Karthik Reddy Lyathakula

Lyathakula, K. R., Cesmeci, S., Demond, M., Hassan, M. F., Xu, H., & Tang, J. (2023). Physics-Informed Deep Learning-Based Proof-of-Concept Study of a Novel Elastohydrodynamic Seal for Supercritical CO<sub>2</sub> Turbomachinery. JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME, 145(12). https://doi.org/10.1115/1.4063326

Lyathakula, K. R., & Yuan, F.-G. (2023). Scalable and portable computational framework enabling online probabilistic remaining useful life (RUL) estimation. ADVANCES IN ENGINEERING SOFTWARE, 181. https://doi.org/10.1016/j.advengsoft.2023.103461

Cesmeci, S., Lyathakula, K. R., Hassan, M. F., Liu, S., Xu, H., & Tang, J. (2022). Analysis of an Elasto-Hydrodynamic Seal by Using the Reynolds Equation. APPLIED SCIENCES-BASEL, 12(19). https://doi.org/10.3390/app12199501

Lyathakula, K. R., & Yuan, F.-G. (2022, May 10). Fatigue Damage Diagnostics-Prognostics Framework for Remaining Life Estimation in Adhesive Joints. AIAA JOURNAL, Vol. 5. https://doi.org/10.2514/1.j060979

Dana, S., & Lyathakula, K. R. (2021, April 22). A framework to quantify uncertainty in critical slip distance in rate and state friction model for earthquakes (Vol. 4). Vol. 4. https://doi.org/10.31224/osf.io/9bf4m

Lyathakula, K. R., & Yuan, F.-G. (2021). A probabilistic fatigue life prediction for adhesively bonded joints via ANNs-based hybrid model. INTERNATIONAL JOURNAL OF FATIGUE, 151. https://doi.org/10.1016/j.ijfatigue.2021.106352

Dana, S., & Lyathakula, K. R. (2021). Arriving at estimates of a rate and state fault friction model parameter using Bayesian inference and Markov chain Monte Carlo. Artificial Intelligence in Geosciences, 2, 171–178. https://doi.org/10.1016/j.aiig.2022.02.003

Lyathakula, K. R. R., & Yuan, F.-G. (2021). Fatigue Damage Prognosis of Adhesively Bonded Joints via a Surrogate Model (D. Zonta, H. Huang, & Z. Su, Eds.). SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2021, Vol. 11591. https://doi.org/10.1117/12.2585280

Lyathakula, K. R., & Yuan, F.-G. (2021). Probabilistic Fatigue Life Prediction for Adhesively Bonded Joints via Surrogate Model. SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2021, Vol. 11591. https://doi.org/10.1117/12.2585281

Structural Optimization Design for Single Layer Surface Acoustic Wave Interdigital Transducer (SAW-IDT). (2021). Xiyou Jinshu Cailiao Yu Gongcheng/Rare Metal Materials and Engineering. Retrieved from https://publons.com/wos-op/publon/52981410/

Dana, S., & Lyathakula, K. R. (2021). Uncertainty quantification in friction model for earthquakes using Bayesian inference. ArXiv. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85106096956&partnerID=MN8TOARS

Wang, Z., Qian, L., Jiang, Z., Xue, X., & Reddy, K. (2020). Vibration effects of standing surface acoustic wave for separating suspended particles in lubricating oil. AIP Advances, 10(4), 045013. https://doi.org/10.1063/5.0004018

Lyathakula, K. R., & Yuan, F.-G. (2019). Demonstration of prognostics health monitoring (PHM) in adhesive lap joints using simulated studies. Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring, 1, 999–1006. https://doi.org/10.12783/shm2019/32213

Wang, Z., Chen, L., Luo, Y., Xue, X., Jiang, Z., & Reddy, K. (2019). Research on Particle Concentration Effect Experiment Based on Microfluidic Chip. Sensor Letters, 17(3), 201–205. https://doi.org/10.1166/sl.2019.4060

Wang, Z.-P., Yin, H., Jiang, Z.-X., Xue, X., Reddy, K., & Li, Y.-F. (2018). A Review of Key Techniques for Online Particle Separation Monitoring. Sensor Letters, 16(4), 259–266. https://doi.org/10.1166/sl.2018.3957

Wang, Z., Xue, X., Li, X., Jiang, Z., & Reddy, K. (2018). Study on Attenuation Properties of Surface Wave of AE Simulation Source Based on OPCM Sensor Element. Journal of Sensors, 2018. https://doi.org/10.1155/2018/6926594

Physics-Informed Deep Learning-Based Modeling of a Novel Elastohydrodynamic Seal for Supercritical CO2 Turbomachinery. https://doi.org/10.1115/POWER2022-86597