Metal hydrides are pivotal materials in the hydrogen energy landscape, serving as one of the few safe and compact solid-state options for hydrogen storage. These materials function by chemically binding hydrogen to metal lattices through chemisorption, effectively locking hydrogen atoms within the structure. Unlike high-pressure gaseous tanks or cryogenic liquid hydrogen storage, metal hydrides offer improved safety profiles and reduce energy-intensive requirements. However, despite these advantages, their widespread application is hindered by their limited durability and performance degradation resulting from repeated hydrogen absorption and desorption cycles.

At the heart of this ambitious research endeavor is the intricate problem of metal hydride vessel degradation. Over numerous charge and discharge cycles, physical changes ensue within the alloy powders comprising the metal hydride bed. These changes include fracturing of particles, densification of the powder bed, and mechanical stresses that collectively impair hydrogen storage capacity and vessel performance. Traditional evaluation methods rely heavily on prolonged, costly experimental testing to assess long-term vessel integrity—a bottleneck that impedes innovation and commercialization.

The project team, headed by Dr. Richard S. Fu of SwRI and Dr. Mohamed Shaat of St. Mary’s University, seeks to transcend these limitations by integrating advanced physics-based simulations with targeted experimental validation. This dual approach aims to create a robust modeling framework capable of forecasting the lifecycle and operational durability of TiFe metal hydride vessels. The predictive tool will account for the coupled chemical, thermal, and mechanical phenomena driving hydrogen cycling performance over hundreds or even thousands of cycles.

Dr. Shaat is spearheading the development of an intricate, multiphysics computational framework at St. Mary’s University. This model is designed to encapsulate the complex interplay between hydrogen diffusion, chemisorption reactions, phase transformations within the metal hydride, heat generation and transfer, and the evolution of mechanical stress within the vessel. By accurately simulating these interconnected processes, the framework seeks to facilitate precise performance predictions, while also identifying design parameters and operational regimes that optimize both efficiency and vessel longevity.

Central to the team’s vision is the translation of fundamental physical and chemical principles of hydrogen interaction into computationally efficient predictive models. Dr. Shaat emphasizes the importance of capturing the coupling mechanisms—particularly how hydrogen transport influences thermal and mechanical responses and vice versa—in order to provide reliable simulations that replace and reduce the dependence on lengthy physical testing. This innovative approach promises to significantly reduce costs, accelerate development cycles, and improve hydrogen storage system designs.

On the experimental front, SwRI’s team, under Dr. Fu’s guidance, is undertaking controlled, long-term cycling experiments to collect essential validation data. By subjecting metal hydride vessels to realistic charge-discharge scenarios, they aim to quantitatively monitor performance degradation mechanisms as they unfold. These experiments will provide the empirical backbone necessary to calibrate and refine the modeling framework, ensuring that simulation outputs faithfully represent real-world behavior under operational conditions.

The synergy between modeling efforts and experiments forms the cornerstone of the project’s iterative design loop. As Dr. Fu explains, modeling insights will direct the focus of experimental tests to critical phenomena, while the resulting data will feedback to correct and enhance the models. This dynamic interplay is expected to unravel previously obscure degradation mechanisms by illuminating the coupled chemical, thermal, and mechanical interactions at work within the metal hydride vessels. Ultimately, this feedback-driven methodology will yield a predictive tool capable of guiding material improvements and engineering decisions.

Hydrogen’s emergence as a clean, versatile energy carrier is a key cornerstone of global efforts to decarbonize energy and transportation sectors. However, to fulfill its potential, advances in storage technology are imperative. Unlike batteries or fossil fuels, hydrogen storage demands novel materials and engineering solutions due to its unique physical and chemical characteristics. By focusing on metal hydride vessels, this project addresses a promising yet underdeveloped storage pathway that could unlock safer, more efficient hydrogen deployment across diverse applications.

