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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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DOE OSTI2025

Integrated System for Methane Emissions Monitoring, Mapping, and Quantification

This report presents the work completed under the DOE iM4 project for the development of a methane emission monitoring system for detection, location, and quantification of methane in oil and gas industries. The task was divided into four main areas including: 1) Sensors and Input, 2) Centralized Cloud Information Center, 3) Algorithms, and 4) Testing and Validation. Task 1 focused on researching and developing an understanding of the current, or soon to be, available methane sensing technologies. Task 2 consisted of developing the architecture, selecting hardware, software and elements for the methane monitoring system. Task 3 focused on the algorithms used for the complex inverse model of going from measured methane signatures to the detection, localization, and quantification of sources that are desired. Finally, Task 4 focused on the methods of testing and validating the operation of the system. Attention was also given to the development method and cost breakdown of the system.

03 NATURAL GAS↗
DOE OSTI2023

Noise analysis for the Sorkin and Peres tests performed on a quantum computer

We use quantum computers to test the foundations of quantum mechanics through quantum algorithms that implement some of the experimental tests as the basis of the theory's postulates. These algorithms can be used as a test of the physical theory under the premise of a perfect hardware or as a test of the hardware under the premise that quantum theory is correct. In this paper, we show how the algorithms can be used to test the efficacy of a quantum computer in obeying the postulates of quantum mechanics. We study the effect of different types of errors on the results of experimental tests of the postulates. A salient feature of this error analysis is that it is deeply rooted in the fundamentals of quantum mechanics as it highlights how systematic errors affect the quantumness of the quantum computer.

Sadana, Simanraj↗
DOE OSTI2024

Systematically tracking the hourly progression of large wildfires using GOES satellite observations

Abstract. In the western United States, prolonged drought, a warming climate, and historical fuel buildup have contributed to larger and more intense wildfires as well as to longer fire seasons. As these costly wildfires become more common, new tools and methods are essential for improving our understanding of the evolution of fires and how extreme weather conditions, including heat waves, windstorms, droughts, and varying levels of active-fire suppression, influence fire spread. Here, we develop the Geostationary Operational Environmental Satellites (GOES)-Observed Fire Event Representation (GOFER) algorithm to derive the hourly fire progression of large wildfires and create a product of hourly fire perimeters, active-fire lines, and fire spread rates. Using GOES-East and GOES-West geostationary satellite detections of active fires, we test the GOFER algorithm on 28 large wildfires in California from 2019 to 2021. The GOFER algorithm includes parameter optimizations for defining the burned-to-unburned boundary and correcting for the parallax effect from elevated terrain. We evaluate GOFER perimeters using 12 h data from the Visible Infrared Imaging Radiometer Suite (VIIRS)-derived Fire Event Data Suite (FEDS) and final fire perimeters from the California's Fire and Resource Assessment Program (FRAP). Although the GOES imagery used to derive GOFER has a coarser resolution (2 km at the Equator), the final fire perimeters from GOFER correspond reasonably well to those obtained from FRAP, with a mean Intersection-over-Union (IoU) of 0.77, in comparison to 0.83 between FEDS and FRAP; the IoU indicates the area of overlap over the area of the union relative to the reference perimeters, in which 0 is no agreement and 1 is perfect agreement. GOFER fills a key temporal gap present in other fire tracking products that rely on low-Earth-orbit imagery, where perimeters are available at intervals of 12 h or longer or at ad hoc intervals from aircraft overflights. This is particularly relevant when a fire spreads rapidly, such as at maximum hourly spread rates of over 5 km h−1. Our GOFER algorithm for deriving the hourly fire progression using GOES can be applied to large wildfires across North and South America and reveals considerable variability in the rates of fire spread on diurnal timescales. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates. The resulting GOFER product has a broad set of potential applications, including the development of predictive models for fire spread and the improvement of atmospheric transport models for surface smoke estimates (https://doi.org/10.5281/zenodo.8327264, Liu et al., 2023).

