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At least 55 records · Page 3

Jupiter Laser Facility Annual Report, FY 2025

Dear JLF community, I cannot believe I am now entering my third year as JLF director — time definitely flies when you are having fun! FY25 was another pivotal year for the Jupiter Laser Facility, marked by both scientific achievement and growing visibility for our community. Building on the successful reopening and refurbishment of the facility, we continued to demonstrate how JLF drives innovation in high energy density and fusion energy science, laser technology, and workforce development. Across Janus, Titan, and COMET, users executed a diverse portfolio of experiments, from dynamic compression and opacity measurements to laser plasma interactions, laboratory astrophysics, and advanced diagnostics. These efforts are highlighted in this report, including the development of new probes that capture the time evolution of plasmas on a single shot, and diagnostics and platforms that are already impacting experiments at NIF and other large facilities. JLF continues to serve as both a testbed for new ideas and a bridge to larger scale campaigns. FY25 also showcased the broader role of JLF within the Laboratory and the national HED science ecosystem. The NIF JLF User Groups Meeting in February brought nearly 180 participants to Livermore and highlighted the scientific progress made during JLF’s first full year of renewed operations. JLF research and users were recognized with Director’s Institutional Awards and Early and Mid Career awards, underscoring the quality and impact of the work performed here. Our team also contributed prominently to national conversations about laser safety, plasma physics, and inertial fusion energy through invited talks, conferences, and professional society leadership. JLF’s integration with LaserNetUS deepened this year as well. We launched a new technical exchange program across LaserNetUS facilities and kicked it off with a JLF team visit to the BELLA Center at Lawrence Berkeley National Laboratory. These exchanges are strengthening operations, sharing best practices, and improving the user experience across the network. Filming for the LaserNetUS “Behind the Scenes” series and participation in the annual LaserNetUS meeting further increased the visibility of our facility and our users. At the same time, JLF continues to play a central role in ambitious new programs, such as the Big Aperture Thulium laser effort funded through one of the DOE Office of Science Microelectronics Science Research Centers, which will use JLF infrastructure to explore next generation high rep rate lasers for EUV and x-ray source development. A core part of our mission remains training the next generation of scientists. In FY25, we welcomed another cohort of summer students, who joined experimental teams on Titan and presented their research at LLNL’s student poster symposium and national inertial fusion energy meetings. JLF users and early career scientists showcased their work at conferences across the country, highlighting experiments performed at the facility. These hands on experiences, and the mentoring provided by our staff and user teams, are central to JLF’s identity as a true user facility. Finally, FY25 reinforced JLF’s role as a focal point for partnerships and outreach. We hosted visits from international collaborators, science leaders, and we shared the story of the facility through venues such as the Big Ideas Lab podcast. These interactions help connect our work to a broader scientific and policy audience and open new pathways for collaboration. As we look ahead, the combination of refurbished hardware, new capabilities like STILETTO and enhanced short pulse performance on Titan, strong partnerships across LLNL and LaserNetUS, and a growing user community positions JLF for an even more ambitious program in the coming years. I am deeply grateful to our technical and operations staff for their dedication, to our LLNL partners for their continued support, and to our users for bringing bold, creative ideas to the facility. I look forward to more experiments, capabilities, partnerships, and groundbreaking science in the years to come! With brightest regards, Félicie Albert, JLF Director.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Emissivity measurements of CuCrZr alloy

The Facility for Rare Isotope Beams (FRIB) heavy-ion accelerator, in user operation since 2022, produces rare isotope beams via interactions of high-intensity stable ion beams with a graphite production target. Approximately 20–40% of the primary beam power is deposited in the target, while the remaining 60–80% is absorbed by the beam dump. The minichannel beam dump (MCBD), currently operated at 20 kW and designed for operation up to 50 kW, uses CuCrZr alloy absorber plates. Thermal validation and thermal cycling tests of the MCBD were conducted at the Applied Research Laboratory (ARL) at Pennsylvania State University. Temperature measurements were obtained from an infrared (IR) camera. Since accurate temperature determination requires reliable emissivity values, the emissivity of CuCrZr was measured using the IR camera validated against thermocouple reference temperatures up to approximately 650 °C. The measurements were conducted under a vacuum level of approximately 10 -5 torr to minimize emissivity variations due to surface oxidation. The emissivity of CuCrZr was determined to be 0.057 ± 0.009 using a constant fit to the measured data over the surface temperature range from 100–650 °C.

