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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Planar Transformer With Asymmetric Integrated Leakage Inductance Using Horizontal Air Gap

This article presents a novel planar-based transformer winding and core structure with controllable leakage inductance generation for integrated magnetics applications. As a result of limitations in integrated magnetics from the literature, a new approach is proposed utilizing semi-interleaved windings and controllable leakage via a leakage flux core leg featuring a horizontal air gap. Here, the proposed transformer design is analyzed via detailed reluctance modeling to determine closed-form equations for the magnetizing and asymmetrically distributed leakage inductances. Next, a genetic-algorithm-based multiobjective design optimization problem is developed, seeking to minimize the core and winding losses of the proposed transformer subject to a set of parametric and geometric constraints in a dc-ac dual-active-bridge topology for microinverter applications. The optimization was extended to include other integrated magnetic structures from the literature, where it is determined that the proposed transformer is superior from the perspectives of efficiency, footprint area, and parasitic capacitance. Based on the results of the optimization analysis, two designs with theoretical transformer California Energy Commission (CEC) efficiency drops (i.e., CEC efficiency reduction specifically due to the transformer loss mechanisms) of 1.19% and 0.83% were fabricated and evaluated for electrical and thermal performance in the proposed 40 V, 400 W microinverter.

42 ENGINEERING↗
DOE OSTI2024

A Bayesian Multi-fidelity Neural Network to Predict Nonlinear Frequency Backbone Curves

The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. Here, this paper presents a multi-fidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics Research Challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model's uncertainty. Once trained, the multi-fidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.

42 ENGINEERING↗
DOE OSTI2021

Achieving extreme light confinement in low-index dielectric resonators through quasi-bound states in the continuum

Obtaining large field enhancement in low-refractive-index dielectric materials is highly relevant to many photonic and quantum optics applications. However, confining light in these materials is challenging, owing to light leakage through coupling to continuum modes in the surrounding environment. Here, we investigate the possibility of achieving high quality factors in low-index dielectric resonators through the bound states in the continuum (BIC). Our simulations demonstrate that destructive interference between leaky modes can be achieved by tuning the geometrical parameters of the resonator arrays, leading to the emergence of quasi-BIC in resonators that have a small index contrast to the underlying substrates. The resultant large field enhancement gives rise to giant quality factors and Purcell effects. By introducing vertical mirror symmetry, the quasi-BIC can be tuned into an ideal BIC. In addition, the quasi-BIC can modify the emission patterns of the coupled emitters, rendering highly directional and focused far-field emission. These findings may provide a path for the practical implementation of photonic and quantum devices based on low-index dielectric materials.

42 ENGINEERING↗
DOE OSTI2025

Probing the role of local tunnel variations in early-stage lithiation of α-MnO₂ nanowires via in situ TEM

Understanding lithium-ion transport in tunnel-structured manganese oxides is essential for designing high-performance lithium-ion battery electrode materials. Here, we elucidate the early-stage lithiation mechanism of potassium-stabilized α-MnO 2 nanowires using in situ transmission electron microscopy (TEM) coupled with electron energy-loss spectroscopy (EELS), high-resolution TEM (HRTEM), and geometric phase analysis (GPA). Real-time TEM imaging reveals clear volume expansion at the reaction front, while EELS analysis uncovers lithium-ion diffusion far beyond this region, where no visible expansion is observed, indicating fast, defect-assisted transport. GPA and HRTEM analyses show that localized tensile and compressive strain fields, originating from pre-existing local tunnel structural variations, persist after lithiation. The tensile-strained regions enable lithium-ion insertion with minimal lattice distortion, offering additional free volume that facilitates rapid lithium-ion accommodation ahead of the structural transformation. Our results demonstrate a local tunnel variation-mediated fast diffusion pathway that precedes bulk reaction, underscoring the critical role of local strain in enabling early-stage lithium transport. Given the structural versatility of MnO 2 and its ability to accommodate diverse atomic arrangements beyond the well-known tunnel phases (β-, γ-, δ-, λ-, R-phases), our findings highlight the importance of understanding and engineering local structural environments. This work provides fundamental insights into the interplay between defects, strain, and ion dynamics, and presents defect engineering as a promising approach to enhance both rate performance and structural stability in manganese-based cathodes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Non-equilibrium molecular dynamics simulation to evaluate the effect of confinement on fluid flow in silica nanopores

