Large Scale Extraordinary Mechanics

Sigrid Adriaenssens, Ph.D., F.SEI, A.M.ASCE

Professor of Civil and Environmental Engineering, Director, Program in Mechanics, Materials and Structures, Princeton University

Abstract: Builders throughout history have made significant strides in exploiting forms to enclose three‐ dimensional spaces, to provide shelter or to bridge two‐dimensional voids, such as water and roadways. In absence of numerical prediction methods, they resorted to trial and error construction practices or structural theory to establish a good enough structural form.

Pier Luigi Nervi, structural engineer and designer of the exquisite Little Sports Palace (Rome, Italy, 1958), stated: ‘Resistance due to form, although the most efficient and the most common type of resistance to be found in nature, has not yet built in our minds those subconscious intuitions which are the basis for our structural schemes and realizations’. I place my scholarship in this force‐modeled form tradition and focus on the advancement of analytical and computational approaches to predict and design the overall properties, stability, and failure of structural surfaces. In particular, I am interested in shells, membranes, and rod networks because they exhibit fascinating mechanical behaviors. I will talk about how we discovered, studied, designed and built surfaces that efficiently carry extreme loading, self-lock, adjust their stiffnesses, morph shape, or amplify motion. We have used these extraordinary mechanics to innovate systems ranging from macro-scale adaptive shading devices to medium-scale robotically constructed waste-free vaults and large-scale storm surge barriers.

Biosketch: Sigrid Adriaenssens’s research interests lie in the mechanics of large‐span structural surfaces under extreme loading and more recently under construction. She has been working on a comprehensive framework with advanced analytical formulations, numerical form finding and optimization approaches, fluid/structure interaction, and machine learning models and algorithms to open new avenues for accelerated discoveries and automated optimal designs. In terms of applications, she has used this framework to successfully innovate structural and architectural systems ranging from macroscale adaptive shading shell devices to large‐scale storm surge membrane barriers.

This year, she holds the Francqui Chair (Ghent University, Belgium, 2024). She was the Myron Goldsmith Visiting Chair at the College of Architecture at Illinois Institute of Technology (2023), named Fellow of the Structural Engineering Institute of the American Society of Civil Engineers (ASCE), elected vice‐president of the International Association of Shell and Spatial Structures (IASS), and received the DigitalFUTURES Matthias Rippmann Award (Tongji University, China) and the Pioneers’s Award (Spatial Structures Research Centre of the University of Surrey, UK) (2021). In 2018 she received the ASCE George Winter Award. She is the IASS Technical Activities coordinator and co-chairs the IASS Continuous Shells Working Group. She is the co‐editor of the International Journal of Space Structures and directs the Form Finding Lab at Princeton University, where she teaches courses on (non‐)linear mechanics of solids and slender structures, structural design, and the integration of engineering and the arts.

Variational Phase-field Modeling of Fracture: Towards Second-Generation Models

Laura De Lorenzis, Ph.D.

Professor, Department of Mechanical and Process Engineering, Deputy Head of Dept. of Mechanical and Process Engineering, Deputy Head of Institute of Mechanical Systems, ETH Zürich

Abstract: Variational phase-field modeling of fracture, first proposed in 2000 for brittle fracture of homogeneous and isotropic materials in predominant mode I, has since been further developed in several directions, encompassing the extension to multiaxial stress states, to heterogeneous and anisotropic materials, to ductile, dynamic and rate-dependent fracture. While the first model was based on the regularization of a variational reformulation of Griffith’s fracture criterion, for many of the subsequent extensions the structural rigidity of the variational framework led to the proliferation of non-variational models, which give up the theoretical and practical advantages of the variational framework in exchange for a greater flexibility to reproduce experimental results.

In this presentation, we discuss some ideas by which variational phase-field models can be endowed with sufficient flexibility to overcome the limitations of existing models, possibly leading to a second generation of variational phase-field fracture models. We show some first results in this direction concerning fracture under multiaxial stress states, fracture of anisotropic materials, and dynamic fracture.

Biosketch: Laura De Lorenzis received her Engineering degree and her PhD from the University of her hometown Lecce, in southern Italy, where she first stayed as Assistant and later as Associate Professor of Solid and structural mechanics. In 2013 she moved to the TU Braunschweig, Germany, as Professor and Director of the Institute of Applied Mechanics. There she was founding member and first Chair (2017-2020) of the Center for Mechanics, Uncertainty and Simulation in Engineering. Since 2020 she is Professor of Computational Mechanics at ETH Zürich, in the Department of Mechanical and Process Engineering. She was visiting scholar in several renowned institutions, including Chalmers University of Technology, the Hong Kong Polytechnic University, the Massachusetts Institute of Technology (as holder of a Fulbright Fellowship in 2006), the Leibniz University of Hannover (with an Alexander von Humboldt Fellowship in 2010-2011), the University of Texas at Austin and the University of Cape Town.

