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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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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

MiniDAQ-3: Providing concurrent independent subdetector data-taking on CMS production DAQ resources

The data acquisition (DAQ) of the Compact Muon Solenoid (CMS) experiment at CERN, collects data for events accepted by the Level-1 Trigger from the different detector systems and assembles them in an event builder prior to making them available for further selection in the High Level Trigger, and finally storing the selected events for offline analysis. In addition to the central DAQ providing global acquisition functionality, several separate, so-called “MiniDAQ” setups allow operating independent data acquisition runs using an arbitrary subset of the CMS subdetectors. During Run 2 of the LHC, MiniDAQ setups were running their event builder and High Level Trigger applications on dedicated resources, separate from those used for the central DAQ. This cleanly separated MiniDAQ setups from the central DAQ system, but also meant limited throughput and a fixed number of possible MiniDAQ setups. In Run 3, MiniDAQ-3 setups share production resources with the new central DAQ system, allowing each setup to operate at the maximum Level-1 rate thanks to the reuse of the resources and network bandwidth. Configuration management tools had to be significantly extended to support the synchronization of the DAQ configurations needed for the various setups. We report on the new configuration management features and on the first year of operational experience with the new MiniDAQ-3 system.

Amoiridis, Vassileios↗
DOE OSTI2022

A robust deep learning workflow to predict multiphase flow behavior during geological C O 2 sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO 2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO 2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO 2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO 2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO 2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO 2 as a deep learning feature, leads to the most accurate prediction of CO 2 saturation. For the post-injection period, it is key to use cumulative CO 2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

58 GEOSCIENCES↗
DOE OSTI2026

Performance Enhancement of the Transcritical CO2 Cycle with a Near-Isothermal Liquid Piston Compression by an Ejector

This study investigates the use of an ejector in a transcritical CO2 cycle employing a near-isothermal liquid piston compressor, with the objective of mitigating refrigerant degassing in mineral oil. While the liquid piston compressor demonstrated high isothermal efficiency (˜90%), overall cycle performance was limited by CO2 degassing during chamber pressure fluctuations. A thermodynamic model was developed to compare baseline and ejector-assisted cycles, incorporating solubility correlations, entrainment ratio characteristics, and a degassing correction factor. Results indicate that the ejector increased suction pressure and reduced CO2–oil solubility variation, which improved refrigerant intake and reduced degassing losses. Despite these benefits, the ejector-assisted cycle showed a lower coefficient of performance (COP) than the baseline. Possible contributing factors include the limited marginal benefit of pressure reduction in a highly efficient isothermal compressor, reduced effectiveness of the separator due to the use of a suction-line heat exchanger in both cycles, and uncertainties in the solubility data under transient conditions. These findings suggest that degassing mitigation is essential for realizing the potential of liquid piston compression, but ejector integration alone is unlikely to enhance COP. Future work should focus on ejector optimization, improved oil–refrigerant management, introducing less-soluble working liquids, and experimental validation under realistic operating conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Wind Supply Chain Security: Hardware Enumeration and Analysis

This project, undertaken by Idaho National Laboratory (INL) for the Department of Energy (DOE) Wind Energy Technologies Office (WETO), focused on the enumeration and analysis of six key devices important to wind technologies. The devices analyzed included Beckhoff Bus Terminal Controllers (BK1120 and BC9000), a Beckhoff Economy Built-in Panel PC (CP6231), an N-Tron Managed Industrial Ethernet Switch (711FX3), a Bachmann M1 Gateway, and a Bachmann Smart Power Plant Controller. Device selection was driven by availability and budget constraints, with several components sourced from existing wind farms and others procured through a co-agreement with another WETO-funded project. The project's primary objective was to create a hardware bill of materials (HBOM) for each device, identifying and documenting all components to assess potential security and supply chain risks. A detailed analysis revealed over 750 unique components across the six devices, with 80% successfully identified and accompanied by datasheets. Notably, Texas Instruments emerged as the leading supplier, providing over 16% of the components, followed by ON Semiconductor at 11.3%, Analog Devices at 5.3%, and Renesas Electronics Corp at 4.1%. Other notable vendors included Toshiba Corporation, iC-Haus Corporation, Atmel, Vishay, and STMicroelectronics. The enumeration process involved thorough documentation of each component, including its designation, quantity, identifiers, pin package, description, vendor, model, and country of origin. This process provided valuable insights into the complexity and diversity of the electronic systems within these wind devices. It also highlighted the distinct separation of components between vendors, suggesting a trend of vendor-specific component usage. Key findings from the project emphasized the importance of broadening the scope of vendor analysis in future research to gain a comprehensive understanding of component distribution and commonality. The identification of vendor-specific component usage patterns offers new avenues for research and underscores the significance of continued investigation in this field. Overall, this project provides critical insights into the component composition of wind devices, aiding in the development of improved supply chain management and component sourcing strategies. The results contribute valuable knowledge to the wind technology sector, laying the groundwork for enhanced security and resilience in wind energy systems.