The flow of fluids in nano-confinement has applications in separations, water purification, medical systems and in the recovery of fluids in petroleum systems. It is believed that fluids do not obey continuum laws when flowing in nanopores (or in confinement). This paper attempts to shed light on various nano confinement effects such as fluid-wall interactions, pore size and molecular geometry, using molecular dynamic simulation. Here, water, hexane, and methanol flow behaviors were simulated for pore diameters ranging from 1 to 8 nm. In addition to the density analysis of the confined fluids, the fluid flows in saturated nanopores were simulated by the sectional flow method. Water and methanol molecules were completely stabilized in the 1 nm pore. Hexane molecules could not enter the 1 nm pore, due to geometric considerations. For the 2 – 8 nm pores, all fluids showed reduced flow rate compared to the Hagen–Poiseuille flow, due to an interfacial molecular layer stabilized at the pore surface. Flow reduction observed in these studies is contrary to significant flow enhancements observed for fluid flow in carbon nanotubes of similar dimensions. Water flow showed almost constant stick length for all the pore sizes. Methanol flow had the largest stick length. Hexane flow was reduced because of overcrowding of molecules at the pore surface. The effect of confinement diminished with an increase in pore diameter.

42 ENGINEERING↗
DOE OSTI2020

Design Optimization of Medium-Frequency Transformer for DAB Converters With DC Bias Capacity

Medium-frequency transformer (MFT) dc bias is critical for the safe operation of dual-active-bridge (DAB) converters. To analyze the dc bias, expressions of dc bias are derived for SiC MOSFET DAB converters. It shows that the dc bias is a current source in steady state; thus, a dc bias current capacity is defined and used as a design constraint in the optimization of MFT. The dc bias current capacity is related to core materials, geometric parameters, and flux density, which is analyzed with analytical expression. Optimization equations with geometric variables are derived, which can be solved theoretically or numerically for both interleaved winding (IW) and separated winding (SW) MFT without and/or with leakage inductance design. Based on the optimization equations, different MFT designs for a 150-kW SiC MOSFET DAB converter are compared on the aspects of core materials, winding structures, and without/with leakage inductance design. The combination of IW structure MFT with auxiliary phase shift inductors is chosen, and the assembled prototype is tested with the 150-kW DAB converter. Here, the designed dc bias capacity, temperature rise, and efficiency are verified.

42 ENGINEERING↗
DOE OSTI2020

Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)

Biological processes such as processive enzyme turnover and intracellular cargo tracking involve the dynamic motion of a small "article" along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical difraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle's trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. Lastly, this work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.

42 ENGINEERING↗
DOE OSTI2023

Investigation on the explosion dynamics of large-format lithium-ion pouch cells

Explosion is the most extreme case of thermal runaway of lithium-ion (Li-ion) batteries. In this study, explosion dynamics of large-format Li-ion cells are investigated experimentally and numerically. Overcharge-to-explosion tests are conducted on 40 Ah Li-ion cells with Li[Ni 0.8 Co 0.1 Mn 0.1 ]O 2 cathode. Based on the explosion physics, shockwave and detonation models are used to characterize the shock effect of the cell explosion and evaluate the explosion equivalent. Von Neumann peak is observed on the pressure curves, and the shockwave velocity is supersonic at this time; the unwrinkled spherical flame phenomenon observed in the experiment indicates that it is detonation. Additionally, a geometric model is established based on the real testing scenario, and the explosion behavior is numerically studied. The characteristics of the explosion dynamics process are interpreted and the propagation mechanism of the shockwave are revealed; the deflagration to detonation transition (DDT) phenomenon in this process is caused by the formation of “hot spots”, and the explosion of the cells eventually turns into stable combustion. Here this study fills the gap in the research on thermal runaway of Li-ion cells, especially in the extreme cases of fire and explosion, and provide useful guidance for battery safety.

