Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Process based assessment”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Residential and Small Commercial Solar Photovoltaic and Storage Permitting, Inspection, and Interconnection Timelines: A Retrospective Review (2017-2023)

This report is part of the ongoing Solar Time-Based Residential Analytics and Cycle Time Estimator (SolarTRACE) project, led by the National Renewable Energy Laboratory (NREL). The SolarTRACE project utilizes time-stamped project-level data provided by installer-partners to assess nationwide and AHJ- and utility-level PI&I and other solar PV adoption timelines since 2017. Our dataset now covers 22% of residential solar and 33% of residential storage installs in the U.S. since 2017. This report provides an update to our previous 2022 report (Cruce et al., 2022b) and includes: updated 2017 2023 project timelines for residential rooftop solar PV up to 20kW; updated tracking of permitting process changes at nearly 4,000 AHJs nationwide; and first-ever reporting of timelines for residential PV+storage projects up to 20kW.

14 SOLAR ENERGY↗
DOE OSTI2022

Comparative assessment of neutron flux sensor technologies for advanced reactors

This report documents the comparative assessment of the rhodium-based self-powered neutron detectors (Rh-SPND), micro-pocket fission detectors (MPFD), and dosimetry wires in heated irradiations at the Neutron Radiography reactor facility at Idaho National Laboratory. The sensors performances are evaluated high-temperature environments (upwards of 850°C) during maximum reactor power to simulate use in advanced reactor applications. The performance of the Rh-SPNDs indicates the experiment cartridge heater power supply interferes with the SPND signals. This interference become increasingly significant at temperatures above 500°C with minimal interference observed for temperatures below 500°C. This work also demonstrated the fabrication process for the updated design of the MPFD, but issues related to the seal welds were identified and usable data was limited. Finally, the dosimetry measurements were within the expected range correlated with reactor power; thus, the dosimetry results were used to provide preliminary SPND calibration factors. The results from this experiment serves as a reference for developing and testing of flux sensors that are designed for high-temperature irradiations and advanced reactor deployments. This includes upcoming FY22 irradiations at the Massachusetts Institute of Technology Reactor and the Neutron Radiography (NRAD) reactor utilizing additional fission chambers from Photonis Technologies and fission chambers and SPNDs from The French Alternative Energies and Atomic Energy Commission.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

A Data-Driven Framework for Predicting the Sorting and Screening Performance of an Integrated Biomass Feedstock Preprocessing System

The characteristics of mechanically sorted and screened lignocellulosic biomass, such as the mass contents of corn stover anatomical fractions (leaves, husks, stalks, cobs, etc.), can be used to calculate the intermediate feedstock quality attributes “yield” and “purity” that indicate the conversion efficiency of biocrude. No prior study has investigated the correlations from the characteristics of raw biomass and preprocessing unit operation parameters to those intermediate feedstock quality attributes. This work presents a data-driven framework for assessing and predicting the intermediate feedstock quality attributes in an integrated biomass feedstock preprocessing system. Our study used corn stover as a typical type of herbaceous biomass because of its abundance in the U.S. It began with data acquisition of moisture content, particle size distribution, and anatomical fractions of the materials after each unit operation in the system. The objective of this preprocessing system is to minimize husks and leaves and maximizing cobs and stalks by mechanically separating the materials into three streams via disc screen and air separator. Prototype neural network models were then developed to evaluate the feasibility of predicting process outcomes based on measurable parameters. It is found that incorporating physical constraints into these prediction models significantly enhances the accuracy of the predicted yield and purity against the ground truth data. The experimental data and model predictions indicate that decreasing throughput increases purity, while higher throughput results in lower purity. Finally, an optimization problem was introduced to search optimal combinations of feed material properties and preprocessing unit operation parameters, as the intermediate feedstock quality attributes – yield and purity, appeared to be competing factors. The study also suggests the continual need to improve the data-driven framework’s predictability by incorporating more accurate physical models to describe the dynamics in the preprocessing units such as the air separator.

