub-tag. Here we present results of the three IMPRINT models. Two of these models were built using IMPRINT’s Operations Model capability, with detailed task networks for specific systems and scenarios allowing for a comprehensive mental workload analysis on certain crew positions. The third model was built using IMPRINT’s Force Model capability, allowing for a higher-level manpower analysis based on crew engagement in activities performed over a longer Mars transit scenario, and how those activities may be impacted by unplanned events and new tasking unique to the Mars mission. Differences between the models will be presented, as well as examples of model results and a discussion of how those results may inform future Mars mission manpower requirements, system design, and operating procedures.

Donna L Dempsey↗
DOE OSTI2021

High Flux Isotope Reactor Low-Enriched Uranium Low Density Silicide Fuel Design Parameters

High Flux Isotope Reactor (HFIR) highly enriched uranium (HEU) to low-enriched uranium (LEU) conversion activities are ongoing as part of the Department of Energy (DOE) National Nuclear Security Administration (NNSA)’s nuclear nonproliferation mission. Design activities studying the conversion of HFIR from HEU to LEU fuel explored different fuel design features and shapes with a low density uranium-silicide dispersion (U 3 Si 2 -Al) fuel, which has a uranium density of 4.8 gU/cm 3 . The goal of these studies is to generate several HFIR LEU fuel designs of varying fuel fabrication complexity that meet the current HEU performance metrics and safety requirements. The documented designs will serve as references for fuel fabrication and qualification activities. Recent advancements in modeling and simulation tools enable quick prototyping of fuel designs. Shift, a Monte Carlo neutron transport and depletion tool optimized for high-performance computing (HPC) architectures, is used for efficient fuel cycle and performance metrics calculations. The HFIR Steady State Heat Transfer Code (HSSHTC) is used to vet the thermal safety margin. Also, a new automation tool that connects all fuel design analysis steps, named Python HFIR Analysis and Measurement Engine (PHAME), has been developed to expedite the design study in an efficient and reproducible manner. Leveraging these tools, several candidate fuel designs were selected for varying fabrication complexity. This report provides design feature details for four selected HFIR LEU low density U 3 Si 2 -Al fuel designs and their corresponding performance and safety metrics. Nominal, best-estimate design parameters and irradiation conditions, including fission rate densities, power densities, heat fluxes, and cumulative fission densities are provided for candidate fuel designs relevant to framing irradiation experiments to support fuel qualification efforts. Simulations show that the low density U 3 Si 2 -Al, with design features to enhance safety, can meet HEU core performance metrics and safety requirements if the reactor power is increased from 85 MW (HEU) to 95 MW (LEU) and if the active fuel length is increased from 50.80 cm (HEU) to 55.88 cm (LEU).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

Department of Energy. Jobs and Innovation Accelerator Challenge (JIAC) Program

1.1 NCMS Digital Manufacturing Initiative The people and businesses of Southeast Michigan have long been known for their prowess in the automotive industry, a sector built on the innovation of the assembly line and the rise of mass production as a manufacturing model. Just as the assembly line was the key to a strong manufacturing base a century ago, a digital manufacturing infrastructure is critical to the future of industry. Economic uncertainty has slowed innovation, but access to cutting-edge tools such as high performance modeling, simulation and analysis (MSA) provides a bold path forward, ensuring global competitiveness and transforming our manufacturing processes. Digital manufacturing is, essentially, the virtualization of processes that had been physical. Many larger manufacturers have embraced it, but the majority of small and medium-sized manufacturers (SMMs) have not. The Digital Manufacturing Initiative is a bold, national effort by the National Center for Manufacturing Sciences (NCMS) to put manufacturing innovation on fast forward, and bring the future of industry into the present. SMMs need a broader array of access options, training, support, and guidance. Providing access will supercharge any organization with tomorrow’s tools, as positively disruptive and potential-laden as the assembly line once was. Sustainable success in the State of Michigan requires the development of foundational infrastructure, the exploration of initial inroads with various manufacturers of all sizes, and the initiation of a prototype engagement mechanism applicable for other future regional efforts. To accomplish this NCMS leveraged complimen-tary State and Federal funding opportunities (shown in Figure 1) along with a coupled voice of industry market research study. A brief summary of each opportunity is found in Appendix A. At the heart of the Michigan effort was the development of an access portal (www.doitindigital.com) and the development of partnerships with local large manufacturers (OEMs) who could provide pull to encourage SMMs (current and future suppliers) to participate. Central to this entire effort was the opportunity that this Final Report documents corresponding to the specific tasks associated with the U.S. Department of Energy (DOE) funded component of the InnoState Jobs Innovation Accelerator Challenge (JIAC) Program.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Reactivity Effects at Mayak Production Association January 2, 1958 Criticality Accident Using Serpent 2 and OpenFOAM

