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At least 1,369 records · Page 76

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Measuring thermal diffusivity and gap conductance in uranium nitride and Zircaloy relevant for microreactor applications

Heat transfer across nuclear fuels and structural interfaces is an important factor for evaluating the performance of nuclear power systems. Specifically, heat generated as nuclear fuel fissions must be transported through the cladding material and through the reactor to reach the steam turbine for power generation. As new microreactor designs emerge, maximizing the efficiency of this heat transfer process becomes crucial to make them commercially viable. This article examines thermal diffusivity and gap conductance in uranium nitride (UN) fuel and Zircaloy-4 (Zry4) cladding using light flash analysis (LFA). Thermal diffusivity measurements were made on monolithic UN pellets and Zry4 exposed to carbon at peak operating temperatures of microreactors and show that carbon ingress has a minimal effect on thermal diffusivity when compared with identical materials not exposed to carbon. Evaluation of gap conductance at the UN-Zry4 interface was done using one-dimensional two-layer thermal transport models as a function of applied pressure. Here the results show that increasing pressure on the UN-Zry4 interface leads to gains in gap conductance per unit area in fuel-cladding assemblies at microreactor operating temperatures. While many other variables are expected to influence UN-Zry4 interfacial gap conductance (e.g. contact surface roughness, porosity, localized heating, environmental gas pressure), the work offers a demonstration of using a conventional LFA apparatus to determine this parameter at elevated temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

A real-time distributed solid oxide electrolysis cell (SOEC) model for cyber-physical simulation

System integration and dynamic operability between SOEC and balance-of-plant (BoP) components are major technical challenges before realizing rapid load following of SOEC systems. Cyber-physical simulation (CPS) is a leading-edge digital engineering approach and is regarded as the next step beyond Digital Twins. CPS approach can be used to research SOEC system integration and develop dynamic controls prior to actual pilot testing without using a real SOEC. To seamlessly couple with BoP hardware and access non-observable operational parameters (e.g., local temperature gradient) during transients, a distributed one-dimensional (1D) real-time SOEC model was developed. Its real-time execution was demonstrated for 20 to 640 nodes at the fixed time step of 5 ms. A higher excess air ratio enabled smaller local temperature gradients on SOEC solid materials and faster transients upon current density step change from 0.15 to 0.55 A cm -2 . During the transients, the magnitude of the peak temperature gradient nearly doubled in 10 s from -3.5 to -5.9 °C cm -1 . This represents a significant operating risk that can impact the dynamic operability of SOEC systems. In addition, the local temperature gradient was found to change directions on all nodes in SOEC solid materials, with the greatest impact on the upstream nodes. The SOEC model was also tested at the thermal neutral voltage using actual process air flow parameters as variable model inputs. Variable process air temperatures were found to induce alternating local temperature gradients on SOEC solid materials. These are new operational mechanisms for SOEC degradation relevant for load following operational modes yet distinct from previous reports. To mitigate these unfavorable features, the SOEC can be operated at voltages that are slightly (±20 mV) deviated from the thermal neutral voltage. Here, the corresponding net thermal energy change was less than 1.6% of the electric power consumption. This 1D real-time SOEC model established the basis of cyber-physical simulation of SOEC hybrid systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Environmental Controls on Water Vapor Deuterium Excess in the Coastal Boundary Layer: An Information Theory Perspective

We use information theory to quantify the environmental controls on water vapor deuterium excess (D-excess) in coastal Southern California from June 2023 through February 2024. Using Shannon entropy, mutual information (MI), and joint mutual information, metrics that capture both linear and nonlinear relationships, we identify the most informative variables and variable combinations governing D-excess across contrasting marine and continental regimes. Relative humidity with respect to sea surface temperature (RHS) is consistently the strongest individual predictor, explaining up to 27% of D-excess variability during marine conditions but only 10% in continental air masses. The Relative humidity(RHS) + sea surface temperature (SST) combination demonstrates synergistic effects, where their joint influence (explaining up to 36% of D-excess variability) exceeds what either variable achieves individually, confirming their coupled influence on deuterium excess. Wind direction complements RHS most effectively during continental conditions. The best three-variable combination (RHS + SST + Planetary Boundary Layer height) explains 38% of D-excess variability in marine air, while no combination exceeds 20% explanatory power during continental periods. Information theory shows that heteroscedasticity in D-excess relationships indicates regime shifts in controlling processes and quantifies fundamental constraints on predictor variables: some environmental factors like surface pressure or water vapor flux contain insufficient information content to explain D-excess variability regardless of their physical relevance. These results highlight the different predictability limits between marine and continental regimes, challenging the adequacy of linear models and providing a rigorous framework for quantifying the information content of isotope-climate relationships with implications for both modern and paleoclimate applications.