09 - BIOMASS FUELS↗
DOE OSTI2025

Elasto-viscoplastic fast Fourier transform modeling framework for assessing microstructural effects on stress intensity factors characterizing fracture toughness

A large-strain elasto-viscoplastic fast Fourier transform (LS-EVPFFT) model with non-periodic (NP) velocity-based boundary conditions is adapted to simulate the sensitivity of stress intensity factors on microstructure for 304L stainless steel. The material was characterized via electron backscattered diffraction (EBSD) serial-sectioning to obtain a measured 3-D microstructural cell to perform simulations. The NP-LS-EVPFFT model, including the simulation setup and boundary conditions, was verified using a crystal plasticity finite element (CPFE) model. To this end, the generation of meshes of notched specimens was developed, which involved creating Python scripts for mesh “cutting” in Abaqus, and Sculpt scripts in Cubit for meshing of the measured microstructural cell processed with DREAM.3D. The complexity of the mesh preparation highlighted the advantages of the FFT-based model, which circumvents the mesh generation process. Given the efficiency of the FFT-based model, statistical distribution of stress intensity factors in function of crystal orientation at the crack tip, grain structure, and crystallographic texture surrounding the crack tip were predicted. Further, the distributions reveal about 10% variation of stress intensity factors with microstructure with the most significant sensitivity found to be the crystal orientation at the crack tip. The methodology developed in this work is discussed as a practical simulation tool for predicting the sensitivity of stress intensity factors on microstructural variability in metallic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

System Health and Status Reporting is Used to Improve Infrastructure Systems at the Hanford Site - 20449

Reliable physical infrastructure systems are vital to completing the clean-up mission and the start-up and commissioning of new nuclear waste processing facilities at the Hanford Site. Key infrastructure systems at Hanford include water, sewer, and electrical utilities, roads, telecommunications/network systems, and fire systems. All of the infrastructure systems are non-nuclear, General Service systems that are being maintained under formal engineering configuration control and none are specifically credited in any documented safety analysis (DSA). Starting in calendar year 2017, the Hanford Site's infrastructure and services contractor started producing system health and status reports for a total of 15 critical infrastructure systems as means to get a better understanding of each system's health and how it could be improved to ensure better and more reliable service delivery. As the reporting, tracking, and use of the metrics has matured, the number of systems for which reports are being developed has been expanded to a total of 23. Each of the critical infrastructure systems are assigned primary and alternate design authorities that complete qualifications on each assigned system. The design authorities actively manage and maintain configuration control on each of their systems and track metrics associated with availability, maintenance (corrective and preventive), and configuration of the technical baseline. The metrics are based on the principles established in Chapter V of DOE O 420.1C, Facility Safety, [1] and have been tailored as appropriate to each of the key infrastructure systems. An overall assessment of system health is performed and documented on a routine basis (e.g., quarterly). Included in this documentation is a quantitative score for each of the metrics (i.e., availability, maintenance, configuration management) along with results of system walk-downs, description of any permanent design modifications, identification of new or emerging issues and vulnerabilities affecting the system, and an assessment by the design authority of actions necessary to improve the system health score. The overall status scores are calculated using a formula and based on a pre-determined grading scale, a color is assigned for simple presentation. The information gained from developing the reports is being used to (1) assist with making timely and better informed decisions through the use of solid technical bases that are increasing the overall reliability and capabilities of the Site's infrastructure systems as new expense and capital projects in the Reliability Projects Investment Portfolio (RPIP) are prioritized and executed; (2) feed the risk management program, and (3) integrate the technical content of Master Plans that describe the Site's needs for the system and the system's capabilities to meet those future needs. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Ageing of PBF‐Grade Poly(Phenylene Sulfide) Powder and its Effect on Critical Printability Properties

