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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Scale-Up of Electrode Coating and Flow-Field for Commercial Hydrogen Peroxide Electrolyzer: Cooperative Research and Development Final Report, CRADA Number CRD-17-00687

Hydrogen peroxide is currently produced at central chemical plants via the anthraquinone oxidation process. This process produces environmental pollutants that are costly to remediate, requires hazardous long distance shipping of highly concentrated peroxide (50% or 70%), and necessitates extra handling costs related to storage and dilution. Peroxygen Systems, Inc. (PSi) is developing breakthrough technology for on-site hydrogen peroxide production. PSi’s on-site on-demand electrolyzer can reduce the cost of producing hydrogen peroxide by 50%, while also completely eliminating the cost and safety issues associated with shipping and handling of high concentration hydrogen peroxide. The challenge for PSi is scaling. To support the next step toward commercialization (customer pilot tests), scaling the prototype into larger single cells and 20-40 cell stacks is required. In addition to internal hardware and flow-field design efforts at PSi, NREL will address three critical problems for this scale-up effort: (1) demonstrating a large scale roll-to-roll (R2R) process to coat uniform electrode materials for 100 cm2 and 500 cm2 stack testing, (2) demonstrating an in-line diagnostic to achieve better electrode quality control, and (3) performing in situ cell/stack testing to better understand and optimize the performance of the flow field design.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2022

Exploiting heat transfer to achieve efficient photoelectrochemical CO 2 reduction under light concentration

Photoelectrochemical (PEC) conversion of carbon dioxide into valuable chemicals and fuels represents a promising path towards combating anthropogenic CO 2 emissions. However, the limited conversion efficiencies, operation lifetimes and CO 2 utilization efficiencies of PEC devices currently prohibit their application beyond the laboratory scale. Here, a wireless device converting CO 2 and water into carbon monoxide and hydrogen at a peak solar conversion efficiency exceeding 16% under an illumination intensity of 5 suns is demonstrated. A CO/H 2 product ratio between 10–20 is measured during a 17 h stability test. Fluctuations in device performance are rigorously analyzed via deconvolution of electrochemical and photoabsorber contributions. Furthermore, it is demonstrated that beneficial heat dissipation is enabled by wireless integration of the photoabsorber and electrocatalyst components, accounting for roughly 10% of the achieved conversion efficiency, an achievement unattainable with physically separated photoabsorber and electrolyzer components.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Two-Phase Cooling for High Packaging Density Planar Inverter: Cooperative Research and Development Final Report, CRADA Number CRD-15-00592

The project objective was to utilize two-phase heat transfer to enable compact and power-dense inverters. The high heat transfer capacity and near isothermal characteristics of two-phase cooling can enable higher power densities, reducing the size and cost of the power electronics system. This project focused on developing efficient, passive (no pump or compressor) two-phase cooling strategies that consist of an air-cooled condenser and evaporator. This report describes the modeling work conducted to design the evaporator and air-cooled condenser for a prototype inverter development work underway in the John Deere. Prototypes of the final evaporator and condenser designs were then fabricated via 3D printing for testing. Experiments on the evaporator confirmed that this component can meet the heat load and junction temperatures requirements for Si IGBT (insulated-gate bipolar transistor) inverter. Experiments with the air-cooled condenser were not possible due to leaks through the condenser walls.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Cooperative Education

M-3 Focused Experiments is a group at Los Alamos National Lab (LANL) and is under the Dynamic Experiments Division. Their work surrounds the diagnostics and study of explosives and their interaction with materials. The data collected is used to better understand the erratic nature of explosions and high velocity projectiles. Through better comprehension of this science, the overall national security will be improved while innovations are made along the way. Despite the strong drive for research and development, the lab holds safety above all else and constantly updates procedures to protect LANL employees and their environment. At any time, there are multiple projects going on at once within the team and I am tasked with assisting the Firing Site Leaders and various research diagnosticians in any way possible with the setup, testing, and execution of data collection for each experiment. Such data includes velocity, fragmentation characteristics, and the spread of energy across the material to name a few. During my summer employment with the lab, my main goal is to maintain a questioning attitude and learn as much possible about the research and development side of science and engineering. Throughout the duration of the Co-op, many experiments will take place at specially designed firing sites which is where I will be spending most of my time.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Three-dimensional cooperative inversion of airborne magnetic and gravity gradient data using deep-learning techniques

