@article{lyathakula_cesmeci_demond_hassan_xu_tang_2023, title={Physics-Informed Deep Learning-Based Proof-of-Concept Study of a Novel Elastohydrodynamic Seal for Supercritical CO<sub>2</sub> Turbomachinery}, volume={145}, ISSN={["1528-8994"]}, DOI={10.1115/1.4063326}, abstractNote={Abstract}, number={12}, journal={JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME}, author={Lyathakula, Karthik Reddy and Cesmeci, Sevki and Demond, Matthew and Hassan, Mohammad Fuad and Xu, Hanping and Tang, Jing}, year={2023}, month={Dec} }
 @article{lyathakula_yuan_2023, title={Scalable and portable computational framework enabling online probabilistic remaining useful life (RUL) estimation}, volume={181}, ISSN={["1873-5339"]}, DOI={10.1016/j.advengsoft.2023.103461}, abstractNote={This work demonstrates a framework that enables online prognostics in adhesive joints by estimating the real-time probabilistic remaining useful life (RUL) using ANNs based hybrid physics models and vectorized sequential Monte Carlo (SMC) simulations. The framework is developed by integrating the physics-based damage degradation model and uncertainty quantification (UQ) techniques to estimate both probabilistic fatigue failure life and RUL. The fatigue damage growth (FDG) simulator, a hybrid surrogate model that simulates real-time fatigue degradation in adhesive joints, is used. In the initial set of results, the generalized framework is validated by estimating the probabilistic fatigue failure life using two UQ methods: Markov Chain Monte Carlo (MCMC) and SMC method. The computational results are successfully compared against experimental data. The conventional MCMC sampling methods are inherently serial, which limits the exploitation of the computational speed-up provided by the FDG simulator and hinders the real-time life predictions. The SMC method quantifies the uncertainties by parallelizing the sampling process, significantly reducing computational time and enabling real-time prediction. Next, the generalized framework is used to estimate probabilistic RUL from the fatigue crack propagation data. The parallel SMC method showed very good speedup compared to the MCMC method. To further enhance the computational speed-up with SMC method, vectorized FDG simulations are introduced into the framework and good scalability is achieved. Finally, the portability of the framework is demonstrated by deploying it on the portable Raspberry Pi cluster.}, journal={ADVANCES IN ENGINEERING SOFTWARE}, author={Lyathakula, Karthik Reddy and Yuan, Fuh-Gwo}, year={2023}, month={Jul} }
 @article{cesmeci_lyathakula_hassan_liu_xu_tang_2022, title={Analysis of an Elasto-Hydrodynamic Seal by Using the Reynolds Equation}, volume={12}, ISSN={["2076-3417"]}, url={https://doi.org/10.3390/app12199501}, DOI={10.3390/app12199501}, abstractNote={This paper reports numerical studies of an Elasto-Hydrodynamic (EHD) seal, which is being developed for supercritical CO2 (sCO2) turbomachinery applications. Current sCO2 turbomachinery suffers from high leakage rates, which is creating a major roadblock to the full realization of sCO2 power technology. The high leakage rates not only penalize the efficiencies but also create environmental concerns due to greenhouse effects caused by the increased CO2 discharge to the atmosphere. The proposed EHD seal needs to work at elevated pressures (10–35 MPa) and temperatures (350–700 °C) with low leakage and minimal wear. The unique mechanism of the EHD seal provides a self-regulated constriction effect to restrict the flow without substantial material contact, thereby minimizing leakage and wear. This work utilizes a physics-based modeling approach. The flow through the gradually narrowing seal clearance is modeled by the well-known Reynolds equation in EHD lubrication theory, while the deformation of the seal is modeled by using the governing equations of three-dimensional solid mechanics. As for the solution methodology, COMSOL’s Thin-Film Flow and Solid Mechanics modules were employed with their powerful capabilities. The numerical results were presented and discussed. It was observed that the Reynolds equation fully coupled with the surface deformation was able to successfully capture the constriction effect. The maximum and minimum leakages were calculated to be 2.25 g/s and 0.1 g/s at P = 5.5 MPa and P = 11 MPa for the design seal, respectively. It was interesting to observe that the seal leakage followed a quadratic trend with increasing pressure differential, which can become advantageous for high-pressure applications such as sCO2 power generation technology.}, number={19}, journal={APPLIED SCIENCES-BASEL}, publisher={MDPI AG}, author={Cesmeci, Sevki and Lyathakula, Karthik Reddy and Hassan, Mohammad Fuad and Liu, Shuangbiao and Xu, Hanping and Tang, Jing}, year={2022}, month={Oct} }
