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DOE OSTI2017

Helium interactions with alumina formed by atomic layer deposition show potential for mitigating problems with excess helium in spent nuclear fuel

Helium gas accumulation from alpha decay during extended storage of spent fuel has potential to compromise the structural integrity the fuel. We report results obtained with surrogate nickel particles which suggest that alumina formed by atomic layer deposition can serve as a low volume-fraction, uniformly-distributed phase for retention of helium generated in fuel particles such as uranium oxide. Thin alumina layers may also form transport paths for helium in the fuel rod, which would otherwise be impermeable. Micron-scale nickel particles, representative of uranium oxide particles in their low helium solubility and compatibility with the alumina synthesis process, were homogeneously coated with alumina approximately 3–20 nm by particle atomic layer deposition (ALD) using a fluidized bed reactor. Particles were then loaded with helium at 800 °C in a tube furnace. Subsequent helium spectroscopy measurements showed that the alumina phase, or more likely a related nickel/alumina interface structure, retains helium at a density of at least 10 17 atoms/cm 3 . High resolution transmission electron microscopy revealed that the thermal treatment increased the alumina thickness and generated additional porosity. Results from Monte Carlo simulations on amorphous alumina predict the helium retention concentration at room temperature could reach 10 21 atoms/cm 3 at 400 MPa, a pressure predicted by others to be developed in uranium oxide without an alumina secondary phase. This concentration is sufficient to eliminate bubble formation in the nuclear fuel for long-term storage scenarios, for example. Finally, measurements by others of the diffusion coefficient in polycrystalline alumina indicate values several orders of magnitude higher than in uranium oxide, which then can also allow for helium transport out of the spent fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Radiation Effects on High Thermal Conductivity Fuels (Final Scientific Report)

High thermal conductivity nuclear fuels offer important potential advantages over traditional oxide-based fuels such as higher burnup, reduction in fission gas release, and better overall safety of the reactor system. One interesting approach to high thermal conductivity fuels utilizes high thermal conductivity nonfissile additives to UO 2 fuel to lower the fuel operating temperature and thereby take advantage of the highly favorable radiation resistance of UO 2 at lower operating temperatures. However, differential swelling between the matrix and high conductivity additive phases during high dose irradiation could lead to internal cracking and poor performance. In the current study, ceria (CeO 2 ) and zirconia (ZrO 2 ) surrogate matrices were used to model UO 2 behavior. Al 2 O 3 or SiC in the form of nanoscale powder or platelets, respectively, were used for the high conductivity second phase. The nuclear fuel surrogates were sintered to achieve densities greater than 93% of the ideal values. Scanning electron microscopy (SEM) imaging and X-ray diffraction confirmed the uniform distribution of the second phase and that no intermetallic second phase was formed during sintering. The thermal conductivity of the sintered samples was measured from 50°C to 900°C and revealed an important increase compared to pure CeO2 and ZrO2 pellets. Samples were irradiated with 20 MeV Ni6+ ions at midrange doses ranging from 1 to 15 displacements per atom (dpa) and temperatures from 300°C to 700°C. Post irradiation characterization revealed a good stability of the samples at low to medium doses (1 to 5 dpa) but showed a significant microstructural deterioration and decrease of the mechanical properties at 15 dpa. Although microcracking is not observed at the interface between the matrix and high conductivity phase for any investigated irradiation condition, the strength degradation at 15 dpa suggests that these high thermal conductivity fuel forms may not be suitable for high burnup applications in current or proposed fission reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1993

The rhesus measurement system: A new instrument for space research

The Rhesus Research Facility (RRF) is a research environment designed to study the effects of microgravity using rhesus primates as human surrogates. This experimental model allows investigators to study numerous aspects of microgravity exposure without compromising crew member activities. Currently, the RRF is slated for two missions to collect its data, the first mission is SLS-3, due to fly in late 1995. The RRF is a joint effort between the United States and France. The science and hardware portions of the project are being shared between the National Aeronautics and Space Administration (NASA) and France's Centre National D'Etudes Spatiales (CNES). The RRF is composed of many different subsystems in order to acquire data, provide life support, environmental enrichment, computer facilities and measurement capabilities for two rhesus primates aboard a nominal sixteen day mission. One of these subsystems is the Rhesus Measurement System (RMS). The RMS is designed to obtain in-flight physiological measurements from sensors interfaced with the subject. The RMS will acquire, preprocess, and transfer the physiologic data to the Flight Data System (FDS) for relay to the ground during flight. The measurements which will be taken by the RMS during the first flight will be respiration, measured at two different sites; electromyogram (EMG) at three different sites; electroencephalogram (EEG); electrocardiogram (ECG); and body temperature. These measurements taken by the RMS will assist the research team in meeting the science objectives of the RRF project.