She is the recipient of several prizes, including the RILEM L’Hermite Medal 2011, the AIMETA Junior Prize 2011, the IIFC Young Investigator Award 2012, the Euromech Solid Mechanics Fellowship 2022, the IACM Fellowship 2024, two best paper awards and two student teaching prizes. In 2011 she was awarded a European Research Council Starting Researcher Grant. She has authored or co-authored more than 150 papers on international journals on different topics of computational and applied mechanics. Since 2023 she is Editor of Computer Methods in Applied Mechanics and Engineering.

Gaussian Processes for Scientific Machine Learning: From Function Approximation to Model Discovery

Houman Owhadi, Ph.D.

Professor, Applied and Computational Mathematics and Control and Dynamical Systems, California Institute of Technology (Caltech)

Abstract: Many challenges in science and engineering involve discovering functional relationships between variables and uncovering the underlying graphical structures that govern complex systems. These challenges can be categorized into three levels of increasing complexity:

• Type 1: Approximate an unknown function (e.g., a stress-strain relationship) using input/output data.
• Type 2: Represent a system as a graph of variables and functions (some unknown) indexed by nodes and edges. Given partial observations (e.g., boundary conditions or sensor measurements), estimate unobserved variables and unknown functions while ensuring consistency with the physics and dependencies encoded in the graph’s structure.
• Type 3: When the graph structure itself is unknown, use partial observations to infer the structure (e.g., interdependencies in a mechanical system) and approximate the unknown functions.

Examples of Type 2 problems include solving nonlinear partial differential equations (PDEs) and learning (possibly stochastic and/or differential) governing equations from limited data. Type 3 problems encompass discovering dependencies in mechanical systems, identifying chemical reaction networks, determining relationships in protein-signaling networks, and, more generally, data-driven model discovery.

Although Gaussian Process (GP) methods are sometimes perceived as a well-founded but old technology limited to Type 1 curve fitting, they can be generalized into an interpretable framework for solving Type 2 and Type 3 problems all while maintaining the simple and transparent theoretical and computational guarantees of kernel methods. This extension leverages the variance decomposition and nonlinear sensitivity analysis properties of GPs, which lack natural counterparts in neural network methods. These properties allow GPs to seamlessly integrate traditional physics-based modeling with data-driven techniques, offering engineers a transparent, computationally efficient, and theoretically grounded approach to model discovery, uncertainty quantification, and predictive analysis.

Biosketch: Houman Owhadi is a professor of applied and computational mathematics and control and dynamical systems at the California Institute of Technology. His expertise includes uncertainty quantification, numerical approximation, statistical inference/learning, data assimilation, stochastic and multiscale analysis, and scientific machine learning. He was a plenary speaker at SIAM CSE 2015 and SIAM UQ 2024 and a tutorial speaker at SIAM UQ 2016. He received the 2019 Germund Dahlquist SIAM Prize. He is a SIAM Fellow (class of 2022) and a Vannevar Busch Fellow (class of 2024).

Mastering Reactive Flow through Fracture Networks in the Deep Subsurface: A Multiscale Grand Challenge for Clean Energy

Kevin M. Rosso, Ph.D.

Lab Fellow, Chemist; CUSSP Director, Physical Sciences, Pacific Northwest National Laboratory

Abstract: The global imperative to develop and rely on clean sources of energy puts our mastery of the Earth’s deep subsurface into focus. Whether for accessing the mantle’s glow for bountiful geothermal heat or for safe disposal of hazardous wastes, humankind is now venturing more ambitiously deep underground where we are blinder than seeing into the far reaches of outer space. This talk will highlight how a multidisciplinary team is tackling a piece of this subsurface science challenge through the Center for Understanding Subsurface Signals and Permeability (CUSSP), a DOE Office of Science Energy Earthshot Research Center focused on enabling broad deployment of Enhanced Geothermal Systems (EGS).

The EGS goal of maintaining heat production for decades from the flow of pressurized water through fracture networks induced in miles-deep hot crystalline rock requires revolutions in two main areas: 1) we must fundamentally understand how fluid pressures and chemical reactions in hot stressed rock alter flow pathways from the micro- to field-scales, and 2) develop new geophysical sensing tools to remotely detect those changes in real-time. To make advances on both fronts, CUSSP integrates a highly instrumented field-scale EGS testbed at the Sanford Underground Research Facility (SURF) established by DOE’s Geothermal Technologies Office (GTO) in combination with core-to-microscale laboratory experiments and multi-continuum and molecular reactive flow simulations.