Using multiple geophysical methods has become a prevailing approach in numerous geophysical applications to investigate subsurface structures and parameters. These multimethod-based exploration strategies have the potential to greatly diminish uncertainties and ambiguities encountered during geophysical data analysis and interpretation. One of the applications is the cooperative inversion of airborne magnetic and gravity gradient data for the interpretation of data obtained in mineral, oil and gas, and geothermal explorations. In this paper, a unified cooperative inversion framework is designed by combining the standard separate inversions with a deep neural network (DNN), which serves as the link between different types of data. A well-trained DNN takes the separately inverted susceptibility and density models as the inputs and provides improved models that will be used as the initial models of deterministic inversions. A two-round iteration strategy is adopted to guarantee the reasonability of the recovered models and overall efficiency of the inversion. In addition, this deep-learning (DL)-based framework demonstrates excellent generalization abilities when tested on models that are entirely distinct from the training data sets. The framework can easily incorporate multiphysics without necessitating any structural changes to the network. Synthetic experiments validate that our DL-based method outperforms conventional separate inversions and cross-gradient-based joint inversion in view of the accuracy of the recovered models and inversion efficiency. Successful application to field data further verifies the effectiveness of our DL-based method.

Geochemistry & Geophysics↗
DOE OSTI2025

Toward machine learning interatomic potentials for modeling uranium mononitride

Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at finite temperatures. We constructed a training set using density functional theory (DFT) calculations that was enriched through an active learning procedure, and two neural network potentials were generated. Both potentials successfully reproduce key thermophysical properties of interest, such as temperature-dependent lattice parameter, specific heat capacity, and bulk modulus. We also evaluated the energy of stoichiometric defect reactions and defect migration barriers and found close agreement with DFT predictions, demonstrating that our potentials can be used for modeling defects in UN. Additional tests provide evidence that our potentials are reliable for simulating diffusion, noble gas impurities, and radiation damage.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Reconstructing Ly$α$ Fields from Low-resolution Hydrodynamical Simulations with Deep Learning

Hydrodynamical cosmological simulations are a powerful tool for accurately predicting the properties of the intergalactic medium (IGM) and for producing mock skies that can be compared against observational data. However, the need to resolve density fluctuation in the IGM puts a stringent requirement on the resolution of such simulations, which in turn limits the volumes that can be modeled, even on the most powerful supercomputers. In this work, we present a novel modeling method that combines physics-driven simulations with data-driven generative neural networks to produce outputs that are qualitatively and statistically close to the outputs of hydrodynamical simulations employing eight times higher resolution. We show that the Ly$α$ flux field, as well as the underlying hydrodynamic fields, have greatly improved statistical fidelity over a low-resolution simulation. Importantly, the design of our neural network allows for sampling multiple realizations from a given input, enabling us to quantify the model uncertainty. Using test data, we demonstrate that this model uncertainty correlates well with the true error of the Ly$α$ flux prediction. Ultimately, our approach allows for training on small simulation volumes and applying it to much larger ones, opening the door to producing accurate Ly$α$ mock skies in volumes of Hubble size, as will be probed with DESI and future spectroscopic sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Isotherm Modeling and Techno-Economic Analysis of Contactor Technologies for New Tetraamine-Appended MOF for NGCC Applications

