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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multidimensional simulations of Mckenna-driven flow tube configuration: Investigating non-ideality in NO x formation flow tube experiments

Multidimensional simulations have been conducted to simulate atmospheric pressure, flat-flame/McKenna-burner-driven-flow tube experiments targeted to obtain NO x speciation data for predicting/analyzing syngas combustion emissions. In a prior work, we demonstrated the impacts of multidimensional transport on post flame region prediction departures from those assuming unidimensional flow/transport conditions. In this work, we develop and utilize a multidimensional laminar reacting flow solver to simulate the fully coupled flame and post flame regions to further elucidate the impacts of the earlier unidimensional modeling assumptions on interpreting post flame NO x experimental data. The model is used to simulate a lean, premixed syngas/air flame and its associated post flame regions within a cylindrical flow-tube-like arrangement. The combustion process takes place under atmospheric condition with trace amount of NO x seeding fed into the inlet gas stream. The spatial evolution of NO x species (NO and NO 2 ) in the flame and in the post-combustion zone suggests two distinct regions: 1) a region encompassing the flame structure itself; and 2) a post flame region in which the temperature decays due to both axial and radial transport processes. The predictions show that for the conditions studied, a pulsatile flow field exists due to the formation of an expanding and contracting recirculation zone in the outer periphery of the flow tube. By resolving the nature of the flow, the resulting time-averaged temperature and species concentrations show improved agreement with existing experimental measurements. The flow-field interaction results in radial inhomogeneities in the NO 2 profiles with the maximum concentration offset from the flow centerline. The location of the peak in NO 2 is coupled with radial temperature gradients from wall cooling effects and their significant influence on NO/NO 2 interconversion kinetics, producing notable NO 2 accumulation in regions near the wall. Geometrical configurations capable of suppressing/minimizing the pulsatile nature are also investigated and the results are compared. Other experimental configurations could be considered in parametric simulations to determine the optimal configuration that would minimize non-idealities in the observations. The work shows the value in performing such computations in advance of settling on a particular design for flow tube/flow reactor experiments.

42 ENGINEERING↗
DOE OSTI2023

Defining a compact dry cooler design to reduce LCOE contribution in a CSP facility

Concentrating solar power (CSP), when coupled with a supercritical carbon dioxide (sCO 2 ) power cycle and sensible heat storage, presents a renewable and clean alternative for utility-scale power generation. However, in order to be competitive in the current and future markets, CSP facilities must limit their levelized cost of electricity (LCOE) by minimizing capital costs and reducing operating costs over the lifetime of the plant. Targeting this goal, this study investigates the LCOE impact of the power cycle pre-cooler. This study considers a compact dry cooler with micro-channel technology on the CO 2 side and formed fin geometry on the air side, using directly-coupled centrifugal fans and a transition duct to improve air distribution across the fins as well as protect the fins from contaminants which may cause blockage, soiling, fouling, and damage. In an effort to better understand the dry cooler impact on LCOE, a sensitivity study was conducted using various combinations of end-to-end approach temperatures, air-side pressure drop values, CO 2 -side pressure drop values, fan types, cooler turndown control schemes, cooler module sizes, and design-point ambient temperatures. Furthemore, off-design cycle performance data was calculated for each dry cooler design using NPSS simulation software; cycle performance data were then input to System Advisor Model (SAM) along with the associated capital costs for LCOE prediction of a 100 MW system over a 30 year plant lifetime. Results of this study show the LCOE is most sensitive to air-side performance, followed by heat transfer effectiveness and capital cost. It was found that a power cycle with a mid- to high-performance dry cooler will produce the most competitive power-production costs. Designing at the extreme ends for approach temperature (or effectiveness), design-point ambient temperature, and compactness (footprint) produce higher LCOE values; mid-range values for these parameters balance performance, operating costs, and associated capital cost to optimize LCOE.

14 SOLAR ENERGY↗
DOE OSTI2021

Sensitivity and Effective Parameterization of a Multi-Scale Model of Proton-Exchange-Membrane Water Electrolysis

The development of proton-exchange-membrane water electrolysis (PEMWE) is crucial to establishing a green-hydrogen infrastructure. Active research in hydrogen technologies is focused on minimizing costs, improving efficiency, and mitigating safety risks. In this study, sensitivity analyses were performed using a physics-based model and used to identify critical parameters capable of reducing overpotentials and safety risks. These parameters include operational (i.e. temperature), structural (i.e. thickness of the components), and material properties (i.e. exchange current density). Simulated results suggest that better engineering of the membrane electrode assemblies (MEA) can offset intrinsic limitations. Membrane thickness and conductivity yielded the largest percent change on performance. Gas crossover was observed to be sensitive to diffusion, solubility, and bubble formation. Current density regimes were identified, where each crossover parameter was most influential. MEA parameterization serves as a framework for theoretical PEMWE optimization simulations and input for design criteria for future research targets.

