Computational Mechanics seminars

Mechanochemical recycling uses collisions in ball mills to create reactive environments within solid plastic feedstocks. Carsten Sievers examines how these environments enable depolymerization and where they impose kinetic and thermodynamic limits. For PET milled with sodium hydroxide, reaction rates depend on interfaces between reactants. Raman microscopy, focused-ion-beam transmission electron microscopy, small-angle X-ray scattering and material-point simulations characterize individual impact craters. Amorphous regions react above a minimum impact force, whereas crystalline regions require impacts exceeding PET’s yield point. Milling polystyrene produces styrene at steady rate and selectivity alongside minor products, including oxygenates. Removing monomer continuously prevents repolymerization, while thermodynamic analysis identifies limits on olefin release from chain-end radicals and motivates more energetic, longer-lived reaction environments. Coupling chain cleavage to hydrogenation or oxidation provides another driving force. An example uses hydrogen peroxide and an iron-oxide catalyst during polyethylene milling, producing oxidation at positions along the backbone followed by carbon-carbon bond cleavage.

This seminar investigates Anderson localization in subwavelength elastic periodic systems with random perturbations. For the unperturbed structure, layer-potential methods recast the eigenvalue problem as boundary integral equations, yielding asymptotic expressions for subwavelength eigenvalues and establishing a band gap above the subwavelength band. A Floquet transform then brings the perturbed problem into a periodic formulation and produces equations for resonant frequencies under general perturbations. Numerical experiments on monomer and dimer structures test agreement with the analysis. Increasing the strength and number of random perturbations demonstrates localization, providing a mathematical basis for elastic metamaterial design.

Richard Huang investigates how DNA nanostructures deform under uneven hydrodynamic forces, a question relevant to drug delivery, vaccines and biosensors. Coarse-grained molecular dynamics and mechanical models first examine linear and circular DNA in extensional flow. Drag concentrates tension near the middle of linear duplexes, disrupting base pairs and causing overstretching. Deforming minicircles straighten along their sides while concentrating bending into opposing tips and localized kinks. Models based on DNA elasticity and hydrodynamic-force distributions explain these responses, then extend to wireframe DNA origami to study force transmission and how local structural transitions produce whole-particle deformation. The results inform structures designed for stability or controlled deformation in flow. Online via Zoom. Friday 2 October 2026, 12:00–13:00 EDT (America/New_York; UTC−4). Open the Zoom link on the official event page; the audience explicitly includes the public. Richard Huang is affiliated with MIT. Organized by MIT Department of Mathematics — Computational Research in Boston and Beyond Seminar.

Landslides remain a major global hazard, with thousands of events each year posing significant impacts to human life, infrastructure, and ecosystems. While advanced constitutive models enable detailed slope-stability analyses at the single-hillslope scale, a critical gap persists in assessing landslide hazards at the community scale. Existing regional-scale models often struggle to balance physical rigor, predictive accuracy, and computational efficiency. They rely either on empirical approaches with limited physical basis or on mechanistic formulations that rely on major simplifying assumptions. This study presents CRISIS (Coupled Regional Rainfall-Induced and Seismic Slope Instability Simulations), a physics-based regional landslide prediction framework. It couples a pseudo-3D slope-stability formulation with a transient three-dimensional groundwater flow model. CRISIS operates in back-analysis and forward-prediction modes, integrating multi-scale topographic, geotechnical, hydraulic, and hydrological data. Back-analysis of both failed and stable slopes from prior events generates high-resolution spatial estimates of shear strength. These estimates are iteratively refined and used for forward prediction of landslide location, size, depth, and timing under future triggering events. Results reveal a strong geospatial connection between hillslope location and dominant failure mechanisms, emphasizing that landslide initiation is inherently geospatial and governed by coupled surface–subsurface processes. Application of the framework to Hurricane Maria in Puerto Rico, where over 70,000 landslides were triggered, demonstrates close agreement between predicted and observed landslide locations, sizes, and timing. The broader vision of this work is to advance regional characterization, assessment, and mitigation of cascading hazards through data-model integration.