Recently, a family of tetraamine-functionalized metal-organic frameworks (MOF) has been reported as promising sorbent materials for capturing CO2 from flue gas conditions relevant to natural gas combined cycle (NGCC) applications. The main advantages of these materials are their two-step cooperative CO2 adsorption, which gives rise to unusual two step-shaped CO2 adsorption profiles and their high thermal stability. This work presents the modelling of the two-transition isotherm of the tetraamine-appended MOF, N,N'-bis(3-aminopropyl)-1,4-diaminobutane (3-4-3)-appended Mg2(dobpdc), and the techno-economic analysis (TEA) of carbon capture processes utilizing this sorbent. Due to the unusual isotherm shapes of the experimental CO2 adsorption data for tetraamine-appended Mg2(dobpdc) and the strong nonlinearity of CO2 loading with respect to temperature and pressure, we tested two different models which use logistic functions for representing the different isotherm behaviors in the different pressure ranges. The first model uses the quadratic isotherm model in the low-pressure region, the Langmuir isotherm model in the middle pressure range, and the dual site Langmuir isotherm model in the high-pressure range. To model the transition between regions we used the arctangent functions independent of temperature and the thermal effect was accounted by using the Clausius-Clapeyron relation]. The second model is an extension of the weighted dual-site Langmuir isotherm model presented by Hughes et al.. In this extended model, the dual-site Langmuir isotherm is employed in the three transition regions, using temperature-dependent logistic functions to activate or deactivate the isotherm model in the low, middle, and high-pressure ranges. Both models fit the experimental data quite well with root mean squared errors (RMSE’s) of 0.41 and 0.17 for model 1 and model 2, respectively. Since model 2 resulted in a lower RMSE, it was leveraged for the development of the gas/solid contactor models used by the TEA. Specifically, two different contactor models, an axial-flow fixed bed and moving bed contactor, were developed as part of this work. These models are dynamic, pressure-driven, and consist of mass, energy, and momentum conservation equations. A kinetic model was also developed by performing parameter estimation using experimental fixed bed breakthrough data. These models are then used to simulate CO2 capture processes from the flue gas generated from a ~600 gross MW NGCC power plant. A cost model was developed which considers the capital cost of the reactors and the significant operating costs such as steam and electricity. Using NETL’s Framework for the Optimization and Quantification of Uncertainty of Uncertainty and Surrogates tool (FOQUS), which has the capability of linking models built using numerous modelling platforms with derivative-free optimization solvers, a techno-economic optimization of the carbon capture processes was performed which minimizes the cost of capture.

Caballero, Daison↗
DOE OSTI2021

Improvement of the $\mathrm{BISON U_3Si_2}$ modeling capabilities based on multiscale developments to modeling fission gas behavior

Uranium silicide (U 3 Si 2 ) is a concept explored as a potential alternative to UO 2 fuel used in light water reactors (LWRs) since it may improve accident tolerance and economics due to its higher thermal conductivity and increased uranium density. U 3 Si 2 has been previously used in research reactors in the form of dispersion fuel, but operated at lower temperatures than commercial LWRs. The research reactor data illustrated that significant gaseous swelling occurs as the fuel burnup increases. Therefore, it is imperative to understand the fission gas behavior of U 3 Si 2 under higher temperature LWR operating conditions. In this work, molecular dynamics and phase-field modeling techniques are used to reduce the uncertainty in select modeling assumptions made in developing the fission gas behavior model for U 3 Si 2 in the BISON fuel performance code. These lower length scale informed models are then utilized in the validation of BISON U 3 Si 2 modeling capabilities to simulate the ATF-1 experiments irradiated in the Advanced Test Reactor (ATR). Sensitivity analysis (SA) and uncertainty quantification (UQ) are included as part of the validation process to identify where further experiments and lower length scale modeling would be beneficial. Here, the multiscale modeling approach utilized in this work can be applied to new fuel concepts being explored for both LWRs and advanced reactors (e.g., uranium nitride, uranium carbide).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

NPRL2 reduces the niraparib sensitivity of castration-resistant prostate cancer via interacting with UBE2M and enhancing neddylation

In this study, we explored the regulatory effects of nitrogen permease regulator 2-like (NPRL2) on niraparib sensitivity, a PARP inhibitor (PARPi) in castrate-resistant prostate cancer (CRPC). Data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) program were retrospectively examined. Gene-set enrichment analysis (GSEA) was conducted between high and low NRPL2 expression prostate adenocarcinoma (PRAD) cases in TCGA. CCK-8 assay, Western blot analysis of apoptotic proteins, and flow cytometric analysis of apoptosis were applied to test niraparib sensitivity. Immunofluorescent (IF) staining and co-immunoprecipitation (co-IP) were conducted to explore the proteins interacting with NPRL2. Results showed that the upregulation of a canonical protein-coding transcript of NPRL2 (ENST00000232501.7) is associated with an unfavorable prognosis. Bioinformatic analysis predicts a physical interaction between NPRL2 and UBE2M, which is validated by a following Co-IP assay. This interaction increases NPRL2 stability by reducing polyubiquitination and proteasomal degradation. Depletion of NPRL2 or UBE2M significantly increases the niraparib sensitivity of CRPC cells and enhances niraparib-induced tumor growth inhibition in vivo. NPRL2 cooperatively enhances UBE2M-mediated neddylation and facilitates the degradation of multiple substrates of Cullin-RING E3 ubiquitin ligases (CRLs). In conclusion, this study identified a novel NPRL2-UBE2M complex in modulating neddylation and niraparib sensitivity of CRPC cells. Therefore, targeting NPRL2 might be considered as an adjuvant strategy for PARPi therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Cooperative Research and Development Agreement among National Energy Technology Laboratory, Advanced Manufacturing LLC, and Connecticut Center for Advanced Technology [Abstract]