Geology↗
DOE OSTI2023

The wave energy converter control competition (WECCCOMP): Wave energy control algorithms compared in both simulation and tank testing

The wave energy control competition established a benchmark problem which was offered as an open challenge to the wave energy system control community. The competition had two stages: In the first stage, competitors used a standard wave energy simulation platform (WEC-Sim) to evaluate their controllers while, in the second stage, competitors were invited to test their controllers in a real-time implementation on a prototype system in a wave tank. The performance function used was based on converted energy across a range of standard sea states, but also included aspects related to economic performance, such as peak/average power, peak force, etc. This paper compares simulated and experimental results and, in particular, examines if the results obtained in a linear system simulation are borne out in reality. Overall, within the scope of the device tested, the range of sea states employed, and the performance metric used, the conclusion is that high-performance WEC controllers work well in practice, with good carry-over from simulation to experimentation. However, the availability of a good WEC mathematical model is deemed to be crucial.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Development of a Hardware-in-The-Loop Testbed for a Decentralized, Data-Driven Electric Vehicle Charging Control Algorithm

This study presents the design of an electric vehicle (EV)-grid integration (EVGI) hardware test-bed to implement smart EV charging algorithms. Here, the proposed test-bed also allows to create different grid events via flexible integration of other power hardware (e.g., controllable loads and battery energy storage systems) and test their impacts on EV charging. The design uses a real-time digital simulator to realize a complex distribution grid model with primary and secondary networks. A grid simulator physically realizes the selected nodes of the simulated grid to power an actual EV, forming a hardware-in-the-loop (HIL) test setup. The EV-grid integration is demonstrated based on the custom hardware and software implementation of the J1772 charging protocol using dSPACE MicroLabBox, operating as a custom EV Supply Equipment (EVSE). The HIL test-bed features a novel testing platform for accurate implementation and analysis of scalable charging algorithms. To this end, a data-driven, decentralized, model-free charging controller based on the Additive Increase and Multiplicative Decrease (AIMD) algorithm is presented and validated on an EV using the HIL test-bed. We tested the proposed algorithm under various case studies, and presented a comparison study with an existing droop-based, decentralized charging solution. The results showed that the EV successfully performed charging commands generated by the EVSE and regulated its charging power to effectively reduce the system loading caused by high EV penetration.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Subfield crop yields and temporal stability in thousands of US Midwest fields

Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha –1 for maize, 0.39 Mg ha –1 for cotton, 0.34 Mg ha –1 for soybean, and 0.59 Mg ha —1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

V1G Frequency Regulation: Algorithm Development, Validation & Analysis at Scale

Researchers at Argonne National Laboratory developed and validated a high-fidelity digital twin of a smart charging (V1G) ecosystem to model the participation of up to 1,000 unique electric vehicles (EVs) in the PJM frequency regulation market. Utilizing a discrete-event framework, the simulation models complex interactions, from dynamic grid signals (updated every 2 seconds) to individual EV charging dynamics. The simulation incorporates multiple EV models created from real-world lab test data. Researchers tested multiple control algorithms to balance the dual objectives of maximizing aggregator’s revenue and driver charging needs. Results demonstrate that aggregated EVs function as a controllable, highly effective grid resource, achieving high PJM Performance Scores (80–90%). Additionally, an optimized, market-aware bidding strategy was identified as key to profitability. The platform was shown to provide drivers with an average charging discount of nearly 50%. The algorithm was further validated in the lab using production EVs and charging stations to compare simulation results with real-world performance.