 @article{lyathakula_yuan_2022, title={Fatigue Damage Diagnostics-Prognostics Framework for Remaining Life Estimation in Adhesive Joints}, volume={5}, ISSN={["1533-385X"]}, url={http://dx.doi.org/10.2514/1.j060979}, DOI={10.2514/1.j060979}, abstractNote={This work presents an integrated damage diagnostics–prognostics framework for remaining useful life (RUL) estimation in the adhesively bonded joints under fatigue loading. A matching pursuit algorithm is proposed as the diagnostics technique for estimating the damage extent followed by the fatigue damage growth (FDG) simulator as the predictive model for simulating fatigue degradation. The framework calibrates the FDG simulator by quantifying uncertainties in fatigue model parameters using the damage extent data. Bayesian inference via the Markov chain Monte Carlo method is used to quantify uncertainties and estimate the probabilistic RUL from the quantified uncertainties. The FDG simulator encompasses a physics-based fatigue damage degradation model with an artificial neural network-based hybrid machine-learning model for tracing the damage progression. In the diagnostic technique, ultrasonic guided waves are excited into the structure using a pair of piezoelectric wafers, and the damage extent is quantified by reconstructing the reflected signal from the bond region. The proposed diagnostic technique is verified using the ultrasonic signal obtained from the finite element simulations. The damage prognostics part of the integrated framework is verified by estimating RUL in a mixed-mode failure joint specimen using the experimental fatigue damage progression data. In addition, the integrated framework is then verified by estimating RUL in two adhesively bonded joints: a single lap joint and a tapered single lap joint using Gaussian noise added synthetic data and diagnostic damage extent data.}, journal={AIAA JOURNAL}, publisher={American Institute of Aeronautics and Astronautics (AIAA)}, author={Lyathakula, Karthik Reddy and Yuan, Fuh-Gwo}, year={2022}, month={May} }
 @article{dana_lyathakula_2021, title={A framework to quantify uncertainty in critical slip distance in rate and state friction model for earthquakes}, volume={4}, url={https://doi.org/10.31224/osf.io/9bf4m}, DOI={10.31224/osf.io/9bf4m}, abstractNote={This work presents a framework to inversely quantify uncertainty in the model parameters of the friction model using earthquake data via the Bayesian inference. The forward model is the popular rate- and state- friction (RSF) model along with the spring slider damper idealization. The inverse model is to determine the model parameters using the earthquake data as the response of the RSF model. The conventional solution to the inverse problem is the deterministic parameter values, which may not represent the true value, and quantifying uncertainty in the model parameters increases confidence in the estimation. The uncertainty in the model parameters is estimated by the posterior distribution obtained through the Bayesian inversion.}, publisher={Center for Open Science}, author={Dana, Saumik and Lyathakula, Kartik Reddy}, year={2021}, month={Apr} }
 @article{lyathakula_yuan_2021, title={A probabilistic fatigue life prediction for adhesively bonded joints via ANNs-based hybrid model}, volume={151}, ISSN={["1879-3452"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85108536903&partnerID=MN8TOARS}, DOI={10.1016/j.ijfatigue.2021.106352}, abstractNote={The paper is aimed at developing an efficient and robust probabilistic fatigue life prediction framework for adhesively bonded joints. This framework calibrates the fatigue life model by quantifying uncertainty in the fatigue damage evolution relation using a set of experimental fatigue life data. Probabilistic assessment of fatigue life is simulated through damage evolution along the bondline and Bayesian inference via the Markov chain Monte Carlo (MCMC) sampling method for inverse uncertainty quantification (UQ). To expedite the fatigue life simulation, a hybrid model composed of physics-based fatigue