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Water Resource Assessment In The New Mexico Permian Basin: BLM 2023 Assessment Report

The Permian Basin is the highest producing oil field in the United States and is comprised of three component basins including; the Midland Basin, Delaware Basin and the Marfa Basin. This report describes the work conducted by Sandia National Laboratories (SNL) for the Bureau of Land Management (BLM) to investigate the occurrence of usable water (quality, and depth to water) in the Delaware Sub-basin of the Permian Basin. High Production Areas (HPAs) were identified for the region based on Reasonable Foreseeable Development Scenario (RFD) published by New Mexico Tech University. HPAs were established based on potential for future development of oil reserves. The study was initiated by the BLM-Carlsbad Field Office (CFO) based on concerns that special protections for groundwater in these HPAs may be warranted. A combination of analysis of existing data and field work to collect new data for analysis were used to complete the investigation objectives. This study summarizes the most recent analyses in an ongoing project and builds on previously completed work as listed in Table 1. Advancements in directional drilling and well completion technologies have resulted in an exponential growth in the use of hydraulic fracturing for oil and gas extraction in the Permian Basin. Within the New Mexico portion of the Delaware Sub-basin, water demand to complete each hydraulically fractured well is estimated to average 7.3 acre-feet (2.4 million gallons), resulting in 30 billion barrels of water over the life of the plan or 1.5 billion barrels per year. This rising demand is creating concern for the regions ability to provide the necessary water in a manner that fulfills BLM’s role of protecting human health and the environment while sustainably meeting the needs of various water users in the region. This report documents water-level and water chemistry baselines to aid the BLM in understanding the regional water supply dynamics under various management, policy, and growth scenarios and to pre-emptively identify risks to water sustainability.

54 ENVIRONMENTAL SCIENCES↗

Ultrasonic Jet Bat Deterrent System Advancement

General Electric Renewable Energy (GE) and the Department of Energy (DOE) worked to develop a project with an objective to increase confidence in acoustic ultrasonic bat deterrent technologies in wind turbines, by mitigating stakeholder investment risks that currently hinder the adoption of deterrent technologies. Many wind turbine sites utilize a bat conservation plan that curtail wind turbines and stop energy production. Curtailment is a known bat impact mitigation solution that has an history of environmental regulatory acceptance. To help support a solution that allows for simultaneous bat conservation and energy production with regulatory acceptance of acoustic bat deterrents, GE proposed a study to compare four treatments: control (no curtailment or deterrent), deterrent only, deterrent plus curtailment, and curtailment only. The concept was to provide a direct comparison of bat deterrent and curtailment effectiveness within the same wind farm and bat migration season. This comparison was intended to advocate for the adoption of bat deterrents with a large-scale field study by addressing customer concerns on regulatory acceptance of deterrents. The study was to be hosted at a forested habitat site during a bat migration season, by Stony Creek Energy, LLC (SCE), the owner of the Orangeville Wind Farm (OWF) in the state of New York. Included was a behavioral study plan with two turbines to monitor bat flight paths with thermal imaging cameras.

17 WIND ENERGY↗

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

Pilot-Scale Testing of an Integrated Circuit for the Extraction of Rare Earth Minerals and Elements from Coal and Coal Byproducts Using Advanced Separation Technologies