42 ENGINEERING↗
DOE OSTI2024

Real-time kinematic (RTK) Drone-collected Data and Processed Models of Port Arthur Coastal Neighborhood and Pleasure Island Golf Course, June 2024

The Southeast Texas Urban Integrated field lab’s Co-design team captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through autonomous flight, and models were processed through the DroneDeploy engine. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point Cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857.For using these data:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset will support researchers' decision-making processes under uncertainties.

2D mapping↗
DOE OSTI2022

Formation and dissociation of shear-induced high-energy dislocations: insight from molecular dynamics simulations

Solid-phase processing (SPP) allows one to create complex microstructures, not achievable via thermal processing alone. The resulting structures exhibit a rich palette of defects, both thermal and non-thermal, including defect substructures, such as dislocation networks. It is essential to understand the mechanisms of deformation and defect structure formation to guide SPP towards achieving desired microstructures and material properties. In this study, large-scale molecular dynamics simulations are used to investigate the effects of inhomogeneous strain distribution, that mimics deformation conditions of tribological tests, on the evolution of defects under severe shear deformation in polycrystalline Al. Analysis of defect nucleation and reaction pathways reveals that strong geometric constraints suppress the nucleation and slide of low energy dislocation 1/2$\langle110\rangle${111} but promote the nucleation and slide of high energy dislocations, such as [$1\bar1$0] (001) and 1/2 [$1\bar1$$\bar2$]($1\bar1$1). Further, a rough contact surface, characteristic to tribological tests, imposes an inhomogeneous stress field leading to inhomogeneous defect substructures due to location-dependent activation of slip systems. The results suggest that high-energy dislocations can dominate the evolution of grain structures in highly constrained environments, which should be considered in modeling plastic deformation and grain refinement during SPP.

42 ENGINEERING↗
DOE OSTI2024

Aerial Data and Processed Models of Port Arthur Coastal Neighborhood and Pleasure Island Golf Course, June 2024

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024.Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area.The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857.For using these data:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

2D mapping↗
DOE OSTI2025

Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds

Low Earth Orbit (LEO) satellites play a crucial role in enhancing global connectivity, serving a complementary solution to existing terrestrial systems. In wireless networks, scheduling is a vital process that allocates time-frequency resources to users for interference management. However, LEO satellite networks face significant challenges in scheduling their links towards ground users due to the satellites’ mobility and overlapping coverage. This paper addresses the dynamic link scheduling problem in LEO satellite networks by considering spatio-temporal correlations introduced by the satellites’ movements. The first step in the proposed solution involves modeling the network over Riemannian manifolds, thanks to their representation as symmetric positive definite matrices. We introduce two machine learning (ML)-based link scheduling techniques that model the dynamic evolution of satellite positions and link conditions over time and space. To accurately predict satellite link states, we present a recurrent neural network (RNN) over Riemannian manifolds, which captures spatio-temporal characteristics over time. Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. Simulation results demonstrate that both RNN and CNN over Riemannian manifolds deliver comparable performance to the fractional programming-based link scheduling (FPLinQ) benchmark. Remarkably, unlike other ML-based models that require extensive training data, both models only need 30 training samples to achieve over 99% of the sum rate while maintaining similar computational complexity relative to the benchmark.