Granular materials compose some of the most complex natural systems on Earth, governing the dynamics of hillslope creep, rock avalanches, landslides, debris flows, and concentrated pyroclastic density currents. Despite their prevalence, the mechanics of these granular systems remain highly enigmatic. To link external observations to internal states, we utilize particle-resolved numerical methods. By simulating both gravity-driven and fluid-destabilized granular beds, we track the fundamental grain-scale processes that govern macroscopic failure. We find that the entire lifespan of a granular flow—from the static state through yielding, fluidization, and final arrest—can be continuously mapped using two distinct probes: one isolating boundary stress fluctuations, and another measuring internal kinematic and structural evolution. We then validate this internal volumetric probe using optical Particle Image Velocimetry (PIV) data from scaled laboratory experiments, demonstrating how these numerical insights may be directly applied to remote geophysical monitoring.

Postfire debris flows pose a hazard to human life, property, and infrastructure when they travel from steep source areas to urbanized alluvial fans or other developed areas. Existing methods for rapid (< 1 week) postfire debris-flow hazard assessment document the increase in the likelihood and size of debris flows as the magnitude of high-intensity rain necessary to initiate debris flows increases but do not indicate the extent of downstream debris-flow runout. Although many models for the simulation of debris-flow motion are available, there is no established approach for using these models to delineate locations susceptible to postfire debris-flow runout that (1) is feasible to use at the spatial scale of an entire fire; (2) is appropriate for runout onto unconfined areas; (3) reproduces observed relations between runout and rainfall intensity; and (4) characterizes inherent uncertainty in runout, even without spatiotemporally variable rainfall. We propose and evaluate a method for generating postfire debris-flow runout hazard maps that has all the above qualities. Selection of case studies prioritized events triggered by a range of rainfall intensities, locations within and outside of southern California, and observed runout onto unconfined topography. Qualitative and quantitative assessment of performance for four events indicate that simulation results broadly match observations albeit with some discrepancies at a scale larger than structure or land parcel level (approximately 20-m by 20-m). The method may be used to identify potentially hazardous areas immediately following a fire and to provide approximate runout forecasts when a storm is imminent.

The eye is a multi-component biological system, where mechanics, optics, transport phenomena and chemical reactions are strictly interlaced, characterized by the typical bio-variability in sizes and material properties. The eye’s response to external action is patient-specific and it can be predicted only by a customized approach, that accounts for the multiple physics and for the intrinsic microstructure of the tissues, developed with the aid of forefront means of computational biomechanics. Our activity in the last years has been devoted to the development of a comprehensive model of the cornea that aims at being entirely patient-specific. While the geometrical aspects are fully under control, given the sophisticated diagnostic machinery able to provide a fully three-dimensional images of the eye, the major difficulties are related to the characterization of the tissues, which require the setup of in-vivo tests to complement the well documented results of in-vitro tests. The interpretation of in-vivo tests is very complex, since the entire structure of the eye is involved and the characterization of the single tissue is not trivial. The availability of micromechanical models constructed from detailed images of the eye represents an important support for the characterization of the corneal tissues, especially in the case of pathologic conditions. In this presentation I will provide an overview of the research developed in our group in terms of computational models and experimental approaches developed for the human cornea.

The fusion of cell aggregates widely exists during biological processes such as development, tissue regeneration, and tumor invasion. Cellular spheroids (spherical cell aggregates) are commonly used to study this phenomenon. In previous studies, with approximated assumptions and measurements, researchers found that the fusion of two spheroids with some cell type is similar to the coalescence of two liquid droplets. However, with more accurate measurements focusing on the overall shape evolution in this process, we find that even in the previously-regarded liquid-like regime, the fusion process of spheroids can be very different from regular liquid coalescence. We conduct numerical simulations using both standard particulate models and vertex models with both Molecular Dynamics and Brownian Dynamics. The simulation results show that the difference between spheroids and regular liquid droplets is caused by the microscopic overdamped dynamics of each cell rather than the topological cell-cell interactions in the vertex model. Our research reveals the necessity of a new continuum theory for “liquid” with microscopically overdamped components, such as cellular and colloidal systems. Detailed analysis of our simulation results of different system sizes provides the basis for developing the new theory.

