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DOE OSTI2023

Performance Testing of a Moving-Bed Gasifier Using Coal, Biomass, and Waste Plastic Blends to Generate White Hydrogen

The objective of this DOE-funded project by the Electric Power Research Institute, Inc. (EPRI), Hamilton Maurer International (HMI) and Sotacarbo S.p.A. (Sotacarbo), is to qualify coal, biomass, and plastic waste blends based on performance testing of selected pellet recipes in a pilot-scale updraft moving-bed gasifier. The testing provides relevant data to advance the commercial-scale design of the moving-bed gasifier to be able to successfully use these feedstocks to produce hydrogen. In particular, the effects of the waste plastics on feedstock development (i.e., blending and pelletizing) and the resulting products (i.e., syngas compositions, organic condensate production, and ash characteristics) are a focus. The gasifier is Hamilton Mauer International, Inc. (HMI)’s moving-bed gasifier, which has been proven capable of gasifying nearly all coal ranks. It has also shown the ability in prior testing work to gasify wood chips. However, mixtures of these fuels with plastic wastes had not yet been prepared and gasified together. The feedstocks were prepared by California Pellet Mill (CPM) under contract to HMI. The technical tasks for this research project included: • Feed Procurement and Preparation: Nine different feedstocks were prepared from varying compositions of PRB coal, corn stover biomass, and car fluff waste plastics. Fuel pellets were produced by California Pellet Mill (CPM) and shipped to Sotacarbo’s test facility in Italy. • Test Plan Development: A test plan was created to define the test runs to be performed. The test plan detailed the different tests that were run, instrumentation used, extractive samples taken, and relevant figures of merit. • Gasifier Testing: Tests were performed in the pilot-scale gasifier at Sotacarbo using nine different fuel feedstocks generated from varying mixtures of coal, biomass, and plastic wastes. The testing provides information on gasification reaction efficiency/performance, yielding relevant data for models used to scale up the gasifier design. This task will also included work to reassemble the gasifier at Sotacarbo and perform a baseline 100% coal run. • Data Analysis and Reporting: Review of the data, determination of figures of merit, and interpretation of the results was reported. The results can be used to specify the range of feedstock blends that can be successfully gasified as well as quantify gasifier outputs based on specific blends. The tri-fuel pelletizing conducted at CPM and gasification testing results from Sotacarbo’s 30mm up draft moving bed gasifier are significant. Providing data for an established gasifier to help accelerate its updated design to be able to accommodate feedstocks composed of coal, biomass, and plastic waste. This should ultimately lead to development and commercialization of a lower cost, white hydrogen generation system.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2021

Gasification of Mixed Blends of Coal, Biomass, and Plastic Waste

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS1979

Development of a portable, high-energy radiographic source

A two-year program has been undertaken to develop a portable high-energy (3-4 MeV) radiographic system for in-service and repair inspections of components at nuclear power stations. The basic design concept uses a lightweight portable linear accelerator. This paper describes the design objectives, concepts employed, and progress to date. Specific potential applications and accompanying radiographic techniques are discussed, along with novel beam-angulation devices that permit utilization in areas of highly restricted access.

Reinhart, E. R.↗

NASA NTRS2004

Test of the Equivalence Principle in an Einstein Elevator

The scientific goal of the experiment is to test the equality of gravitational and inertial mass (i.e., to test the Principle of Equivalence) by measuring the independence of the rate of fall of bodies from the composition of the falling body. The measurement is accomplished by measuring the relative displacement (or equivalently acceleration) of two falling bodies of different materials which are the proof masses of a differential accelerometer. The goal of the experiment is to measure the Eoetvoes ratio sigma g/g (differential acceleration/common acceleration) with an accuracy goal of several parts in 10(exp 15). The estimated accuracy is about two orders of magnitude better than the present state of the art. The main goal of the study to be carried out under this grant is part of the flight definition of the experiment and laboratory testing of key components. The project involves an international cooperation in which the responsibility of the US side is the flight definition of the experimental facility while the responsibility of the non-US partners is the flight definition and laboratory prototyping of the differential acceleration detector.In summary, the experiment to be designed is for taking differential acceleration measurements with a high-sensitivity detector (the sensor) during free fall conditions lasting up to 30 s in a disturbance-free acceleration environment. The experiment strategy consists in letting the sensor free fall inside a few meters long (in the vertical direction) evacuated capsule that is falling simultaneously in the rarefied atmosphere after release from a helium balloon flying at a stratospheric altitude.