The process nuclear criticality accident that occurred at the Mayak Production Association (Chelyabinsk 40) on January 2, 1958 involving a vessel of uranyl nitrate solution claimed the lives of three workers and left a fourth worker with continuing health problems. There are a myriad of uncertain parameters involved with this accident: What was the molarity of the solution? How much solution was in the vessel at the time of the accident? In what position was the vessel and the solution when it went critical? How important was the impact of reflection due to the workers and/or the floor? These uncertain parameters have made this accident particularly difficult to analyze in the past. This work aims to lower the uncertainty on some of these parameters. A most-probable solution composition is determined by comparing literature on the physical properties of uranyl nitrate solutions to those presented in LA-13638, which describes the accident in question. Using this most-probable solution, the main contributions to the reactivity of the system and hence the eventual accident, are identified through Serpent 2 and OpenFOAM analyses. Serpent 2, a Monte Carlo software tool, is used to perform calculations of the reactivity effects of lowering the vessel toward the floor and the reactivity added by the close proximity of workers. OpenFOAM, a C++ partial differential equation solver toolkit, is used to simulate the fluid inside the vessel as the vessel is tipped. This is done by treating the solution and air inside the vessel as two incompressible, isothermal, and immiscible fluids using a volume of fluid (VoF) approach. The goal of this approach is simply to track the interface between the two fluids, and hence give an accurate description of the geometrical structure of the solution as the vessel is tipped. These two unique tools are then coupled to provide a time-dependent flow simulation to study the effect that the changing geometrical structure had on the criticality of the system, which is novel to the criticality safety field. This work provides a more accurate picture of the accident going forward.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Reactivity effects at the Mayak production association, January 2, 1958 criticality accident using serpent 2 and openFOAM - 14444

The process nuclear criticality accident that occurred at the Mayak Production Association (Chelyabinsk-40) on January 2, 1958 involving a vessel of uranyl nitrate solution claimed the lives of three workers and left a fourth worker with continuing health problems. There are a myriad of uncertain parameters involved with this accident: What was the molarity of the solution? How much solution was in the vessel at the time of the accident? In what position was the vessel and the solution when it went critical? How important was the impact of reflection due to the workers and/or the floor? These uncertain parameters have made this accident particularly difficult to analyze in the past. This work aims to lower the uncertainty on some of these parameters. A most-probable solution composition is determined by comparing literature on the physical properties of uranyl nitrate solutions to those presented in LA-13638 [1], which describes the accident in question. Using this most-probable solution, the main contributions to the reactivity of the system and hence the eventual accident, are identified through Serpent 2 and OpenFOAM analyses. Serpent 2, a Monte Carlo software tool, is used to perform calculations of the reactivity effects of lowering the vessel toward the floor and the reactivity added by the close proximity of workers. OpenFOAM, a C++ partial differential equation solver toolkit, is used to simulate the fluid inside the vessel as the vessel is tipped. This is done by treating the solution and air inside the vessel as two incompressible, isothermal, and immiscible fluids using a volume of fluid (VoF) approach. The goal of this approach is simply to track the interface between the two fluids, and hence give an accurate description of the geometrical structure of the solution as the vessel is tipped. These two unique tools are then coupled to provide a time-dependent flow simulation to study the effect that the changing geometrical structure had on the criticality of the system, which is novel to the criticality safety field. This work provides a more accurate picture of the accident going forward. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Qutrit Randomized Benchmarking