Manne, Nithin↗
DOE OSTI2018

Development of a Crosstalk Suppression Algorithm for KID Readout

The GroundBIRD telescope aims to detect B-mode polarization of the cosmic microwave background radiation using the kinetic inductance detector array as a polarimeter. For the readout of the signal from detector array, we have developed a frequency division multiplexing readout system based on a digital down converter method. These techniques in general have the leakage problems caused by the crosstalks. The window function was applied in the field programmable gate arrays to mitigate the effect of these problems and tested it in algorithm level.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Rigid registration algorithm based on the minimization of the total variation of the difference map

Image registration is broadly used in various scenarios in which similar scenes in different images are to be aligned. However, image registration becomes challenging when the contrasts and backgrounds in the images are vastly different. This work proposes using the total variation of the difference map between two images (TVDM) as a dissimilarity metric in rigid registration. A method based on TVDM minimization is implemented for image rigid registration. The method is tested with both synthesized and real experimental data that have various noise and background conditions. The performance of the proposed method is compared with the results of other rigid registration methods. It is demonstrated that the proposed method is highly accurate and robust and outperforms other methods in all of the tests. The new algorithm provides a robust option for image registrations that are critical to many nano-scale X-ray imaging and microscopy applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Model and Standard Operating Procedures Supporting Signal Variation Flow Graph Analysis

This document will describe the principles of the Signal Variation Flow Technique, and the uncertainty models generated using it. We focus on the capture of the variation between the ideal signal and the measured signal. The ideal signal is defined to represent the signal output of a system whose full state behavior is known, with no variation in environment or during operation, and whose photons trajectories are not modified by the object. A CT uncertainty analysis is the result of two main steps. First, the radiography regime extends from the source to the collected image, which is 2D in the case of standard CT. This maps all upstream uncertainties into the variation observed on the radiograph and captures all variation in the physical domain. Second, the reconstruction regime extends from the captured images to the reconstructed 3D image. This regime is purely in the mathematical domain and corrects reconstruction algorithm artifacts/anomalies. The present work focuses on building the model through the radiography regime. The reconstruction regime is expected to be largely a study of algorithmic sensitivity, requiring the definition of a range of standardized tests through which the algorithms would be run. Radiographic variation would then be mapped through reconstruction sensitivities to predict the signal variation in the final image. An incomplete list for the reconstructed image variation output basis includes voxel density, edge blur and length variation, in analogous fashion to the basis functions presented in this work. The signal variation occurs in several forms at the radiograph. These forms are gathered into a complete basis set of functions describing all variation on the radiograph. The scale of each basis function is calculated independently via a specific SVFG. This includes 0D pixel noise (0DI), 0D energy noise (0DE), 1D blur (1DB), 1D length (1DL) or 2D position (2DP). These models are orthogonal in that they each explore a space in the signal variation domain that cannot be reached by the other basis functions. The variation basis functions, and associated SVFG models are split by output dimensionality, a term loosely used for categorization, and explained further below.

42 ENGINEERING↗
DOE OSTI2018

Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)

Land surface models (LSMs), which form the land component of earth system models, rely on numerous processes for describing carbon, water and energy budgets, often associated with highly uncertain parameters. Data assimilation (DA) is a useful approach for optimising the most critical parameters in order to improve model accuracy and refine future climate predictions. In this study, we compare two different DA methods for optimising the parameters of seven plant functional types (PFTs) of the ORCHIDEE LSM using daily averaged eddy-covariance observations of net ecosystem exchange and latent heat flux at 78 sites across the globe. We perform a technical investigation of two classes of minimisation methods – local gradient-based (the L-BFGS-B algorithm, limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints) and global random search (the genetic algorithm) – by evaluating their relative performance in terms of the model–data fit and the difference in retrieved parameter values. We examine the performance of each method for two cases: when optimising parameters at each site independently (“single-site” approach) and when simultaneously optimising the model at all sites for a given PFT using a common set of parameters (“multi-site” approach). We find that for the single site case the random search algorithm results in lower values of the cost function (i.e. lower model–data root mean square differences) than the gradient-based method; the difference between the two methods is smaller for the multi-site optimisation due to a smoothing of the cost function shape with a greater number of observations. The spread of the cost function, when performing the same tests with 16 random first-guess parameters, is much larger with the gradient-based method, due to the higher likelihood of being trapped in local minima. When using pseudo-observation tests, the genetic algorithm results in a closer approximation of the true posterior parameter value in the L-BFGS-B algorithm. We demonstrate the advantages and challenges of different DA techniques and provide some advice on using it for the LSM parameter optimisation.