The National Energy Technology Laboratory (NETL), Advanced Manufacturing LLC (AMLLC) and Connecticut Center for Advanced Technology (CCAT) will collaborate to develop and manufacture cost-effective, oxide dispersion-strengthened, NiCrFeCo-rich high entropy alloys that are superior to Nickel-based superalloys (e.g. IN740) for repair or replacement service in extreme environments. The new alloys can result in higher efficiency and reduced cost of advanced power systems such as advanced ultra-supercritical (AUSC) boilers and gas turbines. High throughput, multiscale computational modeling will be performed to accelerate alloy discovery by interrogating the intrinsic properties of the alloys including thermodynamic, kinetic, and mechanical properties. Validation and evaluation will be performed, including additive manufacturing, microstructure characterization, and mechanical properties tests.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Demonstration of NREL Modeling Capability to Design the Next Generation of Floating Offshore Wind Turbines with Stiesdal and Magellan Wind (Cooperative Research and Development Final Report)

This Technology Commercialization Fund (TCF) CRADA involved demonstration of NREL modeling capability using OpenFAST (formerly known as FAST) to design the next generation of floating offshore wind turbines (FOTW) with Stiesdal’s TetraSpar design. The objective of the project was to enable the design and optimization of next generation FOWT that show promise to make FOWT cost-competitive with other energy technologies by upgrading, verifying, and validating improvements to OpenFAST. This objective was achieved by (1) upgrading OpenFAST to compute floating substructure flexibility and member-level loads, which is critical to enable the design of floating substructures—especially newer designs that are streamlined, flexible, and cost-effective; (2) verifying the new OpenFAST capabilities through model-to-model comparisons and validating the capabilities through comparisons to empirical data generated with wave-tank testing, using TetraSpar data provided by Stiesdal; and (3) making available the upgraded OpenFAST tool to the wind energy community to enable next-generation floating wind designs.

17 WIND ENERGY↗
DOE OSTI2021

Spontaneous Cyclogenesis without Radiative and Surface-Flux Feedbacks

Tropical cyclones (TCs) are among the most intense and feared storms in the world. What physical processes lead to cyclogenesis remains the most mysterious aspect of TC physics. Here, we study spontaneous TC genesis in rotating radiative–convective equilibrium using cloud-resolving simulations over an f plane with constant sea surface temperature. Previous studies proposed that spontaneous TC genesis requires either radiative or surface-flux feedbacks. To test this hypothesis, we perform mechanism-denial experiments, in which we switch off both feedback processes in numerical simulations. We find that TCs can self-emerge even without radiative and surface-flux feedbacks. Although these feedbacks accelerate the genesis and impact the size of the TCs, TCs in the experiments without them can reach similar intensities as those in the control experiment. We show that TC genesis is associated with an increase in the available potential energy (APE) and that convective heating dominates the APE production. Overall, our result suggests that spontaneous TC genesis may result from a cooperative interaction between convection and circulation and that radiative and surface-flux feedbacks accelerate the process. Furthermore, we find that increasing the planetary rotation favors spontaneous TC genesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