information theory↗

Integrated Advanced Microwave Sounding Unit-A (AMSU-A). Performance Verification Report: EOS AMSU-A1 and AMSU-A2 Receiver Assemblies

The AMSU-A receiver subsystem comprises two separated receiver assemblies; AMSU-A1 and AMSU-A2 (P/N 1356441-1). The AMSU-A1 receiver contains 13 channels and the AMSU-A2 receiver 2 channels. The AMSU-A1 receiver assembly is further divided into two parts; AMSU-A1-1 (P/N 1356429-1) and AMSU-A1-2 (P/N 1356409-1), which contain 9 and 4 channels, respectively. The receiver assemblies are highlighted and illustrate the functional block diagrams of the AMSU-A1 and AMSU-A2 systems. The AMSU-A receiver subsystem stands in between the antenna and signal processing subsystems of the AMSU-A instrument and comprises the RF and IF components from isolators to attenuators. It receives the RF signals from the antenna subsystem, down-converts the RF signals to IF signals, amplifies and defines the IF signals to proper power level and frequency bandwidth as specified for each channel, and inputs the IF signals to the signal processing subsystem. This test report presents the test data of the EOS AMSU-A Flight Model No. 1 (FM-1) receiver subsystem. The tests are performed per the Acceptance Test Procedure for the AMSU-A Receiver Subsystem, AE-26002/6A. The functional performance tests are conducted either at the component or subsystem level. While the component-level tests are performed over the entire operating temperature range predicted by thermal analysis, the subsystem-level tests are conducted at ambient temperature only.

Ma, Y.↗

Potential of deep learning methods to enhance satellite-based monitoring of nuclear power plants focusing on remote operation evaluations

The anticipated expansion of the nuclear industry and the deployment of new nuclear reactors (200 + GW of new nuclear capacity by 2050) require the development of monitoring systems that align with safety and security concerns, providing enhanced evaluation capabilities. A remote monitoring system using satellites and deep learning techniques was evaluated for its ability to detect anomalies and capture various features of nuclear reactors independently of the conditions on the ground. Satellite images of current operational and under-construction nuclear power plants were collected from Google Earth Pro as a surrogate database. Subsequently, five datasets were created from the collected images. Transfer learning technique was used for several classification tasks utilizing VGG16, ResNet50V2, Xception, DenseNet121, and MobileNetV2 pre-trained models. In the first task, the capability of the monitoring system to detect abnormal conditions or processes in a nuclear power plant was investigated. In the second task, the ability to capture operational features remotely was examined. As an example, for the purposes of this study, these features included classifying reactors based on type, power range, or onsite condition. Several evaluation metrics were used to compare the performance of the pre-trained models and the overall monitoring system. Here, the evaluation results demonstrated that deep learning techniques and pre-trained models applied to satellite images have the potential to facilitate further and expand capabilities in monitoring systems to assess plant operation details.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

High-Resolution WRF-Based Downscaling of Earth System Model Projections for Energy Applications across CONUS

Evaluating energy resources under future scenarios requires meteorological information that adequately resolves regional-scale variability and is suitable for regional energy system studies. Although Earth system model (ESM) outputs provide essential large-scale context, their coarse resolution and inherent biases limit direct use in energy system applications. This work presents a high-resolution dynamical downscaling framework using the Weather Research and Forecasting (WRF) model to generate energy-relevant regional fields for future scenarios over the contiguous United States (CONUS). The framework first identifies an optimal WRF configuration through sensitivity experiments, then evaluates raw and bias-corrected ESM initial and boundary conditions, with soil moisture and soil temperature bias correction implemented as an integral component of the bias-corrected ESM atmospheric forcing prior to WRF dynamical downscaling to improve land-atmosphere interactions. Simulations performed at 4-km resolution show that uncorrected ESM forcing leads to systematically dry and cold soil states, which propagate into elevated near-surface air temperature and solar irradiance biases, particularly during summer for the period 2000-2014. Incorporating bias-corrected atmospheric forcing together with soil state bias correction substantially reduces these errors and improves the representation of surface energy processes in WRF simulations. The results highlight the importance of bias-aware initialization strategies in high-resolution dynamical downscaling for future energy system analysis and planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Highly Integrated THz Receiver Systems for Small Satellite Remote Sensing Applications