42 ENGINEERING↗
DOE OSTI2024

Advanced Reactors Spent Fuel & Waste Science and Technology Program

Based on the higher interest in Advanced Reactor (AR) deployment (e.g., ARDP ) for potential new fuel cycles, the Spent Fuel & Waste Science and Technology (SFWST) Program has begun to evaluate the possible implications of long-term management and final disposition of potential Advance Reactor spent nuclear fuels (SNF) that would be generated in potential advanced reactors. Safely managing and dispositioning the potential future AR SNF, and any other associated radioactive wastes, is the primary focus of this initial preliminary assessment of those. This paper summarizes three primary tasks the Spent Fuel & Waste Science and Technology (SFWST) Program is executing (or collaborating on) related to the back end of the nuclear fuel cycle (BENFC) for potential future advanced reactors: 1. Advanced Reactors Spent Fuel and Waste Streams: Strategies for the BENFC This set of activities define a high-level strategy for how to systematically approach, identify, and close research and development (R&D) gaps/potential issues associated with long-term management and final disposition of AR SNF and other possible AR waste streams. This task involves summarizing advanced reactor concepts, their likely SNF and other waste forms, and identifying previous experience with similar materials, for example from DOE-managed SNF, with closely related characteristics to the potential future AR SNF. Technical R&D gaps between the breadth of detailed understanding for safe storage, transportation and disposal of the existing light water reactor SNF fuel cycle (e.g., see NASEM, 2022) and potential future fuel cycles based on advanced reactors would then be identified. 2. Characterization and Packaging Options of Advanced Reactor SNF These activities evaluate characteristics and packaging options for advanced reactor spent fuel forms. The fuel forms are categorized into three types: (1) tri-structural isotropic (TRISO), (2) metallic, and (3) fuel salt. Emphasis is given to TRISO and metallic SNF and additional waste streams from such AR as driven by the near-term anticipated operation of the Xe-100 and the Natrium reactors as advanced-reactor demonstrations1. Preliminary information for the spent-fuel salt discharged from molten-salt reactors (MSRs) is examined to provide a baseline for future efforts. All calculations and assumptions in this work are based on publicly available information. The following characteristics are calculated or estimated for use in the preliminary assessments: SNF volume and mass, radiation/activity levels through time, thermal conditions through time, potential radionuclide source terms, chemical interactions and evolutions, disposal inventories, and waste-form lifetime. Using those characteristics, calculations to determine the applicability of existing canister designs were performed. These evaluations included geometric (e.g., dimension, volume) and mass/weight considerations, known operational approaches and loading procedures, physical and chemical considerations/conditions for storage environments, as-loaded radiation, thermal, and criticality analyses to identify constraints for storage, transportation, and disposal. 3. Back-End Management of Advanced Reactors (BEMAR) The DOE NE-8 organization has defined an Integrated Project Team to evaluate the Back End Management of Advanced Reactors (BEMAR) (includes DOE staff from a range of organizations (e.g., NE-81, NE-82, OCED) and national laboratory technical staff within the DOE NE-81 and NE-82 programs). This BEMAR group works directly with advance reactors developers to assess for the DOE the technical feasibility of storage, transportation, and disposal of AR SNF based on the characteristics provided by the developers to DOE (much of which is proprietary). The BEMAR is also tasked to develop rough-order-of-magnitude cost estimates to compare the waste management system for individual advanced reactors to existing light-water reactor management practices. To accomplish this, the BEMAR group is implementing a Systems Engineering approach.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

A materials data framework and dataset for elastomeric foam impact mitigating materials

The availability of materials data for impact-mitigating materials has lagged behind applications-based data. For example, data describing on-field helmeted impacts are available, whereas material behaviors for the constituent impact-mitigating materials used in helmet designs lack open datasets. Here, we describe a new FAIR (findable, accessible, interoperable, reusable) data framework with structural and mechanical response data for one example elastic impact protection foam. The continuum-scale behavior of foams emerges from the interplay of polymer properties, internal gas, and geometric structure. This behavior is rate and temperature sensitive, therefore, describing structure-property characteristics requires data collected across several types of instruments. Data included are from structure imaging via micro-computed tomography, finite deformation mechanical measurements from universal test systems with full-field displacement and strain, and visco-thermo-elastic properties from dynamic mechanical analysis. These data facilitate modeling and design efforts in foam mechanics, e.g., homogenization, direct numerical simulation, or phenomenological fitting. The data framework is implemented using data services and software from the Materials Data Facility of the Center for Hierarchical Materials Design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Integrated Turbine Component Cooling Designs Facilitated by Additive Manufacturing and Optimization