RAPID Manufacturing Institute Final Report

The Rapid Advancement of Process Intensification Deployment (RAPID) Manufacturing Institute, founded in 2017, is a public/private partnership between the U.S. Department of Energy and the American Institute of Chemical Engineers (AIChE). RAPID promotes the development, deployment and commercialization of Process Intensification (PI) and Modular Chemical Process Intensification (MCPI) technologies, enabling U.S. manufacturing to reduce energy consumption, improve process efficiencies and lower investment and operating costs. This mission was carried out through parallel work breakdown structure elements including the establishment of committees to guide the operations and technical direction of RAPID, the establishment of management practices and institute processes, education and workforce development (EWD), and six technical focus areas for the development of technologies to advance PI and MCPI. Throughout the initial six-year cooperative agreement, RAPID worked to meet performance metrics which focused on the operation and sustainment of the institute, education and workforce development and the development of PI and MCPI for the advancement of U.S. manufacturing. All these metrics were successfully met through a total of 43 projects which leveraged $\$$70M Federal with $\$$90M cost share. As a result of these efforts, 84 private and public organizations were brought together by RAPID as members to co-invest in R&D, commercialization and deployment of innovative technologies. In the research portfolio, 82% of the 38 projects achieved > 20% energy efficiency improvement. A RAPID Test Network was developed with 51 testbed facilities to enable access to resources, facilities, tools, and expertise. Eight EWD programs were also developed with over 13,000 impressions. RAPID’s efforts to research, develop, demonstrate, and deploy high-impact PI and modular process technology solutions have enabled reduced energy use, increased sustainability, and improved profitability for U.S. manufacturing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection Description This dataset contains input and output data for the manuscript Mongird, K. et al. (under review) titled "Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection". Input data corresponds to gridded spatial siting attributes that are necessary to conduct a random forest machine learning analysis of siting feature importance. Output data includes SHAP feature analysis outputs, and classification report values. For data on power plant siting results referred to in the manuscript, please refer to the CERF: IM3 Projected Western US Power Plant Locations data download page. The downloadable data includes values for eight different future scenarios for the Western US. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 Technical Information The dataset includes two sets of data files: (1) CERF gridded siting parameters and (2) Feature analysis outputs and classification reports. All downloadable data is in csv file format. Files with x/y coordinate information use the Albers Equal Area Conic projection (ESRI:102003). 1. CERF Gridded Siting Parameters This directory provides a balanced sample of gridded CERF siting parameters data for eight different scenarios for the Western US through 2055, seven different technologies, and eight timesteps. This data serves as input to the feature analysis. It contains the following parameters. region_name - name of region (i.e., state) sited - binary value representing whether the grid cell received a siting of that technology type (1=True) rcp - binary value representing scenario resource concentration pathway (0 = RCP4.5, 1 = RCP8.5) ssp - binary value representing scenario shared socioeconomic pathway (0 = SSP3, 1 = SSP5) climate - binary value representing cooler (0) or hotter (1) GCM forcing tech_name - generation technology name sited_year - year that values correspond to transmission_cost - cost of transmission interconnection pipeline_cost - cost of natural gas pipeline interconnection interconnection_cost - total interconnection cost (sum of transmission cost and gas pipeline cost) lmp - associated locational marginal value ($/MWh) associated with the grid cell, timestep, scenario, and technology xcoord - x-coordinate of location ycoord - y-coordinate of location 2a. Feature Analysis Output The dataset includes the feature analysis shap output for locational marginal price and interconnection cost. It contains the following parameters. technology - generator technology name scenario - name of scenario feature - name of feature, either locational_marginal_price or interconnection_cost value - the mean of absolute value of SHAP values for given feature 2b. Feature Analysis Classification Report This download includes the classification report associated with each random forest model. The dataset contains the following parameters. technology - generation technology name scenario - name of scenario test - one of precision (the proportion of predicted positives that are actually correct), recall (the proportion of actual positives that were correctly identified), f1-score (the harmonic mean of precision and recall) 0.0 - value of test for classification of 0 (grid cell not chosen for siting) 1.0 - value of test for classification of 1 (grid cell chosen for siting) accuracy - accuracy of model (i.e., fraction of all predictions that were right) macro avg - Simple average of test values for all classes weighted avg - Weighted average of test values for all classes, weighted based on Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
DOE OSTI2022

First Leptophobic Dark Matter Search from the Coherent–CAPTAIN-Mills Liquid Argon Detector

We report the first results of a search for leptophobic dark matter (DM) from the Coherent–CAPTAIN-Mills (CCM) liquid argon (LAr) detector. An engineering run with 120 photomultiplier tubes (PMTs) and 17.9 × 10 20 protons on target (POT) was performed in fall 2019 to study the characteristics of the CCM detector. The operation of this 10-ton detector was strictly light based with a threshold of 50 keV and used coherent elastic scattering off argon nuclei to detect DM. Despite only 1.5 months of accumulated luminosity, contaminated LAr, and nonoptimized shielding, CCM’s first engineering run has already achieved sensitivity to previously unexplored parameter space of light dark matter models with a baryonic vector portal. With an expected background of 115 005 events, we observe 115 005 + 16.5 events which is compatible with background expectations. For a benchmark mediator-to-DM mass ratio of m V B =m χ = 2.1, DM masses within the range 9 MeV ≲ m χ ≲ 50 MeV are excluded at 90% C. L. in the leptophobic model after applying the Feldman-Cousins test statistic. CCM’s upgraded run with 200 PMTs, filtered LAr, improved shielding, and 10 times more POT will be able to exclude the remaining thermal relic density parameter space of this model, as well as probe new parameter space of other leptophobic DM models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