damage evolution relation and a data-driven artificial neural networks (ANNs) model is employed. The degradation of the adhesive is evaluated by the fatigue damage evolution relation which is then mapped to the strain redistribution along the bondline using the ANNs model. Once the mapping is learned by the ANNs, through data from FEA simulations, the probabilistic fatigue life prediction framework involves three successive modules: (I) fatigue damage growth (FDG) simulator, (II) uncertainty quantification (UQ), and (III) confidence bounds for fatigue life prediction. The FDG simulator can be used for simulating fatigue degradation rapidly for a given geometric configuration under any arbitrary fatigue loading spectra. The quantified uncertainties from the framework correspond to the intrinsic statistical material properties that can be used for probabilistic fatigue life prediction in any joint configuration with the same adhesive material. The probabilistic framework is verified using a single lap joint (SLJ) by quantifying uncertainties which are then used for probabilistic fatigue life prediction in laminated doublers in the bending (LDB) joint, that uses the same adhesive material as SLJ, and successfully compared with experimental data. The framework is also tested and validated by estimating probabilistic fatigue life in other joint configurations under constant and variable amplitude fatigue loading spectra.}, journal={INTERNATIONAL JOURNAL OF FATIGUE}, author={Lyathakula, Karthik Reddy and Yuan, Fuh-Gwo}, year={2021}, month={Oct} }
 @article{dana_lyathakula_2021, title={Arriving at estimates of a rate and state fault friction model parameter using Bayesian inference and Markov chain Monte Carlo}, volume={2}, url={http://dx.doi.org/10.1016/j.aiig.2022.02.003}, DOI={10.1016/j.aiig.2022.02.003}, abstractNote={The critical slip distance in rate and state model for fault friction in the study of potential earthquakes can vary wildly from micrometers to few me-ters depending on the length scale of the critically stressed fault. This makes it incredibly important to construct an inversion framework that provides good estimates of the critical slip distance purely based on the observed ac-celeration at the seismogram. To eventually construct a framework that takes noisy seismogram acceleration data as input and spits out robust estimates of critical slip distance as the output, we first present the performance of the framework for synthetic data. The framework is based on Bayesian inference and Markov chain Monte Carlo methods. The synthetic data is generated by adding noise to the acceleration output of spring-slider-damper idealization of the rate and state model as the forward model.}, journal={Artificial Intelligence in Geosciences}, publisher={Elsevier BV}, author={Dana, Saumik and Lyathakula, Karthik Reddy}, year={2021}, month={Dec}, pages={171–178} }
 @article{lyathakula_yuan_2021, title={Fatigue Damage Prognosis of Adhesively Bonded Joints via a Surrogate Model}, volume={11591}, ISSN={["1996-756X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85106139756&partnerID=MN8TOARS}, DOI={10.1117/12.2585280}, abstractNote={This paper demonstrates a diagnostic-prognostics framework to estimate probabilistic remaining useful life (RUL), in adhesively bonded joints subjected to fatigue loading, by calibrating the predictive model using the diagnostics data and quantifying uncertainty in the model parameters. The matching pursuit algorithm is used as the diagnostic method to measure the crack length and the rapid fatigue damage growth (FDG) simulator is used as the predictive model to estimate the remaining useful life (RUL). In the diagnostic method, Lamb waves are excited in the structure using piezo transducers, and the matching pursuit algorithm is used to quantify the damage from the reflected signal. The proposed diagnostic technique is verified using the signal obtained from finite element simulations and artificial noise is added to mimic the signal from a real structure. The diagnostic method is applied periodically to measure the crack length in the single lap joint (SLJ) subjected to fatigue loading and the crack length data is used to calibrate the parameters of the predictive model, which can estimate the RUL. However, the noise in the signal and assumptions in the diagnostic technique result in errors in the measured crack length. These errors in the crack length contribute to the parameter uncertainties during the predictive model calibration. To quantify the model parameter uncertainties, the Bayesian inference via the Markov chain Monte Carlo method is used, and to expedite the uncertainty quantification problem, the rapid FDG simulator is used as the predictive model. The approach is demonstrated using a fatigue damage growth simulation in the SLJ and promising results were achieved.}, journal={SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2021}, publisher={SPIE}, author={Lyathakula, Karthik Reddy R. and Yuan, Fuh-Gwo}, editor={Zonta, Daniele and Huang, Haiying and Su, ZhongqingEditors}, year={2021} }