This research program involved the development of improved gas turbine cooling by optimizing the combined internal and film cooling configurations used for turbine blades and vanes. Improved cooling of turbine airfoils allows the gas turbines to be operated at higher temperatures which improves efficiency for gas turbine-based power generation. This also facilitates operation with hydrogen-based fuels which inherently generate higher operating temperatures. Designs of the new cooling configurations were based on the added geometric flexibility that is available when using metal additive manufacturing techniques. When making these designs, adjustments were made to account for imperfections occurring in the metal additive manufacturing process. New designs for film cooling holes were developed using computational adjoint gradient optimization techniques which generated a unique film cooling hole with 70% greater film cooling effectiveness compared to conventional shaped film cooling holes. A major factor in improving the film cooling effectiveness were small external protrusions on either side of the new hole which generated vortices that pushed coolant towards the surface and increased the lateral spreading of the coolant. Our studies also included optimization of film cooling holes that can be built with conventional manufacturing techniques and optimizing the internal coolant channels that supply coolant to the film cooling holes. Various internal coolant channel shapes were investigated and designs that maximized the cooling of the airfoil while minimizing pressure losses in the channel flows were identified. Testing and verification of the performance of the final configurations was done with a transonic wind tunnel facility which allowed testing at engine realistic high velocities. Final testing of the combined internal and film cooling configurations was done by incorporating the configurations in Penn State’s National Experimental Turbine (NExT) vane. These tests confirmed the enhanced overall cooling effectiveness provided by the new cooling configuration designs.

03 NATURAL GAS↗
DOE OSTI2024

Domain nucleation across the metal-insulator transition of self-strained V 2 O 3 films

Bulk V 2 O 3 features concomitant metal-insulator (MIT) and structural (SPT) phase transitions at T C ~ 160 K. In thin films, where the substrate clamping can impose geometrical restrictions on the SPT, the epitaxial relation between the V 2 O 3 film and substrate can have a profound effect on the MIT. Here we present a detailed characterization of domain nucleation and growth across the MIT in (001)-oriented V 2 O 3 films grown on sapphire. By combining scanning electron transmission microscopy (STEM) and photoelectron emission microscopy (PEEM), we imaged the MIT with planar and vertical resolution. We observed that upon cooling, insulating domains nucleate at the top of the film, where strain is lowest, and expand downwards and laterally. This growth is arrested at a critical thickness of 50 nm from the substrate interface, leaving a persistent bottom metallic layer. As a result, the MIT cannot take place in the interior of films below this critical thickness. However, PEEM measurements revealed that insulating domains can still form on a very thin superficial layer at the top interface. Our results demonstrate the intricate spatial complexity of the MIT in clamped V 2 O 3 , especially the strain-induced large variations along the c axis. Finally, engineering the thickness-dependent MIT can provide an unconventional way to build out-of-plane geometry devices by using the persistent bottom metal layer as a native electrode.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Watertight Tensor-Product Spline Reconstruction (KCNSC PDRD Technical Report)

The standard for representing geometric models in Computer Aided Design (CAD) systems is boundary representation with trimmed surfaces. However, the approximate nature of surface-surface intersections in CAD systems leaves many small gaps between mating surfaces. These gaps lead to problems for downstream analysis and manufacturing applications, requiring time consuming repair operations and further approximation of the geometry as polygonal meshes, which do not support design iteration. In this study, nVariate, Inc. evaluated its watertight reconstruction technology on a test part. The resulting watertight model has the same absolute model accuracy of the original model zero gaps, providing a representation that requires no repair and supports further design iterations in the CAD system.

42 ENGINEERING↗
DOE OSTI2024

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

47 OTHER INSTRUMENTATION↗