Shapiro, Irwin I.↗

DOE OSTI2026

Reliability Assessment of Solid-State Circuit Breakers (CRADA CRD-21-21469, Project 4 Final Report)

Growth in power requirements and the complexity of emerging distribution systems are creating the need for new solid-state products for many applications. The qualification requirements, testing standards and life-cycle management practices of solid-state devices used in protection applications is not established. The project will leverage newly developed accelerated testing capabilities design specifically for protection applications to generate large data sets that will enable advanced digital techniques for data analysis and embedded health monitoring.

14 SOLAR ENERGY↗

DOE OSTI2026

Physics model enhancements in MARLEY version 2

Large next-generation neutrino detectors, such as those designed for accelerator-based oscillation experiments, are expected to have impressive sensitivity to MeV astrophysical neutrinos generated by the Sun and by supernovae. There are also numerous related prospects to search for MeV-scale phenomena beyond the Standard Model. In contrast to detectors constructed with water and hydrocarbon targets, which will be primarily sensitive to electron antineutrinos via inverse beta decay, the upcoming Deep Underground Neutrino Experiment (DUNE) will provide unparalleled capabilities to measure low-energy electron neutrinos from cosmic sources. Interpreting such future measurements in DUNE will require a high-quality simulation of MeV interaction physics in the argon nucleus. The MARLEY (Model of Argon Reaction Low Energy Yields) neutrino event generator has been designed with the goal of delivering the needed simulation capabilities for DUNE and other argon-based neutrino detectors. In this poster, I will present recent improvements to MARLEY's suite of interaction models, which will be released for general use by the neutrino community in version 2 of the software.

Gardiner, Steven James [Fermilab] (ORCID:000000028↗

DOE OSTI2026

Physics model enhancements in MARLEY version 2

Large next-generation neutrino detectors, such as those designed for accelerator-based oscillation experiments, are expected to have impressive sensitivity to MeV astrophysical neutrinos generated by the Sun and by supernovae. There are also numerous related prospects to search for MeV-scale phenomena beyond the Standard Model. In contrast to detectors constructed with water and hydrocarbon targets, which will be primarily sensitive to electron antineutrinos via inverse beta decay, the upcoming Deep Underground Neutrino Experiment (DUNE) will provide unparalleled capabilities to measure low-energy electron neutrinos from cosmic sources. Interpreting such future measurements in DUNE will require a high-quality simulation of MeV interaction physics in the argon nucleus. The MARLEY (Model of Argon Reaction Low Energy Yields) neutrino event generator has been designed with the goal of delivering the needed simulation capabilities for DUNE and other argon-based neutrino detectors. In this talk, I will present recent improvements to MARLEY's physics models, which will be released for general use by the neutrino community in version 2 of the software.

Gardiner, Steven James [Fermilab] (ORCID:000000028↗

NASA NTRS1993

Microgravity acceleration measurement environment characterization

The Space Acceleration Measurement System (SAMS) is a general-purpose instrumentation system designed to measure the accelerations onboard the shuttle Orbiter and shuttle/Spacelab vehicles. These measurements are used to support microgravity experiments and investigations into the microgravity environment of the vehicle. Acceleration measurement can be made at locations remote from the SAMS main instrumentation unit by the use of up to three remote triaxial sensor heads. The SAMS was developed by NASA's Lewis Research Center (LeRC) in support of NASA's microgravity science programs. In the past, numerous acceleration measurement systems have flown on various space missions. These systems were tailored to measure accelerations for a narrow set of requirements and were limited in bandwidth, dynamic range, and recording capability. In addition, these systems were mission-peculiar and not easily modified for other applications or missions. The result has been an inability to accurately assess the expected microgravity environment prior to a mission for a particular experiment and/or location. The prime science objective for SAMS on the SL-J mission will be to measure the accelerations experienced by a multitude of experiments in the two racks of the Japanese First Materials Processing Test (FMPT). The FMPT consists of a variety of materials science and life science experiments contained in racks no. 7 and no. 10. The SAMS data will be made available to the FMPT principal investigators after the mission for their analysis with the FMPT data. A secondary science objective for SAMS will be the characterization of the acceleration environment of the Spacelab module. This will include an analysis of the acceleration transfer function of the Spacelab module which will utilize the FMPT acceleration measurement along with measurements at the rack no. 9 structure. Another analytical effort to be undertaken is a general characterization of the acceleration environment of the Spacelab as an orbiting laboratory. These analysis efforts will be in conjunction with similar measurements and analyses on other SAMS Spacelab missions.