We are developing miniaturized, highly integrated Schottky receiver systems suitable for use in CubeSats or other small spacecraft platforms, where state-of-the-art performance and ultra-low mass, power, and volume are required. Current traditional Schottky receivers are too large to employ on a CubeSat. We will develop highly integrated receivers operating from 520-600 GHz and 1040-1200 GHz that are based on state-of-the-art receivers already developed at Jet Propulsion Laboratory (JPL) by using novel 3D multi layer packaging. This process will reduce both mass and volume by more than an order of magnitude, while preserving state-of-the-art noise performance. The resulting receiver systems will have a volume of approximately 25 x 25 x 40 millimeters (mm), a mass of 250 grams (g), and power consumption on the order of of 7 watts (W). Using these techniques, we will also integrate both receivers into a single frame, further reducing mass and volume for applications where dual band operation is advantageous. Additionally, as Schottky receivers offer significant gains in noise performance when cooled to 100 K, we will investigate the improvement gained by passively cooling these receivers. Work by Sierra Lobo Inc., with their Cryo Cube technology development program, offers the possibility of passive cooling to 100 K on CubeSat platforms for 1-unit (1U) sized instruments.

Receiver systems↗

Safe Deep Reinforcement Learning for Robust Frequency and Voltage-Constrained Networked Microgrid Restoration

Here, this paper proposes a safe soft actor-critic reinforcement learning (RL) algorithm–based controller for networked microgrid restoration. It formulates the post black-start start as a finite-horizon constrained Markov decision process. The RL agent co-optimizes real and reactive power set-points for both grid-forming and grid-following inverters under explicit voltage and frequency constraints, while enforcing proper power sharing via the Mean Active Power Sharing Index (MPSI) and Mean Reactive Power Sharing Index (MQSI). Numerical results obtained on the IEEE 123-bus distribution system show that the proposed method achieves a mean voltage build-up time of 0.01 s without breaching the 5% sharing-violation budget under various load scenarios, considering MPSI and MQSI indices. These findings demonstrate that the proposed method yields fast and safe black-start schedules without resorting to heuristic penalties.

Selim, Alaa [Dartmouth College, Hanover, NH (Unite↗

Auto Recycler Report: Results from a Questionnaire about Processing Electric Vehicles and Handling High-Voltage Batteries

With the transition to clean energy the demand for electric vehicles in the United States is rapidly growing. As demand continues to rise in the next couple of decades, sourcing materials (e.g., lithium, nickel, cobalt, graphite, etc.) to put into new batteries for electric vehicles will be critical. The U.S. does not have significant reserves of most of these materials and relies heavily on foreign countries. The lithium-ion batteries in electric vehicles are large and contain substantial amounts of critical materials, therefore, they are a promising source of materials for new batteries. Recycling electric vehicle batteries when they reach their end-of-life will enable the United States to generate a domestic supply of materials needed for future batteries. [1-3] The auto recycling industry is the largest collective owner of end-of-life vehicles in the U.S. and will be the primary handlers of battery-powered vehicles (i.e., hybrid or electric) when they reach the end of their useful life. In an effort to better understand how the auto recycling industry currently handles battery-powered vehicles and their high-voltage batteries, DOE’s ReCell Center for Advanced Battery Recycling (ReCell) developed a questionnaire and distributed it to various trade organizations in the automotive recycling industry.

25 ENERGY STORAGE↗

Interactive Corpus Analysis Tool

The Interactive Corpus Analysis Tool (ICAT) is an interactive machine learning dashboard for unlabeled text/natural language processing datasets that allows a user to iteratively and visually define features, explore and label instances of their dataset, and simultaneously train a logistic regression model. ICAT was created to allow subject matter experts in a specific domain to directly train their own models for unlabeled datasets visually, without needing to be a machine learning expert or needing to know how to code the models themselves. This approach allows users to directly leverage the power of machine learning, but critically, also involves the user in the development of the machine learning model.