17 - WIND ENERGY↗
DOE OSTI2021

Opportunities and Challenges to Capturing Distributed Battery Value via Retail Utility Rates and Programs

Distributed battery deployment is increasing with advanced metering, control, and communication technologies, leaving electric utilities with an under-utilized, flexible grid resource in aggregate. Rates can reflect locational and temporal prices while utility incentive-based programs allow DERs to provide direct grid services. However, utilities must balance accurately reflecting dynamic grid conditions versus simple and feasible design that encourages customer participation. Currently, most rates and incentive-based programs are simple, but as penetration of DER and advanced controls increase, dynamic designs could become prevalent. Utilities could encourage providing multiple services to optimize distributed battery dispatch and value streams, however, challenges persist when stacking services across distribution and bulk systems. A DER committed to multiple discrete services concurrently necessitates coordination between operators and a clear hierarchy of commitments. One way to address this is to separate commitments by time or capacity. For services that follow cyclic, predictable patterns, or those that are peak driven with predictability, an operator could ensure sufficient state of charge for participation, leaving time where a distributed battery could otherwise provide different services by segmenting participation temporally. To provide continuous or unexpected services, a battery operator may use state of charge management to reserve some percentage of the battery and segment participation by capacity. Macroeconomic trends, load patterns, generation profiles, and grid configurations drive variation in value and the subsequent implications for utility offerings and how a customer might participate. As distributed battery adoption increases, both regulators and utilities will need to ensure no adverse grid impacts and encourage provision of societal value beyond the customer domain.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Fine-Grained Power and Energy Attribution on AMD GPU/APU-Based Exascale Nodes

Modern exascale GPU- and APU-based systems provide multiple power and energy sensors, but differences in scope, update rate, timing, and filtering complicate the attribution of short-lived accelerator activity. This paper presents a methodology to characterize and correct these effects on Cray EX systems with AMD Instinct MI250X GPUs (Frontier) and MI300A APUs (Portage). Using controlled square-wave workloads, we quantify update intervals, delay, aliasing, and variability across up to 512 GPUs and 480 APUs with on-chip (rocm-smi/amd-smi) and off-chip Cray Power Management sensors. We reconstruct power from cumulative energy counters to achieve faster response times, validate it against on-chip, off-chip, and node-level sensors, and integrate the resulting streams into a Score-P/PAPI-based tool for time-aligned, phase-level attribution. Applied to rocHPL, rocHPL-MxP, and HPG-MxP, the method separates energy savings due to reduced runtime from changes in power. Mixed precision reduces node energy on Frontier by 79% for rocHPL-MxP and 31% for HPG-MxP, with similar trends on Portage. These results provide portable guidance for sensor validation and power-aware optimization on current and future exascale systems.

Mcdaniel, Adam [ORNL] (ORCID:000000016926028X)↗
DOE OSTI2022

Nonlinear burn control in ITER using adaptive allocation of actuators with uncertain dynamics

Abstract ITER will be the first tokamak to sustain a fusion-producing, or burning, plasma. If the plasma temperature were to inadvertently rise in this burning regime, the positive correlation between temperature and the fusion reaction rate would establish a destabilizing positive feedback loop. Careful regulation of the plasma’s temperature and density, or burn control, is required to prevent these potentially reactor-damaging thermal excursions, neutralize disturbances and improve performance. In this work, a Lyapunov-based burn controller is designed using a full zero-dimensional nonlinear model. An adaptive estimator manages destabilizing uncertainties in the plasma confinement properties and the particle recycling conditions (caused by plasma–wall interactions). The controller regulates the plasma density with requests for deuterium and tritium particle injections. In ITER-like plasmas, the fusion-born alpha particles will primarily heat the plasma electrons, resulting in different electron and ion temperatures in the core. By considering separate response models for the electron and ion energies, the proposed controller can independently regulate the electron and ion temperatures by requesting that different amounts of auxiliary power be delivered to the electrons and ions. These two commands for a specific control effort (electron and ion heating) are sent to an actuator allocation module that optimally maps them to the heating actuators available to ITER: an electron cyclotron heating system (20 MW), an ion cyclotron heating system (20 MW), and two neutral beam injectors (16.5 MW each). Two different actuator allocators are presented in this work. The first actuator allocator finds the optimal mapping by solving a convex quadratic program that includes actuator saturation and rate limits. It is nonadaptive and assumes that the mapping between the commanded control efforts and the allocated actuators (i.e. the effector model) contains no uncertainties. The second actuator allocation module has an adaptive estimator to handle uncertainties in the effector model. This uncertainty includes actuator efficiencies, the fractions of neutral beam heating that are deposited into the plasma electrons and ions, and the tritium concentration of the fueling pellets. Furthermore, the adaptive allocator considers actuator dynamics (actuation lag) that contain uncertainty. This adaptive allocation algorithm is more computationally efficient than the aforementioned nonadaptive allocator because it is computed using dynamic update laws so that finding the solution to a static optimization problem is not required at every time step. A simulation study assesses the performance of the proposed adaptive burn controller augmented with each of the actuator allocation modules.