Accelerator Subsystems and Technologies↗

The FRIB Decay Station: New Horizons with Rare Isotopes

In May 2022, the Facility for Rare Isotope Beams (FRIB), located on the campus of Michigan State University (MSU), began delivering exotic isotopes to an international community of scientists. New discoveries are now being reported from radioactive decay of neutron-rich nuclei near N = 20 and N = 28.FRIB is expected to produce roughly 80% of the unstable or radioactive isotopes predicted to exist up to uranium (Z = 92). The new user facility is supported by the U.S. Department of Energy, and it is operated by MSU. A high-power superconducting linear accelerator, shaped like a paper-clip, drives the production of these rare isotopes by colliding stable nuclei moving at half the speed of light with a rotating, water-cooled graphite tar-get. These collisions cause the primary stable beam to fragment into a wide variety of unstable nuclei, which can be subsequently filtered through a multistage magnetic separator, the Advanced Rare Isotope Separator, and transported to one of several experimental stations. The FRIB Decay Station initiator (FDSi) (see Figure 1) was developed to enable comprehensive radio-active decay studies of the exotic nuclei produced by FRIB and it was used in the first two experiments in 2022. Further, the FDSi is a highly reconfigurable multidetector system with two focal planes (FP1 for discrete spectroscopy and FP2 for total absorption spectroscopy) that can be optimized for the specific science goals of each experiment. It is designed, built, and operated by a community of users with the sup-port of U.S. funding agencies, including the Department of Energy and National Science Foundation.

07 ISOTOPE AND RADIATION SOURCES↗

Transmission electron microscopy of ion irradiated ODS MA956 samples

Oxide dispersion strengthened (ODS) alloys are promising candidate materials for the next generation advanced nuclear reactors due to their superior irradiation resistance and mechanical properties. To better understand the effect of irradiation on MA956, it is essential to study higher dose (50-100 dpa) samples, so that the general trend of microstructural evolution and the resulting radiation-hardening can be deduced. Currently, ion irradiations are considered the only way to achieve doses beyond ~50 dpa in a practical time frame relevant to alloy and welding development programs. This dataset contains TEM characterization results of ODS MA956 samples that were ion irradiated under different conditions: Sample #5 (1.25 dpa at 190℃); Sample #9 (50 dpa at 190℃); Sample #15 (1.25dpa under 320℃); and Sample 17 (25 dpa at 320℃). TEM characterization focused on the irradiation induced defects (dislocation lines and loops) using the on-zone axis bright field STEM technique. These data were collected using a FEI Tecnai G2 F30 S/TEM at Microscopy and Characterization Suite (MaCS) at Center for Advanced Energy Studies (CAES), Idaho Falls, ID. This project (ion irradiation and TEM studies) was supported by the U.S. Department of Energy, Office of Nuclear Energy under DOE Idaho Operations Office Contract DE-AC07-05ID14517 as part of Nuclear Science User Facilities award #18-14784 (PI: Ramprashad Prabhakaran, PNNL).

Prabhakaran, Ramprashad↗

North Slope of Alaska XSAPR b1 Data Processing Report: April 2024-April 2025

The North Slope of Alaska (NSA) atmospheric observatory, operated by the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) User Facility, is a measurement site in the Arctic that has been collecting crucial atmospheric data for more than 25 years. The central facility located in Utqiaġvik, Alaska (formerly known as Barrow) hosts a suite of instruments that are used to better understand arctic processes, which are often not well represented in earth system models. The NSA site sits only a few kilometers from the Arctic Ocean, which also makes it a prime location to study complex ocean-atmosphere-ice interactions. Arctic cloud and precipitation processes are also of scientific interest, and remote-sensing instruments including radars are a key component of the NSA instrument suite. One of the radars at NSA is the X-band Scanning ARM Precipitation Radar (XSAPR). This report evaluates one year of recent XSAPR data from April 2024 through April 2025 and details the process of generating b1-level data. This analysis marks the first effort by ARM staff to quality-control NSA XSAPR data with the goal of routinely producing b1-level data in the future depending on radar operations.