Martindale, Nathan [Oak Ridge National Lab. (ORNL)↗

Torrefaction of almond shell as a renewable reinforcing agent for plastics: techno-economic analyses and comparison to bioethanol process

Abstract In 2016, the US state of California alone produced nearly 3.5 billion kilograms of almonds, accounting for approximately 84% of the world’s almond production. This generated about 2.58 million metric tons (MTs) of almond residues. Almond shells are currently either burned to generate power or disposed of in landfill. Valorizing almond shells and hulls provides an opportunity to replace petroleum-derived products and divert organic material from landfill. Here we demonstrate a detailed techno-economic analysis (TEA) of an almond shell torrefaction process capable of utilizing the 520 000 MTs of almond shells produced annually in California. Our process also includes preprocessing the torrefied biomass to exploit it as a reinforcing agent for plastics. We further compared the revenue generated from the torrefied biomass and bioethanol derived from the same quantity of almond shells. We considered three different torrefaction facility scales to evaluate trade-offs between economies of scale at the facility and trucking costs to deliver almond shells. A facility that takes in 200 000 MT yr –1 of almond shells results in lower per-unit-output basis capital and operating cost relative to other smaller-scale torrefaction facilities, including 10 000 MT yr –1 and 50 000 MT yr –1 , considered for analysis in this study. The large-sale facility results in a minimum selling price (MSP) of the torrefied biomass of $311.4 MT –1 . An analogous TEA on converting almond residues into bioethanol is also investigated. The MSP of almond shell derived ethanol ($1.71 kg −1 ) is higher than that of corn ($0.48 kg −1 ) or cellulosic biomass ($0.88 kg −1 ) derived ethanol. Compared with the bioethanol route, the torrefied almond shells result in three times more revenue if utilized as a reinforcing agent for plastics.

Liu, Dupeng (ORCID:0000000269514064)↗

PMU Data Quality and Sensor Health Monitoring

Phasor Measurement Units (PMUs) play a critical role in the evolution of the electric power industry by providing high-precision, real-time monitoring of essential power system metrics. However, effectively detecting abnormalities and critical events from PMU data is a complex task, complicated by intricate temporal patterns, a scarcity of labeled data for training algo- rithms, and constraints on online computational power. In this study, we apply TranAD, an innovative algorithm that combines transformer architectures with the refinement of adversarial learning, to both synthetic and real-world PMU datasets for developing a data quality and sensor online health monitoring platform for utilities. Our findings reveal that TranAD not only provides efficient detection and localization but also enhances the detail with which abnormalities are detected, marking a a significant step forward in the field of clean data acquisition processes for power system monitoring

deep neural network, machine learning (ML)↗

Fluidized-Bed Gasification of Coal-Biomass-Plastics for Hydrogen Production

Coal is one of the most abundant fossil energy resources in the United States and in the world. The recoverable reserves in the United States are estimated to be about 252 billion tons – more than 350 years of supply at current rates of usage. However, the share of coal in total primary energy consumption in the US has been declining over the years. The decline of coal is mainly attributed to cheap natural gas and precipitous declines in the cost of electricity production from renewable technologies such as wind and solar. Coal can potentially be used if it is coupled with carbon-neutral feedstock such as biomass-agricultural residues, forest biomass, and forest residues. Co-gasification of coal and biomass can become a negative carbon emission technology if the carbon dioxide (CO 2 ) is captured and sequestered. Although the biomass gasification process has a lot of similarities to coal gasification, the large-scale adaptation of power production sourced from biomass has not come to fruition. The main reason is that the power production from biomass is still expensive when compared with natural gas or coal power technologies. To address the feedstock cost, one approach is to use low-cost feedstocks, such as municipal solid wastes (MSW) or plastics, for gasification. Gasification involves the partial oxidation of coal and/or biomass feedstocks to produce a combustible fuel called synthesis gas (syngas) which is composed of carbon monoxide (CO), hydrogen (H 2 ), CO 2 , methane (CH 4 ), nitrogen (N 2 ), water (H 2 O), and other compounds that might be considered as contaminants. Raw syngas from gasification must go through multiple steps to produce high-purity hydrogen. The specific steps depend upon the quality (gas composition, contaminants, and their concentration) and condition (pressure and temperature) of syngas. The long-term goal of the project was to utilize coal and plastics together with biomass to produce energy and fuels using a gasification platform while reducing greenhouse gas emissions. The main objective of this research was to examine gasification performance in a laboratory-scale fluidized-bed gasifier for hydrogen production. The specific objectives of the research were to: (i) study coal-plastic-biomass mixture flowability for consistent feeding in the gasifier; (ii) understand the gasification behavior of the mixture in steam and oxygen environments; (iii) perform thermal property characterization of ash and slag from the mixture feedstock and refractory-ash interface of the mixture under gasification conditions; and (iv) develop process models to determine the technology needed for syngas cleanup and contaminants. The study found that there was no apparent segregation when biomass, coal, and waste plastics were mixed together during feeding. Although there were differences in hydrogen production when individual feedstock were fed, the hydrogen concentration remained almost constant with various blends. Therefore, blending waste plastics with biomass and coal, which are all abundant, is a better approach for energy production. Results of the techno-economic analysis suggested that integration of advanced gasification (GTI’s R-GAS™) and syngas cleanup and conditioning technologies (RTI’s WDP and AFWGS) for clean hydrogen production resulted in substantial benefits, including significant capital cost and operating cost reductions. Advanced technologies resulted in 16% reduction in the hydrogen production cost (COH) from 2.94 $\$$/kg to 2.47 $\$$/g, with further scope for optimization and cost reduction. These advanced technologies also result in lower emissions, and improved energy efficiency.