Ternary quantum processors offer significant potential computational advantages over conventional qubit technologies, leveraging the encoding and processing of quantum information in qutrits (three-level systems). Therefore, to evaluate and compare the performance of such emerging quantum hardware it is essential to have robust benchmarking methods suitable for a higher-dimensional Hilbert space. We demonstrate extensions of industry standard randomized benchmarking (RB) protocols, developed and used extensively for qubits, suitable for ternary quantum logic. Using a superconducting five-qutrit processor, we find an average single-qutrit process infidelity of 3.8×10 -3 . Through interleaved RB, we characterize a few relevant gates, and employ simultaneous RB to fully characterize crosstalk errors. Finally, we apply cycle benchmarking to a two-qutrit CSUM gate and obtain a two-qutrit process fidelity of 0.85. Our results present and demonstrate RB-based tools to characterize the performance of a qutrit processor, and a general approach to diagnose control errors in future qudit hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Municipal Portfolio Performance and Policy Opportunities

A wide range of policy tools are in place to enhance energy efficiency in the built environment. However, the lion’s share of today’s energy savings opportunities have to do with energy consumption in existing buildings currently in operation. Many of the policy tools in play, including energy codes, have little to no effect on these existing buildings. New tools and resources are needed to bring existing buildings up to higher levels of energy performance. This project was focused on building a replicable framework, complete with open-source software tools and resources, to empower cities around the country to significantly enhance the energy performance of their own existing buildings and identify opportunities for future improvements. These issues were addressed through the evaluation of the performance of the municipal building stock and policy environments of four mid-size cities: Eugene, Oregon; Providence, Rhode Island; Grand Rapids, Michigan; and Tacoma, Washington. The project team worked closely with these cities to help city staff lead by example and create actionable, implementation-focused strategic energy plans for their own municipal building portfolios and to evaluate and improve city policies. A major focus throughout has been replicability and knowledge transfer (“teaching them to fish” rather than doing the work for the city).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2016

Utilizing Commercial Hardware and Open Source Computer Vision Software to Perform Motion Capture for Reduced Gravity Flight

Long duration space travel to Mars or to an asteroid will expose astronauts to extended periods of reduced gravity. Since gravity is not present to aid loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize the loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft is limited. Therefore, compact resistance exercise device prototypes are being developed. The NASA Digital Astronaut Project (DAP) is supporting the Advanced Exercise Concepts (AEC) Project, Exercise Physiology and Countermeasures (ExPC) project and the National Space Biomedical Research Institute (NSBRI) funded researchers by developing computational models of exercising with these new advanced exercise device concepts. To perform validation of these models and to support the Advanced Exercise Concepts Project, several candidate devices have been flown onboard NASAs Reduced Gravity Aircraft. In terrestrial laboratories, researchers typically have available to them motion capture systems for the measurement of subject kinematics. Onboard the parabolic flight aircraft it is not practical to utilize the traditional motion capture systems due to the large working volume they require and their relatively high replacement cost if damaged. To support measuring kinematics on board parabolic aircraft, a motion capture system is being developed utilizing open source computer vision code with commercial off the shelf (COTS) video camera hardware. While the systems accuracy is lower than lab setups, it provides a means to produce quantitative comparison motion capture kinematic data. Additionally, data such as required exercise volume for small spaces such as the Orion capsule can be determined. METHODS: OpenCV is an open source computer vision library that provides the ability to perform multi-camera 3 dimensional reconstruction. Utilizing OpenCV, via the Python programming language, a set of tools has been developed to perform motion capture in confined spaces using commercial cameras. Four Sony Video Cameras were intrinsically calibrated prior to flight. Intrinsic calibration provides a set of camera specific parameters to remove geometric distortion of the lens and sensor (specific to each individual camera). A set of high contrast markers were placed on the exercising subject (safety also necessitated that they be soft in case they become detached during parabolic flight); small yarn balls were used. Extrinsic calibration, the determination of camera location and orientation parameters, is performed using fixed landmark markers shared by the camera scenes. Additionally a wand calibration, the sweeping of the camera scenes simultaneously, was also performed. Techniques have been developed to perform intrinsic calibration, extrinsic calibration, isolation of the markers in the scene, calculation of marker 2D centroids, and 3D reconstruction from multiple cameras. These methods have been tested in the laboratory side-by-side comparison to a traditional motion capture system and also on a parabolic flight.