The primary objective of this project was to develop and demonstrate an integrated pilot-scale circuitry for recovering high-value rare earth elements (REEs) from coal and coal byproducts. The target performance was to produce a mixed REE product with content of at least two percent by weight on a dry mass basis in a cost-effective and environmentally benign manner. During the first nine months of the project period (Phase 2 Budget Period 2), pilot plant construction was completed including all field site startup activities such as permitting, engineering design, procurement/bidding, unit fabrication, site construction, equipment installation, module assembly, safety training, and circuit shakedown. During the remaining 21 months of project period (Phase 2 Budget Period 3), detailed field-testing activities were performed including feedstock sample collection and preparation, exploratory testing, circuit modification, detailed parametric study, and performance optimization. A detailed techno-economic analysis was performed based on the pilot plant testing findings which provided various scenarios for REE production. The project successfully accomplished the proposed target performance by producing mixed rare earth oxide (REO) with greater than 90% purity by weight in a continuous pilot scale operation from two distinctly different coarse refuse materials (i.e., West Kentucky No. 13 and Fire Clay coal seams), and at least three secondary sources (i.e., heap leach process and naturally formed acid mine drainage system). Project partners included the University of Kentucky, Virginia Tech, West Virginia University, Alliance Coal, Blackhawk Mining, Mineral Refining Company, and Mineral Separation Technologies. The pilot scale test facility was constructed at a former mining complex owned by Alliance Natural Resource Partners (Alliance Coal). The site was rehabilitated to accommodate the equipment installation, construction and fabrication, electrical power requirement, water line management and containment. The process units constructed and installed included X-ray sorting unit, crushing and grinding unit, physical separation unit, acid leaching unit, solvent extraction unit, and wastewater management unit. A rare earth mineral concentration unit was constructed as a standalone unit for flexible operation. A detailed environmental assessment and control plan was carried out to identify and quantify any potential impacts of the pilot-scale processing circuitry on the human and eco-system health and well-being. Corresponding mitigation strategies and control measures were provided. A conceptual flowsheet was developed to effectively remove thorium and uranium from high purity rare earth oxide mix or any potential radionuclide enriched stream. The two distinct feedstock materials were secured from the Blackhawk Mining Complex in eastern Kentucky where the Fire Clay (Hazard No. 4) seam is processed. The West Kentucky No. 13 (Baker) coarse refuse material was collected from an active process stream at an Alliance coal preparation plant located in western Kentucky. Characterization analysis indicated that both of feed materials generated from the two sources contained >300 ppm of TREEs on a dry whole mass basis which met the requirements for a qualified feed stock. The two feedstocks were further upgraded using a dual x-ray sorter to prepare the feed material for hydrometallurgical circuit. Thermal treatment on feed material prior to leaching was found to: 1) improve the leaching recovery of REEs, 2) increase the leaching kinetics, and 3) allow the leaching reaction to occur at lower acidity. Roasting at 600°C was selected as the pre-treatment condition for both West Kentucky No. 13 and Fire Clay coarse refuse material. Over 40% of leaching recovery was achieved by roasting West Kentucky No. 13 material having a top particle size of 3 mm in the pilot scale operation using 1.2M sulfuric acid leaching at 75OC. Initial pilot scale testing involved continuous operation of the pilot plant for 94 hours. The leaching unit was operated at solid-to-liquid ratio of 1 to 10 (w/v) using 0.5M sulfuric acid solution at a temperature of 75°C. The continuous solvent extraction circuit utilized rougher and cleaner units with DEHPA and TBP as the extractants. An innovative stripping circuit was developed to accumulate the REE concentration in the stripping solution to a level above 600 ppm. A bleed stream from the recycled strip solution was treated using oxalic acid precipitation which produced a high grade rare earth oxalate. The oxalate product was roasted to remove the oxalate which produced a rare earth oxide product having a purity greater 90%. Due to high concentrations of contaminant ions in the pregnant leach solution (PLS), a modified flowsheet was developed that involved pre-concentration of the REEs using multiple stages of precipitation and redissolution. The advantage of this process was improved removal of contamination before the downstream purification process and a significant cost reduction relative to the circuit that utilized the solvent extraction process. The modified circuitry included processes involving leaching, multistage precipitation, redissolution, and oxalate