Research Scholar, Indian Institute of Technology Kharagpur, India.
Dr. Prateek Chandrakar is an emerging expert in structural mechanics, known for his work on stochastic modeling of composite laminates. He holds a Ph.D. in Aerospace Engineering from IIT Kharagpur, with a thesis on machine learning-assisted uncertainty analysis in thermally loaded and damaged composite structures. A recipient of multiple international Award grants and reviewer for reputed journals, his innovative integration of artificial intelligence with classical mechanics has contributed to designing robust aerospace and mechanical structures. His research has led to high-impact publications in Q1 journals like Composite Structures and European Journal of Mechanics A/Solids. His dedication to computational mechanics and his skillful use of tools like ABAQUS, MATLAB, and RBF networks make him a key contributor to the next generation of structural mechanics solutions.
👤Author’s Profile
🎓 Education
Prateek Chandrakar completed his Ph.D. in Structures from IIT Kharagpur (2020–2025), achieving a stellar CGPA of 9.36. His research focused on variable fiber spacing and curvilinear fiber composites under uncertain environments. He earned his M.Tech. in Machine Design from IIT Guwahati (2017–2019) and B.E. in Mechanical Engineering from CSVTU Bhilai (2012–2016). Prior to this, he secured 90% in his matriculation and 80.8% in his intermediate studies from the Chhattisgarh Board. He holds additional certifications in machine learning, MATLAB, ABAQUS, and HyperMesh. Throughout his academic journey, Prateek has consistently demonstrated academic excellence and an aptitude for both foundational and advanced subjects in solid mechanics and composite structures.
💼 Experience
Prateek has served as a Senior Research Fellow and Teaching Assistant at IIT Kharagpur (2022–2025), contributing to courses such as Finite Element Methods and Engineering Mechanics. Previously, he was a Junior Research Fellow (2020–2022) in Aerospace Structures. During his M.Tech. at IIT Guwahati, he worked as a Teaching Assistant for courses like Computational Continuum Mechanics. His industrial exposure includes a short training stint at NTPC-SAIL Power Company. Beyond teaching, he has completed hands-on projects in hydraulic mechanisms, nonlinear FEM, and RVE modeling. Prateek’s blend of academic and applied experience highlights his ability to bridge theory with practical research problems.
🏅 Awards & Honors
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Best Conference Grant – IIT Kharagpur, ECCM 2024 (France)
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SERB ITS Grant – For ICTAM 2024 (Korea)
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ICTAM Support Grant Award – Full fee waiver for outstanding research
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MHRD Fellowships – Junior & Senior Research Fellow at IIT Kharagpur (2020–2025), PG Fellowship at IIT Guwahati (2017–2019)
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GATE 2017 – 98.98 percentile in Mechanical Engineering
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Peer Reviewer – Journals: Acta Mechanica, Awards: ISTAM, AIAA SciTech
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Vaidik Maths Contest – Regional 2nd Position
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Volunteer and Mentor – DISHA (Free education initiative at IIT Guwahati)
🔬 Research Focus
Prateek’s research is centered around stochastic structural mechanics—particularly the buckling and dynamic responses of variable stiffness composite laminates under thermal loads and manufacturing defects. He has advanced the application of machine learning (RBFN, SVR, ABC) to create surrogate models for high-fidelity simulations in nonlinear environments. His studies tackle the challenges of material variability, geometric imperfection, and delamination in aerospace-grade composite materials. His Ph.D. thesis innovatively integrates third-order shear deformation theory and SPR techniques to improve stress prediction accuracy. His goal is to bridge uncertainty quantification with practical design optimizations, particularly for thermal buckling, nonlinear flexure, and damage mechanics.
📚 Publication Top Notes
1. Characterizing Flexural Randomness in Delaminated Curvilinear Fiber Composites
Journal: Composite Structures (2025)
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This study develops a machine learning-assisted surrogate modeling framework to predict the nonlinear flexural behavior of delaminated curvilinear fiber-reinforced composites. By combining Support Vector Regression (SVR) and Radial Basis Function Networks (RBFN), the research significantly reduces computational effort while accurately estimating the impact of tow angle randomness and damage. The paper proposes optimal tow angle layups for enhancing stiffness while accommodating uncertainties, demonstrating a reliable and fast alternative to traditional finite element simulations. The research is particularly valuable for lightweight structural applications in aerospace engineering where manufacturing defects like delamination are unavoidable.
