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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Common pressure vessel development for the nickel hydrogen technology

The design of a pressure vessel nickel hydrogen cell is described. The cell has the following key features: it eliminates electrolyte bridging; provides for independent electrolyte management for each unit stack; provides for independent oxygen management for each unit stack; has good heat dissipation; has a mechanically sound and practical interconnection; and has the maximum in common with state of the art individual pressure vessel technology.

Holleck, G.↗
NASA NTRS2008

Ares I-X Flight Test - On the Fast Track to the Future

In less than two years, the National Aeronautics and Space Administration (NASA) will launch the Ares I-X mission. This will be the first flight of the Ares I crew launch vehicle, which, together with the Ares V cargo launch vehicle, will send humans to the Moon and beyond. Personnel from the Ares I-X Mission Management Office (MMO) are finalizing designs and fabricating vehicle hardware for an April 2009 launch. Ares I-X will be a suborbital development flight test that will gather critical data about the flight dynamics of the integrated launch vehicle stack; understand how to control its roll during flight; better characterize the severe stage separation environments that the upper stage engine will experience during future flights; and demonstrate the first stage recovery system. NASA also will modify the launch infrastructure and ground and mission operations. The Ares I-X Flight Test Vehicle (FTV) will incorporate flight and mockup hardware similar in mass and weight to the operational vehicle. It will be powered by a four-segment Solid Rocket Booster (SRB), which is currently in Shuttle inventory, and will include a fifth spacer segment and new forward structures to make the booster approximately the same size and weight as the five-segment SRB. The Ares I-X flight profile will closely approximate the flight conditions that the Ares I will experience through Mach 4.5, up to approximately130,OOO feet and through maximum dynamic pressure ("Max Q") of approximately 800 pounds per square foot. Data from the Ares I-X flight will support the Ares I Critical Design Review (CDR), scheduled for 2010. Work continues on Ares I-X design and hardware fabrication. All of the individual elements are undergoing CDRs, followed by an integrated vehicle CDR in March 2008. The various hardware elements are on schedule to begin deliveries to Kennedy Space Center (KSC) in early September 2008.

Davis, Stephan R.↗
DOE OSTI2023

Mechanisms for high creep resistance in alumina forming austenitic (AFA) alloys

Castable alumina forming austenitic (AFA) alloys have demonstrated superior creep life and oxidation resistance at temperatures exceeding 800⁰C. Despite the success in the applicability of these alloys in extreme environments, there is a limited understanding of the deformation modes and the influence of each alloying element guiding the alloy design strategies that could further enhance the creep strength of these AFA alloys, particularly at temperatures at and above 900⁰C. In this study, we reveal the mechanism underpinning the superior creep performance of castable AFA alloys that involves suppressing primary carbide formation through minor compositional modification. This approach results in a three-fold increase in creep strength at 900⁰C and 50 MPa. Here, by employing integrated characterization techniques, we analyzed the microstructures of two AFA alloys, both before and after the creep process. We discovered that the suppression of primary carbides permits the in-situ clustering of now-available interstitial elements such as C, Si, and O during high-temperature creep. This improved solid solution strengthening and reduced stacking fault energy of the alloy. Moreover, it also enabled controlled secondary carbide formation during testing, further improving the creep resistance. These findings underline the important interplay between alloy composition, microstructure, and creep properties, and offer a promising design strategy for developing economical high-temperature Fe-based alloys suitable for advanced applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Characterization and pulse-shape discrimination of a multi-stacked structure of ZnS: Ag/PMMA for fast-neutron detection in high-flux environments

The Micro-Layered Fast Neutron Detector (MLFD) is a proton-recoil scintillator that incorporates the basic concept of a Hornyak button detector with an improved design using a layered structure of ZnS:Ag and PMMA (polymethyl methacrylate). The MLFD was predicted to have low sensitivity to gamma rays, hence pulse shape discrimination was performed to determine the gamma-ray contribution. Presented in this paper are the pulse-shape discrimination characteristics of the MLFD. Additionally, the charge integration technique was employed to discriminate between the neutrons and gamma rays using a fast digitzer. The MLFD was found to exhibit excellent neutron/gamma-ray discrimination, with the highest Figure-of-Merit (FoM) being 4.56. With PSD, the neutron signal is distinct from gamma-ray, Ĉerenkov and electronic noise signals. This clear separation was achieved due to the large differences in decay times of the neutron and gamma-ray-induced pulses in the ZnS:Ag phosphor. The MLFD can be employed in high gamma-ray exposure fields, up to at least 1287.7 mR/hr, without appreciable degradation in PSD performance.