Filaments (slender, microscopic elastic bodies) are prevalent in biological and industrial settings. In the biological case, the filaments are often active, in that they are driven internally by motor proteins, with the prime examples being cilia and flagella. For cilia in particular, which can appear in dense arrays, their resulting motions are coupled through the surrounding fluid, as well as through surfaces to which they are attached. In this talk, I present numerical simulations exploring the coordinated motion of active filaments and how it depends on the driving force, density of filaments, as well as the attached surface. In particular, we find that when the surface is spherical, its topology introduces local defects in coordinated motion which can then feedback and alter the global state. This is particularly true when the surface is not held fixed and is free to move in the surrounding fluid. These simulations take advantage of a computational framework we developed for fully 3D filament motion that combines unit quaternions, implicit geometric time integration, quasi-Newton methods, and fast, matrix-free methods for hydrodynamic interactions and it will also be presented.

Many microorganisms and cells function in complex (non-Newtonian) fluids, which are mixtures of different materials and exhibit both viscous and elastic stresses. For example, mammalian sperm swim through cervical mucus on their journey through the female reproductive tract, and they must penetrate the viscoelastic gel outside the ovum to fertilize. In micro-scale swimming the dynamics emerge from the coupled interactions between the complex rheology of the surrounding media and the passive and active body dynamics of the swimmer. We use computational models of swimmers in viscoelastic fluids to investigate and provide mechanistic explanations for emergent swimming behaviors. I will discuss how flexible filaments (such as flagella) can store energy from a viscoelastic fluid to gain stroke boosts due to fluid elasticity. I will also describe 3D simulations of model organisms such as C. Reinhardtii and mammalian sperm, where we use experimentally measured stroke data to separate naturally coupled stroke and fluid effects. We explore why strokes that are adapted to Newtonian fluid environments might not do well in viscoelastic environments.

Microorganisms can swim in a variety of environments, interacting with chemicals and other proteins in the fluid. In this talk, we will highlight recent computational methods and results for swimming efficiency and hydrodynamic interactions of swimmers in different fluid environments. Sperm are modeled via a centerline representation where forces are solved for using elastic rod theory. The method of regularized Stokeslets is used to solve the fluid-structure interaction where emergent swimming speeds can be compared to asymptotic analysis. In the case of fluids with extra proteins or cells that may act as friction, swimming speeds may be enhanced, and attraction may not occur. We will also highlight how parameter estimation techniques can be utilized to infer fluid and/or swimmer properties.

Bacteria exhibit a bewildering diversity of morphologies, but despite their impact on nearly all aspects of life, they are frequently classified into a few general categories, usually just “spheres” and “rods.” Curved-rod bacteria are one simple variation observed in many environments, particularly the ocean. However, why so many species have evolved this shape is unknown. We used a regularized Stokeslet Boundary Element Method to model the motility of flagellated, curved bacteria. We show that curvature can increase swimming efficiency, revealing a widely applicable selective advantage. Furthermore, we show that the distribution of cell lengths and curvatures observed across bacteria in nature is predicted by evolutionary trade-offs between three tasks influenced by shape: efficient swimming, the ability to detect chemical gradients, and reduced cost of cell construction. We therefore reveal shape as an important component of microbial fitness.

Hydra is a fascinating model organism for neuroscience. It is transparent; new genetic lines allow one to image activity in both neurons (Dupre and Yuste, 2017) and muscle cells (Szymanski and Yuste, 2019) ; it exhibits rich behavior, and it continually rebuilds itself. Hydra’s fairly simply physical structure as a two-layered fluid-filled hydrostat and the accessibility of information about neural and muscle activity opens the possibility of a complete model of neural control of behavior. This requires understanding the transformations that occur in the muscle cell layers and a biomechanical model of the body column. We show that we can use this modeling to reverse engineer how neural activity drives behavior.

Epithelial cell sheets form a fundamental role in the developing embryo, and also in adult tissues including the gut and the cornea of the eye. Soft and active matter provides a theoretical and computational framework to understand the mechanics and dynamics of these tissues.I will start by introducing the simplest useful class of models, active brownian particles (ABPs), which incorporate uncoordinated active crawling over a substrate and mechanical interactions. Using this model, I will show how the extended ’swirly’ velocity fluctuations seen in sheets on a substrate can be understood using a simple model that couples linear elasticity with disordered activity. We are able to quantitatively match experiments using in-vitro corneal epithelial cells.Adding a different source of activity, cell division and apoptosis, to such a model leads to a novel 'self-melting' dense fluid state. Finally, I will discuss a direct application of this simple particle-based model to the steady-state spiral flow pattern on the mouse cornea.