precipitation followed by roasting of the oxalate product. The circuit produced a mixed REO that was 92.96% pure from the initial test. A detailed parametric test plan was carried out which involved varying key parameters including solids feed rate, acid flowrate, acid concentration, multistage precipitation pH, redissolution pH, oxalate precipitation dosage and pH. The response variables included REE recovery, contaminant recovery, REO product grade and overall chemical consumption. Test results indicated that the acid-to-solid ratio is the key parameter to leaching efficiency as performance deteriorated with an increase in solids concentration. The optimal pH determined for REE precipitation and redissolution was 6.5 and 2.5, respectively. Additional tests were conducted to further improve the flowsheet. Recirculating a portion of the PLS to the feed of the leach tanks improved the leaching performance by lowering the pH of the leaching system and reducing the contamination recovery by shortening the residence time. Moreover, the removal of Al prior to REE precipitation significantly reduced the oxalic acid consumption in the oxalate precipitation circuit. The modified circuit produced over 90% grade REO by weight from both West Kentucky No. 13 and Fire Clay coarse refuse material in pilot scale continuous test programs. A case analysis model was developed to project the REE and major contaminants concentration in each PLS stream based on the leaching condition, pH cut point, and oxalic acid dosage. A correlation was established using empirical and semi-empirical models. Using the models, chemical consumption required for each stage was predicted based on the projected performance of the hydrometallurgy circuit. After identifying the optimum conditions, validation tests were carried out for the treatment of both West Kentucky No. 13 and Fire Clay coarse refuse materials in the pilot plant. The actual circuit performance and chemical consumptions were very close to the model predictions. Other than the two coarse refuse sources, several secondary feedstocks were also tested in the pilot plant facility. A “heap leach” system was constructed using the coarse refuse material generated from cleaning the West Kentucky No. 13 seam coal. Using the two stage SX rougher and cleaner circuit, a concentrate with a grade >90% REO was produced while recovering >97% of the REEs from the heap leach PLS. Naturally generated acid mine drainage (AMD) from West Kentucky No.13 mine was processed using the multistage precipitation circuit in the pilot plant in a test conducted for a period of 32 hours. The final grade of the mix RE oxide produced from the AMD was 90.84% with an overall circuit recovery of 64%. The primary source of REE was the selective precipitation steps involving iron and aluminum rejection. The hydrophobic-hydrophilic separation (HHS) process was proven to effectively recover coal from fine waste materials. For REM recovery, the HHS process was able to produce concentrates at grades of approximately 1.8% REE on an ash basis; however, recovery values were typically low, <10%, under the optimal conditions determined in the laboratory-scale testing. Staged testing of the pilot-scale HHS process for coal recovery and semi-continuous laboratory testing for REM testing showed that a total concentration ratio of more than 15x was observed for the REM recovery process. A circuit simulation package was developed for REE extraction and purification using a spreadsheet-based platform (Microsoft Excel). The REESim circuit simulation package is configured to track the mass and volume flows of components passing through a series of unit operations specified and configured by the user. The mass rates can then be utilized by the user to determine important performance indicators such as product mass yields, concentrate purity levels, element-by-element recoveries, and so forth. The techno-economic analysis showed that the roasting and leaching operations were the most expensive capital items, each contributing approximately 30% to the total capital cost. One notable contributor to the high production costs was the low REE recovery observed in the pilot scale trials. The product basket price was shown to have a strong influence on the economic viability of the scenarios, with the scandium price being the most significant influencer. Operating cost was shown to be extremely sensitive to REE recovery, REE feed grade, and leaching acid consumption. An analysis of ten different scenarios for a 500 t/h commercial operation revealed that three were economically favorable, producing internal rates of return varying from 27.7% to 33.1% and payback periods of 4 to 5 years. The project successfully developed and demonstrated a process to recover REEs from coal and coal byproducts in a pilot-plant operation which consistently produced over 90% grade REO mix from varies types of feedstocks. Commercialization analysis showed that the technology readiness level successfully achieved TRL 6 at the end of the project and demonstrated the need and the potential for scaling the process to further advance the technologies toward the goal of providing a domestic supply of REEs at a commercial scale.