Schonfeld, Julie E.↗

DOE OSTI2018

Structural and biophysical characterization of Rv3716c, a hypothetical protein from Mycobacterium tuberculosis

Highlights: • The dimeric MtbRv3716c has a “tweezers” like structure with a head and two tails. • The spacing between the tails is appropriate to accommodate double helical DNA. • The N- and the proline rich C-terminal ends exhibit flexibility. • Packing of molecules in the crystal structure is mediated by Cd{sup 2+} ions. • The {sup 25}QQQ{sup 27} motif might be important for divalent metal ion binding. Latent tuberculosis (TB) is the main hurdle in reaching the goal of “Stop TB 2050”. Tuberculin skin and Interferon-gamma release assay tests used currently for the diagnosis of TB infection cannot distinguish between active disease and latent tuberculosis infection (LTBI) and hence new and sensitive protein markers need to be identified for the diagnosis. A protein Rv3716c from Mycobacterium tuberculosis (MtbRv3716c) has been identified as a potential surrogate marker for the diagnosis of LTBI. Here, we present characterization of MtbRv3716c (∼13 kDa) using both biophysical and X-Ray crystallographic methods. EMSA study showed that MtbRv3716c binds to double stranded DNA. X-ray diffraction data collected on a crystal of MtbRv3716c at 1.9 Å resolution was used for structure determination using the molecular replacement method. Significant electron density was not observed for the N-terminal 21 and C-terminal 41 residues in the final electron density map. The C- terminal disordered region is proline rich and displays characteristics of intrinsically disordered proteins. Although the crystal asymmetric unit contained a protomer, a tight dimer could be generated by the application of the crystal two-fold symmetry parallel to the b axis. Packing of dimers in the crystal is mediated by a cadmium ion (Cd{sup 2+}) occurring at the interface of two dimers. Molecular packing analysis reveals large cavities that are probably occupied by the disordered segments of the N- and C-termini. Structural comparison with other homologous hypothetical DNA binding proteins (PDB codes: 1PUG, 1YBX) highlights structural features that might be significant for DNA binding.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2022

High Speed Inlet Distortion Test for the X 59 Low Boom Flight Demonstrator in the NASA Glenn 8-by 6-Foot Supersonic Wind Tunnel

The Low Boom Flight Demonstrator (LBFD) High Speed Inlet Distortion Test (HSIDT) was completed in the NASA Glenn Research Center 8- by 6-Foot Supersonic Wind Tunnel (8x6 SWT) using a 9.5 percent-scale propulsion model of the X–59 to evaluate the compatibility and operability of the inlet and engine integration, focusing on the inlet dynamic effects at the engine face. The model was instrumented with steady-state and high-response total pressure probes in the conventional 40-point, equal-area array at the aerodynamic interface plane (AIP). The HSIDT successfully collected high response distortion data points for the X–59 from Mach = 0 to 1.55 for a wide range of angle-of-attack, angle-of-sideslip, and inlet flow. Analyses showed that inlet dynamic distortion and inlet planar wave (or inlet buzz) were within engine limits through most of the flight envelope. The test data showed that inlet planar wave was present at Mach 1.35 and above, at airflows between idle and maximum engine airflow. Engine throttle back will need to be limited when decelerating from supersonic flight Mach to avoid these regions of inlet planar wave. The HSIDT data was also used to compare the metric SUM40, computed from the 40 high-response AIP total pressure probes, with the metric SUM4, computed from four high-response inlet static pressure probes. The data showed that SUM4 can be used as a surrogate for SUM40 during the X–59 flight test to detect inlet planar wave.