47 OTHER INSTRUMENTATION↗
NASA NTRS2006

Photodetector Arrays for Multicolor Visible/Infrared Imaging

Monolithic focal-plane arrays of photodetectors capable of imaging the same scenes simultaneously in multiple wavelength bands in the visible and infrared spectral regions have been proposed. In prior visible/infrared imaging systems, it has been standard practice to use separate optical trains to form images in visible and infrared wavelength bands on separate visibleand infrared-photodetector arrays. Because the proposal would enable the detection of images in multiple wavelength bands on the same focal plane, the proposal would make it unnecessary to use multiple optical trains. Hence, multispectral imaging systems could be made more compact and the difficulties of aligning multiple optical trains would be eliminated. Each pixel in an array according to the proposal would contain stacks of several photodetectors. The proposal is a logical extension of prior concepts of arrays of stacked photodetectors for imaging in two or three wavelength bands. For example, such an array was described in Three-Color Focal-Plane Array of Infrared QWIPs (NPO-20683), NASA Tech Briefs, Vol. 24, No. 5 (May 2000), page 26a. In one proposed design, (see figure), each pixel would be divided into four subpixels, one being dedicated to a visible- and-near-infrared (V) band, one to a combination of the V band and a verylong- wavelength infrared (VLWIR) band, one to a combination of the V band and a long-wavelength infrared (LWIR) band, and one to a combination of the V band and a medium-wavelength infrared (MWIR) band. For this purpose, each subpixel would include a GaAs-based positive/intrinsic/negative (PIN) photodiode for detection in the V band stacked with three quantum-well infrared photodetectors (QWIPs), each optimized for one of the aforementioned infrared bands. The stacks of photodetectors in all the subpixels would be identical except for the electrical connections, which would be configured to activate the various wavelengthband combinations.

Gunapala, Sarath↗
DOE OSTI2020

Scalable Data-Intensive Geocomputation: A Design for Real-Time Continental Flood Inundation Mapping

The convergence of data-intensive and extreme-scale computing enables an integrated software and data ecosystem for scientific discovery. Developments in this realm will fuel transformative research in data-driven interdisciplinary domains. Geocomputation provides computing paradigms in Geographic Information Systems (GIS) for interactive computing of geographic data, processes, models, and maps. Because GIS is data-driven, the computational scalability of a geocomputation workflow is directly related to the scale of the GIS data layers, their resolution and extent, as well as the velocity of the geo-located data streams to be processed. Unique in high user interactivity and low end-to-end latency requirements, geocomputation applications will dramatically benefit from the convergence of high-end data analytics (HDA) and high-performance computing (HPC). The application level challenge, however, is to identify and eliminate computational bottlenecks that arise along a geocomputation workflow. Indeed, poor scalability at any of the workflow components is detrimental to the entire end-to-end pipeline. Here, we study a large geocomputation use case in flood inundation mapping that handles multiple national-scale geospatial datasets and targets low end-to-end latency. We discuss benefits and challenges for harnessing both HDA and HPC for data-intensive geospatial data processing and intensive numerical modeling of geographic processes. We propose an HDA+HPC geocomputation architecture design that couples HDA (e.g., Spark)-based spatial data handling and HPC-based parallel data modeling. Key techniques for coupling HDA and HPC to bridge the two different software stacks are reviewed and discussed.

Liu, Yan↗
DOE OSTI2026

Oak Ridge Computing Academy: An HPC cluster deployment and management pilot

The High Performance Computing Technologies (HPCT) course is a hands-on High Performance Computing (HPC) cluster deployment and management training program offered as part of the International School for Advanced Studies (SISSA) and the International Center for Theoretical Physics (ICTP) Master in High Performance Computing (MHPC) specialization. Here, this training program introduces students to key concepts in cluster configuration. which include networking, software stack provisioning, job scheduling, and monitoring. The publicly available course materials feature several examples and underlying methods that are broadly applicable to cluster deployment and management. This paper discusses the design of a new workforce development program at the Oak Ridge National Laboratory that is based on HPCT, the Oak Ridge Computing Academy (ORCA). The ORCA pilot program was hosted by the Oak Ridge Leadership Computing Facility (OLCF) in Summer 2025. As a part of this discussion, HPCT and ORCA course contents and infrastructure are outlined, ORCA participant experiences are detailed, and potential opportunities for improvement are discussed.