Dizon, A↗
DOE OSTI2020

Production of ent-kaurene from lignocellulosic hydrolysate in Rhodosporidium toruloides

Abstract Background Rhodosporidium toruloides has emerged as a promising host for the production of bioproducts from lignocellulose, in part due to its ability to grow on lignocellulosic feedstocks, tolerate growth inhibitors, and co-utilize sugars and lignin-derived monomers. Ent -kaurene derivatives have a diverse range of potential applications from therapeutics to novel resin-based materials. Results The Design, Build, Test, and Learn (DBTL) approach was employed to engineer production of the non-native diterpene ent -kaurene in R. toruloides . Following expression of kaurene synthase (KS) in R. toruloides in the first DBTL cycle, a key limitation appeared to be the availability of the diterpene precursor, geranylgeranyl diphosphate (GGPP). Further DBTL cycles were carried out to select an optimal GGPP synthase and to balance its expression with KS, requiring two of the strongest promoters in R. toruloides , ANT (adenine nucleotide translocase) and TEF1 (translational elongation factor 1) to drive expression of the KS from Gibberella fujikuroi and a mutant version of an FPP synthase from Gallus gallus that produces GGPP. Scale-up of cultivation in a 2 L bioreactor using a corn stover hydrolysate resulted in an ent -kaurene titer of 1.4 g/L. Conclusion This study builds upon previous work demonstrating the potential of R. toruloides as a robust and versatile host for the production of both mono- and sesquiterpenes, and is the first demonstration of the production of a non-native diterpene in this organism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The Dark Energy Spectrographic Instrument (DESI) Optical Fiber System

The specific research of the DESI project is to study in detail the expansion history of the Universe over the past 10 billion years. In order to do this, the project designed, fabricated, tested and commissioned the DESI instrument which has been deployed at the Mayall Telescope at the Kitt Peak National Observatory near Tucson, Arizona. The DESI project was conducted by approximately 30 US and foreign national laboratories. The complete instrument includes a prime focus corrector assembly, a focal plane with 5,000 optical fibers whose position can be set by robotic positioners, and ten spectrographs. Each spectrograph can accommodate the light from 500 fibers and include three color channels that are illuminated by a common collimator mirror and other planar transfer optics. In order for this entire system to work, optical fibers are required to connect each positioner to the spectrographs. Our CRADA with Durham University in the UK provided the 10 crucial fiber cables required to connect the DESI focal plane to the 10 spectrographs. The key major goals of this CRADA were achieved. Survey planning was another key component to the Durham contribution. Drs. Frenk, Baugh, Cole, Edge, Lacey, Li, Lucey, Massey, Metcalfe, Norberg and Shanks were all important contributors to the working groups to which they were assigned. Their work to implement emission lines and QSO in lightcone mocks, mocks for non-standard cosmologies/modified gravity, optimization of completeness of BGS in crowded regions and extended source photometry have been vital in the early planning for targets during commissioning. The major role in the project involved Dr. Ray Sharples and his team. They provided a significant portion of the design, fabrication and testing of the optical fiber system. In particular, they were responsible for the design, production of engineering drawings, component procurements, and fabrication of the 10 DESI Science fiber cables. Working in close collaboration with Dr. Claire Poppett, they also designed and fabricated the slit assemblies, the specialized termination of the groups of 500 fibers which plug into the spectrographs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Solid Phase Gradient Alloying Method via ShAPE