Abstract Poly(phenylene sulfide) (PPS) is a high‐performance polymer suiting the needs of powder bed fusion (PBF) early‐adopter industries. Although there are many benefits to PBF's powder bed‐based, one drawback is thermal ageing of the powder not incorporated into printed parts. Ideally, unfused powder can be reused in future builds; however, it is unlikely that critical printability properties of the thermally aged powder will remain unchanged. Changes in properties lead to either limited reuse through a practice of mixing used and new powder, or elimination of all powder after each build. In this paper, the authors report effects of thermal ageing in simulated printing conditions on properties of PPS critical to PBF processing. PBF‐grade PPS powder is exposed to process‐mimicking conditions. Properties relevant to the three PBF manufacturing process sub‐functions are assessed for the aged powders. Single‐layer prints are made using aged powder to observe polymer‐PBF interactions ad machina. Significant and systematic deviations from the as‐received state of the powder are observed for thermal and coalescence related properties with increasing exposure time and temperature. These changes are interpreted both in terms of physical and chemical changes in PPS and in terms of how these changes may impact the PBF printing process.

Chatham, Camden A.↗
DOE OSTI2025

Economic and environmental performance of biomass gasification for renewable natural gas production in the context of the U.S. natural gas supply

Bioenergy technologies offer potential for reducing greenhouse gas (GHG) emissions. One such promising technology is biomass gasification, which is the conversion of biomass into renewable natural gas (RNG) for use with a natural gas combined-cycle power generation system. However, the associated economic and emission effects need to be better understood to enable optimal decision-making and avoid missed opportunities for enhancing efficiency and increasing system circularity. This analysis explores opportunities to (1) decarbonize natural-gas-based systems and (2) leverage the extensive US natural gas infrastructure to mobilize biomass resources to achieve environmental and economic benefits. Here, in this analysis, the research team used a spatially explicit biomass logistics model (integrated with relevant biomass availability, technoeconomic analysis, and life cycle assessment information) to simulate economically optimal biomass allocation for RNG production and use for decarbonization in the United States. Results show that the United States has the potential to produce 9203 million GJ of RNG within the expected range of $\$$12–30/GJ. Further analyses tested the overall RNG production system's sensitivity to economic and emissions parameters of nine different processes. The sensitivity analysis results indicate that the median carbon abatement cost of RNG is most sensitive to changes in emissions associated with conversion processes and land use changes. These findings provide a deeper understanding of RNG's economic and emission potential for decision-making and guiding future research.

09 BIOMASS FUELS↗
DOE OSTI2022

Peak Rain Rate Sensitivity to Observed Cloud Condensation Nuclei and Turbulence in Continental Warm Shallow Clouds During CACTI

Abstract Warm clouds strongly affect Earth's energy budget but remain imperfectly represented in climate models, partly due to the complexity and covariability of relevant processes influencing warm rain. This work presents a detailed analysis of different factors affecting rain rate peak intensity (RR) in continental warm clouds. Clouds were identified with vertically pointing radar and lidar observations and categorized via a temperature‐based cloud type classification algorithm from which warm clouds were isolated. Observations and retrievals of liquid water path (LWP), cloud condensation nuclei concentration (N CCN ), cloud depth, and cloud duration of more than 3,000 separate warm clouds sampled during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign are analyzed in this work. Multiple linear regression (MLR) and random forest (RF) models are applied to assess the relative impact of these variables on RR. Overall, RR tends to increase as cloud depth, LWP, and cloud duration increase, or N CCN decreases. Cloud depth affects RR the most while N CCN impacts it the least. When considering over 170 warm clouds observed at least 1 hr in which in‐cloud turbulence is retrieved, the effect of N CCN on RR remains most likely suppressive, but it is not significant at a 75% level for MLR and is highly uncertain for RF. The impact of in‐cloud turbulence depends on the moment and location it is sampled. Cloud base turbulence around the time of RR suppresses RR, while cloud top turbulence effects are inconclusive. Possible difficulties in isolating robust CCN and turbulence effects on RR are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A review of laser materials processing paradigms

Laser-based processing of materials has progressed from traditional applications such as macroscale cutting and welding, to sophisticated techniques, including ultrafast micromachining, additive manufacturing, and surface engineering at micro- and nano-metric scales. Innovations in laser source technology, particularly the advent of high-power and ultrashort-pulse lasers, have expanded the range of processable materials, enabled a plethora of manufacturing applications, and propelled groundbreaking research in optics, photonics, electronics, and biomedical domains. In this article, we provide a concise, yet broad review of the congruent evolution of lasers and materials processing, and highlight seminal developments in the field over the years, combined with a critical assessment of the state of the art. Finally, we also provide an outlook on emerging needs, as well as a roadmap of anticipated developments in laser technologies and materials platforms over the next 50 years.

