David H. Mollenhauer grew up in central Texas and received a B.S. degree in aerospace engineering from Texas A&M University in 1990. He received an M.S (’92). and a Ph.D. (’97) in engineering mechanics from the Virginia Polytechnic Institute and State University in Blacksburg, Virginia.
He is a research scientist and program manager in the Polymer Matrix Composites Branch at the Materials and Manufacturing Directorate, Air Force Research Laboratory (AFRL), Wright-Patterson Air Force Base, Ohio, where he has just completed his 34th year of employment. His research is focused on experimental mechanics of polymer matrix composites (PMC) and developing digital engineering tools for the processing-to-performance simulation of PMCs and ceramic matrix composites for aerospace.Dr. Mollenhauer is a member of the American Society for Composites and the American Institute for Aeronautics and Astronautics. He has won numerous technical and program management awards and recently was chosen as an AFRL Fellow.
Confined spaces represent one of the most dangerous environments in industry, with over 2.1 million entries annually in the U.S. and an average of two fatalities per week—60% of which involve would-be rescuers. These risks, combined with high operational costs and inconsistent inspection outcomes, demand not just incremental improvement, but a bold rethinking of what’s possible.
This plenary session challenges conventional thinking through the introduction of a groundbreaking IECEx/ATEX Zone 0-certified, non-electric, fully mechanical robotic platform. Developed in direct collaboration with oil, gas, and chemical industry leaders, the system was purpose-built for in-service inspections of floating roof tanks—but its capabilities have quickly outgrown that scope. By removing the need for human entry, it delivers transformative gains in safety, repeatability, and cost-efficiency across hazardous and confined environments
The presentation will cover lessons learned from working in “The Valley of Death”, transitioning NDT technology from research to practical applications. The talk covers ten rules-of-thumb acquired over the years, and highlight progress on transitioning oblique ultrasonic inspections, AI/ML, model-based inversion, and model-assisted POD evaluation to wider use today.
Dr. John C. Aldrin is the principal of Computational Tools, specializing in nondestructive evaluation modeling and simulation, data analysis, inverse methods, AI/ML and reliability assessment. He has co-authored over 180 journal, conference and book publications in the field of nondestructive testing and is a Fellow of ASNT..
The Association of American Railroads (AAR), through its subsidiary MxV Rail, has a proven track record of advancing rail technology across North America. Guided by its owners and stakeholders, AAR funds and supports research at MxV Rail through the Strategic Research Initiatives (SRI) program that drives innovation in equipment, infrastructure, and operating practices - enhancing safety, reliability, and efficiency. This comprehensive program spans from foundational science in understanding root cause analysis to real-world implementation of new technologies.
This presentation presents a rigorous examination of key NDE/NDT inspection technologies developed and/or evaluated by MxV Rail under the SRI program that have measurably advanced operational efficiency and elevated railway safety across North America. These innovations have redefined the capabilities of current NDE/NDT inspection systems, with a tangible industry-wide impact. The discussion will focus on cutting-edge developments in rail inspection, advanced cracked/broken wheel detection systems for freight car wheels, and the Locomotive Undercarriage Thermal Inspection System (LUTIS) for real-time traction motor monitoring. A unifying theme underscores that the future of NDE/NDT systems must be automated, data-rich, and seamlessly integrated with the rail industry's evolving big data and predictive analytics infrastructure - essential for reducing derailments, minimizing unscheduled maintenance, and maximizing safety, reliability, and performance.
Anish Poudel is the Scientist for Nondestructive Evaluation within the Research and Innovation department of MxV Rail (formerly Transportation Technology Center, Inc.). Dr. Poudel holds a Bachelor of Science, Master of Science, and Doctor of Philosophy degrees in Mechanical Engineering, focusing on Experimental Mechanics and Nondestructive Evaluation/Testing (NDE/NDT).