Alloying has been used to confer desirable properties to various metallic systems since the bronze age. For example, Ni, Mn, and Cr are alloyed into an Fe matrix to make steel, which has improved strength and corrosion resistance (as displayed in Fig. 1). In addition to the type of alloying elements, the amount of alloying elements added into a matrix is also very critical for a material’s mechanical property, physical property, machinability, and cost. Conventionally, new alloys are discovered by combining alloy precursors and elemental constituents into a mixture and melting them together. The solidified product or ingot represents a usually non-homogeneous combination of the chemistry with a microstructure dictated by the physics of solidification. It is time and energy intensive to optimize the alloying amounts via the casting process and hence, economically not conducive to rapid alloy discovery. In addition, the cast microstructure seen in the as-cast ingot is often not the ideal, nor even desired structure for optimized performance. Cast materials often need further processing such as homogenization, annealing, and deformation work put in through rolling, forging, extruding, etc. to have favorable microstructures and properties. A faster method to discover new alloy combinations and evaluate them in their worked or processed form is needed. Recently, Laser Engineered Net Shaping was applied to fabricate gradient compositional material for alloy designing. However, the mechanical properties of this gradient alloy were poor due to the existence of impurities, oxidation and cracks that are inherent in the melt-solidification process. Because of these limitations, we propose to use a solid-phase process (ShAPE) to create bulk materials (extrudates) that vary in composition from one end of the extruded solid to the other. Various manufacturing methods for alloy design including conventional casting method, laser based combinatorial method and current solid phase gradient alloying method are displayed and compared in Fig. 2. ShAPE machine and a schematic of ShAPE process are displayed in Fig. 3. Starting material is processed by a rotating and plunging die to form an extrudate. We are proposing through this methodology to invent a new, bulk-scale combinatorial technique. With this novel technique, alloying element can be dissolved into the matrix with a continued gradient without bulk melting. Formation of inter-metallics and defects originating from melt processing can be avoided. In addition, the ShAPE process creates the mixed alloy chemistry, and meanwhile subjects the material to severe plastic strain, which induces the favorable “worked” microstructure. Products from the ShAPE process can be tested in hardness directly providing a path to rapid evaluation of mechanical properties. The ability to create graded structures using friction stir processing (FSP) has been demonstrated, however using the ShAPE process we believe will lead to much faster and higher fidelity results. When combined with high throughput screening methods of physical, mechanical and microstructural property characterization, efficiency and accuracy of alloy design can be significantly improved.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Smart Vision-Aided RICH (Robotic Interface Control and Handling) System for VULCAN

High-flux neutron beams and high-efficiency detectors enable rapid neutron diffraction measurements at the Engineering Materials Diffractometer (VULCAN) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL). To optimize beam time utilization, efficient sample exchange, alignment, and automated measurements are essential. Recent advances in artificial intelligence (AI) have expanded the capabilities of robotic systems. Here, we report the development of a Robotic Interactive Control and Handling (RICH) system for sample handling at VULCAN, designed to support high-throughput experiments and reduce overhead time. The RICH system employs a six-axis desktop robot integrated with AI-based computer vision models capable of recognizing and localizing samples in real time from instrument and depth-resolving cameras. Vision algorithms combine these detections to align samples with designated measurement positions or place them within complex sample environments such as furnaces. This integration of machine learning-assisted vision with robotic handling demonstrates the feasibility of autonomous sample detection and preparation, offering a pathway toward fully unmanned neutron scattering experiments.

automation↗
DOE OSTI2024

Summary of a Workshop on Integrating Energy Equity into Power System Planning and Operation

On November 29 and 30, 2023, the Energy Systems Integration Group convened a one-and-a-half-day workshop in Arlington, Virginia, to improve knowledge and practices for integrating energy equity considerations into power system planning and operation. Workshop participants were invited to discuss approaches for integrating energy equity aspects into models and tools and to explore possible pathways for enhanced technical practices. Participants included utility planning and modeling practitioners, social science and energy justice academics, representatives from energy and environmental justice nongovernmental organizations, non-profits, consumer advocates, and representatives from the U.S. Department of Energy, national labs, and other government agencies. This report summarizes the discussion points and outcomes of the workshop, which prioritized the technical aspects of incorporating the challenges and solutions identified by electricity-futures stakeholders and communities. These technical activities include the fundamental grid structure and hardware, detailed modeling approaches that benchmark energy system performance and justify future investments, and engineering designs and limitations for potential solutions to meet energy equity goals in communities.

energy equity↗
DOE OSTI2022

Topology optimization of an airfoil fin microchannel heat exchanger using artificial intelligence