58 GEOSCIENCES↗
DOE OSTI2022

On-policy learning-based deep reinforcement learning assessment for building control efficiency and stability

Artificial intelligence technologies have emerged as a game changer not only in specific applications such as image recognition and machine translation but also in many scientific domains. In particular, as deep reinforcement learning (DRL) has shown great success in complex control problems, DRL-based control has been considered as a potential solution to efficiently control and manage building systems. However, broad assessment of DRL-based building control is still required to characterize their pros and cons in comparison with conventional building control methods (e.g., rule-based feedback controls). In this paper, we assessed DRL-based controls with on-policy learning-based algorithms and continuous control actions for cooling control of large office buildings in the summer season to minimize whole-building energy use and occupant discomfort. We compared DRL-based control methods with two baseline control methods: (1) a pre-determined schedule with supply temperature and static pressure setpoints, and (2) advanced reset method that adjusts setpoints based on heuristic rules, i.e., ASHRAE Guideline 36. We also tested the DRL algorithms to evaluate their performances in multiple climate locations. We found that DRL-based control methods outperformed the baseline control methods in terms of energy savings while maintaining a thermal comfort. DRL reduced energy use between ~4%–22% on average compared to the baseline methods, depending on climate location. We also evaluated DRL-based control in terms of control stability and showed that DRL-based methods should address the span of hardware lifetimes in practical operations.

control stability↗
DOE OSTI2018

An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments

We present that x-ray standing-wave photoemission experiments involving multilayered samples are emerging as unique probes of the buried interfaces that are ubiquitous in current device and materials research. Such data require for their analysis a structure optimization process comparing experiment to theory that is not straightforward. In this work, we present a new computer program for optimizing the analysis of standing-wave data, called SWOPT, that automates this trial-and-error optimization process. The program includes an algorithm that has been developed for computationally expensive problems: so-called black-box simulation optimizations. It also includes a more efficient version of the Yang X-ray Optics Program (YXRO) [Yang, S.-H., Gray, A.X., Kaiser, A.M., Mun, B.S., Sell, B.C., Kortright, J.B., Fadley, C.S., J. Appl. Phys. 113, 1 (2013)] which is about an order of magnitude faster than the original version. Human interaction is not required during optimization. We tested our optimization algorithm on real and hypothetical problems and show that it finds better solutions significantly faster than a random search approach. The total optimization time ranges, depending on the sample structure, from minutes to a few hours on a modern laptop computer, and can be up to 100x faster than a corresponding manual optimization. Finally, these speeds make the SWOPT program a valuable tool for realtime analyses of data during synchrotron experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Multimodal Nondestructive Dry Cask Basket Structure and Spent Fuel Evaluation