X-59↗

DOE OSTI2025

Moltensaltpropnet

MoltenSaltPropnet is a physics-informed machine learning framework that aims to predict the thermophysical properties of molten fluoride and chloride salt mixtures, which are crucial for the design and safety of Generation IV molten salt reactors. The code processes data from the Molten-Salt Thermal Properties Database (MSTDB-TP) and the Janz compendium, converting critically evaluated correlations into fast, differentiable surrogate models for density, viscosity, thermal conductivity, and heat capacity across 448 distinct salt systems. The implementation consists of several key components: 1. Data Curation: The code parses and cleans the raw data, normalizing elemental mole fractions and extracting relevant regression coefficients for various thermophysical properties. 2. Feature Engineering: It generates fixed-length numerical descriptors that encapsulate the composition and temperature, incorporating polynomial interaction terms and dimensionality-reduction techniques to optimize model performance. 3. Coefficient Learning: Four different machine learning architectures are employed: a deep residual network (ResNet), a Kolmogorov–Arnold network (KAN), a sparsity-inducing neural network (SNN), and classical regression models. Each model learns to predict coefficients that define the temperature-dependent correlations for the thermophysical properties. 4. Property Reconstruction: The predicted coefficients are used to compute temperature-dependent property values, ensuring positivity and monotonic trends through a composite loss function that enforces physical constraints. 5. User Interface: An open-source web application enables users to filter the database, train task-specific models, and visualize the results, allowing for rapid exploration of candidate salt mixtures. MoltenSaltPropnet bridges the gap between limited experimental data and high-fidelity reactor simulations, providing a powerful tool for researchers in the field of molten salt reactors and advanced nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗

NASA NTRSDate not supplied

NASA and Blue Origin Collaborative Assessment of Precision Landing Algorithms and Computing

NASA’s Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project is developing sensor, algorithm, and compute technologies for precision landing and hazard avoidance. These technologies are being tested as an integrated Precision Landing and Hazard Avoidance (PL&HA) system on Blue Origin’s New Shephard suborbital vehicle. A key goal for the computing element of this technology development is to characterize the performance of the SPLICE software workloads on the project’s Descent and Landing Computer (DLC). The DLC is a multi-core processor designed as a surrogate for NASA’s High-Performance Space Computer (HPSC). Measurements of the SPLICE workload performance on the DLC provides NASA insight on how PL&HA capabilities will perform on the HPSC, and guidance on how the SPLICE algorithms can be implemented to best utilize the DLC platform. This insight can also be used to derive requirements to guide trade studies on candidate computing architectures, for use on platforms like Blue Moon. NASA and Blue Origin are collaborating under an agreement to pursue this mutual benefit. Performance metrics collected are based on measurement of common compute resources such as percentage used of memory bandwidth, I/O utilization, interrupt latency, and kernel vs. user space code residency. Where possible existing performance counters and metrics that are part of the operating system kernel are used. As the design has a significant FPGA component, performance counters are identified and instantiated in the fabric to measure DMA performance and interface metrics. Collection of metrics is performed on the DLC with a representative workload that simulates a full landing cycle of the Blue Origin New Shepard vehicle. Consideration is given to the other compute implementations and whether they can run SPLICE algorithms at the same rate and with the same latency as the DLC. One option being considered is the use of a RISC-V soft core instantiated in a radiation resilient FPGA fabric such as the Xilinx KU60. Select algorithms from the SPLICE code will be run for comparison with the DLC. This paper describes how the DLC is instrumented to collect performance measurements of the SPLICE workloads, preliminary results from these measurements, and their implications on SPLICE algorithm implementation. The results of experimentation to derive candidate requirements for architecture trades on a PL&HA computing system are also presented.

computer performance↗

DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Evaluation of lithium as a tritium storage medium for betavoltaics

Lithium foils were demonstrated to absorb surrogate protium for tritium-powered betavoltaics. 20μm thick lithium foils were hole-punched from a ribbon of electrodeposited lithium on copper foil. The lithium foils were loaded with hydrogen in a custom Sievert apparatus where the pressure drop showed full hydriding at a hydrogen pressure of 2 bar and at all loading temperatures above the lithium melting point at 190, 200, 225, 250, and 300. Lithium hydride formation was confirmed with Raman spectroscopy after hydrogen loading. The kinetics of experimental hydride formation was compared to the diffusion-limited Mintz–Bloch model. While the Mintz–Bloch model showed good fit with the experimental loadings, the model overpredicted the loading kinetics starting at 250 °C and at higher temperatures. In conclusion, The overprediction was either caused by lithium hydride outgassing due to some reduction with some residual lithium hydroxide created from brief air exposure when sealing the lithium in the reactor or a transition from diffusion-limited hydride growth to surface or metal–hydride interface-limited hydride growth.