Physics↗
DOE OSTI2025

Integrated Distribution Planning

The contemporary distribution planning landscape is comprised of an increasing number of factors that require integration into the engineering of the modern electric grid. Expectations for electric utilities to accommodate heightened awareness of stakeholders' interest in things like decarbonization, resilience and equity are growing. As these interests are formed into objectives, many jurisdictions will experience increasing levels of load modifying technologies like DER, building and industrial electrification and electric vehicles which prove not only to challenge the capabilities of the grid; but the processes by which planning for it is traditionally done. Other related factors that strain the conventional distribution planning mold are the swelling amount and sources of data associated with these technologies and the need it creates for improved capabilities in the processes and tools that manage it. As the complexity of the distribution system expands, so will the distribution system's effects on the transmission and generation systems that it is a part of. Forecasting distribution system load and DER are examples of areas where this complexity will manifest, and harmonizing distribution forecasting with transmission and generation forecasting requires higher amounts of intentionality as these typically separate processes become a solitary one. Of course, core activities do not cease as a utility begins to integrate these other factors, and in this webinar we explore specifics of how distribution planning can be expected to evolve as progress towards Integrated Distribution System Planning is made.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Models and Strategies for Optimal Demand Side Management in the Chemical Industries

Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

K in an Urban World: New Contexts for Hydraulic Conductivity

Abstract Hydraulic conductivity ( K ) is a key hydrologic parameter widely recognized to be difficult to estimate and constrain, with little consistent assessment in disturbed, urbanized soils. To estimate K , it is either measured, or simulated by pedotransfer functions, which relate K to easily measured soil properties. We measured K in urbanized soils by double‐ring infiltrometer ( K dring ), near‐saturated tension infiltrometry ( K minidisk ), and constant head borehole permeametry ( K borehole ), along with other soil properties across the major soil orders in 12 United States cities. We compared measured K with that predicted from the pedotransfer function, ROSETTA. We found that regardless of soil texture, K dring was consistently larger than K minidisk ; with the latter having slightly less sample variance. K borehole was dependent upon specific subsurface conditions, and contrary to common expectations, did not always decrease with depth. Based on either soil textural class, or percent textural separates (sand, silt clay), ROSETTA did not accurately predict measured K for surface nor subsurface soils. We go on to discuss how K varies in urban landscapes, the role of measurement methods and artifacts in the perception of this metric, and implications for hydrologic modeling. Overall, we aim to inspire consistency and coherence when addressing K ‐related challenges in sustainable urban water management.

Shuster, W. D.↗
DOE OSTI2024

Microwave-assisted catalytic gasification of mixed plastics and corn stover for low tar, hydrogen-rich syngas production

The challenge for efficient management of post-consumer plastic and biomass waste has grown over the past few decades due to their dramatic increases. In comparison to conventional gasification, microwave-assisted co-gasification of plastics and corn stover offers many benefits, including increased H 2 yield and gas components compared to unfavorable char/tar. Nonetheless, for future commercialization of the process and ease of product separation, further reduction of the undesirable tar is necessary, which can be achieved over the catalytic route. Here, in this work, we studied the catalytic effect of magnetite for microwave-assisted co-gasification of corn stover and plastic to make syngas with higher H 2 and lower tar selectivity over non-catalytic conditions. A 1:1:1 ratio of plastic-corn stover-magnetite was used to evaluate the reaction parameters such as temperature, space velocity, heating media, and catalytic cycles under gasification conditions. In comparison with the microwave non-catalytic route, a 100% increase in the total H 2 yield with 76% higher H 2 production efficiency (mmol/kWh) was achieved in the presence of the magnetite catalyst, while reducing the overall tar formation from 9% to 2%. When magnetite was reduced in situ during the reaction, it coupled with microwave and delivered oxygen radicals that cracked down plastic and corn stover intermediates generated from the synergistic effect under microwave heating. Soon after the oxygen transfer process initiated, magnetite reached its final oxidation state consisting of microwave-active Fe and Fe 3 C phases that continued coupling with microwaves along with the generated graphitic carbon to maintain the heat necessary to further reduce the generated tar and make additional gaseous products, as confirmed by XRD, Raman, and TGA analyses.