High-performance microchannel heat exchangers are needed to supply heat for power conversion for nuclear microreactors. An airfoil fin microchannel design, constructed of Alloy 617 with helium as the working fluid, is analyzed and optimized using a design of experiments with artificial intelligence techniques. The use of airfoil fins offers the potential to reduce pressure drop across the heat exchanger, as compared to other types of channel configurations. A framework for topology optimization of airfoil fin printed circuit heat exchangers (PCHEs) has been developed that can be readily extended to different fin sizes and shapes, as well as different inlet and operating conditions, materials of construction, and working fluids. An optimization procedure is developed that employs computational fluid dynamics for a set of design points identified using Latin hypercube sampling. Computational fluid dynamics is used to analyze a simplified two-channel configuration where five design parameters are varied – inlet angle, fin scale, extent of staggering, transverse and longitudinal pitches. Two methods (a 5D polynomial and a regression neural network) are compared for generating surrogate models and the resulting response surface approximation is input to a genetic algorithm that is used to identify a set of optimal parameters. The optimal geometries are found across six channel Reynolds numbers ranging from 1000 to 5000, since inlet conditions affect flow through the heat exchanger. Additionally, a set of optimal designs that maximizes heat transfer and minimizes pressure drop is identified, and a thermal stress analysis is performed on the optimal design. Correlations for the Nusselt number and Darcy friction factor are developed that can be useful for thermal hydraulic analyses using system codes. Thermal stresses are analyzed and a brief discussion of the status of code cases of PCHEs for nuclear applications is given. Testing and thermomechanical modeling is needed to facilitate future code compliance of PCHEs for high pressure and high temperature applications.

42 ENGINEERING↗
DOE OSTI2026

Microchannel-based Membrane-less Extraction of Li from Unconventional Lithium Sources & the Separation of REE

This final report provides an overview of the Project's entire duration, covering July 1, 2021 to December 31, 2023. It primarily focuses on the achievements, technological developments, and unique challenges the team faced while working on separating and extracting Lithium from produced waters. The project's primary aim was to create an integrated, high-throughput, membrane-less, and modular microfluidic platform that could extract Lithium from unconventional sources. We have successfully met all goals and milestones envisioned in the SOPO document. The most critical primary milestones, including the Go-No-Go milestone (refer to the Gantt chart in the Appendices), were successfully accomplished. We demonstrated phase separation (>90%) and extraction (>85%) performance in the MPSE using synthetic, and representative produced water composition feed at 50 ml/min total flow through MPSE 36. We have also performed a parametric study of the MPSE operations, beyond the scope of SOPO, exploring operating conditions of current and broader interest. The extended investigation of operational parameters is concurrent with our efforts to seek further development of the MPSE technology beyond the scope of the Project. Along these lines of development, we have made efforts to be responsive to DOE calls for technological developments of other types of resources (beyond PW) for the recovery of Critical Materials and higher TRL development (beyond TRL 4). During the work on this Project, we developed and implemented three innovative technical approaches that emerged from our efforts to successfully meet the Project milestones. The innovative & original technical approaches developed and implemented in this Project are now the contributions to process engineering that could be clearly credited to the Project. First, Convergent Design Approach is a comprehensive feedforward & feedback loop of four design phases: i) design for functionality, ii) design for manufacturing, iii) design for sustainability, and iv) design for market. Next was Process Intensification. A major aim of this Project was to create an innovative phase separation & extraction microscale-based technology for Li separation – thus the words microchannel-based in the Project title. A microscale-based technology is intrinsically in the center of the Process Intensification domain as defined by its unique principles. Therefore, Process Intensification was implicitly envisioned in the Project’s SOPO. Lastly, Time Scale Analysis is a novel tool for discovering the needs and directions of Process Intensification implementations in any process technology. This Project is fully credited for developing and implementing the three novel technical approaches mentioned above. These are general contributions to process engineering that emerged from this Project. Beyond the original SOPO scope, the OSU-U.Pitt research group utilized a Convergent Design methodology, integrating first-principles mathematical modeling with experimental validation on the Minimum Development Vehicle. By creating these Digital Twins, the team rapidly assessed manufacturing iterations to support TEA analysis. This framework further enabled the development of advanced Surface Modification Techniques, where hydrophobic and oleophobic coating strategies were optimized via Digital Twin tools and validated through rigorous 100-hour longevity testing. TEA Analysis: The closing efforts of this Project were focused on the TEA analysis. TEA analysis had two primary functions: i) enabling critical assessments of design variations withing 10 the Concurrent Design Approach, thus enabling evolution of the MPSE design to reach faster- better-cheaper alternatives; and ii) to create a bridge between the accomplishments of this Project and future projects of higher TRL, beyond TRL 6 level. It is important to note that the TEA model created in the Project stirred the technological solutions for the recovery of critical materials toward a vision of a very profitable modular plant that has unique zero-waste water discharge signature. More importantly, thanks to our experimental performance data and conservative assumptions, the TEA model predicts minimal technological and investment risks. Low cost of a modular unit of a nominal capacity of [1000 tons of Li 2 CO 3 /year] positions the MPSE based technology within the reach of community investors, thus offering a paradigm shift in the development of critical technologies. The project successfully navigated two primary challenges: solvent selection and manufacturing adaptation. Restricted by the SOPO to existing literature for lithium recovery, the team identified a critical need for a "material excellence program" to develop next-generation solvents, eventually concluding with a preliminary investigation into promising Ionic Liquids (ILs). Simultaneously, COVID-19 supply chain disruptions forced a pivot from traditional manufacturing to advanced additive methods at ATAMI-OSU. By transitioning from stainless steel to 3D-printed polymer substrates, the team achieved a transformative three-order-of- magnitude reduction in manufacturing costs and compressed prototyping timelines from several months to just two days. The MPSE technology offers significant energy, environmental, and economic advantages by overcoming the traditional bottlenecks of phase-separation hardware and contactor size. Unlike conventional mixer-settlers or membrane-based systems, MPSE operates without moving parts or fouling-prone membranes, achieving robust performance even with challenging, viscous, or particulate-heavy feeds. Key performance metrics include an energy intensity reduction of 5–50x (3–40 kJ/m 3 ) compared to incumbent technologies and a dramatic reduction of processing time to under 60 seconds, which drastically reduces the physical plant footprint. These technical efficiencies translate into superior economic outcomes; for a 100 t/year Li 2 CO 3 facility, implementing MPSE is projected to nearly halve contactor CAPEX (from $\$$6.08M to $\$$3.01M) and significantly increase the project's Net Present Value (NPV), derisking new investment and enabling distributed critical-mineral processing configurations. The commercialization of MPSE technology is being spearheaded by Vigsur Dynamics Inc., which has adopted a structured, parallel approach to technical and business development since its formation in January 2026. Following extensive customer discovery and engagement with the Oregon State University accelerator, Vigsur Dynamics is working to establish a business model that transitions from pilot demonstrations to modular hardware sales, ultimately aiming for a "build-own-operate" service strategy. Current technical milestones—including 100 hours of continuous operation, superior energy efficiency, and successful 6-unit modular scale-up— provide a foundation for this transition. Backed by ongoing IP licensing and a growing network of industrial and venture advisors, the company is actively de-risking the platform to replace conventional mixer-settler systems in the critical minerals market.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Abstract for CRADA among the National Energy Technology Laboratory, National Technology & Engineering Solutions of Sandia, The Regents of the University of California, and Tri-State Generation and Transmission Association, Inc. (AGMT-0855)