Lasers↗
DOE OSTI2025

Exploring Uncertainty in Moment Estimation for Small Earthquakes in Southern Nevada Using the Coda Envelope Method

Compiling source parameter estimates for small earthquakes is important both for our understanding of earthquake physics and for accurately assessing earthquake hazard. Reliable source parameter estimates are difficult to achieve for small earthquakes, in part due to our inability to accurately model the relevant physical processes at high frequencies. The coda envelope methodology developed by Mayeda and Walter (1996) and Mayeda et al. (2003) can mitigate this concern and estimate the moment of small earthquakes by determining the parameters that control the shape of the S-wave coda envelope while eliminating path effects by minimizing the scatter between seismic stations. Here, we use an open-source implementation of this technique called the Coda Calibration Tool (CCT; Barno, 2017) to calculate CCT-based moment magnitude estimates of small earthquakes (M L 0–3) in the Rock Valley, Nevada, region within the Nevada National Security Site. The Rock Valley data set is of particular interest because it allows us to explore the changes in uncertainties of the coda calibration method with earthquake size and depth. We found that a consistent linear relationship exists between the local magnitude M L and our coda-derived M w estimates for earthquakes as small as M L 0–3, but that current CCT workflows do not accurately characterize very shallow events. We also demonstrate that the epistemic uncertainty in the apparent stress value assumed by the CCT algorithm can influence magnitude estimates of small earthquakes. In conclusion, these results provide valuable insight into the seismicity of this region, and inform future analysis and modeling efforts for nuclear monitoring and seismic hazard.

58 GEOSCIENCES↗
DOE OSTI2024

Evaluating SWAT + model uncertainties for human and natural outcomes: Application in a Great Lakes agricultural watershed

Nutrient exports from agricultural lands in the Great Lakes Region pose significant threats to water quality and ecological health through eutrophication, hypoxia, and harmful algal blooms. Climate change and agricultural adaptation practices complicate future nutrient loading due to intensified hydrologic cycles and land use decisions. Our research focuses on evaluating the Soil and Water Assessment Tool (SWAT) plus model parametric uncertainties for human and natural outcomes across different scales. These factors are integral to ensuring a balance between productive agricultural practices and maintaining the health of watershed hydrology. However, uncertainties in modeling such complex interactions pose significant challenges, limiting our ability to precisely determine critical factors that influence crop yield and soil moisture. Our analysis employs Sobol global sensitivity analysis to evaluate first-order, second order, and total-order indices for SWAT crop growth parameter, ensuring comprehensive assessment of individual and interactive effects on model outputs. The objective is to identify the parameters that significantly affect model outputs for crop yield and soil moisture and improve our understanding of their interactions at the basin and hydrological response unit (HRU) scale. Our case study, the Portage River Watershed, which drains into Lake Erie, is chosen to better capture finer scale interactions crucial for predicting nutrient loading under future climate scenarios. This foundational work is aimed at setting the stage for the future development of an agent-based model (ABM). The ABM model would incorporate SWAT outputs to dynamically simulate decision-making processes.

Bunyon, Enock↗
DOE OSTI2023

Quantifying the environmental benefits of a solvent-based separation process for multilayer plastic films