Biodynamics↗
NASA NTRS2022

Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, and Psychosocial Adaptions within a Team

The Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team (the Team Risk) is primarily performance-focused, with a secondary emphasis on behavioral health outcomes resulting from team performance and interpersonal interactions. Monitoring tools, measures, and countermeasures are aimed at enhancing team processes and team composition configurations to optimize team performance and functioning. Long-duration exploration missions (LDEMs) will include major challenges that could affect team performance, including social isolation, physical confinement, a small and diverse crew, communication delays between crew and ground, limited or no crew rotation or evacuation options, limited or no resupply, and a high-consequence environment. Each of these conditions will affect the crew’s coordination, cooperation, psychological well-being, and performance. Although the International Space Station (ISS) remains important for studies that require spaceflight testing and validation, the current conditions on the ISS do not adequately mimic the exploration environment that is required for National Aeronautics and Space Administration (NASA) teams research, and thus access to terrestrial or ground-based analogs of LDEM conditions is paramount. The emphasis on analogs for research is reflected in this updated evidence review of the Team Risk, and includes data from studies conducted at isolated, confined, extreme (ICE) environments (e.g., Antarctic stations), and from several mission simulation analogs such as the Human Exploration Research Analog (HERA) (HERA Experiment Information Package, 2014), also known as isolated, confined, controlled (ICC) environments. These studies have characterized many team factors regarding LDEMs, and the Team Risk has now matured from risk characterization to focusing more on countermeasure development. Because spaceflight evidence for team-level research is lacking, no reliable data is available to quantify the impact of team-level variables on individual and team-level outcomes during spaceflight missions. Until recently, no systematic attempt had been undertaken to measure the performance effects of team cohesion, team composition, team training, or team-related psychosocial adaptation during spaceflight. The Team Risk is a relatively young research area for NASA, with substantial growth only since the 2000s, and with limited access to spaceflight performance data. As a result, spaceflight evidence is lacking to identify specifically what team composition, level of training, amount of cohesion, or quality of psychosocial adaptation is necessary to reduce the risk of performance errors in space. However, astronaut journals and interviews and reports from spaceflight subject matter experts (SMEs) provide testimonies that team performance during spaceflight is important for mission success and to maintain crew health. Team spaceflight data is now being collected as part of the Spaceflight Standard Measures task (Clement, 2021)—a set of core measurements related to many human spaceflight risks that are collected from astronauts before, during, and after long-duration missions. The team-related standard measures focus on team cohesion, team performance, group living, team climate, and team processes. Collection of standard measures data is ongoing and published data is not yet available. Finally, although spaceflight evidence is lacking, evidence gleaned from ground studies and spaceflight analog studies will help close the gaps outlined in the Team Risk. Ground-based studies provide quantitative evidence for team functioning in ICE environments. Academic research on teams has produced dozens of meta-analyses that can be used to understand the general relationships among team inputs (e.g., team member characteristics and skills, job context), team processes, and emergent states (e.g., coordination, communication, cooperation, cohesion, trust, shared cognition), and team outcomes (e.g., effectiveness, errors, adaptation). Teams are complex, incorporating individual characteristics of team members, but also existing at a level that is greater than the sum of its parts. Therefore, the Team Risk must be integrated with other individual-focused NASA Human Research Program (HRP) risks, including Behavioral Medicine (BMed), Sleep, and Human-Systems Integration Architecture (HSIA), and emerging research indicates more integration may needed between the Team Risk and the physiologically oriented risks. Much of this integration occurs through the Human Systems Risk Board (HSRB). A lack of team functioning may be a stressor in some circumstances, but the team often acts as a countermeasure. For example, support for team leaders and teammates can facilitate individual functioning and encourage psychological and physically healthy behaviors and attitudes. However, more research is needed regarding teams during LDEMs and the remaining gaps in the research are described in the current report.

Lauren Blackwell Landon↗
DOE OSTI2015

Accuracy and Validation of Measured and Modeled Data for Distributed PV Interconnection and Control

The distribution grid is changing to become an active resource with complex modeling needs. The new active distribution grid will, within the next ten years, contain a complex mix of load, generation, storage and automated resources all operating with different objectives on different time scales from each other and requiring detailed analysis. Electrical analysis tools that are used to perform capacity and stability studies have been used for transmission system planning for many years. In these tools, the distribution grid was considered a load and its details and physical components were not modeled. The increase in measured data sources can be utilized for better modeling, but also control of distributed energy resources (DER). The utilization of these sources and advanced modeling tools will require data management, and knowledgeable users. Each of these measurement and modeling devices have accuracy constraints, which will ultimately define their future ability to be planned and controlled. This paper discusses the importance of measured data accuracy for inverter control, interconnection and planning tools and proposes ranges of control accuracy needed to satisfy all concerns based on the present grid infrastructure.