At MxV Rail, Dr. Poudel serves as a program manager, team leader, and subject matter expert (SME), providing advanced technical expertise related to NDE, wayside inspection technologies, and data analytics for the North American Railway Industry, the American Association for Railroads (AAR), and other programs. He is an experienced mentor within MxV’s engineering and research department and is a primary point of contact for NDE-related matters in the railway industry. Dr. Poudel has been instrumental in researching, developing, and implementing several NDE inspection systems at MxV Rail that have enhanced efficiency and, more importantly, driven railway safety to new heights. These advancements have pushed the boundaries of current NDE technology and significantly impacted the North American railway industry.
Electromagnetic tomography (EMT) is a non-destructive imaging technique that facilitates the visualization of the interior of an object or medium by analyzing its response to electromagnetic fields. EMT evaluates variations in electrical properties, including conductivity, dielectric permittivity, and magnetic permeability.
However, reconstructing the internal structure of an object presents challenges. This is largely due to the ill-posed nature of the underlying inverse problem, where minor errors in measurements can result in significant inaccuracies in the reconstruction. In addition, the process is further complicated by the nonlinear relationship between the material properties and the measured data, even when the materials themselves exhibit linear behavior.
In this presentation, we will examine specific imaging methods aimed at tackling the inverse obstacle problem, which involves identifying the shape of an unknown anomaly within a known material. This subject has a broad range of applications, including traditional Non-Destructive Testing (NDT), structural health monitoring, industrial process monitoring, geophysical exploration, biomedicine, and security.
Our primary focus will be on the Monotonicity Principle (MP), which supports a suite of real-time imaging methods that are effective for the inverse obstacle problem, even when working with nonlinear materials. We will introduce the MP, discuss its recent extension to nonlinear materials, and examine the related imaging methods, addressing key challenges such as theoretical limits and robustness (regularization) in the presence of noisy data.
This presentation will begin by briefly summarizing the historical developments of nondestructive evaluation (NDE) and structural health monitoring (SHM) that have led to a general four-step paradigm for implementation of these technologies:
Although these specific terms might not be used explicitly, almost all NDE and SHM approaches used in practice, including those that make use of physics-base models, employ these four steps in some manner with the rigor associated with Step 4 varying the most.
With this background, the hypothesis that damage increases the complexity of a system will be proposed. To provide a detailed justification for this hypothesis, a general overview of the concept of complexity will be presented. Next, a discussion of various damage mechanisms and the complexity associated with them will be presented where it will be shown that the increased complexity associated with damage occurs at all length scales. Following this discussion, a derivation will be presented showing that the system entropy increases because of damage and that increase in entropy can be viewed as a measure of the increase in the system’s complexity. The presentation will then focus on three types of complexity that are germane to NDE and SHM:
Finally, some of the implications of this hypothesis on the four-step paradigm listed above will be presented.
Charles Farrar obtained a Ph.D. in civil engineering from the University of New Mexico in 1988. He is currently a guest scientist at Los Alamos National Laboratory (LANL) where he has worked since 1983. He recently stepped down from his positions as the leader of LANL’s Engineering Institute (for the past 20 years, a research and education collaboration between LANL and the Univ of California San Diego’s (UCSD) Jacobs School of Engineering), and the coordinator for LANL’s Engineering Leadership Council (past 3 years). The first ten years of his career focused on performing experimental and analytical structural dynamics studies for a wide variety of systems including nuclear power plant structures subjected to seismic loading, and weapons components subjected to various portions of their stockpile-to-target loading environments. Since 1992 his research interests have focused on developing integrated hardware and software solutions to structural health monitoring (SHM) problems. The results of this research have been documented in 400+ coauthored journal publications, conference proceedings, reports and a book entitled Structural Health Monitoring A Machine Learning Perspective. Additional professional activities include an adjunct faculty appointment in the structural engineering department at UCSD where he teaches a course entitle “Structural Health Monitoring Principles,” and the development of a structural health monitoring short course that has been offered more than 45 times to industry and government agencies in Asia, Australia, Europe, South America and the U.S. He is the founder of the Los Alamos Dynamics Summer School, and he is a co-developer of the Los Alamos Judicial Science School. He is a Los Alamos National Laboratory Fellow and a Fellow in the American Society of Mechanical Engineers, American Society of Civil Engineers and the Society for Experimental Mechanics.