Advances in Multidisciplinary Design, Analysis and Optimization
Proceedings of the 6th National Conference on Advances in Multidisciplinary Design, Analysis and Optimization 2023
- 2025
- Book
- Editors
- Deepak Sharma
- Sachin Singh Gautam
- Tapan K. Mankodi
- Ujjwal K. Saha
- Book Series
- Lecture Notes in Mechanical Engineering
- Publisher
- Springer Nature Singapore
About this book
This book contains select papers presented during the 6th National Conference on Multidisciplinary Analysis and Optimization. The book focuses on design and analysis as applicable to optimization of engineering systems in aerospace, mechanical, automotive, manufacturing, biomedical, and other domains. The book includes papers on the topics such as metamodeling or surrogate modeling, systems design and optimization, optimization and additive manufacturing, mixed integer and linear programming, multiscale and multiphysics problems, among others. The book can be a valuable reference for researchers and professionals interested in the field of optimization and its use in design for different applications.
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Table of Contents
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Frontmatter
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Basis Splines to Perform Isogeometric Topology Optimisation to Design the Outline of a Bridge Pier and Evaluate the Compliance-Based Performance Index
K. N. V. Chandrasekhar, V. Bhikshma, B. Ramana Kumar, Gugilla Aruna, Bakka Ramya, Mamidi Sai Sagar, Ganta Pushpalatha, K. JyothiThis chapter delves into the innovative use of basis splines for isogeometric topology optimization, focusing on the design of a bridge pier. It begins with an introduction to the historical context of structural optimization, tracing the evolution from early discrete structure optimizations to modern continuum structure designs. The chapter highlights the computational challenges and advancements in topology optimization, particularly in the past three decades. The main focus is on using basis splines to model the geometry of a reinforced concrete bridge pier and applying isogeometric analysis to optimize material distribution. The methodology involves a two-stage process: static analysis to calculate stiffness matrices and force vectors, followed by sensitivity analysis to determine changes in compliance. The optimality criteria are used to iteratively refine the relative densities of each element until convergence is achieved. The chapter also reviews relevant literature, discussing various performance indices and optimization methods. A detailed case study of a bridge pier is presented, including the initial design domain, loading conditions, and material properties. The optimal material distribution is visualized, and the performance index based on compliance is calculated. The results are compared with existing literature, demonstrating the effectiveness of the proposed approach. The conclusion summarizes the application of basis splines in topology optimization, emphasizing the agreement of the calculated forces with established values and the practical implications for structural design.AI Generated
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AbstractThe rapid urban development of the major cities is going through a big transition in terms of rising traffic and increasing urban needs of the people. Millions of people are flowing into the city for various reasons every day in the morning and are leaving by the evening. The use of existing roads is becoming quite congested and hence there are traffic jams wasting several hours. The need of new flyovers is obvious to accommodate smooth flow of traffic and help the businesses and employees to perform their duties. The present study is focused on the design of a bridge pier and find the layout of the material at the ultimate elastic limit. The pier is modelled using first-order basis splines and isogeometric analysis is performed to determine the nodal displacements. The code is developed using MATLAB®. Topology optimisation is performed using optimality criteria to determine the optimal distribution of material. The performance indices based on compliance are determined for the optimal layout of the material. The bridge pier is then designed using Indian standard codes to determine the steel required. Two examples of bridge pier are modelled using IDEA Statica® software and analysed. The results show that the design of the bridge pier is optimal and gives similar results with those existing in the literature. -
Topology Optimisation of Reinforced Concrete Structures Having Openings Using B-Splines
K. N. V. Chandrasekhar, V. Bhikshma, B. Ramana Kumar, Gugilla Aruna, Bakka Ramya, Mamidi Sai Sagar, Ganta Pushpalatha, L. JyothiThis chapter explores the intricate process of topology optimization for reinforced concrete structures featuring openings, utilizing B-splines for precise modeling. The study focuses on deep beams with single and multiple openings, highlighting the challenges posed by tensile stresses and the need for optimal reinforcement layouts. Through a meticulous methodology, the chapter details the use of first-order basis splines and sensitivity analysis to determine the optimal distribution of material within the concrete domain. The analysis reveals significant reductions in compliance and performance indices, demonstrating the effectiveness of the proposed optimization techniques. The chapter presents detailed stress distributions, illustrating the compressive and tensile stresses at optimal convergence, and provides a comprehensive discussion on the structural performance of the optimized designs. The results offer valuable insights into the behavior of reinforced concrete structures under various loading conditions, making this chapter an essential read for those interested in advanced structural optimization and design.AI Generated