01 COAL, LIGNITE, AND PEAT↗

Distributed Energy Resource Cybersecurity Framework Best Practices

Current cybersecurity challenges for distributed energy resources (DERs) stem from the integration of various systems and the cyber-physical security concerns they bring. Although frameworks exist for industrial control systems and other energy systems, a simple guided tool that can prioritize recommended actions for controls specific to DERs is lacking. Additionally, the novelty of the field has made it difficult to create a standardized procedure for DERs with cybersecurity in mind. In response to this critical need to address cybersecurity risk management, the National Renewable Energy Laboratory (NREL) has developed a framework and accompanying web application known as the Distributed Energy Resources Cybersecurity Framework (DERCF) (Powell, 2019). A brief overview of the DERCF can be found in Section 1.2. This document provides a guide to cybersecurity best practices identified by technical research and site visits where the framework was used to assess the cybersecurity posture of DER systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Combined Normal and Disruption, Electromagnetic Transient, Thermal, and Structural Analysis of COMPASS Upgrade

We report that COMPASS Upgrade is a replacement for the COMPASS device at the Institute of Plasma Physics (IPP) of the Czech Academy of Sciences. It is a cryogenic copper machine with a major radius of 0.89 m, with 5 T at the plasma centerline and 2-MA plasma current. It is intended to develop ITER relevant plasma densities and high power fluxes in the divertor region. The entire vacuum vessel is planned to reach 500 °C. The COMPASS-U TF coil is a bitter plate-like design with radial conductor plates that extend the width of the TF inner leg. Currents redistribute radially based on resistive and inductive effects. This necessitates an electromagnetic (EM) transient analysis of the coil current distribution. For copper coils like COMPASS Upgrade, the solution must be coupled with a thermal solution to properly include the temperature-dependent resistive effects. The EM transient solution includes the TF magnetic loads, and a stress pass on the results produces in-plane loads. The EM solution chosen requires inclusion of all the conducting and nonconducting regions surrounding the TF coil. With the TF current distribution solved, the addition of the poloidal coils and resulting background fields allows the determination of the out-of-plane loads. Integration of the Joule heating produced from the EM solution produces temperature distributions throughout the shot, which can be read in to a structural pass to include thermal stresses in the evaluation. This is true of both TF and PF temperature prediction with appropriate packing fractions. Currents in the PF coils, with computed background fields, produce Lorentz loads. Time transients of the PF currents produce startup eddy currents in the structures included in the model; inclusion of the vessel and other passive structures allows these to be included in a stress pass. The EM transient solution includes all the elements needed for a disruption analysis with prescribed motions and quench of the plasma. From a model originally intended to simulate current diffusion in the TF coil, the analysis can be refined to be used to predict the performance of most of the components of the tokamak or provide boundary conditions for more detailed submodels. In this article, the use of the current diffusion model for bounding assessments of the COMPASS Upgrade TF, PF, vessel, external structure, and disruption analyses will be presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.

60 APPLIED LIFE SCIENCES↗

Simulations of Silicon Radiation Detectors for High Energy Physics Experiments

Silicon radiation detectors are an integral component of current and planned collider experiments in high energy physics. Simulations of these detectors are essential for deciding operational configurations, for performing precise data analysis, and for developing future detectors. In this white paper, we briefly review the existing tools and discuss challenges for the future that will require research and development to be able to cope with the foreseen extreme radiation environments of the High Luminosity runs of the Large Hadron Collider and future hadron colliders like FCC-hh and SPPC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Hydrogen Mitigation Process Testing at Nevada Solar One

The National Renewable Energy Laboratory (NREL) and Acciona Solar Power (ASP) developed and installed a process that addresses the issue of hydrogen buildup in Acciona's Nevada Solar One power plant. Our method selectively removes hydrogen from the expansion tanks of the power plant to control hydrogen levels in the circulating heat-transfer fluid (HTF). During previous work, we developed a sensor that measures hydrogen partial pressure in the expansion-tank headspace gas. We demonstrated that our sensor measures hydrogen levels over a wide range of partial pressure from 10 mbar down to 0.003 mbar. More recently, we conceived and developed an integrated process module that performs both hydrogen sensing and separating functions. The sensor/separator measures hydrogen partial pressure in the headspace gas in the same way as our original sensor design. Additionally, the integrated module separates hydrogen from the headspace gas to reduce hydrogen to the level needed to maintain the performance of receivers in the collector field. Laboratory testing at NREL showed that the sensor function had an accuracy of +/-7%, and the hydrogen extraction rate for separator function was consistent with our modeling predictions. The primary benefit of this module is its simple design, both in terms of function and incorporation into the HTF subsystem of the power plant. Most recently, NREL and ASP completed installation and initial testing of a mitigation process at ASP's Nevada Solar One power plant in Boulder City, Nevada. In this paper, we report on the completed installation, initial testing, and plans to bring the process to full automation, so that it can be operated unattended on a daily schedule.

