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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

China’s Booming Plug-in Electric Vehicle Market—How Will It Continue?

The plug-in electric vehicle (PEV) is recognized as a pivotal component in transportation energy security and technology innovation in China. With government incentives and substantial capital investments, the Chinese PEV market has become one of the largest in the world. However, is this market boom ephemeral or will it be continuing? This chapter provides a general review of the Chinese PEV market mainly from the consumer per-spective, by summarizing the characteristics of the vehicle policies and consumer concerns in the PEV market in recent years. Moreover, it intro-duces detailed examples on how the policies, technologies, and infrastruc-ture shape the market. In addition to employing large amounts of reliable data from the China Automotive Technology and Research Center for the review, the authors also adopted a quantitative analysis tool—the New Energy and Oil Consumption Credits model—to project the potential in-dustrial impacts of the government policies in multiple scenarios. Based on the findings, it seems that the prosperity of the PEV market, incentivized by the government, will continue in the short run. The direct subsidies to PEV from the government will gradually phase out, and the supply-side push policies such as the dual-credit policy and other charging infrastruc-ture incentives will work as the invisible hand to force the automakers to invest more in the PEV segment. However, in the long run, innovation in PEV technology and business models, together with consumer acceptance will be the major factors that can sustain further market growth.

Ou, Shawn↗
DOE OSTI2020

Gamma-Ray Mirrors (Quarterly Progress Report Q2 FY20 Jan-Mar 2020)

During the second quarter of FY20, the optic design, or Phase II Task 2 of the project, continued. Our investigation of two full revolution shell designs has shown that the radius of the optic would be small to maintain throws to reasonable limits. These efforts are informing our alternative segmented optics design in Q3. We have also started investigating the Kirkpatrick-Baez Optic as a backup option.

36 MATERIALS SCIENCE↗
DOE OSTI2025

LABQ3: Bayesian method for quantification of mineral compositions and nano-scale elemental mapping of 3D synchrotron XCT data

Quantitative analysis of mineral compositions is essential in understanding geochemical, mineralogical and environmental processes. Fine-resolution 3D imaging is widely done using synchrotron X-ray computed tomography (XCT), but existing analyses are limited to visualization and segmentation. This paper presents a new method, Linear Attenuation Bayesian Quantitative 3D-mapper (LABQ3), based on the linearity of X-ray attenuation with respect to elemental concentrations. To address the random variability in attenuation measurements, LABQ3 employs Bayesian decision theory to minimize classification error, using reference attenuation distributions from scans of pure mineral standards. To demonstrate LABQ3 and test its performance, we studied precipitated carbonate samples. XCT scans were done at multiple energies using the transmission X-ray microscope (TXM) at beamline 32-ID-C of the Advanced Photon Source at Argonne National Laboratory. The reconstructed 3D images have a voxel size of 20 nm. Analyses revealed rich nano-scale compositional heterogeneity within individual particles. A mixture of calcium and cadmium produced an overall stoichiometric composition of (Ca 0.78 ,Cd 0.22 )CO 3 , with some voxels containing nearly pure CdCO 3 . The addition of zinc led to an overall stoichiometric composition of 33% Ca, 28% Cd, 39% Zn, with a nearly pure CaCO 3 core and compositional zonation through the rim. These compositional gradients are related to temporal sequences of carbonate mineral formation where Cd precipitated at the beginning in (Ca,Cd)CO 3 , while Cd and Zn precipitated at the end in (Ca, Cd,Zn)CO 3 . Results differ from bulk analyses using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), showing that LABQ3 provides particle-specific insights. LABQ3 distinguishes itself by quantifying chemical compositions along a continuum, making it different from XCT analyses based on segmentation. LABQ3 allows simultaneous acquisition of morphology and chemical composition in 3D, facilitating the interpretation of chemical gradients of trace elements, quantification of solid solution compositions, inferences about temporal sequences of mineral precipitation, and addressing other concerns about solid-phase chemistry.

58 GEOSCIENCES↗
DOE OSTI2024

Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile

Abstract The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.

58 GEOSCIENCES↗
DOE OSTI2022

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, an approach based on a combination of deep learning-based semantic segmentation, rotationally invariant variational autoencoder (VAE), and non-negative matrix factorization to enable learning of a latent space representation of the data with multiple real-space rotationally equivalent variants mapped to the same latent space descriptors is introduced. By varying the size of training sub-images in the VAE, the degree of complexity in the structural descriptors is tuned from simple domain wall detection to the identification of switching pathways. Importantly, this yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep-learning methods to improve learned descriptors of physical phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Graph Neural Networks for Charged Particle Tracking on FPGAs

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph—nodes represent hits, while edges represent possible track segments—and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml , for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.