Delombard, Richard↗

DOE OSTI2022

Computational Design of Alloys for Energy Technologies

Advanced materials that maintain their mechanical performance under elevated temperatures, corrosive environments, and a range of static and evolving stresses are needed to improve the efficiency and reduce the environmental impact of a wide spectrum of energy technologies. For instance, cost-efficient alloys that can withstand high temperatures (e.g., 700 °C) have a critical role in improving the efficiency and economics of power generation to support decarbonization of the energy sector; such is true of both the nuclear and fossil energy sectors. Considering both the threats of the energy crisis, namely soaring costs of greenhouse gas emission-producing energy and climate change, it is essential to increase the pace of material discovery and enable rapid paths for material qualification to advance clean energy technologies. Conventionally, alloy development has followed a slow Edisonian process that uses repeated cycles of making, characterizing, and modifying to arrive at optimum composition and processing conditions to achieve the desired component performance. This optimization is followed by the necessary stepwise materials qualification. Furthermore, the increasing adoption of sound data management and physics-informed machine learning represents the next step in the acceleration of materials design and development. In the integrated computational materials engineering (ICME) approach, computational modeling and simulation data from different length and time scales can be combined with complex microstructural details from multimodal experimental characterization and selective property testing to close the design loop for rapid alloy development.

Computational Design Of Materials↗

DOE OSTI2025

Model-based Hierarchical Reinforcement Learning for Improved Physical Security Design: A Prototype

Prior work in FY24 developed an adversarial AI agent aid in path analysis of physical protection systems. This agent, trained using a model-based reinforcement learning algorithm, was able to successfully learn the most vulnerable path in facilities. It was able to extend the current state of practice for physical protection design by exhibiting dynamic behavior based on current environmental conditions. Whereas PathTrace largely performs a static, graph-based analysis, the AI agent was able to make decisions based on relative position in the facility, current conditions (was the adversarial agnet discovered?), and proximity to secondary targets. The agent demonstrated some novel capabilities, but had limitations that need to be resolved before it can be used for production purposes. For example, the adversarial agent generalizes poorly and takes a relatively long time to train. Nonetheless, there is still considerable promise for developing the adversarial agent further in order to explore even richer, more dynamic behaviors (e.g., adversary motivations, environmental debris, and more). This work considers a complementary idea; development of a planning agent. The planning agent is envisioned as an auto-complete-like tool that can help accelerate security system design by human experts. The agent would respect existing barriers and sensors placed by a human expert while offering cost-effective suggestions (i.e., implicitly balancing effectiveness with cost) to improve the design. The goal is for this agent to be part of an expert’s toolbox, not to totally upend the current state-of-practice, or to displace human experts. The ultimate goal would be concurrent training of both the adversarial and planning agent together, to learn entirely through self-play. This would represent an entirely new way of performing system deign. We selected a hierarchical, model-based reinforcement learning algorithm to serve as the planning agent. This is an extension of concepts used in the prior FY24 adversarial agent work. There, we had a single agent acting an environment. Here, we have two different sub-agents (policies), working together, to form a complete agent. There is a manager policy, which can select abstract goals on slower time scales, and a worker, which performs primitive actions to reach goals selected by the manager. It is worth noting that this class of algorithm is challenging to work with. From our understanding, our work is one of the first successful uses of model-based reinforcement learning (MBRL) in nuclear energy1 , and likely the first hierarchical model-based reinforcement learning application in nuclear energy. Further, this work is one of the first known attempts to apply AI to perform a design tasks in nuclear energy. Consequently, there were significant implementation challenges and the bulk of the work was focused on successful implementation and algorithm design. The results presented here are very low technology readiness level as a consequence of the lack of related literature, but still represent a significant step forward in the pursuit of applied AI for design.