 @article{lyathakula_yuan_2021, title={Probabilistic Fatigue Life Prediction for Adhesively Bonded Joints via Surrogate Model}, volume={11591}, ISSN={["1996-756X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85108669736&partnerID=MN8TOARS}, DOI={10.1117/12.2585281}, abstractNote={The paper is aimed at developing a probabilistic framework for fatigue life prediction in adhesively bonded joints by calibrating the predictive model, governing adhesive fatigue behavior, using the set of experimental data, and quantifying uncertainty in the model parameters. A cohesive zone model (CZM) is employed to simulate the fatigue damage growth (FDG) along the adhesive bondline and Bayesian inference is used for uncertainty quantification (UQ). The fatigue behavior predicted by FEA modeling for high cycle fatigue, in particular, is computationally intractable, not to mention the inclusion of UQ. To enhance the computational efficiency and yet retain accuracy, a rapid FDG simulator is developed for adhesively bonded joints, by replacing the computationally intensive strain field calculations with the artificial neural networks (ANNs) based surrogate model. The developed rapid FDG simulator is integrated with Bayesian inference and the integrated framework is verified by quantifying uncertainty in fatigue model parameters using the experimental fatigue life data of a single lap joint (SLJ) configuration under constant amplitude fatigue loading. The quantified parameter uncertainties are then used to predict the probabilistic fatigue life in the laminated doublers in bending joint configuration, fabricated using similar adhesive material as SLJ, and successfully comparing it with the experimental data.}, journal={SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2021}, author={Lyathakula, Karthik Reddy and Yuan, Fuh-Gwo}, year={2021} }
 @article{structural optimization design for single layer surface acoustic wave interdigital transducer (saw-idt)_2021, url={https://publons.com/wos-op/publon/52981410/}, journal={Xiyou Jinshu Cailiao Yu Gongcheng/Rare Metal Materials and Engineering}, year={2021} }
 @article{dana_lyathakula_2021, title={Uncertainty quantification in friction model for earthquakes using Bayesian inference}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85106096956&partnerID=MN8TOARS}, journal={arXiv}, author={Dana, S. and Lyathakula, K.R.}, year={2021} }
 @article{wang_qian_jiang_xue_reddy_2020, title={Vibration effects of standing surface acoustic wave for separating suspended particles in lubricating oil}, volume={10}, url={https://doi.org/10.1063/5.0004018}, DOI={10.1063/5.0004018}, abstractNote={The microfluidic chip can be used to separate particles via the acoustic radiation force and can be applied to the separation of impurity particles from lubricating oil. A device that separates particles by controlling the acoustic radiation force via standing surface acoustic waves (SSAWs) was proposed. The concentration and separation of suspended particles were simulated by using the COMSOL Multiphysics modeling software. The force exerted on suspended particles and their subsequent motion in the microfluidic channel were analyzed, and then the concentration of particles exposed to SSAWs was verified. We also investigate how the frequency of the SSAW affects the particle concentration and discuss the advantage of using SSAWs to concentrate and separate particles. The separating feasibility was verified by suspended particles in lubricating oil experiments according to simulation results.}, number={4}, journal={AIP Advances}, publisher={AIP Publishing}, author={Wang, Ziping and Qian, Lei and Jiang, Zhengxuan and Xue, Xian and Reddy, Karthik}, year={2020}, month={Apr}, pages={045013} }