01 COAL, LIGNITE, AND PEAT↗

Heat Rejection Concepts for Brayton Power Conversion Systems

This paper describes potential heat rejection design concepts for closed Brayton cycle (CBC) power conversion systems. Brayton conversion systems are currently under study by NASA for Nuclear Electric Propulsion (NEP) applications. The Heat Rejection Subsystem (HRS) must dissipate waste heat generated by the power conversion system due to inefficiencies in the thermal-to-electric conversion process. Space Brayton conversion system designs tend to optimize at efficiencies of about 20 to 25 percent with radiator temperatures in the 400 to 600 K range. A notional HRS was developed for a 100 kWe-class Brayton power system that uses a pumped sodium-potassium (NaK) heat transport loop coupled to a water heat pipe radiator. The radiator panels employ a sandwich construction consisting of regularly-spaced circular heat pipes contained within two composite facesheets. Heat transfer from the NaK fluid to the heat pipes is accomplished by inserting the evaporator sections into the NaK duct channel. The paper evaluates various design parameters including heat pipe diameter, heat pipe spacing, and facesheet thickness. Parameters were varied to compare design options on the basis of NaK pump pressure rise and required power, heat pipe unit power and radial flux, radiator panel areal mass, and overall HRS mass.

Siamidis, John↗

Development of a NASA 6-U Satellite

NASA/Wallops Flight Facility has focused on the development of new technologies for the advancement of 6 Unit (6U) small satellites. From the design of the structure and instrument support hardware to improvements in the deployer, NASA is concentrating on maximizing the potential of small satellites for the benefit of science. The telemetry system provides much higher data rates than typical I U UHF system. 6U provides up to several hundred kilobits per second and utilizes the existing NASA Ground Network for data reception. The guidance, navigation and control system keeps the satellite pointed within plus or minus 10 degrees of the sun and has knowledge of the sun vector within 1 degree. The 6U power design increased the options for different voltages and power switching capabilities and is capable of supporting instruments with higher power requirements. The Command & Data Handling (C&DH) system includes a low-power 520 MHz flight processor which provides more processing capability than existing I U processor technologies. These optimized technologies will offer the science community a greater opportunity for flying more sophisticated and complex instruments.