54 ENVIRONMENTAL SCIENCES↗

Optimizing inference of segmentation on high-resolution images in MLExchange

MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically segment high-resolution scientific images with minimal manual annotation effort. In this work, we share code optimizations that significantly speed up the segmentation inference workflow of large data in short time. By optimizing the sequence of CPU-GPU data transfers and introducing CPU parallelization to key operations, we improve the per-device, per-image frame computational efficiency and observe close to 3×$$\times$$ speedup over the original segmentation inference workflow run time when utilizing a single GPU. Further adaptations enabling multi-GPU inference yield more than 40×$$\times$$ speedup with 100 GPUs compared to the optimized single GPU inference workflow. This acceleration of the segmentation inference workflow will provide MLExchange users with easy access to segmentation results with little wait time.

Lu, Shizhao↗

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗

TRACER-iso Field Campaign Report

The TRACER-iso project took place from June 1 to September 30, 2022, as part of the intensive operational period (IOP) of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s TRacking Aerosol Convection interactions ExpeRiment (TRACER) at the main project site at the La Porte, Texas, municipal airport. The project sought to use measurements of the stable isotopic composition of water vapor to better understand cloud-aerosol interactions in deep convection in a polluted urban setting.

54 ENVIRONMENTAL SCIENCES↗

Assessment of North Slope of Alaska (NSA) Snow Monitoring Arrays

Arrays of instruments for monitoring winter precipitation (snowfall) and snow cover on the ground installed in 2017 at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s North Slope of Alaska (NSA) C1 and at Oliktok Point sites became operational in 2018. In 2022, the instruments from Oliktok Point were moved to NSA E12, about 5.4 km south of NSA C1, where two arrays now operate. The instrument arrays monitor wind speed and direction, snow depth at multiple locations, the horizontal flux of blowing snow, and the number and fall speed of hydrometeors. The arrays are monitored using digital cameras. Collectively, the instruments produce a wealth of data on falling and accumulated snow, but, as with any instrument array, some data are more reliable and accurate than others. In this document, we present our findings on the efficacy, accuracy, and reliability of each type of instrument. Overall, a key finding is that no single instrument provides sufficient information to determine the source of falling snow particles nor the cause of changes in snow depth. However, if used in concert, the instruments produce a reliable understanding of the processes affecting the snow cover depth distribution and the true winter precipitation.

