JAGATH NARAYANA KAMINENI

EDUCATION

Ph.D.
M-Tech
B-Tech

RESEARCH INTERESTS

Nonlinear analysis of composite stiffened panels, variational asymptotic method, Multifunctional composite structures, Aerospace structures, Nonlinear FEM etc.

Scopus Publications

Automated laser machining and electro chemical honing of stellite alloy
S. Sheeba Rani, P. Vinoth Kumar, E. Mohan, R. Praveena, Jagath Narayana Kamineni, et al.
Interactions, 2025
Data-driven mechanical and aerospace systems: transforming with machine learning algorithms and nascent paradigms
Jagath Narayana Kamineni, Ramesh Gupta Burela, Ankit Gupta, Gunji Venkata Punna Rao, B. Balaji
Multiscale and Multidisciplinary Modeling Experiments and Design, 2025
Modeling, fabrication, and burst testing of type-IV 3D printed plastic liner composite overwrapped pressure vessels
Jagath Narayana Kamineni, Ramesh Gupta Burela
International Journal on Interactive Design and Manufacturing, 2025
Investigation on Material Selection and Optimization for Enhanced Performance in Earth Tube Heat Exchangers
Vamsi Krishna Mamidi, Jagath Narayana Kamineni, M. L. Pavan Kishore, T. V. Niteshkumar Achari
Lecture Notes in Networks and Systems, 2025
Monocular Depth Estimation for UAV Navigation Using Segmental Fully Convolutional Prairie Dog Neural Networks
Indradeep Kumar, A. Elumalai, G. Santha Meena, Jagath Narayana Kamineni, Joshuva Arockia Dhanraj, et al.
4th International Conference on Sentiment Analysis and Deep Learning Icsadl 2025 Proceedings, 2025
Unmanned Aerial Vehicles (UAVs) play an important role in very precise navigation for its usage in applications like disaster management, agricultural surveillance, and as delivery drones. Precise depth estimation is crucial for UAV to safely avoid obstacles and thus obtain proper paths for flying. Monocular Depth Estimation (MDE), is another method which is becoming popular due to its easy implementation as compared to stereo vision and LiDAR systems in which single camera depth information is inferred. However, found that there are still some problems in the current a few deep learning methods of MDE, including high computational cost, low prediction accuracy in dynamic conditions, and unsuitability for real-time or online applications. In order to solve these problems, the current research work introduces a Segmental Fully Convolutional Neural Network (SFCNN) tended by the Prairie Dog Optimization (PDO) for UAV flight. The block structure improves adaptability as well as computational complexity; PDO seeks to minimize network's parameters to increase the result precision of the depth forecast and lessen the computation time. tarting from this background, the proposed approach is intended to support lightweight real-time and reliable means for MDE to enhance UAV navigation in complex environments.
Enhancing Mechanical Properties of Composites Through Hybridization with Bio-composites
Gadhamsetty Gurumahesh, Jagath Narayana Kamineni
Springer Proceedings in Materials, 2024
Advances and applications of biofiber-based polymer composites
Manan Gupta, Akshat Jain, Jagath Narayana Kamineni, Ramesh Gupta Burela
Advances in Bio Based Fiber Moving Towards A Green Society, 2021
Geometric nonlinear analysis of composite stiffened panels using variational asymptotic method
K. Jagath Narayana, Ramesh Gupta Burela, Sathiskumar Anusuya Ponnusami, Dineshkumar Harursampath
AIAA Journal, 2020
Constraint method for laminated composite flat stiffened panel analysis using variational asymptotic method (VAM)
Jagath Narayana Kamineni, Ramesh Gupta Burela
Thin Walled Structures, 2019
Multi-scale modeling and simulation of natural fiber reinforced composites (Bio-composites)
K Jagath Narayana, Ramesh Gupta Burela
Journal of Physics Conference Series, 2019
This work presents the numerical analysis of natural fiber reinforced composites (from renewable sources), to evaluate the mechanical behavior of Bio-composites and elucidated the role of micro-mechanical analytical models (Rule of Mixtures and Halpin-Tsai models). Specifically, this study is carried out to perform the Multi-scale modeling and simulation of Biocomposite that constitute of sisal fiber reinforcement and Epoxidized Soybean-Oil (ESO) based matrix. In general, it is difficult to predict the effective properties of natural fiber reinforced composites due to its heterogeneous properties. The Representative Volume Element (RVE) model is capable to estimate the effective properties of Bio-composites. Therefore, RVE model is designed and analyzed based on micro-mechanics by taking the individual properties of fiber and matrix as an input. As a result, the effective properties of Bio-composite can be obtained. Subsequently, a 3-D model of Bio-composite laminated plate behavior is analyzed based on macro-mechanics by taking the effective properties of Bio-composite obtained from micro-mechanical analysis. The obtained effective properties of Bio-composite are validated with the theoretical (Rule of Mixtures) results. The micro-mechanical analysis is carried out using the Digimat (Multi-scale modeling and simulation tool) and macro-mechanical analysis is performed using Ansys software.
Multifunctional polymer composites for 3D and 4D printing
Ramesh Gupta Burela, Jagath Narayana Kamineni, Dineshkumar Harursampath
3D and 4d Printing of Polymer Nanocomposite Materials Processes Applications and Challenges, 2019
A review of recent research on multifunctional composite materials and structures with their applications
K. Jagath Narayana, Ramesh Gupta Burela
Materials Today Proceedings, 2018