The objective of this CRADA is to facilitate cooperation among Tri-State Generation and Transmission Association, Inc. and collaborators of the Institute for the Design of Advanced Energy Systems (IDAES), including the National Energy Technology Laboratory, Sandia National Laboratories, and Lawrence Berkeley National Laboratory. The goal is to develop steady-state and dynamic optimization models of power plants. Tri-State will provide data related to the design and operation of power plants, which will be used to validate the IDAES team’s models and tools. These models will provide insight into potential improvements which could improve heat rate, reduce operations and maintenance costs, and/or improve reliability of the existing fleet of coal plants in the United States.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Analysis of neptunium oxides produced through modified direct denitration

Production of neptunium-237 ( 237 Np) target materials for plutonium-238 ( 238 Pu) radioisotope thermoelectric generators (RTGs) for deep space exploration requires advanced chemistry and engineering development. Currently, the domestic Pu-238 Supply Program at Oak Ridge National Laboratory produces neptunium dioxide (NpO 2 ) for target material using a modified direct denitration (MDD) flowsheet. Although the chemistry, reaction mechanisms, and product characteristics of MDD are well understood for uranium, corresponding studies of the neptunium system are still needed to continue optimization of target material properties, production equipment design, and production flowsheets. Here, the objective of this work is to characterize crystalline phases, morphology, surface texture, and particle size of NpO 2 produced via MDD reactions. Solid-phase characterization techniques, including powder X-ray diffraction (pXRD) and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), were employed to achieve this objective. Subsequent data processing using the Morphological Analysis for Material Attribution (MAMA) software was performed to analyze particle morphology and size. Broadly, the powders were found to contain a mixture of NpO 2 and Np 2 O 5 after denitration with a variety of morphologies. After high-firing, the product was found to be NpO 2 with a typical polycrystalline oxide morphology and a grain size ranging from 0.72 to 0.94 µm. These analyses provide knowledge on the reaction pathway for a non-traditional NpO 2 synthesis method and offer additional unique insight into production-scale environments for transuranic materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Significantly enhanced near-field coupling via tip engineering

