Mechanics and Computation Group Research
Focus Areas
Metal 3D Printing
Metal 3D printing comes in different forms. The most commonly found strategies are: Direct Energy Deposition (DED), and Power-Bed Fusion (PBF), in which the heat source is either a Laser or an electron beam. In DED the power is blown onto the built part, while in PBF, layers of powder are progressively deposited and regions that need to be fused are heated up by scanning with the laser or the electron beam.
One of the promises of 3D printing is the possibility of building hierarchical structures, in which solid parts are replaced by complex lattices at the sub-centimeter scale. This offers the possibility of tailoring the mechanical properties of a metal part, reducing the weight, and potentially tuning electromagnetic, catalytic and/or acoustic properties.
A challenge or advantage of as-printed parts is that they have different microstructures than conventionally cast or forged parts. This is because during printing the melted material solidifies much faster in the presence of a high temperature gradient. This is why it is important to understand the relation between the processing conditions, temperature history, and resulting microstructure.
We approach this challenge with a combination of advanced computational tools and carefully designed experiments. With computation we can evaluate and then predict the type of temperature fields during printing. An important mechanism of heat transfer in the melted material is the motion of the liquid metal (convection).
The microstructure of the parts can be characterized by cutting, etching, and imaging the sections through optical and electron microscopy. Melt pool boundaries, solidification microstructures, and dislocation patterns can all be observed and compared with the prediction of computational models.
Modeling and Simulation in Solids and Structures
Computational Aided-Engineering tools are widely used in engineering design nowadays and have become an integral part of the design cycle. However, any analyst using the tools will be aware of their predictive limitations for problems involving complex material behavior, failure, inelasticity and damage, phase changes, and changes in the domain (e.g., shape optimization, crack evolution). These shortcomings are all manifested in some of the areas of activity of the Group: additive manufacturing of metals and polymers, stretchable electronics at the interface of engineered and living systems, battery technology, hydraulic fracture and geophysics problems, and the design of advanced material systems, among others also described below.
The Group advances modeling and simulation knowledge and technology through the creation of new numerical methods for both physics-based and data-based modeling. The former includes finite element methods, mesh-free methods, dislocation dynamics, and advanced sampling method in atomistic simulations, and the latter encompasses machine learning and data-science techniques adapted and applied to solids and structures. Machine learning and data science offer the opportunity to go beyond empirical and phenomenological relations between physical quantities and identify new relations purely based on data, either from experiments or first-principle simulation data. They provide new avenues for probabilistic modeling when measurements or observations are sparse, noisy, and uncertain. Applications include the identification and modeling of the constitutive behavior of materials, as well as inverse modeling and optimization based on experimental and large-scale simulation datasets.
Experimental mechanics, advanced microscopy and characterization
“Seeing” phenomena previously limited to theory and characterizing the mechanical behavior at very small scales or of novel multifunctional materials are the current driving forces behind the Group’s effort in this area. Activities include the characterization of the nonlinear mechanical behavior of various polymeric materials and soft systems, nanostructured metals and alloys, and architected composites that are usually subject to thermo-mechanical coupled loading and finite deformation. To see new phenomena, the Group uses and develops new types of advanced microscopes—optical and X-ray—to resolve the multiscale behaviors of tiny defects that can cascade into bulk failure or strength in different environments. Advanced electron microscopy, X-ray diffraction and microscopy, and optical spectroscopy are also used to understand structural changes at atomistic-to-continuum scales, including under extreme environments such as high pressure and in-operando conditions in renewable energy systems (e.g., green hydrogen, batteries). These activities also include exploring how extreme states of matter are generated as materials rapidly deform during shock waves, in systems relevant to aerospace and defense technology.