08 HYDROGEN↗
DOE OSTI2026

Quantitative 14 N NMR with Monte Carlo Uncertainty Analysis of Nitrate/Nitrite in Alkaline Nuclear Waste

While monitoring of nitrate and nitrite concentrations is important for managing corrosion in nuclear waste systems, existing analytical methods are hindered by turbidity, spectral interference, and delays from sample handling. Here, we demonstrate quantitative 14 N nuclear magnetic resonance (qNMR) spectroscopy as a direct, matrix-tolerant approach for nitrate and nitrite detection at natural abundance. Monte Carlo resampling was integrated into the workflow to quantify random error, establish precision–time tradeoffs, and separate noise-limited uncertainty from systematic bias arising from shimming, transmitter offset, or excitation pulse conditions. Quantification of nitrate and nitrite were validated in controlled alkaline matrix challenges and in 18-component Hanford-type simulants. These results establish 14 N qNMR as a practical, uncertainty-bounded tool for monitoring redox-active nitrogen species in chemically complex environments and provide a generalizable framework for quantitative analysis of quadrupolar nuclei.

Graham, Trent R. [Pacific Northwest National Labor↗
DOE OSTI2022

Data for Determining Spatially Varying Profit-Maximizing Management Practices for Miscanthus and Switchgrass Production in the Rainfed United States

The compiled datasets include county-level variables used for simulating miscanthus and switchgrass production in 2287 counties across the rainfed US including 5-year (2012-2016) averaged growing season degree days (GDD), 5-year (2012-2016) averaged growing season cumulative precipitation, National Commodity Crop Productivity Index (NCCPI) values, regional dummies (only for miscanthus), the regional-level random effect of the yield response function, N price, land cash rent, the first year fixed cost (only for switchgrass), and separate datasets for simulating an alternative model assuming a constant N rate. The GAMS codes are used to run the simulation to obtain the main results including the age-varying profit-maximizing N rate, biomass yields, and annual profits for miscanthus and switchgrass production across counties in the rainfed US. The STATA codes are used to merge and analyze simulation results and create summary statistics tables and key figures.

Age↗
DOE OSTI2025

Comparative techno-economic analysis of synthetic renewable natural gas production via reactive CO 2 capture and conversion

Reactive CO 2 capture and conversion (RCC) is an emerging carbon management strategy that integrates CO 2 capture and conversion and avoids intermediate CO 2 purification. In this study, we design an RCC process to capture atmospheric CO 2 and react it with renewable hydrogen to produce synthetic renewable natural gas (SRNG), which serves as a carbon-neutral energy source and a chemical form of long-duration renewable energy storage. We assess the technological potential of RCC through process modeling, techno-economic, carbon footprint, and sensitivity analyses. Our findings demonstrate that RCC offers energy savings and comparable cost to separated capture and conversion processes. The cost is dominated by renewable hydrogen and material replacement cost. SRNG produced via RCC is competitive with existing low-carbon natural gas technologies and presents a promising low-cost option for long-duration energy storage. This work highlights the potential for deploying RCC technologies within a circular carbon economy and the scientific and technical challenges that must be overcome for material and technology developers.

03 NATURAL GAS↗
DOE OSTI2021

Belowground allocation and dynamics of recently fixed plant carbon in a California annual grassland

Plant roots and the organisms that surround them are a primary source for stabilized soil organic carbon (SOC). While grassland soils have a large capacity to store organic carbon (C), few field-based studies have quantified the amount of plant-fixed C that moves into soil and persists belowground over multiple years. Yet this characteristic of the soil C cycle is critical to C storage, soil water holding capacity, nutrient provisions, and the management of soil health. Here, we tracked the fate of plant-fixed C following a five-day 13 CO 2 labeling of a Northern California annual grassland, measuring C pools starting at the end of the labeling period, at three days, four weeks, six months, one year, and two years. Soil organic carbon was fractionated using a density-based approach to separate the free-light fraction (FLF), occluded-light fraction (OLF), and heavy fraction (HF). Using isotope ratio mass spectrometry, we measured 13 C enrichment and total C content for plant shoots, roots, soil, soil dissolved organic carbon (DOC), and the FLF, OLF, and HF. The chemical nature of C in the HF was further analyzed by solid state 13 C nuclear magnetic resonance (NMR) spectroscopy.