Education↗
DOE OSTI2024

Additive manufacturing of LiCoO 2 electrodes via vat photopolymerization for lithium ion batteries

Additive manufacturing has the potential to revolutionize the fabrication of lithium-ion batteries for a diversity of applications including in portable, biomedical, aerospace, and the transportation fields. Standard commercial batteries consist of stacked layers of various components (current collectors, cathode, anode, separator and electrolyte) in a two-dimensional manner. By leveraging the latest advances in additive manufacturing and computer-aided design, an improved geometric and electrochemical configuration of these batteries can maximize energy efficiency while allowing design optimization to reduce dead space for a given application. In this work, a composite UV photosensitive resin was prepared and used as feedstock in a vat photopolymerization system. The resin was loaded with LiCoO 2 acting as electrochemically active material for the cathode of a lithium-ion battery, and was further improved with the addition of conductivity-enhancing carbonaceous additives. Challenges to additive manufacturing arise from the opacity and high viscosity of the composite nature of these electrochemically-active resins, which cause light refraction during selective UV curing. Subsequently, items were printed and subjected to a thermal post-processing step to obtain an adequate compromise between electrochemical performance and mechanical integrity. Both sintered and green state 3D printed cathodes were assembled into half-cell lithium-ion batteries using lithium metal as a reference and counter electrode. Electrochemical cycling of these batteries yielded satisfactory results approaching commercial LiCoO 2 cathodes’ performance, with the potential advantages of additive manufacturing – high surface area anode–cathode configurations for power performance as well as shape conformability.

25 ENERGY STORAGE↗
DOE OSTI2020

Using Machine Learning to Identify Optimum Prospects for Offshore Geologic CO 2 Storage and Enhanced Oil Recovery in the Gulf of Mexico

The SECARB Offshore Partnership is a government-industry partnership focused on assembling the knowledge base required for secure, long-term, large-scale CO 2 subsea storage in saline formations and mature oil reservoirs. The SECARB Offshore Partnership is evaluating potential storage opportunities in the Central and Eastern planning areas of the outer continental shelf (OCS) of the Gulf of Mexico. The evaluation focuses on active and depleted oil and gas fields and potentially associated CO 2 -enhanced oil recovery (CO 2 -EOR), as well as, deep saline storage resources. As part of Subtask 3.0 (Offshore Storage Resources Characterization) and Subtask 4.0 (Risk Assessment, Simulation, and Modeling), Oklahoma State University (OSU) is developing a machine learning system that employs the SAS Institute’s Viya platform with visual data mining and machine learning to identify and characterize optimum prospects for enhanced oil recovery and stacked storage in saline reservoirs. This report summarizes this effort to date and discusses the many variables that can be used to characterize these storage objectives, as well as, the design vision of the Viya machine learning system.

02 PETROLEUM↗
DOE OSTI2024

Transformer Neural Networks with Spatiotemporal Attention for Predictive Control and Optimization of Industrial Processes

In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.

Gallup, Ethan R.↗
NASA NTRS1985

The dynamic response of composite beams to axial impact

Large deformation response of simple composite structural elements to intense dynamic loadings is studied by focusing on the response of beams. Overall goals are to: design a simple test fixture to introduce crash related bending loads in beams; determine any difference between large deformation static and large deformation dynamic response; determine the influence of laminate stacking arrangements on the dynamic response, failure mode, and energy absorbed; and predict the dynamic response. Twelve different laminate orientations were investigated. Modeling of the response was done with an existing finite element program. The program computed nonlinear transient response analysis of a structure objected to a time varying load.

Derian, E. J.↗
DOE OSTI2022

Probabilistic Nanomagnetic Memories for Uncertain and Robust Machine Learning

This project evaluated the use of emerging spintronic memory devices for robust and efficient variational inference schemes. Variational inference (VI) schemes, which constrain the distribution for each weight to be a Gaussian distribution with a mean and standard deviation, are a tractable method for calculating posterior distributions of weights in a Bayesian neural network such that this neural network can also be trained using the powerful backpropagation algorithm. Our project focuses on domain-wall magnetic tunnel junctions (DW-MTJs), a powerful multi-functional spintronic synapse design that can achieve low power switching while also opening the pathway towards repeatable, analog operation using fabricated notches. Our initial efforts to employ DW-MTJs as an all-in-one stochastic synapse with both a mean and standard deviation didn’t end up meeting the quality metrics for hardware-friendly VI. In the future, new device stacks and methods for expressive anisotropy modification may make this idea still possible. However, as a fall back that immediately satisfies our requirements, we invented and detailed how the combination of a DW-MTJ synapse encoding the mean and a probabilistic Bayes-MTJ device, programmed via a ferroelectric or ionically modifiable layer, can robustly and expressively implement VI. This design includes a physics-informed small circuit model, that was scaled up to perform and demonstrate rigorous uncertainty quantification applications, up to and including small convolutional networks on a grayscale image classification task, and larger (Residual) networks implementing multi-channel image classification. Lastly, as these results and ideas all depend upon the idea of an inference application where weights (spintronic memory states) remain non-volatile, the retention of these synapses for the notched case was further interrogated. These investigations revealed and emphasized the importance of both notch geometry and anisotropy modification in order to further enhance the endurance of written spintronic states. In the near future, these results will be mapped to effective predictions for room temperature and elevated operation DW-MTJ memory retention, and experimentally verified when devices become available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Fully Distributed Multi-Material Magnetic Sensing Structures for Multiparameter DAS Applications