Elabd, Abdelrahman↗
DOE OSTI2026

Three-dimensional high-content imaging of unstained soft tissue with subcellular resolution using a laboratory-based X-ray microscope

With increasing interest in studying biological systems across spatial scales—from centimeters down to nanometers—histology continues to be the gold standard for tissue imaging at cellular resolution, providing an essential bridge between macroscopic and nanoscopic analysis. However, its inherently destructive and two-dimensional nature limits its ability to capture the full three-dimensional complexity of tissue architecture. Here, we show that phase-contrast X-ray microscopy can enable three-dimensional virtual histology with subcellular resolution. This technique provides direct quantification of electron density without restrictive assumptions, allowing for direct characterization of cellular nuclei in a standard laboratory setting. By combining high spatial resolution and soft tissue contrast, with automated segmentation of cell nuclei, we demonstrated virtual Hematoxylin and Eosin (H&E) staining using machine learning-based style transfer, yielding volumetric datasets compatible with existing histopathological analysis tools. Furthermore, by integrating electron density and the sensitivity to nanometric features of the dark field contrast channel, we achieve stain-free, high-content imaging capable of distinguishing nuclei and extracellular matrix.

3D virtual histology↗
DOE OSTI2020

Multiamorphous Phases in Diketopyrrolopyrrole-Based Conjugated Polymers: From Bulk to Ultrathin Films

The glass transition temperature (T g ) of conjugated polymers is a crucial physical parameter that governs their mechanical and electrical properties for applications from sensor technology to skin-like electronics. Despite some well-developed methods that exist for detecting T g of conjugated polymers, there is still a need for the combination of advanced calorimetric methodologies to characterize T g in a broad range of temperatures and scales from bulk to nanometer-thin films. In this work, alternating current (ac) chip calorimetry and flash differential scanning calorimetry (flash DSC) are applied to successfully identify the Tgs and segmental relaxations of conjugated polymers. The detected relaxations were interpreted, in agreement with existing observations in similar systems, as a side-chain relaxation (low temperature), mobile amorphous fractions (MAF), and rigid amorphous fractions (RAF). T g s of MAF and RAF increase and decrease with increasing size of fused thiophene units in the backbone, respectively. The T g s of diketopyrrolopyrrole-based conjugated polymer thin films are also identified by the same method, with both T g s showing a decreased value compared to those of bulk samples. Furthermore, the weight fractions of MAF and RAF with various film thicknesses are qualitatively analyzed based on the heat capacity step of each amorphous fraction during the glass transition. This facile method gives insights into the physical nature of amorphous phases in conjugated polymers for better revealing their structure-property relationships, which will guide the designs of new materials with optimized electrical and mechanical properties in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Effective Li-Ion Transport Quantification in Composite Cathodes for All-Solid-State Batteries via Multiscale Modeling and Experiments

The tortuosity factor of composite cathodes significantly affects the rate performance of all-solid-state batteries (ASSBs) and has significant differences from systems with liquid electrolytes. Here, in this work, we report a simulation-experiment combined approach that quantifies the effective Li-ion transport in an ASSB composite cathode, which links tortuosity factor on ∼ μm scale to terminal voltage during cycling at the cell level (on ∼ cm scale). Two independent approaches of tortuosity factor quantification are considered: fitting electrochemical cycling data and verifying at different cycling rates and calculating from segmented tomography images, with the tortuosity factor quantified from both methods reaching self-consistency. The simulated terminal voltage using the quantified tortuosity factor has a small relative error of <3% compared to the experimental measurements. We find a significantly reduced value of the Bruggeman exponent of the catholyte phase (1.75), and using shape analysis, we show that rod-shaped catholyte particles play an important role in lowering the tortuosity factor.

Yao, Archie Mingze [Univ. of Michigan, Ann Arbor, ↗
DOE OSTI2023

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Phase-contrast 3D tomography of HeLa cells grown in PLLA polymer electrospun scaffolds using synchrotron X-rays

Advanced imaging is useful for understanding the three-dimensional (3D) growth of cells. X-ray tomography serves as a powerful noninvasive, nondestructive technique that can fulfill these purposes by providing information about cell growth within 3D platforms. There are a limited number of studies taking advantage of synchrotron X-rays, which provides a large field of view and suitable resolution to image cells within specific biomaterials. In this study, X-ray synchrotron radiation microtomography at Diamond Light Source and advanced image processing were used to investigate cellular infiltration of HeLa cells within poly L-lactide (PLLA) scaffolds. This study demonstrates that synchrotron X-rays using phase contrast is a useful method to understand the 3D growth of cells in PLLA electrospun scaffolds. Two different fiber diameter (2 and 4 µm) scaffolds with different pore sizes, grown over 2, 5 and 8 days in vitro , were examined for infiltration and cell connectivity. After performing visualization by segmentation of the cells from the fibers, the results clearly show deeper cell growth and higher cellular interconnectivity in the 4 µm fiber diameter scaffold. Finally, this indicates the potential for using such 3D technology to study cell–scaffold interactions for future medical use.