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AbstractThe inevitable use of reinforced concrete for large constructions and the need to provide openings require a proper layout of reinforcement steel to be provided within the design domain. An Engineer can apply topology optimisation to determine the optimal layout of the tensile areas where steel can be provided using a strut and tie model. The maximum stress at the optimal convergence and the energy of structure can be determined by performing topology optimisation. The main focus of this study is to model the design domain using first-order basis splines and perform isogeometric analysis to determine the maximum stress and displacement in the material. The topology optimisation is performed by gradually removing the low-stress carrying material and the final layout of the material can be obtained at the optimal point of convergence. This layout can give an indication to the engineer to provide the steel in the concrete domain. The results obtained when design domain is modelled using basis splines are similar to the layout of material when the design domain is modelled using quadrilateral elements in the literature. These results have shown that basis splines can be very useful to model and analyse the concrete domains. -
Topology Optimization of Metamaterials Using Functionally Graded Material
U. Meenu Krishnan, Abhinav Gupta, Rajib ChowdhuryThis chapter investigates the cutting-edge intersection of topology optimization and functionally graded materials (FGMs) to engineer metamaterials with exceptional mechanical properties. It begins by highlighting the advantages of FGMs in mitigating interfacial stress concentrations, making them ideal for high-performance applications. The chapter then delves into the computational methods of topology optimization, focusing on the density-based method with the solid isotropic material with penalization (SIMP) approach. It explores the variational formulations used to predict the response of a given domain by minimizing the total potential energy, providing a robust framework for designing structures with optimal performance. A significant focus is placed on the design of metamaterials with a negative Poisson's ratio, known as auxetics, using FGMs. The chapter presents a comprehensive numerical study that implements the proposed topology optimization algorithm to create auxetic metamaterials, examining the influence of control parameters such as material gradient index and volume fraction on the properties and topologies of unit cells. The detailed analysis and innovative approach make this chapter a compelling read for those interested in pushing the boundaries of material science and structural engineering.AI Generated
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AbstractFunctionally graded material (FGMs) holds significant relevance in diverse engineering applications. It should be noted that Topology Optimization (TO) is a potent method utilized to optimize the distribution of materials within a designated design area with the aim of attaining optimal performance. However, its application to design auxetics which has a negative Poisson ratio has been limited due to various challenges emanating from the computational demand. To address this challenge, we describe the necessary technology and methodologies to performTOwithFGMs. Numerical examples demonstrate the effectiveness of theTOapproach to design negative Poisson’s ratio metamaterial withFGMs. The results shown in this study also demonstrate the potential for utilizingTOin practical applications with auxetics and different material gradation. -
Adaptive Topology Optimization in Fourth-Order Plate Bending Problems Using Isogeometric PHT-Splines
Philip Luke Karuthedath, Abhinav Gupta, Bhagath Mamindlapelly, Rajib ChowdhuryThis chapter delves into the innovative use of isogeometric analysis (IGA) with Polynomial Splines over Hierarchical T-meshes (PHT-splines) to address the challenges in topology optimization (TO) of plate structures. The study focuses on the Kirchhoff-Love plate formulation, which requires higher continuity and accuracy that classical finite element methods struggle to provide. By employing PHT-splines, the research introduces an adaptive mesh refinement (AMR) strategy that balances computational efficiency and solution accuracy. This approach allows for localized refinement, enhancing the resolution of solid-void boundaries and ensuring smooth, feasible topologies. The chapter presents a detailed methodology, including the weak form derivation of the plate formulation and the implementation of the SIMP approach for material distribution. Numerical examples, such as the clamped square plate and cantilever plate problems, demonstrate the efficacy of the proposed method. The results showcase significant reductions in degrees-of-freedom compared to globally refined strategies, highlighting the computational advantages and improved design outcomes achieved through this novel framework.AI Generated