This dissertation demonstrates the first of its kind distributed magnetic field sensor based on a fiber optic distributed acoustic sensing (DAS) scheme. Ferromagnetic nickel and Metglas® were dispersed internally within a fiber optic preform and then drawn on an in-house fiber optic draw tower to lengths in the kilometers. Due to the close proximity of the ferromagnetic metals and fiber optic core, the magnetostrictive strain response of the ferromagnetic materials when exposed to a magnetic field would perturbate within the fiber cladding and transfer that strain, internally, to the fiber optic core. Strain resulting from the magnetostrictive effect allows the DAS based sensor to accurately translate strain into readable magnetic field data. Due to the high sensitivity seen in this sensor design, multiparameter sources, acoustic and magnetic fields, were tested and validated and a three dimensional magnetic-field vector sensor was proposed. Numerical analysis of the novel sensor design was first implemented using COMSOL Multiphysics, where inputs such as magnetostrictive element shape, size, distance, and number were first investigated. Upon optimizing system constraints, the sensor design was further modified such that single mode operation was consistent across multiple fiber draws while retaining high strain transfer from the ferromagnetic elements to the fiber optic core. Ferromagnetic material selection was evaluated as a function of the saturation magnetostriction constants and a total of 4 modules were used to fully characterize the complex physics involved in this sensor design. All fabrication and testing were performed in-house using a full scale 3-story fiber draw tower and custom environmental testing stations to imitate naturally occurring events such as magnetic or acoustic point sources. A unique stacking method was used to embed ferromagnetic nickel and Metglas® into a fiber optic preform which when combined with a custom fiber draw process resulted in consistent multi-material fibers drawn to lengths of 1-km. In-house testing facilities included different types of electromagnetic generators, in addition to a soil test bed, and an outdoor test bed which allowed 100 meters of fiber to be tested simultaneously. All tested sensors demonstrated high strain transfer capabilities on the order of 0.01-10 μϵ depending on the materials used, ferromagnetic rod number, and core to metal spacing. Due to the sensitivity of the system the difference between AC and DC was distinct, and directional magnetostriction was studied. Transverse and longitudinal magnetic wave propagation was controlled through a solenoid and rectangular Helmholtz coil, both built in-house. A three-dimensional magnetic field vector sensor was proposed due to the success of the magnetic field sensor, and a design was proposed and initially tested to validate direction as a function of field strength and distance. To summarize, this dissertation explores the first fully distributed magnetic field sensor using DAS based techniques and one of the first multi-material fiber draw processes which can produce consistent single mode fiber up to 1-km. Due to extensive FEA modeling, multiple iterations of the magnetic sensor were fully characterized and an equation describing the relationship between sensor design and strain transfer has been created and validated experimentally. Multi-parameter tests including acoustic and magnetic fields were implemented and an algorithm was developed to separate the mixed signals. Finally, a test was performed to demonstrate the feasibility of sensing magnetic fields directionally. Cumulative results demonstrate a high-quality sensor alternative to current designs which may surpass other magnetic sensors due to innate multi-parameter capabilities, in addition to the inexpensive production cost and extremely long operating lengths.