3D culture↗
DOE OSTI2024

Quantitative Insight to Fission Gas Pores Distribution in Irradiated Annular U-10Zr Metallic Fuel Using Machine Learning

Metallic fuels, particularly U-10Zr and its performance in reactor irradiation conditions, have been thoroughly investigated and are a promising candidate for next-generation sodium-cooled fast spectrum nuclear reactors. Irradiation in reactors can lead to the formation of fission gas and increased pore formation which can significantly impact fuel performance. Due to the large number of pores and various phases formed in metallic fuel during irradiation, a quantitative description of fission gas pores as a function of irradiation conditions is not yet available, undermining the fidelity of fuel performance modeling to support fuel qualification. It has been difficult to clearly detect pore boundaries and distinguish matrix phases from fission gas pores using optical microscopy by using simple threshold methods working with low magnification images. The pre-trained deep learning model for fission gas pore detection was applied to ~10,260 high magnification scanning electron microscopy images. The model increased the accuracy of fission gas pore segmentation to obtain statistical features, which cannot be processed manually. A pre-trained decision tree model was used to classify pores as isolated or connected pores, providing new insight into the correlation between the movement of lanthanides, solid fission products, and the radial temperature gradient developed in fuel irradiation conditions. This paper emphasizes the potential that artificial intelligence-based machine learning models have to accelerate qualification and support nuclear fuel development.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Application of sulfur SAD to small crystals with a large asymmetric unit and anomalous substructure

The application of sulfur single-wavelength anomalous dispersion (S-SAD) to determine the crystal structures of macromolecules can be challenging if the asymmetric unit is large, the crystals are small, the size of the anomalously scattering sulfur structure is large and the resolution at which the anomalous signals can be accurately measured is modest. Here, as a study of such a case, approaches to the SAD phasing of orthorhombic Ric-8A crystals are described. The structure of Ric-8A was published with only a brief description of the phasing process [Zeng et al. (2019), Structure , 27 , 1137–1141]. Here, alternative approaches to determining the 40-atom sulfur substructure of the 103 kDa Ric-8A dimer that composes the asymmetric unit are explored. At the data-collection wavelength of 1.77 Å measured at the Frontier micro-focusing Macromolecular Crystallography (FMX) beamline at National Synchrotron Light Source II, the sulfur anomalous signal strength, |Δ ano |/σΔ ano ( d ′′/sig), approaches 1.4 at 3.4 Å resolution. The highly redundant, 11 000 000-reflection data set measured from 18 crystals was segmented into isomorphous clusters using BLEND in the CCP 4 program suite. Data sets within clusters or sets of clusters were scaled and merged using AIMLESS from CCP 4 or, alternatively, the phenix.scale_and_merge tool from the Phenix suite. The latter proved to be the more effective in extracting anomalous signals. The HySS tool in Phenix , SHELXC / D and PRASA as implemented in the CRANK 2 program suite were each employed to determine the sulfur substructure. All of these approaches were effective, although HySS , as a component of the phenix.autosol tool, required data from all crystals to find the positions of the sulfur atoms. Critical contributors in this case study to successful phase determination by SAD included (i) the high-flux FMX beamline, featuring helical-mode data collection and a helium-filled beam path, (ii) as recognized by many authors, a very highly redundant, multiple-crystal data set and (iii) the inclusion within that data set of data from crystals that were scanned over large ω ranges, yielding highly isomorphous and highly redundant intensity measurements.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Reduced volume of diabetic pancreatic islets in rodents detected by synchrotron X-ray phase-contrast microtomography and deep learning network

The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3.2 mm × 15.97 mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After quantifying the volumes of the islets, we found that the number of larger islets (=>1500 μm 3 ) was reduced by 2-fold (wt 1004 ± 94 vs GK 419 ± 122, P < 0.001) in chronically developed diabetic GK rat, while in STZ-treated diabetic mouse the large islets were decreased by half (189 ± 33 vs 90 ± 29, P < 0.001) compared to the untreated mice. Our study provides a label-free tool for detecting and quantifying pancreatic islets in situ. It implies the possibility of monitoring the state of pancreatic islets in vivo diabetes without labelling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Néel domain walls with bistable chirality in a perpendicularly magnetized ferrimagnetic insulator