25 ENERGY STORAGE↗

DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗

DOE OSTI2026

The high explosives & affected targets (HEAT) dataset

Artificial Intelligence (AI) surrogate models offer a computationally efficient alternative to full-physics simulations, yet no existing datasets are publicly available for training, testing, and validation of machine learning models of the dynamics of high-explosive driven shocks through multiple materials. Shock propagation through materials is a computationally challenging problem because simulations must include material-specific equations of state (EOS) along with descriptions of other physical processes such as plastic deformation, phase change, damage processes, fluid instabilities, and multi-material interactions. Shocks are typically initiated by high-velocity impacts or explosive loading. The latter case necessitates the addition of models of reactive materials to represent high-explosive (HE) detonation. Here, to address the lack of an expansive dataset for multi-material shock propagation in the AI/ML community, we present the High-Explosives and Affected Targets (HEAT) Dataset. HEAT is a physics-rich collection of two-dimensional, cylindrically symmetric, simulations generated using an Eulerian, multi-material, shock-propagation code developed at Los Alamos National Laboratory. The dataset includes two partitions: (1) the expanding shock-cylinder (CYL) simulations, Figs. 1, and (2) the Perturbed Layered Interface (PLI) simulations, Fig. 2. Entries in both partitions consist of time series of arrays of thermodynamic fields (pressure, density, and temperature), kinematic fields (position and velocity), and additional fields that depend on thermodynamic and/or kinematic fields (e.g., material stress). Materials in the CYL partition include solids (aluminium, copper, depleted uranium, stainless steel, tantalum, and a generic polymer), a liquid (water), gases (air, nitrogen), and a generic detonating material (high explosive, HE). The PLI partition spans a highly varying geometry but consists of fixed materials across entries: Copper, aluminium, stainless steel, generic polymer, and generic HE. HEAT captures critical phenomena such as momentum transfer, shock propagation, plastic deformation, and thermal effects, making HEAT a valuable benchmark for development of AI/ML emulation of multi-material shock propagation.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

m:N ConOps/R&R Remote Simulation

This presentation details the experimental design of an investigation of small unmanned aircraft system (sUAS) operations involving multiple vehicle management by a remote operator. The study is part of an ongoing effort to explore multiple vehicle control by multiple operators, i.e., the control of N vehicles by m operators (m:N operations). For this effort, NASA and collaborators have developed prototypes of a concept of operations (ConOps), roles and responsibilities (R&R) for operators and supervisors, and a ground control station (GCS), including software displays and interfaces. Participants in this study acted as the pilot-in-command of twelve aircraft flying pre-approved routes in a simulation of a food delivery operation utilizing sUAS in the San Diego, CA area. Each participant experienced four experimental trials. Twice within the course of each trial, participants were responsible for responding to a sudden, unanticipated, and high-priority contingency: an airspace restriction for sUAS operations known as a UAS Volume Reservation (UVR). Upon issuance of a UVR, pilots were expected to reroute affected vehicles around the airspace. The level of automation (LoA) and workload of the flight rerouting task were varied. The LoA was manipulated by providing reroute suggestions ("auto" condition) for aircraft or by requiring pilots to manually reroute ("manual" condition) affected vehicles. Workload was varied as a function of the number of vehicles affected by the UVR contingencies: 2 vehicles ("low workload" condition) versus 4 vehicles ("high workload" condition). Additionally, some vehicles required pilots to adjust for terrain conflicts while avoiding the UVR region. Due to the COVID-19 pandemic, in-person data collection for this study was not possible. Researchers adapted to this circumstance through the development of remote data collection protocol. Participants were able to view adapted GCS displays using the Microsoft Teams teleconferencing platform and responded to events by using a verbal protocol developed for the experiment. Using this protocol, participants provided instructions for actions to a researcher, referred to as the surrogate, to carry out on their behalf. This presentation describes the experiment design, including special details for remote data collection via a subject-surrogate configuration, and concludes with planned data analysis and results to be presented at a later date.

multi-UAS↗

DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Plasma production and ion heating systems for the Material Plasma Exposure eXperiment