02 PETROLEUM↗
NASA NTRS2007

CEV Trajectory Design Considerations for Lunar Missions

The Crew Exploration Vehicle (CEV) translational maneuver Delta-V budget must support both the successful completion of a nominal lunar mission and an "anytime" emergency crew return with the potential for much more demanding orbital maneuvers. This translational Delta-V budget accounts for Earth-based LEO rendezvous with the lunar surface access module (LSAM)/Earth departure stage (EDS) stack, orbit maintenance during the lunar surface stay, an on-orbit plane change to align the CEV orbit for an in-plane LSAM ascent, and the Moon-to-Earth trans-Earth injection (TEI) maneuver sequence as well as post-TEI TCMs. Additionally, the CEV will have to execute TEI maneuver sequences while observing Earth atmospheric entry interface objectives for lunar high-latitude to equatorial sortie missions as well as near-polar sortie and long duration missions. The combination of these objectives places a premium on appropriately designed trajectories both to and from the Moon to accurately size the translational V and associated propellant mass in the CEV reference configuration and to demonstrate the feasibility of anytime Earth return for all lunar missions. This report examines the design of the primary CEV translational maneuvers (or maneuver sequences) including associated mission design philosophy, associated assumptions, and methodology for lunar sortie missions with up to a 7-day surface stay and with global lunar landing site access as well as for long duration (outpost) missions with up to a 210-day surface stay at or near the polar regions. The analyses presented in this report supports the Constellation Program and CEV project requirement for nominal and anytime abort (early return) by providing for minimum wedge angles, lunar orbit maintenance maneuvers, phasing orbit inclination changes, and lunar departure maneuvers for a CEV supporting an LSAM launch and subsequent CEV TEI to Earth return, anytime during the lunar surface stay.

Condon, Gerald L.↗
DOE OSTI2021

Numerical and experimental evaluation of mechanical performance of the multifunctional energy storage composites

This work presents numerical simulation methods to model the mechanical behavior of the multifunctional energy storage composites (MESCs), which consist of a stack of multiple thin battery layers reinforced with through-the-hole polymer rivets and embedded inside carbon fiber composite laminates. MESC has been demonstrated through earlier experiments on its exceptional behavior as a structural element as well as a battery. However, the inherent complex infrastructure of the MESC design has created significant challenges in simulation and modeling. A novel homogenization technique was adopted to characterize the multi-layer properties of battery material using physics-based constitutive equations combined with nonlinear deformation theories to handle the interface between the battery layers. Second, mechanical damage and failure modes among battery materials, polymer reinforcements, and carbon fiber-polymer interfaces were characterized through appropriate models and experiments. The model of MESCs has been implemented in a commercial finite element code in ABAQUS. A comparison of structural response and failure modes from numerical simulations and experimental tests are presented. The results of the study showed that the predictions of elastic and damage responses of MESCs at various loading conditions agreed well with the experimental data. © 2021

Materials Science↗
DOE OSTI2024

Properties of Nb x Ti (1–x) N thin films deposited on 300 mm silicon wafers for upscaling superconducting digital circuits

Scaling superconducting digital circuits requires fundamental changes in the current material set and fabrication process. The transition to 300 mm wafers and the implementation of advanced lithography are instrumental in facilitating mature CMOS processes, ensuring uniformity, and optimizing the yield. Here, this study explores the properties of Nb x Ti (1–x) N films fabricated by magnetron DC sputtering on 300 mm Si wafers. As a promising alternative to traditional Nb in device manufacturing, Nb x Ti (1–x) N offers numerous advantages, including enhanced stability and scalability to smaller dimensions, in both processing and design. As a ternary material, Nb x Ti (1–x) N allows engineering material parameters by changing deposition conditions. The engineered properties can be used to modulate device parameters through the stack and mitigate failure modes. We report characterization of Nb x Ti (1–x) N films at less than 2% thickness variability, 2.4% T c variability and 3% composition variability. Film resistivity (140–375 Ωcm) shows a strong correlation with the film oxygen content, while the critical temperature T c (4.6 K–14.1 K) is strongly affected by film stoichiometry and its microstructure has only a moderate effect on modifying T c . Our results offer insights about the interplay between film stoichiometry, film microstructure and critical temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Sensitivity of a closed dielectric haloscope to axion dark matter

We present a method to determine the sensitivity of a closed dielectric haloscope to axiondark matter. Dielectric haloscopes aim to probe the theoretically well-motivated axion mass rangeof ∼ 26 µeV to ∼ 500 µeV by utilizing a stack of dielectric disksand a mirror to enhance the axion-photon conversion within an external magnetic field. Theirconversion volume is nearly axion-mass independent, thereby favoring large-scale designs toincrease sensitivity. The large volume causes simulations to be computationally expensive andtime-consuming. This paper presents a simple model that can be used to determine the sensitivityof the experiment with minimal computational resources. The model is able to describe theelectromagnetic response of a closed dielectric haloscope, accounting for realistic geometricimperfections, as well as the noise introduced by the receiver system. It is applied to datataken with a MAgnetized Disk and Mirror Axion Experiment (MADMAX) prototype within the 1.6 TMorpurgo magnet at CERN. This work underpins the first axion dark matter search using adielectric haloscope and provides the foundation for future dark matter searches with MADMAX.

Ivanov, A. [Munich, Max Planck Inst. Quantenopt.]↗