Tanmoy Chatterjee

Tanmoy Chatterjee
University of Surrey · School of Mechanical Engineering Sciences

Doctor of Philosophy

About

40
Publications
5,462
Reads
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351
Citations
Citations since 2017
33 Research Items
338 Citations
2017201820192020202120222023020406080
2017201820192020202120222023020406080
2017201820192020202120222023020406080
2017201820192020202120222023020406080
Introduction
Tanmoy Chatterjee currently works as a post-doctoral research fellow at the Department of Aerospace Structures Engineering, Bay Campus, Swansea University. Tanmoy's expertise lies in Structural Dynamics and Vibration Control, Machine Learning Techniques, Uncertainty Quantification and Robust and Reliability based Optimization of Structural Systems.

Publications

Publications (40)
Conference Paper
Modelling the uncertainties at the joint interface is crucial as it affects the dynamics of the whole system. But modelling the interface using only a physics-based (mathematical) model may lead to large differences between predictions and observations as the physics of the actual system is only partially known. The research focuses on the Bayesian...
Conference Paper
Uncertainties arising in the parameters and modelling of complex systems need to be considered for credible predictions of their dynamic response. For most practical systems, the detailed information regarding these two types of uncertainties is not available. In this paper, the Wishart random matrix model is presented to quantify the total uncerta...
Conference Paper
Physics-informed neural networks have received considerable attention for the solution and data-driven discovery of physical systems governed by differential equations. Despite their immense success, it has been observed that the first-generation PINNs suffer from a drawback arising from the regularization of the loss function. The motivation of th...
Chapter
Parametric excitation is introduced to amplify the external harmonic excitation and extend the capabilities of a nonlinear piezoelectric energy harvester device. To investigate the efficiency of the parametrically amplified energy harvester, the time responses of the voltage and power are computed using the state space formulation. It is assumed th...
Chapter
Parametric excitation is introduced to amplify the external harmonic excitation and extend the capabilities of a nonlinear piezoelectric energy harvester device. To investigate the efficiency of the parametrically amplified energy harvester, the time responses of the voltage and power are computed using the state space formulation. It is assumed th...
Article
Full-text available
This paper addresses the influence of manufacturing variability of a helicopter rotor blade on its aeroelastic responses. An aeroelastic analysis using finite elements in spatial and temporal domains is used to compute the helicopter rotor frequencies, vibratory hub loads, power required and stability in forward flight. The novelty of the work lies...
Article
Full-text available
We demonstrate that the consideration of material uncertainty can dramatically impact the optimal topological micro‑structural configuration of mechanical metamaterials. The robust optimization problem is formulated in such a way that it facilitates the emergence of extreme mechanical properties of metamaterials. The algorithm is based on the bi‑di...
Article
Periodic structures attenuate wave propagation in a specified frequency range, such that a desired bandgap behaviour can be obtained. Most periodic structures are produced by additive manufacturing. However, it is recently found that the variability in the manufacturing processes can lead to a significant deviation from the desired behaviour. This...
Article
To reduce the computational cost of assembled stochastic linear structural dynamic systems, a three-staged reduced order model-based framework for forward uncertainty propagation was developed. First, the physical domain was decomposed by constructing an equivalent reduced order numerical model that limited the cost of a single deterministic simula...
Article
This paper characterizes the stochastic dynamic response of periodic structures by accounting for manufacturing variabilities. Manufacturing variabilities are simulated through a probabilistic description of the structural material and geometric properties. The underlying uncertainty propagation problem has been efficiently carried out by functiona...
Conference Paper
Parametric excitation is introduced to amplify the external harmonic excitation and extend the capabilities of a nonlinear piezoelectric energy harvester device. To investigate the efficiency of the parametrically amplified energy harvester, the time responses of the voltage and power are computed using the state space formulation. It is assumed th...
Article
This paper investigates the elastic wave propagation, mode veering, and in-plane vibration of pre-stressed hexagonal lattice embedded in an elastic medium and composed of axially loaded Timoshenko beams with attached point masses. The frequency band structure of the lattice system is obtained by solving the corresponding eigenvalue problem based on...
Article
Robust design optimization (RDO) of large-scale engineering systems is computationally intensive and requires significant CPU time. Considerable computational effort is still required within conventional meta-model assisted RDO frameworks. The primary objective of this article is to minimize further the computational requirements of meta-model assi...
Conference Paper
Stochastic model updating of joints is a common solution for realistic modelling of built-up structures. However, detailed large-scale models render the simulations to be computationally expensive. This issue is addressed in the following two ways. The first approach involves stochastic decomposition of the functional space by machine learning tech...
Article
The steady-state response of a nonlinear piezoelectric energy harvester subjected to external and parametric excitation is investigated based on the Mathieu-Duffing nonlinear oscillator model. The parametric excitation is introduced to amplify the external harmonic excitation and extend the capabilities of the nonlinear piezoelectric energy harvest...
Article
This paper addresses computational aspects in dynamic sub-structuring of built-up structures with uncertainty. Component mode synthesis (CMS), which is a model reduction technique, has been integrated within the framework of domain decomposition (DD), so that reduced models of individual sub-systems can be solved with smaller computational cost com...
Conference Paper
The response evaluation of assembled systems formed by the combination of different sub-components may often prove to be computationally expensive. The problem can escalate in the presence of uncertainties as multiple simulations of the deterministic finite element (FE) code are employed. The present work aims to address this issue efficiently by r...