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AbstractThis paper introduces an Adaptive Mesh Refinement (AMR) methodology for Topology Optimization (TO) of fourth-order plate structures using Isogeometric PHT-Splines. We focus on the benefit of Isogeometric Analysis (IGA) to inherently discretize the \(C^1\) continuous weak form of plate structures. Our approach offers several advantages over traditional methods, including a discrete density field for the material distribution filtered through a first-neighbourhood strategy and a hierarchical tree structure for the structural mesh that enables effortless implementation of an AMR strategy. Utilizing the Geometry Independent Field approximaTion (GIFT), we discretized the design and adaptive analysis stages independently through NURBS and PHT-Splines, respectively, enabling easy transfer of geometries from industry-standard packages. Numerical examples demonstrate the superiority of our proposed methodology over traditional methods in terms of solution accuracy and computational efficiency. -
A Coupled Fluid-Structural Solver
Akshay Prakash, Mohammad Rabius Sunny, Kavi Pradhap, Kushan Verma, D. K. Maiti, Dibya Ranjan Sahoo, P. C. JainThe chapter investigates the critical interplay between fluid dynamics and structural mechanics in various aerospace applications, emphasizing the need for coupled solutions to optimize performance. It highlights the importance of time-dependent fluid flow parameters in reentry flows, cooling mechanisms in jet exhaust CD nozzles, and the optimization of turbine blades in jet engines. The text delves into the challenges of aerodynamic flutter, requiring simultaneous computation of fluid and structural solutions, and the impact of high-speed flows on aero heating. The governing equations and numerical methods are thoroughly discussed, including the use of the compressible Navier–Stokes equations and the finite element formulation for structural analysis. Two immersed boundary methods are implemented and tested: the sharp interface method using ghost points and the diffused interface method using source terms. The chapter provides a detailed comparison of these methods, showcasing their advantages in handling moving boundary problems. Validation studies against established benchmarks, such as shock standoff distance and pressure coefficient over a cylinder, demonstrate the accuracy and reliability of the proposed methods. The chapter also presents coupled fluid–structure simulations of a slender object, illustrating the dynamic interaction between aerodynamic loading and structural deformation. The results highlight the potential for optimizing aerodynamic and structural designs through coupled solutions, offering valuable insights for advanced research and development in the field.AI Generated
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AbstractVarious applications in aerospace require a coupled solution of the surrounding airflow and structural response of the vehicle to the external loads. Flutter prediction is an essential part of design. Traditionally, fluid dynamics are solved using the simplified piston theory or steady solutions from CFD, which predict flutter behavior when coupled with a structural solver. An integrated solver consisting of a structural and fluid flow solver is developed and presented. The immersed boundary technique is used in the fluid solver to model time-varying geometry more efficiently, thereby reducing simulation time for the CFD solver. The weak formulation through the Newmark time integration scheme solves the structural formulation. -
Data-Driven Model of Thomson Coil Using Dimensionless Parameters
Vishakha Harlapur, Salil KulkarniThis chapter investigates the behavior of Thomson coils, which are crucial for high-speed operations in various applications such as circuit protection devices, mechanical switches, and robotic actuators. The study focuses on the multi-physics interactions within Thomson coils, including electrical circuits, electromagnetic fields, and structural mechanics, to understand the dynamics of these systems comprehensively. A significant contribution of this work is the development of a data-driven model using dimensionless parameters derived from the Buckingham Pi theorem. This approach reduces the number of independent variables and avoids dimensional inconsistencies, making the modeling process more efficient. The chapter details the use of finite element simulations and advanced regression techniques, including ensemble learning, to predict the stroke length of the Thomson coil actuator at specified times. The results highlight the sensitivity of the coil's displacement to various design parameters, such as voltage and capacitance, and demonstrate the effectiveness of the data-driven models in optimizing the design of Thomson coils for high-speed applications. The chapter concludes with insights into future work, including sensitivity analysis and optimization studies, to further enhance the performance and reliability of Thomson coil actuators.AI Generated
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AbstractThomson coil can generate large forces in a short time and hence finds application in fast acting actuators. Behaviour of Thomson coil actuator depends on multi-physics which involves various fields including electrical, electromagnetic, structural and their interactions. As a result, many design parameters related to the different physical domains are involved in the analysis of Thomson coil. Thus, it becomes complicated to have a closed-form equation which can describe the behaviour of this system. In this paper, we derive dimensionless parameters associated with the variables involved in the design of Thomson coil using Buckingham Pi theorem. These dimensionless parameters are then used to develop data-driven models which help to predict the displacement of Thomson coil disc at a specified time interval and gain insights about the actuator when it is subjected to changes in the design parameters. -
Multi-disciplinary Design Optimization of Conceptual Design of Hybrid Drone Using Evolutionary Algorithm