Field-free spin-orbit torque-driven domain wall motion in magnetic thin films with perpendicular magnetic anisotropy (PMA) requires the domain walls to have Néel character. Conventionally, Néel domain walls are stabilized by the Dzyaloshinskii-Moriya interaction (DMI) in ultrathin films. Here, in a europium iron garnet thin film with PMA and an additional uniaxial in-plane anisotropy, we demonstrate two bistable Néel domain wall states in the absence of DMI, and the capability to toggle the wall states with an in-plane field pulse and consequently their directions of motion under a current pulse. We present a phase diagram for the bistable Néel domain wall states as a function of in-plane field pulse width and amplitude. By fitting the experimental data to an analytical model of Néel wall reversal through the nucleation and propagation of Bloch lines, we extract the length of the initial reversed domain wall segment and Bloch line nucleation energy barrier. Current-driven motion of in-plane anisotropy stabilized Néel walls is qualitatively different from that of DMI-stabilized ones owing to the different symmetry of the effective fields that stabilize the Néel configuration. Furthermore, we present a proof of principle demonstration for 2-bit random number generation based on the stochastic reversal of domain wall chirality. These results provide critical insight into the topological energy barrier of Bloch lines and identify paths towards domain wall-based memory and computing devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Design, Prototyping, Fabrication and Test of the Mu2e AC-Dipole Magnet

Fermilab Magnet Systems is building three High-frequency AC-Dipole Magnets for the Mu2e experiment at Fermilab. These magnets are composed of three single-loop one-meter-long ferrite loaded segments. The excitation consists of a copper tube, which is also used as a means for its cooling with the inherited challenges of Voltage and Frequency uncoupling. These magnets are designed to operate either at 300 kHz or 4.4 MHz via resonance tuning. Following the completion of the design, prototyping, and analysis phases, all ferrites and other components were procured, and the magnets were fabricated to meet vacuum compatibility requirements using strict procedures. Each magnet undergoes thorough baking and testing before being installed in the experiment. This paper discusses the magnets' role in proton background suppression as it represents a unique and essential device for the Mu2e experiment at Fermilab. We present the magnet design choices, modeling approach, and the challenges encountered during fabrication. Additionally, we outline the power supply driving mechanism, the testing that was performed, and our results.

Elementi, Luciano [Fermilab] (ORCID:00000002766372↗
DOE OSTI2020

Installation of the Dark Energy Spectroscopic Instrument at the Mayall 4-meter telescope

The Dark Energy Spectroscopic Instrument (DESI) is a Stage IV ground-based dark energy experiment that will measure the expansion history of the Universe using the Baryon Acoustic Oscillation technique. The spectra of 35 million galaxies and quasars over 14000 square degrees will be measured during the life of the experiment. We describe the installation of the major elements of the instrument at the Mayall 4m telescope, completed in late 2019. The previous prime focus corrector, spider vanes, and upper rings were removed from the Mayall’s Serrurier truss and replaced with the newlyconstructed DESI ring, vanes, cage, hexapod, and optical corrector. The new corrector was optically aligned with the primary mirror using a laser tracker system. The DESI focal plane system was integrated to the corrector, with each of its ten 500-fiber-positioner petal segments installed using custom installation hardware and the laser tracker. Ten DESI spectrographs with 30 cryostats were installed in a newly assembled clean room in the Large Coude Room. The ten cables carrying 5000 optical fibers from the positioners in the focal plane were routed down the telescope through cable wraps at the declination and hour angle axes, and their integral slitheads were integrated with the ten spectrographs. The fiber view camera assembly was installed to the Mayall’s primary mirror cell. Servers for the instrument control system replaced existing computer equipment. The fully integrated instrument has been commissioned and is ready to start its operations phase.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Deployment and validation of predictive 6-dimensional beam diagnostics through generative reconstruction with standard accelerator elements

Understanding the 6-dimensional phase space distribution of particle beams is essential for optimizing accelerator performance. Conventional diagnostics such as use of transverse deflecting cavities offer detailed characterization but require dedicated hardware and space. Generative phase space reconstruction (GPSR) methods have shown promise in beam diagnostics, yet prior implementations still rely on such components. Here we present the first experimental implementation and validation of the GPSR methodology, realized by the use of standard accelerator elements including accelerating cavities and dipole magnets, to achieve complete 6-dimensional phase space reconstruction. Through simulations and experiments at the Pohang Accelerator Laboratory X-ray Free Electron Laser facility, we successfully reconstruct complex, nonlinear beam structures. Furthermore, we validate the methodology by predicting independent downstream measurements excluded from training, revealing the reconstruction closely resembling ground truth. This advancement establishes a pathway for predictive diagnostics across beamline segments while reducing hardware requirements and expanding applicability to various accelerator facilities.

Kim, Seongyeol [Pohang Univ. of Science and Techno↗