Thompson, Linda D.↗

Spent Nuclear Fuel Storage and Radioactive Waste Disposal in the United States: A Law and Policy Analysis - 20302

While Congress plays political football, spent nuclear fuel continues to sit in de facto interim nuclear waste storage sites throughout the country. This ad hoc approach - if it can even be called an approach - to nuclear waste management is in no one's best interests, because of financial, safety, environmental, and national security concerns with the status quo. Senate leadership had asserted that an up-or-down vote on the Yucca Mountain Project would take place in September or October 2019, but that did not occur. Rather, a conference bill was agreed to, and it provided that, although the nuclear program should receive some funding, funding was not made available for centralized interim storage ('CIS') or the Yucca Mountain Project. The President's FY2021 Budget Request does not contain any proposed funding for the Yucca Mountain Project ('the Project'). This paper will discuss the implications of that process, both for the Project itself and for the U.S. nuclear waste program more generally. In order to provide context for the current situation, this paper will begin by chronicling the history of U.S. nuclear waste management, from the passage of the Nuclear Waste Policy Act of 1982 and to the present day. It will discuss the roles of the U.S. Department of Energy, the Nuclear Regulatory Commission, federal and state political delegations, and the nuclear energy industry. The paper will then identify key themes that can be expected to affect the future of nuclear waste management in the United States. For example, it will consider whether development of additional CIS facilities is a viable short- to medium-term solution. CIS facilities may relieve immediate pressure at decommissioned or soon-to-be-decommissioned nuclear power plants; but with that pressure partially alleviated, support for building a long-term repository may be reduced. It will also discuss opposition by the State of New Mexico and others to the proposed New Mexico CIS project, based not only on siting concerns but also whether it might become a de facto repository, and whether the State of Texas and others may also be opposed, for similar reasons, to the proposed CIS facility in Texas. Additionally, the paper will examine consent-based siting and its implications for Nevada and other states that are potential hosts for spent nuclear fuel storage and disposal facilities. Further, if the Project is not a politically viable solution, the paper will examine whether it is now time for serious consideration of an alternate location for a permanent repository, as well as consideration of other options entirely, such as deep geologic disposal. The paper will conclude by looking ahead to the 2020 elections. It will describe the positions of candidates (particularly Presidential candidates) on spent nuclear fuel storage and disposal options, without advocating for any particular candidate or political party. Perhaps in 2020, we will at least see a start to a solution to the seemingly intractable problem of nuclear waste management in the United States. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

SiC Receiver/Reactor by Additive Manufacturing for Concentrated Solar Thermocatalysis with Thermal Energy Storage (Final Technical Report - Public)

The direct use of solar thermal energy provides opportunities for low-cost heating sources for a variety of applications. Ultra-high temperatures around 1000°C are high value and highly useful for energy-demanding industries. Many materials cannot withstand these conditions. In the area of Sustainable Chemicals, further limitations on material stability exist. Combining state-of-the-art materials with new designs provides a promising pathway for harvesting solar thermal energy and performing high temperature chemical processes. However, conventional manufacturing limits the potential for design flexibility. In this project, Additive Manufacturing was combined with advanced materials and new chemical reactor designs. In addition, 24/7 energy is necessary for chemical processing, and designs for ultra-high temperature thermal storage were devised. Specifically, preliminary design of a novel solar thermal receiver was developed in this project and designed to work with thermocatalytic reactors for producing sustainable chemicals and fuels. An ultra-high temperature particle storage system and heat exchangers were proposed to transport ultra-hot air as thermal fluid for the system. On a broader scale, this system could be used to tap solar thermal energy for a centralized facility with capability of transferring that heat to various segments at a full range of temperatures to 1000°C. The project pushed the temperature boundaries past those in current use, and Additive Manufacturing was envisaged for fabricating the receiver to meet requirements of extreme environments. An extensive analysis of silicon carbide additive manufacturing was performed to compare the thermal and mechanical properties of complex geometries compared to conventional material and those manufactured via other methods. The Additive Manufacturing via Binder-Jet printing was optimized and characterized to provide high quality and reproducible components capable of withstanding the proposed extreme environments. The designs for the concentrating solar thermal cavity with ultra-hot air thermal fluid showed high performance in simulations, attributable to the complex optimized geometries of the 3D printed systems. The bright future of Additive Manufacturing with advanced materials developments should provide more options and even higher quality as the technology further develops. Current costs for Additive Manufacturing of advanced ceramics is relatively low, however post-processing of the materials for extreme environments is currently high. There is little industrial-scale infrastructure for these, but it is growing as niche applications become more mainstream. The results of the project can be translated into similar extreme environments for concentrating solar thermal energy as well as its integration with ultra-hot air thermal fluids. A number of industries that require ultra-high temperatures need to electrify or otherwise decarbonize for climate goals, and this project showed that theoretically there is a pathway to do so with direct concentrated solar thermal power.

10 SYNTHETIC FUELS↗