This NEUP-IRP assembled a team from diverse institutions and a diverse set of expertise to examine the problem of assessing the structural health of dry nuclear fuel storage casks, specifically the TN-32 design. With no physical access designed into these storage casks and the high cost and risk associated with opening casks for direct inspection, the technical difficulty of structural assessment of the basket structure and fuel assemblies is high. In response to this challenge, this IRP brought together experts in both traditional and novel NDE methodologies: active acoustics (University of Mississippi), emissions tomography (University of Florida), muon tomography (Oregon State University), and passive-active ultrasonics (University of South Carolina). Further, our industry partners in the Electric Power Research Institute (EPRI) and AREVA provided invaluable insight into technical design details for the cask, practical knowledge of operating parameters at a storage facility, and key relationships that enabled activities unforeseen at the time of the proposed work but were critical in guiding the path of the research. The team decided early in the project that we had a collective goal of laying the technical ground work for practical solutions that could be implemented in the field with reasonable cost, time, and reliability. A fortunate opportunity arose early in this IRP project to obtain access to a new TN-32 cask at Columbiana High Tech. Both acoustics teams were given access for testing concepts in the first year of the project. This ended up providing critical guidance in the design of subsequent laboratory experiments. Specifically, it became clear that the only reliable acoustic pathways to the interior were through the trunnions, base plate, and top ring of the cask. Based on these field tests and discussions about what would be of practical interest, the team as a whole decided a realistic goal was to detect catastrophic failure of a single fuel assembly, or one that was missing. It is clear from this work that the active acoustics / resonance and muon tomography methodologies would be capable of detecting interior structural integrity problems at this level. The other two areas of investigation (emissions tomography and passive/active ultrasonics) would need more testing to determine a successful implementation, but each may also have other applications such as long term monitoring and monitoring during transportation. This IRP project resulted in technical advances with applications that extend beyond the problem at hand, supported the training of 10 graduate students + 1 post doc, 21 publications, and 13 conference presentations. The remainder of this summary highlights significant activities and achievements in each of the methodologies and a brief discussion on how they can be combined to leverage strengths. Active Acoustics The active acoustics team at UM used both linear and non-linear structural resonance methods to infer information about the internal structure of a cask. Significant activities were a set of four field test measurements on a full scale TN-32 cask at the Columbiana Hi Tech facility in Greensboro, NC and the design and fabrication of a 1/6th scale model TN-32 cask for laboratory measurements. A significant set of acoustic data has been acquired on both the full scale and laboratory scale casks. It was determined early in the project that mechanical resonance techniques were not going to be sensitive to small scale (centimeter) cracks and structural defects in the basket structure or fuel assemblies. We were able to ascertain that both the energy and complexity of the resonance spectra were sensitive to “bulk state” of the fuel assemblies – an intact assembly versus a severely degraded assembly. By developing a novel combination of metrics, we are able to measure a statistically significant shift due to one out of the 32 fuel assemblies being either missing or degraded to a point of complete loss of structural coherence (i.e. “rubble”). We believe this combination of resonance metrics is new to the field and will have applications beyond the fuel storage problem. Emissions Tomography The Emissions Tomography team at the University of Florida explored the concept of using scattering of radiation emissions from the stored fuel as method of evaluating the internal structural health of the cask. Due to the need for emissive fuel assemblies and the difficulties of gaining access to storage facilities, this team primarily relied on simulations to inform feasibility. A key question was the flux estimates of neutrons with a small enough number of scattering events to retain spatial information. Some key conclusions from the transmission component of this work are that the number of scattering events falls rapidly with energy – setting a 11MeV threshold, the average number of scattering events is 8∓4. Rough estimates of scanning times using an ideal detector array would be 1 week. The team also explored the case of significant structural failure resulting in a shift of the source material (fuel). The result is that a +/- 2% mass shift results in a detectable (5.13%) shift in the correlated 2 neutron emission counts and thus may be a candidate as an initial survey. Finally, simulations for predicting the azimuthal dependence of the flux based on specific loading patterns were conducted. Due to self-shielding effects of the assemblies, a periodic pattern results and could potentially be used to identify missing or severely degraded assemblies. This work forms a basis for proper detector placement with the goal of optimizing sensitivity. Muon Tomography Muon tomography (MT) is a relatively novel method of interrogating interior structure on a large scale. One of the advantages of this technique is that the exciting energy source (cosmic muons) is ubiquitous and relatively stable in time. Challenging aspects are relatively low flux rates and appropriate detector technologies. The efforts of the muon group at Oregon State University focused on two areas: (1) simulation of various combinations of muon scattering events and detection schemes to assess sensitivities and time scales for image construction and (2) building a prototype muon detection system applicable to the dry storage cask problem as well as many other similar systems. Significant accomplishments in this area are: (1) demonstration that MT is a viable method for detecting missing or severely degraded rod assemblies, (2) a scintillation based detector with 4 panels and 32 channels of position data each were constructed, tested, and calibrated. Testing of the panels using natural muon background and no scattering objects yielded largely expected results – a straight line path for the muon through the four panels. However some anomalies (paths deviating from a line) were also detected. A redesign of the board improved the signal which greatly improved the muon tracking through the panels. Testing with lead bricks as scattering bodies showed a reduction in the coincidence rate with increasing bricks indicating increased scattering events. A refinement of the scintillator detector design resulted in a spatial image resolution of around 2 cm based on lead brick spacing tests lasting 24 hours. Passive/Active Ultrasonics This team at the University of South Carolina investigated the feasibility of combining passive ultrasonics, in which an elastic pulse is generated by a degradation process (such as a crack extending), and active ultrasonics, in which a known excitation is introduced into the structure and detected by an array of transducers. Significant accomplishments in this area include the development of elastic wave propagation simulation in relevant materials, development of diagnostic algorithms, and laboratory testing and validation using laser doppler vibrometry. This group demonstrated the applicability of various non-contact vibration detection methodologies such as air-coupled ultrasonic transducers and scanning laser doppler vibrometry. While passive acoustic emissions monitoring will likely have a role during the transportation of a fuel storage cask, the level of detailed information about the defect generating the event which can be ascertained from the detections in a complex structure remains for future work. Thermography The EPRI team contributed early in the project a series of thermography measurements in which various levels of water pooled in a mock-up canister were analyzed using commercially available infrared imaging technologies. While large levels of water (100 L, ~10 cm deep) were observable in the thermograms, more realistic levels of pooled water (1 L) were not detectable whether localized or distributed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Autonomous Closed-Loop Experimental Characterization and Dynamic Model Validation of a Scaled Underwater Kite