Plasma production and ion heating in the Material Plasma Exposure eXperiment (MPEX), whose design is nearing completion, is accomplished using continuous wave RF power with average power density up to 1.6 MW/m2 at the plasma interface. Plasma is produced using helicon waves coupled through a single helical antenna at 13.56 MHz with power supplied by three 100 kW fixed-frequency RF generators feeding a power combiner network followed by the matching network and launcher. The helicon source can utilize various gasses including hydrogen, deuterium, and helium, with a magnetic field strength in the source region up to 0.2 T, and maximum device |B| of 2.5 T. Power is coupled to ions via ion cyclotron heating at the fundamental resonance using a pair of phased helical antennas operating in the frequency range 4-9 MHz, that launch waves towards the resonance from the antenna region where ω > ωci. ICH power is supplied by a single 500 kW tunable RF transmitter through a 90° power splitter and matching/decoupling network. In the case of both the helicon and ICH systems the antennas are located external to the vacuum, with power transferred through novel water-cooled coaxial vacuum windows consisting of fused quartz outer cylinders and silicon nitride inner cylinders with forced convection water cooling between them. The antenna enclosures are pressurized with dry air to 3 bar absolute for voltage standoff.Several 3-D COMSOL models have been created to simulate the two systems. A model of the helicon region utilizing a cold plasma dielectric tensor with accurate magnetic field and realistic plasma density profiles has been used to calculate the plasma loading/complex antenna input impedance at the launcher feed for various ne and |B| values, for the purpose of estimating power handling. It also incorporates the geometry of all launcher structures relevant to this determination. A still more detailed model of the launcher together with a lossy dielectric plasma surrogate has been used to determine RF electric field values and power losses in the device components.Similar models have been produced to predict the performance and power handling of the ICH launcher. For this device the impedance matrix of the two-element antenna array is calculated using a warm plasma model, necessary to properly determine the wave propagation and absorption.

Goulding, Richard↗

DOE OSTI2017

Uncertainty Quantification Analysis of Both Experimental and CFD Simulation Data of a Bench-scale Fluidized Bed Gasifier

Adequate assessment of the uncertainties in modeling and simulation is becoming an integral part of the simulation based engineering design. The goal of this study is to demonstrate the application of non-intrusive Bayesian uncertainty quantification (UQ) methodology in multiphase (gas-solid) flows with experimental and simulation data, as part of our research efforts to determine the most suited approach for UQ of a bench scale fluidized bed gasifier. UQ analysis was first performed on the available experimental data. Global sensitivity analysis performed as part of the UQ analysis shows that among the three operating factors, steam to oxygen ratio has the most influence on syngas composition in the bench-scale gasifier experiments. An analysis for forward propagation of uncertainties was performed and results show that an increase in steam to oxygen ratio leads to an increase in H2 mole fraction and a decrease in CO mole fraction. These findings are in agreement with the ANOVA analysis performed in the reference experimental study. Another contribution in addition to the UQ analysis is the optimization-based approach to guide to identify next best set of additional experimental samples, should the possibility arise for additional experiments. Hence, the surrogate models constructed as part of the UQ analysis is employed to improve the information gain and make incremental recommendation, should the possibility to add more experiments arise. In the second step, series of simulations were carried out with the open-source computational fluid dynamics software MFiX to reproduce the experimental conditions, where three operating factors, i.e., coal flow rate, coal particle diameter, and steam-to-oxygen ratio, were systematically varied to understand their effect on the syngas composition. Bayesian UQ analysis was performed on the numerical results. As part of Bayesian UQ analysis, a global sensitivity analysis was performed based on the simulation results, which shows that the predicted syngas composition is strongly affected not only by the steam-to-oxygen ratio (which was observed in experiments as well) but also by variation in the coal flow rate and particle diameter (which was not observed in experiments). The carbon monoxide mole fraction is underpredicted at lower steam-to-oxygen ratios and overpredicted at higher steam-to-oxygen ratios. The opposite trend is observed for the carbon dioxide mole fraction. These discrepancies are attributed to either excessive segregation of the phases that leads to the fuel-rich or -lean regions or alternatively the selection of reaction models, where different reaction models and kinetics can lead to different syngas compositions throughout the gasifier. To improve quality of numerical models used, the effect that uncertainties in reaction models for gasification, char oxidation, carbon monoxide oxidation, and water gas shift will have on the syngas composition at different grid resolution, along with bed temperature were investigated. The global sensitivity analysis showed that among various reaction models employed for water gas shift, gasification, char oxidation, the choice of reaction model for water gas shift has the greatest influence on syngas composition, with gasification reaction model being second. Syngas composition also shows a small sensitivity to temperature of the bed. The hydrodynamic behavior of the bed did not change beyond grid spacing of 18 times the particle diameter. However, the syngas concentration continued to be affected by the grid resolution as low as 9 times the particle diameter. This is due to a better resolution of the phasic interface between the gases and solid that leads to stronger heterogeneous reactions. This report is a compilation of three manuscripts published in peer-reviewed journals for the series of studies mentioned above.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

42 ENGINEERING↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

14 SOLAR ENERGY↗