The S²TAR funding mechanism plays an instrumental role in enabling this interdisciplinary collaboration by providing seed resources that draw on the expertise strengths of both SwRI and St. Mary’s University. This partnership is not only poised to produce high-impact scientific outcomes but also lays the foundation for sustained cooperation. Through this collaboration, the team is well-positioned to attract larger external funding sources from federal agencies and industry players focused on advancing hydrogen infrastructure and clean energy technology.

As work progresses, the team anticipates that their predictive modeling tool will be an invaluable resource for the hydrogen storage community. It will offer designers and researchers an evidence-based platform to evaluate new materials, optimize vessel designs, and predict operational lifetimes with higher confidence. By systematically addressing the core durability challenges of metal hydride storage, this project represents a wide-reaching advancement that could significantly accelerate solid-state hydrogen storage adoption worldwide.

In conclusion, this joint initiative by Southwest Research Institute and St. Mary’s University epitomizes the cutting edge of hydrogen storage research. Through combining rigor in experimental validation and sophistication in physics-based modeling, the project champions a future where hydrogen is stored more safely and efficiently. The knowledge generated here promises to not only rewrite the fundamentals of storage vessel degradation understanding but also to catalyze practical applications in the global hydrogen economy.

Subject of Research: Physics-based modeling and experimental validation of TiFe-based metal hydride hydrogen storage vessel degradation and durability.

Article Title: Advancing Solid-State Hydrogen Storage: Predictive Modeling and Experimental Synergy for Metal Hydride Vessel Durability

News Publication Date: June 24, 2026

Web References: https://www.swri.org/markets/energy-environment/power-generation-utilities/advanced-power-systems/hydrogen-energy-research?&utm_medium=referral&utm_source=eurekalert!&utm_campaign=s2tar-metal-hydrides-pr

Image Credits: Southwest Research Institute

Keywords

Hydrogen storage, metal hydrides, TiFe alloys, chemisorption, physics-based modeling, thermo-mechanical framework, hydrogen diffusion, phase transformation, performance degradation, solid-state hydrogen storage, experimental validation, multiphysics simulations

Metabolic cost — the quantifiable amount of energy the human body consumes during physical activities such as walking, running, or performing everyday tasks — serves as a critical metric in understanding movement efficiency and muscular demands. Dr. Nicholas Vandenberg, a research engineer at SwRI and co-principal investigator, emphasizes the immense value of reliably estimating this parameter: it offers rehabilitative specialists an objective means to tailor therapies aimed at optimizing patients’ energy expenditures, ultimately facilitating improved mobility and reducing fatigue. By advancing predictive capabilities beyond traditional, marker-based systems, the ENABLE platform promises enhanced precision without the encumbrance and complexity of physical markers.

Central to this project is the innovative ENABLE system, which leverages state-of-the-art computer vision and deep learning algorithms to capture three-dimensional kinematics without intrusive markers. This technology transcends conventional biomechanics tools by enabling seamless, markerless motion capture that integrates biomechanical modeling expertise to generate robust datasets ideal for clinical and sports science applications. The deployment of ENABLE particularly focuses on diverse subject groups, including individuals with below-the-knee amputations, reflecting the commitment to addressing mobility challenges through sophisticated engineering solutions.

St. Mary’s University’s expertise in machine learning, led by Dr. Ricardo Ramirez, complements ENABLE’s technological foundation by developing algorithms that interpret 2D video footage to predict metabolic costs. This project significantly expands upon earlier work by incorporating three-dimensional video analyses, thus enriching data granularity and improving prediction fidelity. The synergistic integration of computer vision data and musculoskeletal simulations permits an unprecedented understanding of muscle-specific energy consumption, facilitating the refinement of both biomechanical models and artificial intelligence approaches.

To ensure scientific rigor, the collaboration employs metabolic carts to obtain direct, real-time measurements of energy expenditure from study participants. Such empirical data serve as the ground truth against which the validity of machine learning predictions is tested. By juxtaposing model-generated estimates with observed metabolic rates, the team aims to iteratively enhance algorithmic accuracy, enabling predictive models to capture nuances in human movement efficiency that have traditionally been elusive.