42 ENGINEERING↗

DOE OSTI2026

ROADRUNNER uranium nitride MiniFuel: Experimental design, fabrication and pre-irradiation baseline characterization for accelerated burnup testing

Uranium nitride (UN) is a promising fuel candidate for advanced reactor systems owing to its high uranium density and thermal conductivity; however, its qualification remains constrained by the scarcity of well-controlled irradiation performance data. Here, to address this limitation, the ROADRUNNER (Research On ADvancing the peRformance of UraNium Nitrides in Extreme enviRonments) campaign employs the MiniFuel platform in the High Flux Isotope Reactor (HFIR) to enable accelerated burnup irradiation testing under tightly controlled and largely isothermal conditions. This paper presents the experimental design, fuel fabrication, and pre-irradiation baseline characterization of the ROADRUNNER UN MiniFuel campaign. Thirty-six UN minidisc specimens were fabricated with systematically varied as-fabricated density (86–96% of theoretical density), carbon impurity content (961–5240 ppm), oxygen content (≤ ∼2000 ppm), and grain size (2.5–24 μm). The irradiation matrix spans nominal fuel temperatures of 873 K, 1173 K, and 1473 K and target burnups of 3.75%, 6.0%, and 7.5% fissions per initial metal atom (FIMA). Neutronic and thermal analyses were performed to define specimen-specific burnup accumulation and temperature histories, establishing the boundary conditions for subsequent in-pile behavior. Comprehensive pre-irradiation characterization—including dimensional metrology, density verification, impurity analysis, X-ray diffraction, Raman spectroscopy, scanning electron microscopy, X-ray computed tomography, and confocal profilometry—provides a detailed baseline for post-irradiation examination. Pre-irradiation data were further used to generate predictive estimates of fission gas release and swelling using existing empirical correlations. This quantitative comparison reveals substantial inter-model divergence at intermediate and elevated temperatures that exceeds propagated input uncertainties, highlighting structural gaps in the historical irradiation database. The ROADRUNNER irradiation campaign is currently underway in HFIR, with initial firs cycle completed in late 2025 and remaining targets scheduled through 2027. The experimental design and baseline dataset presented here establish the framework needed to interpret forthcoming post-irradiation measurements and to provide discriminating data for the validation and refinement of physics-based UN fuel performance models.

Lopes, Denise Adorno [Oak Ridge National Laborator↗

DOE OSTI2020

Three-Dimensional CFD Analysis of Construction Design Alternatives for an ERDC Coastal and Hydraulics Laboratory Flow Accelerator

The U.S. Army Engineer Research and Development Center, ERDC, Coastal and Hydraulics Laboratory in Vicksburg Mississippi is planning to build a large flume with a 10-foot-wide flow channel. Three-dimensional Computational Fluid Dynamics (CFD) analysis was used in the design of the flume entry and flow accelerator sections that feed into the 10-foot-wide channel. The design is based on the plans of the existing fiberglass flume inlet at the Turner-Fairbank Highway Research Center (TFHRC) J. Sterling Jones Hydraulics Research Laboratory in McLean Virginia. The TFHRC flume inlet feeds water to a 6-foot-wide flume channel, and therefore a scale up factor of 10/6 in the width of the inlet section of the TFHRC flume would meet the width requirement of the ERDC flume channel. The CFD analysis was performed using STAR-CCM+ commercial CFD software. Additional analysis was done with the open-source CFD software, OpenFOAM, to cross-check the analysis of one of the cases with a code-to-code comparison.