C. Shraddha, Pankaj PriyadarshiThe chapter delves into the multi-disciplinary design optimization (MDO) of a hybrid drone tailored for the DroneNet technology, which focuses on the autonomous mid-air recovery of launch vehicle spent stages. The hybrid drone's design is optimized using an in-house developed Attractor Anchored Multi Objective Evolutionary Algorithm, addressing multiple disciplines such as geometry, aerodynamics, propulsion, flight mechanics, and stability. The mission profile includes vertical take-off, stage capture, and landing, with horizontal flight modes for other segments. Parametric multi-disciplinary design analysis (MDA) is conducted to evaluate the impact of various design variables on take-off weight and other flight vehicle characteristics. The study explores different configurations of the staggered biplane hybrid drone, optimizing objectives such as maximizing endurance, range, and payload while minimizing weight. The MDO framework integrates modules for geometry, aerodynamics, propulsion, mission design, structures, weight estimation, and stability, ensuring a balanced and efficient design. The results highlight the effects of geometry and mission-related parameters on vehicle and flight characteristics, providing valuable insights into the optimization of hybrid drone designs for complex mission profiles.AI Generated
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AbstractHybrid drones have Vertical Take Off and Landing (VTOL), hovering, longer range and endurance and hence proposed for use in DroneNet application where the launch vehicle spent stage is recovered mid air by a swarm of drones on a net. This paper presents the conceptual design of hybrid drone given the mission and payload specifications through a Multi-disciplinary Design Optimization (MDO) employing an in-house evolutionary algorithm \(A^2-MOEA\). Multi-disciplinary Design Analysis (MDA) using Multiple Discipline Feasible (MDF) approach is carried out and driven by \(A^2-MOEA\) to obtain optimal configurations. Parametric studies are carried out to analyze the effect of flight and vehicle characteristics and results are discussed. -
Analyzing Aeroelastic Wing Flutter: Trends in Eigenvalues and System Speeds for Improved Aircraft Design and Safety
N. Akshayraj, B. V. N. RamakumarThis chapter provides an in-depth exploration of aeroelastic wing flutter, a critical phenomenon in aircraft design that can lead to catastrophic structural failure if not properly addressed. The text begins by discussing the fundamental concepts of flutter, including classical binary flutter and the interaction of multiple modes of vibration. It introduces a simplified binary flutter model using strip theory and unsteady aerodynamic terms, which serves as a tool to analyze the dynamics of aeroelastic systems. The chapter delves into the influence of various parameters, such as the position of the elastic axis, mass distribution, and frequency spacing between modes, on the system's behavior. It also examines methods for determining critical flutter speeds and associated frequencies, emphasizing the importance of incorporating unsteady aerodynamic effects for accurate predictions. The chapter presents a detailed analysis of frequency and damping trends under different aerodynamic conditions, highlighting the transition from stable to unstable states. Through the use of MATLAB simulations and eigenvalue solutions, the text provides a comprehensive understanding of flutter dynamics and offers valuable insights for enhancing aircraft design and safety. The exploration of both zero aerodynamic damping and unsteady aerodynamic conditions, along with the validation of results against reference literature, makes this chapter a significant contribution to the field of aeroelasticity.AI Generated
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AbstractAeroelastic wing flutter plays a crucial role in the field of aerospace engineering, as it addresses a significant concern in aircraft design and safety. Flutter refers to the self-excited oscillations that can occur in an aircraft’s wings when subjected to certain aerodynamic forces and structural dynamics. These oscillations can lead to potentially catastrophic consequences if not properly understood and addressed. Understanding aeroelastic wing flutter is essential to ensure the structural integrity, stability, and performance of aircraft. It helps to identify critical conditions, design robust wings, and develop strategies to mitigate or suppress flutter, ultimately contributing to safer and more efficient air transportation. This technical paper presents an investigation on a system of equations, incorporating structural damping, and explores the solution to the corresponding eigenvalue problem across a range of speeds. The primary objective in this research is to analyze and visualize the trends of Vω (eigenvalue) and Vg (system speed), by writing a code in MATLAB. The code facilitates numerical solution of the system equations, allowing for the study of damping effects on the system's behavior. By systematically varying the speed parameter, the resulting eigenvalues and system speeds are obtained and plotted to provide insights into the system's dynamic characteristics. -
Development of a Hybrid BCGA Tuner for Artificial Neural Network in Assessing the Performance of Electromagnetic Forming and Perforation (EMFP) of Al6061–T6 Tube
