Abstract:

A method for aerodynamic shape optimization using adjoint variables is developed and implemented. A stabilized finite element method is used to solve the governing equations. The validation of the formulation and its implementation is carried out via steady flow past an elliptical bump whose eccentricity is used as a design parameter. Results for, both, optimal design and inverse problems are presented. Using different initial guesses, multiple optimal shapes are obtained. A multi-objective function with additional constraints on the volume and the drag coefficient of the bump is utilized. It is seen that as more constraints are added to the objective function the design space is constrained and the multiple optimal shapes become progressively similar to each other. Next, the shape of an airfoil is optimized for various different objectives and for various values of Reynolds number. Very interesting shapes are discovered at low Reynolds numbers. The non-monotonic behavior of the objective functions with respect to the design variables is demonstrated. The method is extended to design airfoils for a range of Reynolds number and angles of attack. Next, the approach for optimizing shapes that are associated with unsteady flows is developed. The objective function is typically based on time-averaged aerodynamic coefficients. Interesting shapes are obtained, especially when the objective is to produce high performance airfoils. The method is utilized to obtain high performance airfoils for Re = 1000 and 10,000 using relatively large number of design variables. Beyond a certain number of control points the optimization leads to a spontaneous appearance of corrugations on the upper surface of the airfoil. The corrugations are responsible for generation of small vortices that add to suction on the upper surface of the airfoil and lead to enhanced lift. Preliminary results will be presented for optimization for finite wings.The method is extended to three dimensions. Its application is demonstrated via optimization of the planform of a finite wing for maximum lift-to-drag ratio. A bi-parametric tensorial NURBS (non-uniform rational bi-cubic spline) surface is interpolated on a 3D control net to represent a wing surface. It is shown that for low Reynolds number, it is possible to design a planform that is more efficient than an ellipse. Unlike the elliptic planform, the optimal wing computed by the present method, is associated with a short winglet-like structure at the wing-tip and the maximum chord length at around mid-span.

Abstract:

• Spinodal architected materials

• Multi-anisotropic-material topology optimization formulation for volume constrained compliance minimization

• Design examples of maximally stiff structures embedded with spinodal architected materials

• Multi-anisotropic-material topology optimization formulation for elastostatic cloaks

• Design examples of elastostatic cloaks in spinodal elastic media

• A few implementation details via our new educational code, PolyAnisoMat (under review)

• Remarks on preparing the designs for manufacturing

Abstract:

High-fidelity simulations such as large-eddy simulations and direct numerical simulations provide unparalleled insight into turbulent flows, yet their extreme computational cost limits its usage in design, prediction, and optimization of our engineering systems. This talk discusses recent advances in data-driven and physics-informed machine learning that aim to bridge this gap, including latent-space modeling, diffusion-based generative inflow and super-resolution models, and physics-aware constraints for reliability and generalization. Beyond accelerating CFD, the talk highlights the emerging role of information theory through measures based on Shannon entropy, in detecting nonlinear interactions and uncovering causal pathways in complex mechanical systems. Together, these approaches point toward interpretable, physically-consistent, and computationally efficient workflows for next-generation CFD of turbulent flows.

Abstract:

Origami, the art of folding paper, has inspired several ideas for engineering applications in recent years. Origami tessellations with periodic lattice symmetries, when studied as metamaterials, were found to exhibit properties that were previously not found in natural or engineered materials. This talk will discuss the unconventional Poisson's ratio behavior of some origami metamaterials and how that can be captured purely from a geometric perspective. A path towards homogenization of origami tessellations will be presented that will allow approximating the system as an effective continuum and will enable calculation of material properties. Future research directions for inverse-design of origami-inspired lattice metamaterials using topology optimization will be discussed.

Abstract:

The talk will focus on issues related to topology optimization methods and their applicability to dynamically loaded members. Novel approaches developed in this direction will be elaborated upon with real-time examples of engineering systems, particularly industrial manipulators. The presentation will highlight a topology optimization methodology for manipulators aimed at reducing link weight, improving structural performance, and minimizing energy consumption. The use of commercial software to achieve these results will also be discussed.

Abstract:

Compliant mechanisms (CMs) achieve motion transmission and force amplification through the elastic deformation of their flexible members rather than using rigid-body joints. In path generation applications, the aim is to design a mechanism whose output point follows a prescribed trajectory under specified input conditions. Such mechanisms are vital in precision engineering, MEMS devices, soft robotics, and biomedical tools, where compactness, smooth motion, and high accuracy are required.

This talk explores topology optimization for compliant mechanisms subjected to complex path generation objectives by employing the Hill Climber Search (HCS) algorithm, while explicitly considering self-contact and contact interaction with external rigid or flexible surfaces.

The design domain is discretized into finite elements, where each element is represented by a topology choice variable. The Hill Climber Search algorithm iteratively perturbs the material distribution—adding or removing elements—and evaluates the resulting structure using nonlinear finite element analysis. Only modifications that improve the objective function are retained, allowing a gradual ascent toward an optimal topology.

The inclusion of contact interactions introduces strong nonlinearity, leading to complex energy redistribution and localized deformation behaviour. However, these effects can be beneficial—contacts can act as functional constraints or motion limiters, helping the mechanism achieve desired trajectories without rigid joints.

This approach provides a computationally efficient, non-gradient-based method to synthesize contact-aided compliant mechanisms capable of realizing nonlinear, multi-segment path generation tasks. The results are expected to contribute to the development of compact, high-performance compliant systems for micro-scale manipulators, deployable structures, and adaptive robotic end-effectors.

Abstract:

This talk explores the transformative potential of wearable robotics in enhancing and restoring human movement through a multidisciplinary lens. With advances in sensing, control, and soft system design, wearable exosuits exemplify how human-centric engineering integrates biomechanics, robotics, and computational optimization. We will discuss the role of wearable sensors in accurately capturing human motion and how sensor-informed models enable adaptive and efficient exosuit performance. The talk will also highlight ongoing research at the Human-Centered Robotics (HCR) Lab, IIT Gandhinagar, focusing on the design, analysis, and optimization of soft wearable systems for movement augmentation and rehabilitation. By bridging human physiology with engineering design and data-driven optimization, this work demonstrates how multidisciplinary approaches can drive the next generation of wearable robotic technologies.

Abstract: 

In contemporary design and engineering practices, decision-making and optimization constitute fundamental components of the development process. Traditionally, research in this domain has primarily focused on determining the global optimum of a given problem by exhaustively exploring the entire search space to identify the best possible solution or a set of optimal alternatives. With the advent of advanced statistical and probabilistic methodologies, designers and decision-makers have increasingly recognized the necessity of incorporating uncertainty quantification and reliability-based concepts into the optimization framework. This integration enhances the robustness and practical applicability of the obtained solutions under real-world variability.

In the realm of multi-objective optimization, additional complexities arise due to the simultaneous consideration of conflicting objectives. Consequently, the task extends beyond finding a set of Pareto-optimal solutions to facilitating an informed and rational decision-making process for selecting the most preferred alternative. In this presentation, an interactive optimization methodology would be shared that synergistically combines the strengths of evolutionary algorithms and classical multi-objective optimization techniques. The approach iteratively and interactively engages the user, enabling comprehensive exploration of the solution space while progressively converging toward a single, user-preferred optimal solution.