Using chemically-controlled circulation tests at the testbed, CUSSP is developing the ability to reconstruct the spatiotemporal distribution of reactive fracture flow properties through joint inversions of time-lapse distributed seismic, strain, and temperature sensing with electrical resistivity tomography (ERT) for novel insights into changes in porosity, stress, and fluid chemistry. This entails developing enhanced strainmeter and ERT sensing capabilities, performing machine learning (ML) analyses of seismic datasets from previous testbed experiments to understand how changes in the coda signal relate to strain in pressurized fractures, enhancing the coupling of geophysical process models in our primary simulator PFLOTRAN, characterizing the petrographic, mineralogic, and geomechanical properties of the testbed host rock from the micro- to core-scale to inform reactive flow simulations, training ML algorithms to guide joint inversions to physically meaningful solutions, and implementing efficient coarse-grained modeling techniques to simulate interfacial reaction dynamics between pore fluids and mineral surfaces in the host rock over timescales relevant to our upcoming testbed experiments.

The project is well on its way of ultimately setting a new standard in the ability to predict and control fluid flow through EGS fracture networks, and in doing so will help pave the way to seeing below ground with unprecedented precision.

Biosketch: Dr. Kevin Rosso is a Laboratory Fellow and the Associate Director of the Physical Sciences Division for Geochemistry at PNNL. He leads the U.S. Department of Energy (DOE) Basic Energy Sciences Geosciences program at PNNL, and is the Director of the Center for Understanding Subsurface Signals and Permeability, a 10-institution Energy Earthshot Research Center funded by DOE’s Office of Science. Dr. Rosso is a well-recognized expert in uncovering mechanisms controlling reaction kinetics at mineral/fluid interfaces, such as for dissolution/precipitation, metal adsorption or incorporation, and electron transfer processes. His work has been applied to a diversity of topics such as metal sulfide oxidation, bacterial reduction of metal oxides, contaminant fate and transport, crystal growth and dissolution, geologic carbon sequestration, electrical energy storage, and stress corrosion cracking.

Dr. Rosso won the Mineralogical Society of America Award in 2004, the Hallimond Lectureship from the Mineralogical Society of Great Britain and Ireland in 2016, and the Stumm Medal from the European Association of Geochemistry (EAG) in 2020. He is a Fellow of the Geochemical Society, the EAG, the American Association for the Advancement of Science, and the American Geophysical Union. His research is compiled in over 430 publications with an H index of 81 (Google Scholar). He has been an active mentor for 50 post-doctoral researchers, graduate students, and early career scientists at PNNL and abroad.

Why Flood Mechanics Matter

Brett F. Sanders, Ph.D., EIT, F.EMI, F.EWRI, A.M.ASCE

Chancellor’s Professor, University of California Irvine

Abstract: Damage and disruption from flooding have rapidly escalated over recent decades. Knowing who and what is at risk, how these risks are changing, and what is driving these changes is of immense importance to flood management and policy. Furthermore, predictions of flood risk are critical to public safety. Robust methods for accurate predictions of flooding have been known for decades, grounded in shallow-water wave theory and computational methods for hyperbolic equations. However, many high-profile research studies reporting risks at national and global scales rely upon a significant oversimplification of how floods behave—as a level pool—an approach known as bathtub modeling that is avoided in flood management practice due to known biases (e.g., >200% error in flood area) compared to physics-based modeling.

With publicity by news media, findings that would likely not be trusted by flood management professionals are thus widely communicated to policy makers and the public, scientific credibility is put at risk, and maladaptation becomes more likely. This presentation will provide an overview of the mechanics and computational methods capable of robustly modeling floods, and document biases and uncertainties that may be introduced by reduced-physics approaches including bathtub models. We will also highlight the feasibility of physics-based flood simulation at regional, national and global scales.

Biosketch: Brett Sanders is a Chancellor’s Professor of Civil and Environmental Engineering, Urban Planning and Public Policy at UC Irvine. He earned a B.S. in Civil Engineering from the University of California, Berkeley and an M.S. and PhD in Civil Engineering at the University of Michigan emphasizing environmental fluid mechanics and computational methods.

Dr. Sanders’ research seeks to promote improved understanding of and responses to flooding and erosion risks. His work addresses coastal, riverine, urban and mountain risks. He is the developer of the ParBreZo and PRIMo flood simulation models for compound risk assessment at local to regional scales, and his work has informed the practice of collaborative flood modeling for effective and equitable flood adaptation. Dr. Sanders focuses research on compound and interconnected climate risks, and he presently leads collaborative flood modeling projects in California and Florida.

Dr. Sanders is a Fellow in the Engineering Mechanics Institute of ASCE, a Fellow of the Environmental and Water Resources Institute of ASCE, Fellow of the Faculty Academy for Teaching Excellence, a Chapter Author for the 6th National Climate Assessment, a recipient of the National Science Foundation CAREER Award, a Member of the Science Advisory Panel for the California Coastal Commission, and the recipient of numerous teaching awards at UCI. 

Find more information at his UCI Flood Lab website.