Avinash Chetry, Arup NandyThis chapter delves into the complexities of electromagnetic forming and perforation (EMFP) of Al6061–T6 tubes, a process that offers a viable alternative to conventional methods plagued by burrs and slivers. The study introduces a hybrid Binary-Coded Genetic Algorithm–Artificial Neural Network (BCGA–ANN) model to tackle the non-linear mapping between input features and tube thickness reduction, a challenge that has hindered the widespread adoption of EMFP. The chapter presents a comprehensive methodology that includes a shallow ANN learning algorithm, genetic algorithms for hyperparameter tuning, and a coupled simulation for validation. It compares the effectiveness of grid search and genetic algorithms in optimizing the ANN architecture, highlighting the superior performance of the genetic algorithm in terms of mean squared error, root mean squared error, mean absolute error, and R-squared scores. The chapter also discusses the working principle of EMFP, the numerical simulation model, and the dataset used for evaluation. It concludes with a validation of the hybrid BCGA–ANN model, demonstrating its accuracy in predicting tube thickness reduction and its potential for process optimization and expanded applicability of multi-point EMFP.AI Generated
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AbstractA hybrid strategy, integrating Binary-Coded Genetic Algorithm (BCGA) tuner and Artificial Neural Network (ANN), enhances the precision and generality of the predictive model. The BCGA tuner optimizes the ANN model’s parameters, resulting in an efficient and robust predictive tool. The research methodology involves collecting a comprehensive dataset with input parameters like discharge energy, tube thickness, number of punches, and spacer length, alongside output data representing thickness reduction and morphological conditions. The BCGA tuner refines the ANN model's architecture and weights, ensuring optimal performance. The trained ANN model's efficacy is evaluated using statistical metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R2). Comparative analysis with simulation data across six datasets indicates a minimal deviation (less than 5%), highlighting the model's accuracy and reliability. This accurate predictive model empowers manufacturers to optimize parameters, minimize waste, and reduce production costs, making it a valuable tool for widespread industrial implementation. -
Transient Analyses of Shape Memory Alloy Structures
Animesh Kundu, Atanu BanerjeeShape Memory Alloys (SMAs) have garnered significant attention due to their unique properties, namely pseudoelasticity and the shape memory effect, which enable large strain recovery and intrinsic dissipation capabilities. This chapter presents a detailed exploration of the dynamic behavior of SMA structures, focusing on transient analyses and advanced finite element modeling. It begins by summarizing the captivating characteristics of SMAs and their extensive applications in various engineering fields, from civil and aerospace to biomedical and automotive. The chapter then delves into the development of constitutive models that capture the thermomechanical behavior of SMAs, highlighting contributions from key researchers in the field. A comprehensive finite element framework is established, employing a composite time integration approach and the Lagoudas SMA constitutive model. The framework is applied to predict the free vibration response of a shape memory alloy cantilever beam, demonstrating the intrinsic damping properties of SMAs and their potential for vibration reduction. The chapter concludes with a discussion on the quantification of damping using the logarithmic decrement method, providing valuable insights into the dynamic behavior of SMA structures and their practical applications.AI Generated
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AbstractShape Memory Alloys (SMAs) represent a distinctive class of smart materials renowned for their capacity to effectively attenuate undesirable vibrations via their intrinsic hysteretic stress-strain response, termed pseudoelasticity. The design of SMA-based damping elements necessitates a numerical framework that can accurately capture the stress-temperature dependent phase transition phenomena in SMAs. In this study, we present a finite element (FE) based numerical model tailored for emulating the dynamic response of structures incorporating SMAs across a spectrum of thermomechanical conditions. Notably, the vibration amplitude exhibits an initial decline until the structure transitions to an elastic response regime, thereafter manifesting as stable oscillations characterized by a constant amplitude. -
Application of Multi-optimization Techniques on Transmission Components
Prudhvi Raj, Yogesh S. Bhalekar, Vipin RaneThis chapter presents a comprehensive exploration of multi-optimization techniques applied to transmission components, focusing on topology and topography optimization. The study begins by defining optimization in both mathematical and engineering contexts, setting the stage for an in-depth analysis of size, shape, and topology optimization methods. Topology optimization is highlighted for its ability to determine optimal material distribution within a given design space, utilizing algorithms like the method of moving asymptotes (MMA) to achieve smooth convergence and handle multiple constraints. The chapter then delves into topography optimization, a specialized form of shape optimization that involves small, controlled movements of nodes in a finite element mesh to refine material distribution and reduce stress. The practical applications of these techniques are illustrated through case studies, such as improving the structural performance of automotive body structures and optimizing transmission components. The chapter also addresses the limitations of individual optimization methods and introduces a combined approach that integrates topology and topography optimization. This hybrid method is demonstrated to effectively reduce stress, enhance stiffness, and meet assembly and manufacturing constraints, ultimately leading to a significant improvement in the fatigue life of transmission components. The detailed analysis and innovative solutions presented in this chapter make it an essential read for those interested in advanced optimization techniques and their practical applications in component design.AI Generated