chemical elements↗

The Single-Volume Scatter Camera

The multi-institution Single-Volume Scatter Camera (SVSC) collaboration led by Sandia National Laboratories (SNL) is developing a compact, high-efficiency double-scatter neutron imaging system. Kinematic emission imaging of fission-energy neutrons can be used to detect, locate, and spatially characterize special nuclear material. Neutron-scatter cameras, analogous to Compton imagers for gamma ray detection, have a wide field of view, good event-by-event angular resolution, and spectral sensitivity. Existing systems, however, suffer from large size and/or poor efficiency. We are developing high-efficiency scatter cameras with small form factors by detecting both neutron scatters in a compact active volume. This effort requires development and characterization of individual system components, namely fast organic scintillators, photodetectors, electronics, and reconstruction algorithms. In this presentation, we will focus on characterization measurements of several SVSC candidate scintillators. The SVSC collaboration is investigating two system concepts: the monolithic design in which isotropically emitted photons are detected on the sides of the volume, and the optically segmented design in which scintillation light is channeled along scintillator bars to segmented photodetector readout. For each of these approaches, we will describe the construction and performance of prototype systems. We will conclude by summarizing lessons learned, comparing and contrasting the two system designs, and outlining plans for the next iteration of prototype design and construction.

Manfredi, Juan↗

Development and Validation of Southeast Asia Solar Resource Data [Slides]

Lack of access to high-quality, publicly available, time series solar data to inform decisions that will transform energy sectors in Southeast Asia is a challenge. The solution is to level the playing field by offering free, high-quality, robust solar data to inform private sector investment and policymaking. This is done by (1) leveraging deep NREL expertise in atmospheric science, solar resource assessment, high-performance computing, and cloud-based data dissemination, (2) producing and validating high spatial and temporal resolution solar resource data, (3) making data available on the USAID-funded global Renewable Energy Data Explorer platform, (4) providing capacity building for data and applications, and (5) informing future demand-driven tool development.

14 SOLAR ENERGY↗

Neutrinos from Stored Muons (nuSTORM)

The 2020 Update of the European Strategy for Particle Physics (ESPP) (see https://cds.cern.ch/record/2720129 ) recommended that muon beam R&D should be considered a high-priority future initiative and that a programme of experimentation be developed to determine the neutrino cross-sections required to extract the most physics from the DUNE and Hyper-K long-baseline experiments. The ENUBET and nuSTORM collaborations have begun to work within and alongside the CERN Physics Beyond Colliders study group and the international Muon Collider collaboration to carry out a joint, five-year R&D programme to deliver a detailed plan for the implementation of an infrastructure in which: (1) ENUBET and nuSTORM deliver the neutrino cross-section measurement programme identified in the ESPP and allow sensitive searches for physics beyond the Standard Model to be carried out; and in which (2) A 6D muon ionisation cooling experiment is delivered as part of the technology development programme defined by the international Muon Collider collaboration. This document summarises the status of development of the nuSTORM and 6D cooling experiments and identifies opportunities for collaboration in the development of the initiative outlined above.

43 PARTICLE ACCELERATORS↗

High-Fidelity Analysis of EV Integration on Real Utility Feeders in Colorado

Residential electric vehicle (EV) charging has the potential to alter long-held assumptions on load characteristics impacting distribution grid planning, operations, and design standards. This study identifies analysis and control methods to increase the affordability of residential EV charging both for Xcel Energy and their customers. The project also provides solutions for more reliable grid interconnection that can support a reliable utility business model prepared for increasing EV charging load in the coming years. For this project, we referenced Level 2 alternating current (AC) onboard charging profiles for various vehicle models and high-fidelity charging data collected at the experimental setup established at the EV Research Infrastructure Laboratory at the National Renewable Energy Laboratory (NREL). Next, we developed EV adoption models for 2030 and 2040 for the Boulder and Aurora regions in Colorado. Moreover, we evaluated different smart charging control algorithms and compared their performance. We developed time-of-use (TOU)-based and grid-aware active EV charging control methods and integrated them within the study region to understand field impacts. Diving deeper, we selected 10 feeders in Boulder and Aurora for high-fidelity grid modeling down to the house level. We executed detailed grid analysis comparing the smart charge management (SCM) algorithms we developed. Finally, we created a novel tool, Electric Vehicle Infrastructure--Distribution System Integration Tool (EVI-DiST), to integrate all the approaches in a single software environment to provide easy integration, fast simulation, and detailed evaluation capability for utility engineers and other stakeholders.