One of the groundbreaking aspects of this research lies in the use of OpenSim models that incorporate individual muscle fibers and dynamics. Through these sophisticated simulations, researchers can dissect the metabolic cost contributions across distinct muscle groups, offering a detailed biomechanical perspective that informs both clinical and athletic interventions. This granular insight is paramount for devising rehabilitation strategies tailored not only to overall movement patterns but also to specific muscular demands that influence fatigue and injury risk.

The implications of this technology extend well beyond clinical rehabilitation. As ENABLE refines its capabilities, its potential application in sports science could redefine athletic training by enabling coaches and therapists to quantify metabolic loads with heightened accuracy. Through personalized data on muscle efficiency and energy expenditure, performance optimization may be approached with precision previously unattainable, fostering advancements in injury prevention and recovery protocols.

Moreover, the practical advantages of using a markerless system facilitate broader accessibility and scalability in real-world settings. The reduction in setup time, participant discomfort, and equipment expenses opens avenues for wider adoption in outpatient clinics, athletic training facilities, and research laboratories. This democratization of biomechanical assessment tools aligns with broader trends toward wearable and non-invasive health monitoring technologies, signaling a paradigm shift in human performance analytics.

The focus on individuals reliant on prosthetic devices underscores the project’s emphasis on addressing critical gaps in mobility research. By illuminating subtle gait inefficiencies and energy expenditure patterns among prosthesis users, the research offers opportunities to tailor prosthetic design and fitting with unprecedented precision. Such customized approaches have the potential to lessen fatigue, enhance comfort, and improve the overall quality of life for millions of individuals experiencing limb loss.

In addition to clinical populations, the robust data generated through this project could catalyze research into neuromuscular diseases, age-related mobility decline, and occupational biomechanics, where metabolic cost assessment is vital. The integration of ENABLE’s motion capture with computational models and machine learning tools exemplifies how interdisciplinary approaches can unravel complex biological processes and translate them into tangible health solutions.

As the research team continues to iterate on the metabolic cost prediction algorithms, their vision encompasses a comprehensive system capable of evaluating a full spectrum of activities, from simple ambulation to high-intensity exercise. By distributing metabolic cost predictions across muscle groups and functional tasks, the technology aspires to offer insights that inform everything from prosthetic user rehabilitation to elite athlete conditioning.

This venture, supported by an investment of $127,750 from the S²TAR program, represents a forward-looking commitment to bridging engineering ingenuity and biomedical science. ENABLE embodies the next frontier in biomechanical evaluation, merging artificial intelligence and biomechanics for a future where movement efficiency metrics are accessible, reliable, and deeply informative. The outcomes of this partnership hold promise for transforming rehabilitative care, enhancing prosthetic technologies, and optimizing human performance across diverse populations.

For a deeper understanding of ENABLE and its applications, interested parties are encouraged to visit the official Southwest Research Institute web page dedicated to this innovative technology.

Subject of Research: Markerless motion capture for metabolic cost prediction in biomechanics and rehabilitation.

Article Title: Revolutionizing Metabolic Cost Estimation: ENABLE™ Markerless Motion Capture and Machine Learning Transform Rehabilitation and Performance Assessment.

News Publication Date: June 23, 2026.

Web References: https://www.swri.org/markets/biomedical-health/biomedical-devices/biomechanics-human-performance/engine-automatic-biomechanical-evaluation-enable?&utm_medium=referral&utm_source=eurekalert!&utm_campaign=s2tar-metabolic-pr

Image Credits: Southwest Research Institute

Keywords

Markerless motion capture, ENABLE™, metabolic cost prediction, musculoskeletal modeling, machine learning, biomechanics, rehabilitation, prosthetics, gait analysis, energy expenditure, computer vision, OpenSim models.