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AbstractDue to recent technological advancements and a competitive market, most automobile OEMs seek lightweight, efficient, reliable, and cost-effective products. In this direction, the industry employs various weight optimization techniques such as topology optimization. This method enhances structural stiffness and reduces weight by optimizing material distribution. However, this technique lacks control over localized stresses. Typically, topology optimization is followed by a couple of detailed stress analyses to achieve desired levels. This will increase analysis time and fall short of an optimal solution. In contrast, topography optimization redistributes mass while closely controlling localized stresses. The current study explores a combined simulation setup that integrates the benefits of both topology and topography optimization. This paper focuses on a transmission component, where the joint optimization approach reduced localized stress by 40% compared to topology optimization alone. GENESIS along with ANSYS® is utilized for model setup and defining loads, and boundary conditions. -
Stress-Driven Topology Optimization Based Design of Auxetic Microstructure
Anurag Gupta, U. Meenu Krishnan, Abhinav Gupta, Rajib ChowdhuryThe pursuit of exceptional material properties through meticulously designed mechanical metamaterials has been a focal point in recent research. Among these, auxetic materials, characterized by their negative Poisson's ratio, have garnered significant attention due to their unique mechanical advantages, such as enhanced shear resistance and indentation resistance. Traditional design approaches for auxetic microstructures have been limited by intuitive methods, but the advent of topology optimization (TO) has expanded the design possibilities. This chapter introduces an innovative energy-based stress-constrained optimization framework that addresses the critical issue of stress concentrations in auxetic microstructures. By integrating stress criteria into the TO process, the framework aims to optimize target material properties while ensuring that local stresses remain below predefined thresholds. The design methodology employs homogenization theory to predict the overall properties of macrostructures with periodic microstructures, utilizing an energy-based homogenization scheme. The von Mises stress is formulated to establish a topology optimization problem with both volume and stress constraints. Numerical studies demonstrate the effectiveness of this approach, showcasing the density layout and stress contours of optimized unit cells. The results highlight the absence of stress concentration regions when stress constraints are incorporated, underscoring the practical applicability and enhanced performance of auxetic materials in various engineering applications.AI Generated
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AbstractThis paper presents a stress-based topology optimization (TO) method for systematic designing of 2D auxetic microstructure Auxetics exhibit unique mechanical properties characterized by lateral expansion (or contraction) when subjected to uniaxial tension (or compression). These materials have gained significant interest due to their potential applications in various fields, including aerospace, automotive, sports industry, defense sector, and so on. Density-basedTO, relying on the Solid Isotropic Material with Penalization (SIMP) model, is used and the P-norm stress measure is adopted. The stress penalization is exploited to overcome the singularity phenomenon arising from the introduction of stress constraints within the framework ofTO. The method of moving asymptotes (MMA) is employed as an optimization solver. -
Linear and Non-linear Reduced Order Model of Scramjet
Arun Govind NeelanThis chapter investigates the application of reduced order modeling (ROM) techniques to enhance the simulation of scramjet engines, focusing on both linear and non-linear approaches. The study highlights the significant advantages of ROM, including substantial reductions in computational costs and memory requirements, as well as the ability to perform real-time simulations and parametric studies. The chapter delves into two prominent linear ROM methods: Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD). POD is shown to effectively capture over 99% of the energy within the first mode, enabling significant data compression and accurate velocity data reconstruction. In contrast, DMD, while useful for linear and periodic systems, struggles with non-periodic flows, as evidenced by the distributed energy across multiple modes. The chapter also explores the use of Artificial Neural Networks (ANN) as a non-linear ROM approach, demonstrating its potential for handling complex flow conditions and optimization tasks. The ANN-based model, despite being computationally more intensive, offers versatility and efficiency in dealing with varied flow scenarios. The comparative analysis of computational times reveals that while linear methods are more efficient for single-flow conditions, ANN-based models provide a robust solution for multi-flow conditions and optimization. The chapter concludes with a detailed examination of the strengths and limitations of each method, providing valuable insights for researchers and engineers seeking to optimize scramjet simulations and enhance their understanding of complex fluid dynamics.AI Generated