33 ADVANCED PROPULSION SYSTEMS↗

Mock-Up Waveguide Loop Development toward a Half-Meter Scale Traveling-Wave SRF Cavity

Mock-up waveguide loop development toward a half-meter scale traveling-wave SRF cavity ABSTRACT = Traveling-Wave (TW) technology can push the accelerator field gradient of niobium SRF cavity to 70MV/m or higher beyond the fundamental limit of 50~60MV/m in Standing-Wave regime. The success of TW resonance excitation in a proof-of-principle 3-cell SRF cavity in 2K liquid helium encouraged to advance TW technologies necessary more for future accelerator-scale one. Fermilab has proposed a preliminary RF design of a half-meter scale TW cavity by considering the physical dimensions of existing SRF facilities and the lessons learned from the 3-cell. It consists of a 7-cell structure and a power feedback waveguide (WG) loop with new TW excitation and control schemes such as, double directional coupler and two WG tuners. Mock-up waveguide loop development was launched under Fermilab LDRD program to demonstrate those new RF schemes at a room temperature. Fabrication drawings of a mock-up loop were completed. More details and plans of the development will be presented.

Furuta, Fumio [Fermilab]↗

Overview of the KSTAR experiments toward fusion reactor

The Korean Superconducting Tokamak Advanced Research has been focused on exploring the key physics and engineering issues for future fusion reactors by demonstrating the long pulse operation of high beta steady-state discharge. Advanced scenarios are being developed with the goal for steady-state operation, and significant progress has been made in high ℓ i , hybrid and high beta scenarios with β N of 3. In the new operation scenario called fast ion regulated enhanced (FIRE), fast ions play an essential role in confinement enhancement. GK simulations show a significant reduction of the thermal energy flux when the thermal ion fraction decreases and the main ion density gradient is reversed by the fast ions in FIRE mode. Optimization of 3D magnetic field techniques, including adaptive control and real-time machine learning control algorithm, enabled long-pulse operation and high-performance ELM-suppressed discharge. Symmetric multiple shattered pellet injections (SPIs) and real-time disruption event characterization and forecasting are being performed to mitigate and avoid the disruptions associated with high-performance, long-pulse ITER-like scenarios. Finally, the near-term research plan will be addressed with the actively cooled tungsten divertor, a major upgrade of the NBI and helicon current drive heating, and transition to a full metallic wall.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Essence2.0 Development and Deployment (Final Report)

The objective of the project was to take two core technologies that have been developed under the Recipient’s solid laboratory products and integrate them into a single CyberPhysical awareness platform and complete development on current field-tested prototypes that will extend the integrated capability of the platform. During the final development phase, the Recipient development team and its selected industry partners tested and hardened the platform to ensure resilient and secure operation of the integrated platform. The team also executed substantial field testing and established the framework for defining the organization and/or commercial infrastructure needed to sustain operations and provide readiness for a national scale deployment. The focus of the project was (1) the improvement, refinement, and deployment of technology for the detection of cyber-attacks on utility operational technology (OT) and information technology (IT) networks and assets, including Supervisory Control and Data Acquisition Systems (SCADA) systems; and (2) support for containment and remediation of adversarial threats and actions against those systems and environments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

C$^3$ Demonstration Research and Development Plan

C$^3$ is an opportunity to realize an e$^+$e$^-$ collider for the study of the Higgs boson at $\sqrt{s} = 250$ GeV, with a well defined upgrade path to 550 GeV while staying on the same short facility footprint. C$^3$ is based on a fundamentally new approach to normal conducting linear accelerators that achieves both high gradient and high efficiency at relatively low cost. Given the advanced state of linear collider designs, the key system that requires technical maturation for C$^3$ is the main linac. This white paper presents the staged approach towards a facility to demonstrate C$^3$ technology with both Direct (source and main linac) and Parallel (beam delivery, damping ring, ancillary component) R&D. The white paper also includes discussion on the approach for technology industrialization, related HEP R&D activities that are enabled by C$^3$ R&D, infrastructure requirements and siting options.

43 PARTICLE ACCELERATORS↗