42 ENGINEERING↗

DOE OSTI2021

Nanomechanical and Fluorescence Characterizations of Weathered PV Module Encapsulation

Nanoindentation and fluorescence spectroscopy provide spatially resolved mechanical and chemical characterization for degradation of poly (ethylene-co-vinyl acetate) (EVA) encapsulants aged under accelerated weathering and in field deployed photovoltaic (PV) modules. For the accelerated weathering tests, two different EVA formulations in the glass/EVA/glass-laminated coupons were exposed to ultraviolet (UV) light for 180 days at three different temperatures (40, 60, and 80 °C) and UV intensities (0.4, 2.2, and 2.9 W/m2/nm at 340 nm). The fluorescence spectra and moduli were uniform through the cross section of both EVA formulations at the lowest temperature and UV intensity; however, under the highest temperature and UV intensity, the UV irradiated sides showed lower moduli and higher fluorescence compared to the opposite sides. For EVA obtained from PV modules deployed 27 years in Sacramento, CA, USA, changes in fluorescence intensity across the thickness were relatively greater than changes in modulus. On the other hand, for EVA obtained from the same type of module stored in the shed for 27 years, no fluorescence was observed, and a uniform modulus was seen through the EVA layer, but with substantially higher values than those obtained from the field-deployed modules. The different extent of changes between laboratory and field exposures suggests the importance of considering effects of temperature and UV intensity for designing a reliable accelerated testing. This study uniquely demonstrates the capability of microscale characterization using nanoindentation and fluorescence microscopy to monitor mechanical and chemical degradation of EVA encapsulants in PV modules.

14 SOLAR ENERGY↗

DOE OSTI2021

Learning with Delayed Rewards—A Case Study on Inverse Defect Design in 2D Materials

Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends strongly on the nature and organization of defects-their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (RL) [Monte Carlo Tree Search (MCTS)] based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low-dimensional materials. Using a representative case of two-dimensional MoS 2 , we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5 to 8% S vacancies)-the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS 2 are discussed. In this study, we introduce a RL strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

60 years of science in ICF: from conception to scientific breakeven on the National Ignition Facility

The recent achievements of a burning plasma, fusion ignition, and scientific energy gain with deuterium-tritium (DT) fuel at Lawrence Livermore National Laboratory’s National Ignition Facility (NIF) represents a major milestone in the development of inertial confinement fusion (ICF) and all of fusion research. In these experiments, fuel pressures well in excess of hundreds of GBars were achieved in the compressed fuel, and robust alpha heating of the fuel, far in excess of the energy provided by the implosion, were demonstrated for the first time. These achievements occurred 60 years after the inception of ICF and the first laser demonstration, and were made possible by more than five decades of research at laser facilities around the world. Advances in laser technology both in wavelength and precision, motivated by improved understanding of laser-plasma interaction physics and the demands of targets; improvements in target fabrication inspired by the need to control and minimize hydrodynamic instabilities in the implosion; and multi-dimensional simulations and diagnostics have been critical to this achievement. This paper will summarize the scientific and technical advances, the surprises, and the challenges that had to be overcome to achieve these goals.

fusion↗

DOE OSTI2020

Performance Evaluation of AG-1 FC HEPA Filters and Medium in Nuclear Complex Facilities - 20434