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AbstractThe model reduction technique exemplifies the approach of representing solutions within higher-dimensional data through a lower-order spatial framework. This methodology serves to alleviate complexity by enabling the representation of solutions through a reduced set of modes. In this context, we shall endeavor to construct a lower-order representation of a scramjet system employing both linear and non-linear techniques. The linear reduced order model (ROM) utilized in our research encompasses dynamic mode decomposition (DMD) and Proper Orthogonal Decomposition (POD). The non-linear ROM, on the other hand, is predicated upon an artificial neural network (ANN). This ROM is poised to facilitate the analysis of flow characteristics, optimization of geometrical configurations, and the design of a scramjet controller. Within the scope of this study, we will conduct a comparative assessment of the performance of ROMs obtained through the linear reduced order model and the ANN-based model, leveraging Computational Fluid Dynamics (CFD) data. Additionally, we will assess and compare the computational efficiency of these distinct methodologies. -
Comparison Study of FEM and IGA Based Topology Optimization Methods for Elastic Structures
Tejdeep Ganekanti, Umesh Mishra, Atanu BanerjeeThis chapter presents a meticulous comparison between Finite Element Method (FEM) and Isogeometric Analysis (IGA) in the context of topology optimization for elastic structures. The study focuses on minimizing compliance under volume constraints, utilizing density-based Solid Isotropic Material with Penalization (SIMP) approach. The chapter begins by outlining the fundamental principles of topology optimization, distinguishing between density-based Material Description Models (MDMs) and Boundary Description Models (BDMs). It delves into the implementation of FEM and IGA, highlighting the numerical deficiencies of FEM, such as checkerboard patterns, and the advantages of IGA in terms of accuracy and reduced degrees of freedom. The chapter provides an in-depth analysis of the Density Distribution Function (DDF) and its role in achieving smooth nodal densities. It then presents a series of case studies, including a cantilever beam, L-beam, and quarter annulus, each subjected to different loading conditions. The results demonstrate that IGA-based topology optimization yields better objective functions and smoother topologies compared to FEM, while also significantly reducing computational time. The convergence studies and optimized topologies illustrated in the chapter offer a compelling argument for the superiority of IGA in structural optimization tasks. The findings underscore the potential of IGA to revolutionize the design process by providing more efficient and accurate solutions.AI Generated
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AbstractTopology Optimization (TO) is an effective numerical technique by which one can obtain the optimal material layout of the design domain satisfying an objective function and a set of constraints. In general, the optimization tool is coupled with an analysis tool to determine the unknowns. Finite Element Method (FEM) is widely used as an analysis tool until the advent of Isogeometric Analysis (IGA). In literature, IGA has been reported to perform better in comparison to FEM, as the former one considers exact geometry for analysis, yielding more accurate results while using lesser degrees of freedom. In this paper, we try to compare the performance of both FEM and IGA based Topology optimization tools in terms of computational time, value of the objective function obtained, and the number of degrees of freedom involved in the analyses. -
Optimization of the Bypass Duct Shape for the Effective Icing Air Flow to the Turboprop Engine Using Numerical Method
C. A. Vinay, S. Allwin, K. Sai UjjwalaThe chapter explores the critical role of bypass duct design in enhancing the performance and safety of turboprop engines, particularly in managing icing particles. Through the use of computational fluid dynamics (CFD) analysis with Ansys software, the study visualizes flow parameters and evaluates the impact of design modifications on a light transport aircraft. A key focus is the optimization of the bypass ratio, which is pivotal in balancing engine efficiency and power output. The research conducts a comprehensive CFD analysis under various operating conditions, revealing significant improvements in engine performance by reducing the bypass ratio. The chapter also discusses the geometry and sizing of the bypass duct, ensuring optimal airflow and pressure recovery. Detailed contour plots and velocity vectors illustrate the flow behavior within the nacelle, highlighting the effectiveness of the modified designs. The findings underscore the importance of bypass duct optimization in enhancing fuel efficiency, reliability, and safety, providing valuable insights for designers and engineers.AI Generated
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AbstractA study has been carried out to optimize the size of the bypass duct, improve engine performance and prevent engine damage caused by icing particles in a commuter category aircraft turboprop engine air intake. Computational fluid dynamics (CFD) analysis using Ansys software is employed to visualize flow parameters and study the impact of design modifications. The study emphasizes the importance of bypass ratio optimization in achieving efficient and resilient engine operation to maintain ram recovery at engine inlet screen. CFD analysis under various aircraft conditions at different altitude provides insights into flow behavior and aids in optimizing the bypass duct size. The results indicate a decrease in the bypass ratio from 28 to 18% at minimum climb and 38 to 27% at maximum cruise conditions leading to improved engine performance and minimizing the ingress of icing particles into critical engine components with higher ram pressure recovery. -