Advanced and Sustainable Manufacturing
Manufacturing is undergoing a revolution propelled by the democratization of design promised by additive manufacturing techniques (3D printing), the application of data science, and the increasing demand for sustainable manufacturing processes. The Group both creates novel ways to print metals and polymers, as well as uses 3D printing as an integral part of research efforts. Multiple additive manufacturing techniques, including direct-ink-writing, digital-light-processing, and fused filament fabrication, are integrated to print polymeric composites and soft materials. These efforts enable the manufacturing of advanced systems such as stimuli-responsive materials for soft robotics, morphing systems, self-assembled materials, strong and lightweight nano-architected materials, and materials with tunable structural and mechanical properties. To print very small structures, two-photon lithography is combined with novel resins to directly write nanoscale metals and ceramics. Finally, a metal 3D printer based on powder-bed fusion (PBF) technology provides the platform for fundamental research on the physical processes in metal printing, as well as for the manufacturing of metal parts with complex shapes for novel applications. The Group has a program to study process design for metal additive manufacturing, both to learn about the physics that gives rise to the unique properties of printed metals, as well as to enable the printing of novel materials demanded by novel applications, such as creating Copper micro heat exchangers and wave guides.
Facilities
High Performance Computing Center
The Stanford High Performance Computing Center operates a number of HPC clusters to enable larger simulations, deeper analyses, and faster computation times than are possible using computers available to individual researchers. The HPC Center has mass data storage and archival systems to store the vast quantities of data the result from performing simulations on these HPC resources.
Cai Lab | Micro and Nano Mechanics Lab
Prof. Cai’s research lies at the intersection of materials science, solid mechanics and high-performance computing. The main goal is to predict the properties of materials through the evolution of their microstructures. An example is the prediction of the stress-strain curves of metals during plastic deformation through large-scale simulation of dislocations, which are line defects inside the crystal. Understanding the synthesis and deformation mechanisms of nano-scale objects (such as nanowires) through atomistic and continuum modeling is another major area of interest. We aim to push the capability of materials modeling through the use of advanced numerical algorithms and machine learning techniques, and taking advantage of new novel computational architectures (such as GPUs). The group is also starting experimental and computational research on metal additive manufacturing (3D printing).
Darve Lab
Prof. Darve’s research lies at the intersection of applied mathematics and computational engineering with a focus on fast algorithms to solve complex engineering problems. One of the core areas is solving large-scale sparse linear systems, which requires novel numerical schemes, mathematics, and the design of parallel algorithms. This is key to many areas including solid mechanics, fluid mechanics, and material science. More recently, novel methods have emerged that promise to revolutionize the way scientific computing is done. They include machine learning (deep neural networks, hierarchical Gaussian processes, random forests) and statistical scientific computing. These new ideas promise to open radically new ways of expressing and solving engineering problems, going beyond what current partial differential equations models are capable of.
Gu Lab
Prof. Gu’s research is on experimental mechanics of nanoscale solids. Her group combines solid mechanics, materials science and chemistry to make nanostructures and architected composites, and develops experimental techniques to detect nanoscale loads, displacements and structural changes. One major area of research in the group is in-situ electron microscope mechanical testing of metallic nanocrystals, in order to learn about the influence of defects, interfaces and free surfaces on strength, ductility and failure in metals. Research findings can be applied to areas such as lightweight structural materials for fuel-efficient vehicles, nanoscale 3D printing, and strong and tough metallic alloys.
Lew Lab
Prof. Lew’s research group works on design and mathematical analysis of numerical methods to simulate solids and fluids, and in the modeling of selected processes is solids. Current areas of focus are: (a) Methods and models to simulate crack propagation, with a focus on hydraulic fracturing, (b) Creation of meshing tools for problems with evolving domains, advancing in particular the applicability of the method of Universal Meshes that the lab introduced, and (c) Creation of methods, models, and experiments to analyze and tailor the microstructure of 3D printed metallic parts.
Pinsky Lab
Prof. Pinsky (Emeritus) works in the theory and practice of computational mechanics with a particular interest in multiphysics problems in biomechanics. His work uses the close coupling of techniques for molecular, statistical and continuum mechanics with biology, chemistry and clinical science. Areas of current interest include soft tissue mechanics and the mechanics of human vision (ocular mechanics). Topics in tissue mechanics include multiphasic (solid-fluid-ionic) modeling based on thermodynamics, statistical mechanics and computational methods. Topics in the mechanics of vision include the mechanics of transparency, which investigates the mechanisms by which corneal tissue self-organizes at the molecular scale using collagen-proteoglycan-ion interactions to provide mechanical resilience and almost perfect transparency; modeling metabolic and swelling processes in soft tissues; imaging applied to microscale organization of soft tissues for clinical applications aimed at improving vision and investigating the mechanical behavior of diseased tissues.