Dr. Y. Dharmendar Reddy Publishes Nanofluid Flow Research
Anurag University proudly recognises the research achievement of Dr. Y. Dharmendar Reddy, Professor in the Department of Mathematics, on the publication of his paper titled 'Thermal and Solutal Transport in Magnetized Nanofluid Flow Across a Rampant Cone: Role of Slip and Internal Heat Generation.' The work appears in the Journal of Thermal Analysis and Calorimetry, a SCIE (Web of Science) indexed Q2 journal with an Impact Factor of 3.1 and an H-index of 129 (DOI: 10.1007/s10973-025-15022-w).
The study explores the behaviour of magnetized nanofluids, advanced fluids enhanced with nanoparticles, examining how heat and mass transport occur across a conical geometry under the influence of slip conditions and internal heat generation. Such research has important applications in heat transfer engineering, thermal management systems, and various industrial and energy-related processes, where understanding and optimising fluid flow and heat exchange is essential. The mathematical modelling of these complex phenomena contributes valuable insight to both theoretical and applied science.
Publication in a reputed high-impact journal reflects the rigour and relevance of the work. This achievement highlights Anurag University's commitment to fostering strong research in the foundational and applied mathematical sciences. The university congratulates Dr. Dharmendar Reddy on this impactful contribution and celebrates his dedication to scholarly excellence.
Written By
By AU Media Team
More Stories You Might Like
Advancing Hexaferrite–Spinel Nanocomposite Research
Dr. M. Srinivasa Reddy, Professor & HoD, Department of Physics, has published a research paper in Next Materials on the comparative synthesis of hexaferrite–spinel nanocomposites, featured in a Q1 journal indexed in WoS, SCIE and Scopus.
Advancing Image Understanding Through Deep Learning
Ms. Palla Chamundeswari, Assistant Professor, Department of AIML, has published the book “Deep Learning Pathways for Image Understanding and Computer Vision Analytics”, contributing to knowledge in Deep Learning and Computer Vision.