Unmanned Aerial Vehicle (UAV) with Heavy Payload Capacity and Enhanced Stability
K. Raghav, T. Karthik, S Sripad Kulkarni, H. G. Prashantha KumarThis chapter explores the intricacies of designing and building a quadcopter with a heavy payload capacity and enhanced stability. It begins with an introduction to quadcopters, highlighting their versatility and the basic principles of their operation. The literature review delves into various studies and advancements in quadcopter technology, focusing on structural analysis, navigation, and autonomous flight. The methodology section provides a detailed, step-by-step guide to fabricating a quadcopter, from frame and motor selection to component integration and testing. Key components such as the Pixhawk flight controller, GPS module, and telemetry kit are discussed in depth, emphasizing their roles in achieving precise flight control and stability. The chapter also includes a comprehensive weight estimation and thrust calculation, crucial for understanding the quadcopter's performance capabilities. Results from test flights demonstrate the quadcopter's ability to handle heavy payloads and navigate autonomously, showcasing its potential for diverse applications. The discussion highlights the advantages of using advanced components and suggests future works to further enhance the quadcopter's capabilities. Overall, this chapter offers a thorough and practical guide to building and optimizing a high-performance quadcopter, making it an essential read for anyone interested in aerial robotics.AI Generated
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AbstractThe paper introduces a versatile drone platform for experimentation and testing. With customizable payloads, advanced control systems, and a controlled environment, it facilitates technology and algorithm assessment. Emphasizing flexibility, scalability, and integration ease, it's a reliable platform for UAV prototyping and concept evaluation. The project aims to advance drone technology by providing a comprehensive test bed, aiding the development and validation of new UAV systems and applications, thus contributing to unmanned aerial vehicle evolution. -
Economic Optimization of Silicon Carbide Insulation for Orbiter Re-entry
Abhishek V. Upadhye, G. S. Kamble, K. D. JoshiThe chapter investigates the economic optimization of insulation materials crucial for spacecraft re-entry, focusing on the thermal and economic analysis of silicon carbide (SiC) foam. It begins by outlining the extreme conditions faced during re-entry, where surface temperatures can reach up to 1650°C, necessitating advanced thermal shields. The study emphasizes the importance of optimizing insulation systems to balance thermal performance, volume, size, weight, and cost. It introduces the use of open-cell sandwich-type materials with SiC foam, which provides both conductive and convective resistance to heat flow. The methodology involves a detailed finite element analysis (FEA) to determine the optimal thickness of SiC insulation, ensuring an ambient temperature of 25°C for human comfort and electronic functionality. The analysis includes varying the thickness of silicon (Si) sheets and SiC foam to find the most cost-effective configuration. The results demonstrate a significant reduction in both cost and mass compared to conventional thermal protection systems, with a direct cost reduction of 2.57% and a mass reduction of 29.18%. The study validates its findings through ANSYS simulation, confirming the accuracy and reliability of the FEA approach. The chapter concludes by highlighting the potential of SiC-based insulation systems to simplify the design process and provide an economically optimal solution for spacecraft thermal protection.AI Generated
This summary of the content was generated with the help of AI.
AbstractOptimization of various sub-systems is crucial in space craft’s design. Thermal insulation system on the outer surface of spacecraft requires great attention at the design stage, as it has to bear very high temperatures (around 1650o C) during orbiter re-entry. Thermal insulation is one of those aspects which proves to be principal for mission success. The focus of this study is to determine the optimized insulation system by considering the trending material, i.e., Silicon Carbide Foam (SiC). SiC is a porous material which can be sandwiched with the Silica (Si) Flexible Sheets, to form the Open Cell structure. In this study, thermal analysis of insulation is focused to form an economical alternative insulation system. This optimization includes multiple variables like cost, mass, volume as well as compactness. The mathematical model of heat transfer is solved considering Finite Element Analysis (FEA). In the results, optimum thickness of insulation was identified to be 0.079 m (at 0.8 open porosity) with greater emphasis on obtaining temperature gradient. The primary aim of this study is to design and optimize an effective insulation system from the economic aspect.
- Title
- Advances in Multidisciplinary Design, Analysis and Optimization
- Editors
-
Deepak Sharma
Sachin Singh Gautam
Tapan K. Mankodi
Ujjwal K. Saha
- Copyright Year
- 2025
- Publisher
- Springer Nature Singapore
- Electronic ISBN
- 978-981-9611-58-4
- Print ISBN
- 978-981-9611-57-7
- DOI
- https://doi.org/10.1007/978-981-96-1158-4
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