Food packaging often appears in the form of multilayer (ML) plastic films, which leverage the functional properties of different polymers to achieve specific food protection goals (e.g., oxygen, water, and temperature barriers). These properties are essential to enable long shelf lives, reduce refrigeration usage, mitigate food waste, and increase food accessibility. However, ML film production processes generate large amounts of plastic waste that cannot be mechanically recycled. Recently, we have proposed a process, called solvent-targeted recovery and precipitation (STRAP), that enables the separation and recycling of the constituent polymers of ML films. This technology uses a series of solvent washes that selectively dissolve and precipitate target polymers. Quantifying the environmental benefits of STRAP over virgin resin production is essential for the commercial deployment of this technology. Further, this work uses life cycle assessment (LCA) methods to evaluate these impacts in terms of carbon footprint, energy use, water use, and toxicity. We analyze three STRAP process variants that use antisolvent and temperature-driven precipitation to treat different ML films. Our analysis reveals that the STRAP-A and STRAP-B process variants can provide environmental benefits over virgin film production. Furthermore, it gives valuable insight into the critical components of ML films (specific polymers) and of the STRAP processes (equipment) that are responsible for the highest impacts. Ultimately, we believe that the proposed analysis framework can lead to the design of more environmentally-friendly ML films and recycling processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Explosion behavior investigation and safety assessment of large-format lithium-ion pouch cells

We report large-format lithium-ion (Li-ion) batteries with high energy density for electric vehicles are prone to thermal runaway (or even explosion) under abusive conditions. In this study, overcharge induced explosion behaviors of large-format Li-ion pouch cells with Li[Ni 0.8 Co 0.1 Mn 0.1 ]O 2 cathode at different current rates (C-rates) (0.5C, 1C, 2C) were investigated. The explosion characteristics of the cells were elucidated by discussing the evolution of the cell voltage, the surface temperature and the shock wave pressure. Generally, the whole overcharge process could be divided into four stages according to the evolution of several key parameters and the overcharge behaviors; the overcharge C-rate has a great influence on cells’ thermal behaviors. The experimental results showed that the thermal runaway process of Li-ion cells caused by overcharging consisted of two kinds of explosions, physical explosion and chemical explosion. The existence of observable negative pressure zone in the pressure curves indicated that the Li-ion cells are not a self-supplying oxygen system during the explosion. Further, the explosion dynamics parameters were matched. An explosion TNT-equivalent conversion strategy that depended on the pressure of the shock wave was utilized to evaluate the released energy and its hazards. In addition, with respect to the overcharge of Li-ion pouch cells, a safety assessment method and a safety management method were proposed based on the explosion behaviors. From the perspective of battery safety, this study is of great significance for the safety design of Li-ion cells and can provide guidance for engineers to optimize the safety function of battery packs.

25 ENERGY STORAGE↗
DOE OSTI2023

Sensitivity of Arctic Surface Temperature to Including a Comprehensive Ocean Interior Reflectance to the Ocean Surface Albedo Within the Fully Coupled CESM2

Abstract Almost all current climate models simplify the ocean surface albedo (OSA) by assuming the reflected solar energy without the ocean interior contribution. In this study, an improved ocean surface albedo scheme is incorporated into the Community Earth System Model version 2 (CESM2) to assess the sensitivity of Arctic surface temperature to including ocean interior reflectance to the OSA. Fully coupled CESM2 simulations with and without ocean interior reflectance are subsequently performed, we focus on the analysis of Arctic surface temperature responses. Incorporating ocean interior reflectance increases absorbed solar radiation and warms the ocean, enhancing seasonal heat storage and release across the Arctic Ocean, and increasing sea ice reduction and positive climate feedbacks that elevates Arctic surface temperature. Seasonal variations in air‐surface temperature differences induce changes in turbulent heat flux patterns, concurrently modifying dynamic advection and moisture processes that affect boundary layer humidity and low clouds, especially in winter. Based on partitioning physical processes in the thermodynamic energy equation, surface air warming is induced primarily through positive heating anomalies of vertical advection, latent heat release, and longwave radiative forcing. Through an examination of the surface energy budget, skin temperature warming is driven predominantly by increased downward longwave radiation, positive surface albedo feedback in summer, and increased conductive heat transport from the ocean particularly in winter. Significant effects of ocean interior reflectance on the Arctic Ocean, including sea surface warming and sea ice reduction, justify the importance of ocean interior reflectance in climate models for better understanding of ongoing Arctic climate changes.

Meteorology & Atmospheric Sciences↗
DOE OSTI2025

Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures

Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.

Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗
DOE OSTI2026

Long-term stabilization of intensity-difference squeezing from four-wave mixing in rubidium vapor

Generation of quantum states of light through off-resonance four-wave mixing in rubidium vapor is a straightforward and well-established technique. However, achieving a sufficiently high and consistent level of intensity difference squeezing (IDS) and intrinsic quantum correlations between photon pairs over an extended timescale, necessary for quantum-light-based nonlinear optical spectroscopy and microscopy, remains challenging and largely unexplored. Here, we report a simple stabilization method combining an active periodic laser frequency retuning with an automatic control algorithm based on quantitatively assessing the factors affecting the IDS level and squeezed light intensity. Validation of our method was performed by acquiring data over a 5-hour period, and the results demonstrate a remarkably stable squeezing level of -7.8 dB in combination with a > 4× reduction of the standard deviation of IDS level from 0.46 dB to 0.10 dB. The achieved stabilization further enables us to quantitatively assess the IDS reduction due to the scattering in a polystyrene bead suspension as a function of sample transmission. Our approach should enable a variety of applications requiring an extended squeezing stability over multiple hours, especially for those following biological processes and chemical reactions in real time.

Allen, Christian Harry [Oak Ridge National Laborat↗
DOE OSTI2021

Microclimate observations associated with snowmelt experiment gradient sites, East River, Colorado, 2017 to 2020

The timing of snowmelt in mountain systems is a main driver of vegetation phenology and production, as well as recharge of soil moisture and ground water. Decreases in maximum snowpack and warmer spring temperatures have led to a higher frequency of early snowmelt. This study combines a natural elevation gradient with an experimental manipulation of snowmelt timing to assess the effect of earlier melt on hillslope and watershed ecohydrological processes. This data package includes csv files with plot and sub-plot level observations of soil temperature (SM_ST.csv), soil moisture (SM_SWC.csv), air temperature (SM_AirT.csv), and relative humidity (SM_rH.csv). The date range of this dataset includes a pre-treatment year (2017), two treatment years (2018 and 2019), and a post-treatment year (2020). Location IDs associated with this data package are: ER-LM, WG-UM, WG-LS, ER-US, and XX-AL. Related data packages include: “Colorado Elevation Gradient Snowmelt Manipulation Plant Phenology 2017-2018” and “Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020”

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Design, Fabrication and On-Sun Performance Evaluation of SiC Receiver Feature Specimens for CST Applications Using Additively Manufactured SiC Materials

Increasing operating temperatures of solar receivers is paramount to the efficiency of concentrated solar thermal (CST) and solar power (CSP) systems. Successful development of CST systems to generate heat for industrial applications requires significant increase in temperature capability and techno-economically viability of the receiver systems. Supported by an award from the Solar Technology Office (SETO), US Department of Energy (DOE), GE Aerospace Research in collaboration with Heliogen Holdings Inc and Sandia National Lab, is engaged in the development of ultra-High Operating Temperature SiC-matrix Solar Thermal Air Receiver (HOTSSTAR) enabled by additive manufacturing. The program objective is to demonstrate SiC receiver with air exit temperatures up to 1100 oC and high thermal efficiencies. We report design, fabrication and on-sun test results of SiC components of a prototype 50kW (thermal) HOTSSTAR module. The receiver module architecture is based on a radial airflow design and consists of a series of radial SiC receiver sectors organized around a SiC center absorber. The SiC components are fabricated using binder-jet printed SiC followed by melt-infiltration reaction bonding process. To enhance the reliability and to minimize the risk of cracking damage of SiC test articles in thermal gradient and thermal shock environment of the application, the components were laminated with GE’s MI SiC-SiC CMC. Following extensive design and lab test analyses, selected SiC component designs are tested at Heliogen Lancaster Solar field under highly concentrated solar fluxes around 2000 suns to assess the thermal performance characteristics under realistic field conditions. We report on the test results and compare the thermal performances of different HOTSSTAR SiC absorbers. Finally, we discuss fabrication and initial assembly of a 50kW prototype test module in preparation of on-sun field tests to assess the performance of our final design.

CST, CSP, high temperature, air receiver, SiC, add↗