47 OTHER INSTRUMENTATION↗

1000 Soils Pilot Dataset, version 8, May 2025

This record hosts data generated by the 1000 Soils Pilot. Data will be updated as more become available. Please see the most recent data upload for current data. A beta visualization tool is available for some data types at https://shinyproxy.emsl.pnnl.gov/app/1000soils. Please submit any suggestions or comments through the 'contact' tab. We are actively working to improve visualizations and value all feedback. Data completed include: Geochemistry, texture, respiration, and enzyme activities FTICR-MS organic matter chemistry Microbial biomass C and N TOC/TDN of water-extractable OM X-ray computed tomography (derived metrics available here, raw data available upon request) Metagenomes; a variety of data formats are available upon request Soil hydraulic properties Data in progress: LC-MS/MS in development, timeline TBD, inquire for status 1000S_processed_BGC_summary.csv contains all available biogeochemical data; microbial biomass C and N; and TOC/TDN of water-extractable OM; and 1000S_Tomography.xslx contains a summary of data generated via X-ray computed tomography. icr_v2_corems2.csv contains FTICR-MS data processed by CoreMS version 2. These data are merged by formula across instrument runs to enable cross-sample comparisons. Technical replicates are merged by retaining peaks present in 2 out of 3 replicates. 1000Soils_Metadata_Site_Mastersheet_v1.csv contains site information. Soil Hydraulics_corrected_02042025.xlsx contains soil hydraulics information. Readme File_v4.xlsx is the readme file. Please contact the MONet project (monet.emsl@pnnl.gov) or Emily Graham (emily.graham@pnnl.gov) with questions. The following file and all raw data are available upon request: icr_by_mass_for_single_sample_analysis_only.csv contains FTICR-MS data processed by CoreMS and is intended for usage in the calculation of biochemical transformations within samples only. These data are not acceptable for cross-sample comparison of masses because they are from multiple instrument runs. For more information, please see: https://www.emsl.pnnl.gov/monet and https://sc-data.emsl.pnnl.gov/monet Acknowledgment: Soil data were provided by the Molecular Observation Network (MONet) at the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), a DOE Office of Science user facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830. The work (proposal: 10.46936/10.25585/60008970) conducted by the U.S. Department of Energy, Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science user facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231. The Molecular Observation Network (MONet) database is an open, FAIR, and publicly available compilation of the molecular and microstructural properties of soil. Data in the MONet open science database can be found at https://sc-data.emsl.pnnl.gov/.

biogeochemistry↗

Nuclear Physics Network Requirements Review (Final Report)

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. ESnet interconnects DOE national laboratories, user facilities, and major experiments so that scientists can use remote instruments and computing resources as well as share data with collaborators, transfer large datasets, and access distributed data repositories. ESnet is specifically built to provide Between July 2023 and October 2023, ESnet and the Nuclear Physics program (NP) of the DOE SC organized an ESnet requirements review of NP-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the NP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

97 MATHEMATICS AND COMPUTING↗

2024 Atmospheric Radiation Measurement (ARM) Annual Report

This report provides an overview of the Atmospheric Radiation Measurement (ARM) user facility and a sample of achievements for fiscal year 2024 (FY2024). Strong collaborations between nine DOE national laboratories enable ARM to successfully operate in remote locations around the world. This unique partnership supports the DOE mission to provide for the energy security of the nation. Without the support of the following laboratories, ARM would not be the state-of-the-art facility that it is today. ARM is a multi-laboratory, U.S. Department of Energy (DOE) Office of Science user facility and a key contributor to national and international atmospheric and climate research efforts. ARM offers scientists cutting-edge, ground-based observatories, aerial observation capabilities, and high-performance computing. ARM’s capabilities have enabled more than 30 years of continuous measurements of cloud and aerosol properties and their effects on Earth’s energy balance.

54 ENVIRONMENTAL SCIENCES↗

Compressed Air Scoping Tool Teaches Energy Efficiency Best Practices

Standards and practices around compressed air systems are evolving as previous golden rules become outdated and unhelpful. One of the most referenced guidelines gives us the perfect example: The previous rule of thumb for compressed air storage – 1-3 gal/cfm1,2 – is now accepted to be 3-5 gal/cfm. This change in recommended storage is only one of many in the industry, resulting in industrial users lost in a sea of contradictory best practices. To help combat confusion and poor information sharing, a collaborative effort between Oak Ridge National Laboratory (ORNL) and the Department of Energy’s (DOE’s) Better Plants program created the Compressed Air (CA) Scoping Tool with the most up-to-date best practices in the industry (as of 2023). It can be used as a nonbiased, one-stop shop for best practice recommendations. The Excelbased tool is designed to be an initial step in understanding the operation of a compressed air system, a baselining tool enabling users to comprehend various aspects of a facility’s system from the production of compressed air to its application by end users.