Article
Full-text available
Robust design optimization (RDO) has been eminent in determining the optimal design of real-time complex systems under stochastic environment. Unlike conventional optimization, RDO involves uncertainty quantification which may render the procedure to be computationally intensive, if not prohibitive. In order to deal with such issues, an efficient a...
Conference Paper
Dynamic sub-structuring (DS) has been observed to play a crucial role in dealing with large-scale complex dynamic systems over the years. The broad classes of DS approaches involve coupling of constituent component elements to obtain the response of the assembled system in modal, frequency, and time-domains [1, 2, 3]. The developments of DS approac...
Article
Full-text available
Two novel surrogate-based approaches have been developed for uncertainty quantification of engineering systems. In doing so, two well-known techniques, namely, high dimensional model representation (HDMR) and Kriging, have been integrated. Specifically, the trend portion of Kriging has been replaced by HDMR such that the approximation accuracy may...
Article
Robust design optimization (RDO) has been eminent, ascertaining optimal configuration of engineering systems in presence of uncertainties. However, computational aspect of conventional RDO can often get computationally intensive as neighborhood assessments of every solution are required to compute the performance variance and ensure feasibility. Su...
Chapter
The increasing complexity of real-time problems has posed a perpetual challenge to existing simulation models. In particular, such models governing any physical system usually entail long hours of simulation, making them computationally intensive for large-scale problems. In order to mitigate this issue, a novel computational tool has been develope...
Article
Full-text available
The role of robust design optimization (RDO) has been eminent, ascertaining optimal configuration of engineering systems in the presence of uncertainties. However, computational aspect of RDO can often get tediously intensive in dealing with large scale systems. To address this issue, hybrid polynomial correlated function expansion (H-PCFE) based R...
Chapter
Robust design optimization (RDO) has been noteworthy in realizing optimal design of engineering systems in presence of uncertainties. However, computations involved in RDO prove to be intensive for real-time applications. For addressing such issues, a meta-model-assisted RDO framework has been proposed. It has been further observed in such approxim...
Article
Full-text available
Moment free sensitivity analysis computes importance of input parameters by taking into account the entire probability distribution of the output response. Due to improvement in the framework of moment free sensitivity analysis, it is widely preferred over other approaches. However, the framework often becomes computationally intensive especially i...
Article
Full-text available
The computational intensiveness inherently associated with uncertainty quantification of engineering systems has been one of the prime concerns over the years. In order to mitigate this issue, two novel approaches have been developed for efficient stochastic computations. Both the approaches have been developed by amalgamating the advantages of two...
Article
Moment free sensitivity analysis computes importance of input parameters by taking into account the entire probability distribution of the output response. Due to improvement in the framework of moment free sensitivity analysis, it is widely preferred over other approaches. However, the framework often becomes computationally intensive especially i...
Chapter
The computational effort associated with uncertainty quantification of engineering systems has been one of the prime concerns over the years. In order to alleviate this issue, three novel approaches have been developed for efficient stochastic computations. All of the approaches have been developed by combining two available techniques, namely, hig...
Conference Paper
Uncertainty quantification (UQ) of structural systems is a well-established scientific domain prevalent in the engineering research community. However, while dealing with large-scale systems, sampling based UQ approaches, such as, Monte Carlo simulation (MCS) suffer from slow rate of convergence. To mitigate this problem of computational intensity,...
Conference Paper
The input excitation is discretized into a large number of random variables in nonlinear stochastic dynamic response analysis. Simulation based approaches, such as Monte Carlo simulation (MCS) may often prove to be computationally intensive in solving such high-dimensional problems. In order to mitigate the above issue of computational effort, effi...
Article
The computational intensiveness inherently associated with uncertainty quantification of engineering systems has been one of the prime concerns over the years. In order to mitigate this issue, a novel approach has been developed for efficient stochastic computations. The proposed approach has been developed by amalgamating the advantages of two ava...
Article
Robust design optimization (RDO) is a field of optimization in which certain measure of robustness is sought against uncertainty. Unlike conventional optimization, the number of function evaluations in RDO is significantly more which often renders it time consuming and computationally cumbersome. This paper presents two new methods for solving the...
Article
Optimization for crashworthiness is of vast importance in automobile industry. Recent advancement in computational prowess has enabled researchers and design engineers to address vehicle crashworthiness, resulting in reduction of cost and time for new product development. However, deterministic optimum design often resides at the boundary of failur...
Article
The computational intensiveness of evolutionary algorithms (EAs) in dealing with large-scale problems has been of great concern over the years. In order to reduce the computational burden associated with EAs initially, an anchored ANOVA decomposition model was integrated with the elitist nondominated sorting genetic algorithm (NSGA-II). Later, an i...

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Projects

Projects (2)
Project
The overall aim of this project is to create robustly-validated digital twins for dynamics applications. This is urgently needed to overcome limitations in current industrial practice that increasingly rely on large computer-based models to make critical design and operational decisions for systems such as wind farms, nuclear power stations and aircraft. The project will deliver the transformative new science required to generate digital twin technology for key sectors of UK industry: specifically power generation, automotive and aerospace. Visit project website for details: https://digitwin.ac.uk/
Project
The goal is to develop efficient methods for reliability based and robust design optimization.