Stewart, Emma↗
DOE OSTI2026

PV Operations Software Transparency: A PVMAC Industry Snapshot

The rapid growth of photovoltaic (PV) deployment has increased reliance on software platforms for monitoring, workflow automation, diagnostics, and performance analytics. As these tools play a central role in asset management and operations and maintenance (O&M), greater transparency in methodologies, data handling, and validation practices benefits the broader PV ecosystem. To better understand current practices and identify opportunities for improved clarity and interoperability, 24 software providers contributed detailed responses through the PV O&M Analytics Collaborative (PVMAC) initiative, the first structured questionnaire of its kind in the industry, covering onboarding, interoperability, data quality, diagnostics, AI/ML, and other operational categories. These providers represent over 1.1 TW of solar assets under management. The analysis shows broad adoption of digital twins, AI/ML, and API integrations, but also highlights challenges in onboarding processes, inconsistent definitions and methodologies, variability in key performance indicator (KPI) calculations, and limited independent validation. Greater standardization, clearer documentation, and stronger validation frameworks could improve transparency, comparability, and trust across PV operations software platforms.

14 SOLAR ENERGY↗
DOE OSTI2021

Synchrotron X-ray metrology of dopant distribution and oxidation state in high pressure CVD grown TM 2+ :ZnSe optical fibers

High pressure chemical vapor deposition (HPCVD) has shown to be a promising method for producing Cr 2+ :ZnSe and Fe 2+ :ZnSe mid-IR optical fiber lasers. The oxidation state of the dopants and their spatial homogeneity are critical for the characterization of effective fiber laser performance; however, this is challenging for small fiber cores and small doping concentrations. We demonstrate using synchrotron micro X-ray fluorescence (XRF) mapping to study the distribution of the dopants throughout the fiber cross-sections. Furthermore, we study the local chemical environment of these unique fiber structures using micro X-ray near edge absorption spectroscopy (XANES). Our study reveals that transition metal doped ZnSe deposited using HPCVD has nanoscale dopant aggregation although the material is chemically identical to the commercially produced diffusion doped laser crystals, as indicated by the presence +2 oxidation state of the dopants. This work demonstrates the power of synchrotron-based X-ray techniques for use as metrology tools for improving the performance of laser materials in fiber geometry.

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

Chapter 6: Modeling and Simulation of Wind-Farm Flows

The subject of this chapter is the modeling and simulation of the flow through full wind farms, also known as wind plants, which are collections of many wind turbines within a region working together to supply power to the grid like any other type of power plant. This topic is interesting and exciting because of the increasing variety of and uses for wind plant flow modeling and simulation. For example, Figure 6.1 shows the computed flow field from a high-fidelity simulation of the Lillgrund offshore wind farm using computational fluid dynamics (CFD) with large-eddy simulation (LES) turbulence modeling and actuator line rotor aerodynamics models. There are many ways to model this type of flow, ranging from very simple models of wind turbine wakes and their interactions that can run in a matter of seconds on a laptop to high-fidelity, turbulence -resolving LES of the atmospheric flow and wakes that require days of use of thousands of cores on a high-performance computing cluster. The uses of wind plant flow simulations are equally varied. Wind plant layout designers run hundreds or thousands of simulations over all possible wind directions and speeds using simple models to predict a future wind plant's annual energy production. Researchers use LESs of the wind plant to study phenomena such as wind turbine wake interactions with each other and with the atmospheric boundary layer (ABL). As computational resources become more readily available, it is even becoming common for turbine manufacturers to use CFD of some form to model wind plants in complex terrain to help in the wind turbine siting and selection process. We are beginning to see the expensive, high-fidelity tools being used to create data and knowledge that improves or enables the creation of new cheaper, lower fidelity tools. We are also beginning to see the low- and high-fidelity tools used together to perform uncertainty quantification of energy estimates and optimization of wind plant layouts. As new computing technologies mature, such as graphics processing unit (GPU)-based computing, the future of full wind plant flow modeling is exciting.

17 WIND ENERGY↗