High Efficiency Particulate Air (HEPA) filters are credited as the final barrier against the release of radioactive aerosol contamination in nearly every operating U. S. Department of Energy (DOE) and National Nuclear Security Agency (NNSA) nuclear facility. The Institute for Clean Energy Technology (ICET) at Mississippi State University maintains a research program that studies various aspects of these components of containment systems and seeks to answer key questions from across the industry. This programmatic overview will include results from recent and ongoing studies, including a study on filter design and performance envelope, degradation due to aging or fatigue, fire event impact on HEPA filters, and accelerated aging of medium and component parts. ICET has completed a study helping to define the threshold for combined elevated temperature and relative humidity resistance of separator style and separator-less style HEPA filters that are used in DOE complexes. The study provides experimental data that considers elevated temperature, elevated relative humidity (RH), and target differential pressure (dP) in an attempt to gain a better understanding or more comprehensive insight into the operating envelope of different configurations of ASME (American Society for Mechanical Engineers) AG-1 Section FC filters. Test variables including air temperature ranges of 48.88 deg. C, 54.44 deg. C, or 60 deg. C (120, 130, or 140 deg. F); air RH ranges including 60-70%, 80%, or 90+% with initial filter dP of either 497.68 Pa, 746.52 Pa, or 995.36 Pa (2 inches water column (in. w. c.), 3 in. w. c., or 4 in. w. c.) included for full bracketing of each set of conditions. A study examining the effects of aging and fatigue on nuclear grade HEPA filters and medium elucidates the physical properties of media along with testing of new and aged ASME AG-1 HEPA filters. In addition to performing autopsies of the tested, aged filters to help better understand the service life of their performance, newly designed accelerated aging chambers allow new medium to undergo accelerated aging and exposure treatments. Evaluation of degradation in physical properties and functionality requires a high population of aged filters of different ages, designs, manufacturers, and operational histories. The limited availability of this population and the impropriety of looking solely at formulations and components from past years requires experimental design that will provide foresight into future filter performance. This undertaking therefore involves not only properties analysis of aged medium from different operational histories, ages, designs, and manufacturers, but also prescience in analytical goals via accelerated aging studies of newly manufactured media. A more complete understanding of the mechanisms of degradation from past formulations as well as current and future formulations is possible. Accelerated Aging of newly manufactured media utilizes exposure treatments based on the Arrhenius equation to artificially age medium and therefore glimpse into the future. Evaluation of current media properties This testing allows for the comparison of performance and durability of new filters under upset or design basis conditions with aged filters that were in service under ambient conditions and other aged filters retained in storage. Susceptibility of HEPA filters to the effects of combustion byproducts, heat, and water is also being studied in an attempt to gain understanding on the prevention of filter failures during fire events in nuclear facilities. ICET seeks to determine the effect of filter performance due to smoke loading using characterized smoke from variable fuel compositions, geometries, and loads; the smoke capacity of various filters with different burning conditions, heat release rates and transport, as well as mass transport from the air stream will be studied in low flow containment systems. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Development and Experimental Optimization of High-Temperature Modeling Tools and Methods for Concentrated Solar Power Particle - Systems

A novel, open-source radiative modeling toolset was developed to extend the functionality of particle-based modeling software (e.g. discrete element method (DEM)) to environmental conditions relevant to concentrated solar power applications. This toolset was optimized for deployment on desktop workstations instead of high-performance computing systems, to render such tools more accessible to the research community. Both particle-based modeling and radiative exchange modeling are computationally expensive and often require specialized programming expertise, making these methods cumbersome to use. Recent developments in DEM software by DCS Computing have greatly reduced these challenges, providing a graphical-user-interface based platform and modeling optimization for desktop workstations, HPCs, and cloud computing. The University of Dayton leveraged the experience of DCS Computing in developing a user-friendly, open-source radiative heat transfer expansion for DEM modeling. The University of Dayton DEM+ radiative modeling toolset was developed using a combination of fundamental experimental measurements, modeling, and simplified flow experiments over a range of temperatures and flow conditions. The toolset provides researchers with access to multiple radiative models including an accelerated Monte-Carlo Ray Tracing (application agnostic, highly computationally expensive), an expanded database of distance-based approximations (application limited, computationally light), and a weighted blending of the two methods capable of achieving over 90% reduction in computation time with equivalent accuracy compared to Monte-Carlo Ray Tracing. Through a graphical user interface, users can customize the radiative models to match their desired accuracy and available computational resources, improving access to particle based modeling for the research community. Ceramic sintered bauxite proppants were used in modeling and experimentally as a baseline. Both the radiative heat transfer and flow properties for particulate systems were investigated at elevated temperatures up to 800 °C. The major accomplishments for this work include a verified, open-source radiative modeling toolset to be distributed amongst the research community and the fabrication of three small-scale test facilities to investigate particle behavior and tune DEM flow properties for operation up to 800 °C. The findings have been shared with the research community via conference modeling workshops, deployment of the tools in DCS Computing Aspherix®, and open-source access to the developed radiative modeling tool. The development of next-generation CSP facilities and thermal energy storage systems based on ceramic particles requires providing access to computationally efficient and accurate modeling tools. Particles will experience a wide range of environments (20-800 °C) and handling conditions (dilute curtains or dense packing), requiring specially designed and optimized equipment. Optimizing solid particle physics models and establishing best-practices for particle modeling in CSP environments will assist researchers with designing optimized equipment, accelerating the deployment of more economically-competitive CSP facilities.

14 SOLAR ENERGY↗