 @inproceedings{lyathakula_yuan_2019, title={Demonstration of prognostics health monitoring (PHM) in adhesive lap joints using simulated studies}, volume={1}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85074414345&partnerID=MN8TOARS}, DOI={10.12783/shm2019/32213}, abstractNote={Adhesively bonded joints are increasingly used in structural applications due to many advantages over classical mechanical fasteners. However, they are susceptible to fatigue damage due to the hostile working environment. It is essential to detect, quantify the damage and estimate the remaining useful life (RUL) under fatigue loading. This paper presents prognostics health monitoring (PHM) framework that can quantify the damage and estimate the RUL in adhesive lap joints. A PHM framework is the synthesis of four disciplines: damage diagnostics, predictive modeling, uncertainty quantification, and uncertainty propagation. Damage diagnostics provide the damage evolution in terms of damage growth rate as input to PHM system. In this work, a new diagnostic method based on ultrasonic Lamb waves is proposed for in-situ measurements of crack length in a single lap joint (SLJ). The idea is to excite single mode using two piezo transducers and extract the wave packet reflected from the crack tip to estimate crack length. The proposed method is verified using computational simulations. A predictive model must be capable of simulating the damage growth physics and can govern the growth rate using model parameters. In this study, the cohesive zone model (CZM) is used to simulate crack growth in SLJ. Uncertainty quantification methods require the evaluation of the predictive model for large parameter sets. To achieve this, setup is built using Python script to run crack propagation simulations in ABAQUS. Convergence issues and computational challenges associated with the setup is addressed. Finally, the procedure to estimate RUL using diagnostics data, predictive model and uncertainty quantification methods is discussed.}, booktitle={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}, author={Lyathakula, K.R. and Yuan, F.-G.}, year={2019}, pages={999–1006} }
 @article{wang_chen_luo_xue_jiang_reddy_2019, title={Research on Particle Concentration Effect Experiment Based on Microfluidic Chip}, volume={17}, url={http://dx.doi.org/10.1166/sl.2019.4060}, DOI={10.1166/sl.2019.4060}, number={3}, journal={Sensor Letters}, publisher={American Scientific Publishers}, author={Wang, Ziping and Chen, Liangbin and Luo, Ying and Xue, Xian and Jiang, Zhenxuan and Reddy, Karthik}, year={2019}, month={Mar}, pages={201–205} }
 @article{wang_yin_jiang_xue_reddy_li_2018, title={A Review of Key Techniques for Online Particle Separation Monitoring}, volume={16}, url={http://dx.doi.org/10.1166/sl.2018.3957}, DOI={10.1166/sl.2018.3957}, number={4}, journal={Sensor Letters}, publisher={American Scientific Publishers}, author={Wang, Zi-Ping and Yin, He and Jiang, Zheng-Xuan and Xue, Xian and Reddy, Karthik and Li, Ye-Fei}, year={2018}, month={Apr}, pages={259–266} }
 @article{wang_xue_li_jiang_reddy_2018, title={Study on Attenuation Properties of Surface Wave of AE Simulation Source Based on OPCM Sensor Element}, volume={2018}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85059068560&partnerID=MN8TOARS}, DOI={10.1155/2018/6926594}, abstractNote={It is of great significance to grasp the ultrasonic attenuation characteristics of materials for the nondestructive testing of materials. The dynamic properties of a piezoelectric composite material (OPCM) with self-developed transverse anisotropy have been analyzed using the experiment in this work. The OPCM sensor is attached to the surface of the iron plate and concrete structures to sense the surface waves generated by acoustic emission (AE). The experiment results show that OPCM sensor elements have unique advantages compared to piezoelectric ceramic materials (PZT). Further, by comparing the signals of isotropic and anisotropic materials, the attenuation characteristics of surface waves propagating in different materials are studied, and a new method for measuring the attenuation coefficient of surface waves is demonstrated.}, journal={Journal of Sensors}, author={Wang, Z. and Xue, X. and Li, X. and Jiang, Z. and Reddy, K.}, year={2018} }
 @article{physics-informed deep learning-based modeling of a novel elastohydrodynamic seal for supercritical co2 turbomachinery, url={https://publons.com/wos-op/publon/54577902/}, DOI={10.1115/POWER2022-86597} }