42 ENGINEERING↗

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Technoeconomic Insights into Metal Hydrides for Stationary Hydrogen Storage

Abstract Metal hydrides (MHs) are promising candidates for storing hydrogen at ambient conditions at high volumetric energy densities. Recent developments suggest hydride‐based systems can cycle and operate at favorable pressures and temperatures that work well with fuel cells used in stationary power applications. In this study, we present a comprehensive design and cost analysis of MH‐based long duration hydrogen storage facilities for a variety of power end users (0 to 20 megawatts (MW) supplied over 0 to 100 hours), to offer insights on technical targets for material development and operation strategies. Our findings indicate that hydride‐based storage systems hold significant size advantage in physical footprint, requiring up to 65% less land than 170‐bar compressed gas storage. Metal hydride systems can be cost competitive with 350‐bar compressed gas systems, with TiFe 0.85 Mn 0.05 achieving $0.45/kWh and complex MH Mg(NH 2 ) 2 ‐2.1LiH‐0.1KH achieving $0.38/kWh. Extending charging times and increasing operating cycles significantly reduce levelized cost of storage, especially for complex MHs. Key strategies to further enhance the competitiveness of MHs include leveraging waste heat from fuel cells, reducing use of critical minerals, and achieving MH production costs of US$10/kg.

08 HYDROGEN↗

Experimental Methods for the Performance Test of Environment-Assisted Cracking (EAC) in Nuclear Industry and Examples through NSUF

Environmentally assisted cracking (EAC) is a critical failure mode in nuclear reactor environments, where materials are exposed to high temperatures, radiation, and corrosive media. Specific EAC studies in nuclear environments include stress corrosion cracking in primary water chemistry, irradiation-assisted stress corrosion cracking (IASCC) under neutron exposure, and hydrogen induced cracking (HIC) in reactor pressure vessels. The experimental methodologies of EAC include slow strain rate testing (SSRT), constant load, and cyclic loading techniques, conducted in autoclaves designed to simulate the reactor’s operational environments. This paper highlights several experimental case studies, especially on IASCC, conducted within the Nuclear Science User Facilities (NSUF).

36 - MATERIALS SCIENCE↗

Post-DTL Beam Delivery

Ensuring that the beam delivered from the upgraded Front-End (FE) meets the Key Performance Parameters (KPPs) at each user facility is critical to the success of the LANSCE Accelerator Modernization Project (LAMP). For a high-intensity, multi-user facility like LANSCE, compliance with beam loss and radiation thresholds is as important as the charge delivered to each target. While early LAMPF/LANSCE operations relied on iterative tuning to minimize losses from beam halo and tail particles, the new FE may introduce different beam distributions and loss modes—making predictive modeling essential. To manage this, the F2E (Front-End to End) effort is developing detailed particle-tracking models that reflect realistic beamline conditions, including halo formation and expected diagnostic readings. These "snapshot" simulations aim to benchmark live machine performance at a given moment. This will help quantify how beam quality from the new FE will propagate downstream through the facility. Only by validating these models can we confidently assess and mitigate the potential impacts of the LAMP FE on beam delivery. Post-DTL, the beam splits to serve five major user facilities. Historically, low-energy beam transport has been modeled using TRACE, and higher-energy sections with TRANSPORT. These have now been unified into MAD-X format and validated with codes such as Elegant, pyOrbit, XSuite, Impact-Z, and HPSim. The primary focus now is on accurate modeling of full particle distributions (including beam halo) as they traverse the accelerator and beamlines to each experimental station. All models are at various stages of validation with empirical data.

43 PARTICLE ACCELERATORS↗

Monitoring Helium Cryogen Usage with iFix Software at IB1 Test Facility

At the IB1 facility of the Applied Physics and Superconducting Technology Directorate within the Cryogenic Division at Fermilab, Liquid Helium is essential to the testing activities. The test stands require large amounts of Helium to test the thermal and superconducting cavities and magnets. The Liquid Helium that enters the test stand is monitored through a series of valves and controls to determine the start and stop for monitoring time. There are 3 phases of cryogen usage that must be considered for each stand, cooldown, warming and overnight mode. With a new system in place, cold hour tracking will be more objective and precise within each test stand ensuring better calculations for User s fees which are charged to various groups that employ the facility and aid replenishment of Helium and continual operation at IB1.

Trillo, Angelica↗