13C-NMR↗
DOE OSTI2020

Overview of research completed with funding from the SciDAC CSPM project (Final Technical Report)

The Center for the Study of Plasma Microturbulence was a SciDAC project managed by the Office of Fusion Energy Sciences in the US Department of Energy. Funding for the University of Maryland was initially modest (40k/year) for the first two years. The remaining four years were funded at 201k/year, with a single $50k supplemental in 2015, and a no-cost extension that ran through March, 2018. The CSPM involved several institutions whose efforts are documented separately. At UMD, the CSPM supported two graduate students (Kanekar and Wilkie), two post-doctoral fellows (Landreman and TenBarge), and summer salary for the UMD PI (Dorland). The supplemental in 2015 provided additional salary for a sabbatical by Dorland in 2015/16. Landreman and TenBarge both transitioned to separately funded research staff positions at Maryland at the ends of their post-doctoral, CSPM-supported, positions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The Dark Energy Spectrographic Instrument (DESI) Corrector Assembly

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. Our CRADA with University College London (UCL) involved both research planning for eventual observing on the DESI telescope; and in the build of the optical corrector, a major part of the instrument. The key major goals of this CRADA were achieved. For survey planning, Drs. Lahav, Abdalla, Peiris, Pontzen and Dr. Farihi were all important contributors to the working groups to which they were assigned. Their modelling efforts, cosmological simulations, target selection and analyses of imaging surveys have been vital in the early planning for targets during commissioning. The major role UCL played in the development of the instrument was performed by Drs. Doel and Brooks. The optical corrector components were developed by scientists and engineers at Lawrence Berkeley National Laboratory and at Fermi National Accelerator Laboratory. UCL was assigned the arduous responsibility for creating the instrumentation necessary to take all the separate components (lenses, cells, rings) and incorporate them, while precisely aligning the optics. Following that, they installed the corrector inside the barrel and shipped everything to the Mayall. It was a great success that the optical barrel arrived safely in good condition. Drs. Doel and Brooks were involved in every step of the fabrication and kept in constant contact with the LBNL DESI Project Manager and Project Director. They were extremely successful in their alignment, which was proved by early observations. First light on the telescope was achieved on October 22, 2020. The first spectrum was taken of a random star at zenith. Shortly thereafter, the telescope slewed to M33 and several spectra were taken there and later in the Persus nebula. It was a major accomplishment for the project.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Design Optimization of A generic Fissile Solution for Mo99 production using Electron Beam-based Neutron Generator using MCNP+CFD

Molybdenum-99 ( 99 Mo) is a medical isotope used in 80% of all medical imaging procedures today. However, 99 Mo is not directly administered to patients for imaging; its decay product, technetium-99m ( 99 mTc), is a metastable isotope with a half-life of ~6 hours. Such a short half-life does not allow for production, separation, and application to a patient before decaying further. Therefore, the solution is to produce the precursor to 99 mTc, 99 Mo. 99 Mo has a half-life of 66 hours, long enough to produce the material and send it to the desired location before it fully decays. Under National Nuclear Security Administration’s (NNSA) highly enriched uranium (HEU) minimization mission, the Material Management and Minimization (M3) office leads the Molybdenum-99 (Mo-99) program. A major goal of the Mo-99 program is to develop methods of producing 99 Mo using low enriched uranium (LEU) opposed to the traditionally used HEU domestically in the U.S. Internationally, LEU is the new standard for use in research reactors and isotope production facility. In this study, we introduce a generic fissile solution system for 99 Mo production facility meeting the design requirement of 1) LEU usage as fissile material and 2) subcritical fissile system coupled with a horizontal electron beam (E-beam) accelerator. The focus of this research is to optimize a fissile solution configuration for maximum 99 Mo production yield using MCNP model, and ultimately implement the optimized design with computational fluid dynamics (CFD) model to understand thermal hydraulic behavior and solution convection characteristic. The report consists of two parts: 1) isotope yield calculation and criticality analysis with a wide range of parametric test matrix, and 2) thermal hydraulic analysis for solution mixing and cooling assessment.

60 APPLIED LIFE SCIENCES↗