2. Damage-Induced Buckling in Thermally Loaded VAT Laminates under Uncertainty
Journal: European Journal of Mechanics – A/Solids (2024)
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This paper investigates the influence of thermal loads and damage mechanisms (e.g., delamination) on the buckling performance of Variable Angle Tow (VAT) composite laminates under uncertainty. Using a probabilistic modeling approach, the authors incorporate variability in material properties and geometrical imperfections. Third-order shear deformation theory is employed along with stochastic finite element analysis to evaluate thermal buckling loads. This research provides essential insight into designing safer composite structures subjected to thermal and mechanical instability, relevant for aerospace panels and automotive body structures.
3. Stochastic Buckling of Variable Fiber Spacing Composite Plates
Journal: Journal of Composite Materials (2023)
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
Focusing on the uncertainty quantification of buckling behavior in variable fiber spacing composites (VFSCs), this work employs Monte Carlo simulations to assess the reliability of such structures under thermal loading. The results reveal the probabilistic distribution of critical buckling loads under realistic imperfections and loading scenarios. The novelty lies in accounting for both deterministic and stochastic fiber layout variations, leading to design guidelines that improve robustness and structural integrity for high-performance composite components.
4. Buckling Variability in Damaged Composite Laminates in Thermal Fields
Journal: Journal of Thermal Stresses (2024)
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This study explores how delamination and temperature variations affect the buckling characteristics of composite laminates. A combination of the improved first-order shear deformation theory and a continuum damage model is applied to simulate thermomechanical behavior. Uncertainty in input parameters is tackled through a stochastic analysis, revealing critical design zones where damage-induced degradation is more severe. The findings can help in the predictive maintenance and thermal stability assessment of structural composite components in sectors such as defense and aviation.
5. Stochastic RBFN-Based Reliability Estimation in Thermal Loading
Journal: International Journal of Advances in Engineering Sciences and Applied Mathematics (2024)
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
In this work, Radial Basis Function Networks (RBFN) are used to build fast and efficient surrogate models for reliability estimation of VFSC laminates under thermal stresses. The study shows that RBFN can effectively capture nonlinearities caused by fiber angle variation, material uncertainties, and temperature-induced stresses. This method significantly reduces computational time compared to traditional Monte Carlo techniques, providing a viable tool for real-time reliability assessments in design optimization of composite structures.
6. Delamination Effects via Polynomial Neural Network on Dynamic Characteristics
Journal: Acta Mechanica (2024)
Authors: N. Sharma, P. Chandrakar, D.K. Maiti
Summary:
This paper introduces a Polynomial Neural Network (PNN) model to quantify the influence of delamination on the uncertain dynamic response of Variable Angle Tow composites. The work includes both free and forced vibration analyses, capturing variability in material damping, boundary conditions, and geometric configurations. The model is validated against high-fidelity simulations, showing high accuracy with minimal computational resources. Applications include condition monitoring and damage detection in aerospace-grade structural panels.
7. Uncertain Buckling with Internal Defects in VFSC Laminates
Journal: Journal of Composite Materials (2024)
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This research evaluates the buckling behavior of variable fiber spacing composites with embedded internal flaws such as voids, microcracks, and fiber waviness. Through stochastic modeling and sensitivity analysis, the study reveals how defect parameters affect critical buckling loads. The study’s contribution is twofold: it quantifies uncertainty due to imperfections and recommends defect-tolerant configurations for high-reliability designs. This work is vital for industrial applications where manufacturing variability cannot be completely avoided.
Conclusion
Prateek Chandrakar is a deserving and promising candidate for a Best Researcher Award, especially within the domains of aerospace structures, uncertainty quantification, and composite mechanics. His combination of deep domain expertise, strong publications, machine learning integration, and international exposure demonstrates a research trajectory that is both impactful and forward-looking.