This paper presents the closed-loop experimental framework and dynamic model validation for a 1/12-scale underwater kite design. The pool-based tow testing framework described herein, which involves a fully actuated, closed-loop controlled kite and flexible tether, significantly expands upon the capabilities of any previously developed open-source framework for experimental underwater kite characterization. Specifically, the framework has allowed for the validation of three closed-loop flight control strategies, along with a critical comparison between dynamic model predictions and experimental results. In this paper, we provide a detailed presentation of the experimental tow system and kite setup, describe the control algorithms implemented and tested, and quantify the level of agreement between our multi-degree-of-freedom kite dynamic model and experimental data. Finally, we also present a sensitivity analysis that helps to identify the most influential parameters to kite performance and further explain the remaining mismatches between the model and data.

42 ENGINEERING↗
DOE OSTI2022

Comparison of Machine Learning and Deep Learning for View Identification from Cardiac Magnetic Resonance Images

Background: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL) and classical machine learning (ML) techniques without user input. We aimed to compare the accuracy of ML and DL for CMR view classification and to identify potential pitfalls during training and testing of the algorithms. Methods: To train our DL and ML algorithms, we first established datasets by retrospectively selecting 200 CMR cases. The models were trained using two different cohorts (passively and actively curated) and applied data augmentation to enhance training. Once trained, the models were validated on an external dataset, consisting of 20 cases acquired at another center. We then compared accuracy metrics and applied class activation mapping (CAM) to visualize DL model performance. Results: The DL and ML models trained with the passively-curated CMR cohort were 99.1% and 99.3% accurate on the validation set, respectively. However, when tested on the CMR cases with complex anatomy, both models performed poorly. After training and testing our models again on all 200 cases (active cohort), validation on the external dataset resulted in 95% and 90% accuracy, respectively. The CAM analysis depicted heat maps that demonstrated the importance of carefully curating the datasets to be used for training. Conclusions: Both DL and ML models can accurately classify CMR images, but DL outperformed ML when classifying images with complex heart anatomy.

artificial intelligence↗