The ability to significantly enhance near-field coupling between light and matter at the nanoscale is crucial for advancing the fields of nanophotonics and nanopolariotonics. However, conventional probes face challenges in achieving optimal light–matter interaction. In this study, we propose a novel, to the best of our knowledge, simulation-based strategy that leverages tip engineering to dramatically amplify the scattering field through tailored double-layer geometries. By employing a core-shell structure with a thin shell layer optimized for specific dielectric permittivity and effective polarizability, we demonstrate a near-field enhancement of up to 10 times compared to conventional probes. Our findings highlight exciting new possibilities for optimizing near-field interactions through probe designs with customized resonances, paving the way for substantially improved nano-optical sensing, imaging, and detection.

Shiravi, H.↗
DOE OSTI2020

A numerical study on the sooting tendencies of bio-derived fuels for spark-ignition engines

While more efficient vehicles have been developed over the decades, their performance is limited by the properties of existing fuels. In response, the Co-Optimization of Fuels and Engines Initiative (Co-Optima) under the U.S. Department of Energy (DOE) has developed a rigorous screening approach to identify the most promising biomass-derived blendstocks that are suitable for advanced spark-ignition (SI) engines. A detailed kinetic model has also been developed to predict the combustion properties of the selected blendstocks. This kinetic model has been designed mainly targeting ignition and flame propagation properties, and it has not previously been validated for soot formation. In this work, we numerically predicted the sooting tendency of 20 Co-Optima SI blendstocks using the Co-Optima kinetic model. The sooting tendencies are determined quantitatively using the Yield Sooting Index (YSI) methodology. As shown in Fig. 1, the predicted YSIs show good agreement with measurements, except for 2,5-dimethylfuran. We also quantify the sensitivity of the YSI predictions to aromatic growth reactions and fuel decomposition reactions of the test fuels. It is found that PAH growth reactions have little impact on YSIs for all 20 fuels under investigation. On the other hand, fuel decomposition reactions have a significant influence on the YSI of 2,5-dimethylfuran. Perturbing their reaction rates by a factor of two is shown to lead to 11 % YSI prediction uncertainty.

09 BIOMASS FUELS↗
DOE OSTI2021

AURORA: Automated Refinement of Coarse-Grained Reconfigurable Accelerators

Coarse-grained reconfigurable arrays (CGRAs), loosely defined as arrays of functional units interconnected through a network-on-chip (NoC), provide higher flexibility than domain-specific ASIC accelerators while offering increased hardware efficiency with respect to fine-grained reconfigurable devices, such as Field Programmable Gate Arrays (FPGAs). Un-fortunately, designing a CGRA for a specific application domain involves enormous software/hardware engineering effort (e.g., designing the CGRA, map operations onto the CGRA, etc) and requires the exploration on a large design space (e.g., applying appropriate loop transformation on each application, specializing the reconfigurable processing elements of the CGRA, refining the network topology, deciding the size of the data memory, etc). Int his paper, we propose AURORA – a software/hardware co-design framework to automatically synthesize optimal CGRA given a set of applications of interest

Tan, Cheng↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Modeling the Charge and Exciton Distributions in Phosphorescent White Organic Light-Emitting Diodes

A comprehensive numerical model is developed to quantitatively analyze the charge and exciton distributions in phosphorescent white organic light emitting diodes (WOLEDs). The model encompasses bipolar charge drift-diffusion transport with doping-dependent charge mobilities, field-dependent charge hopping across multiple interfaces, exciton generation, diffusion, energy transfer, radiative decay, and non-radiative exciton annihilation. The model is validated by direct measurement of the exciton distribution in monochromatic phosphorescent OLEDs. It is then applied to complex, multilayer WOLEDs where it is used to accurately predict their intensity and current-dependent spectral shifts. The magnitudes of different exciton loss channels in WOLEDs are calculated and quantitatively compared. This model provides an alternative for the many experimental iterations commonly used to optimize the color balance of complex OLEDs. In this context, the model inspired the addition of a hole blocking layer in the WOLED to balance the charge conduction and reduce spectral shifts – a design that is verified by experiment.

42 ENGINEERING↗