Welcome to the Product Design and DevelopmentLab, led by Prof. Stephen Ekwaro-Osire. Wespecialize in risk and uncertainty quantification inengineering systems. With our expertise inartificial intelligence and digital twin concept, weenable robust decision-making in complexengineering scenarios.
Join us in shaping the future of product designand development through transformativetechnologies.
Unsupervised Deep Learning of Turbulent Heat Transfer via Generative Adversarial Networks
Nicholas J. Ward*1, Stephen Ekwaro-Osire*1
- Extensive importance of fluids simulations
- Atmospheric flows, heat exchangers
- Current methods are costly
- Correlation between temperature and velocity
- Machine learning for fluid dynamics 
- Deep learning
- Generative adversarial networks (GANs)
- Application of machine learning for studyingturbulence
- Previous works provided insight on modeling turbulence
- Fluids modeling: direct numerical simulation (DNS),near-wall physics
- Artificial intelligence: data-driven models,supervised/unsupervised learning
- Simulate at reduced computational expense
- Improve data-driven models in turbulence research
- Apply deep learning for predicting turbulent heattransfer
Can turbulent heat transfer be eficiently predicted with wall variables?
- Predict heat transfer from wall variables
- Extrapolate temperature gradients to higher Reynolds numbers
1: Texas Tech University, Department of Mechanical Engineering
Prognostics and Health Management of Wind Energy Infrastructure Systems
Yasar Yanik^1, Stephen Ekwaro-Osire^1, João Paulo Dias^2, Edgard Haenisch Porto^3, Diogo Stuani Alves^3, Tiago Henrique Machado^3, Gregory BregionDaniel^3, Helio Fiori de Castro^3, Katia Lucchesi Cavalca^3
- A digital twin (DT) is a combination of integrated multi-physics, multi-scale, probabilistic simulation of a complex product
- Verification and validation (V&V) is the method of determining if the criteria for apart or a system are accurate and complete
- Applications; aerospace, manufacturing, construction, healthcare, automobile, city, agriculture, electricity
- Implementing V&V of a single componentinstead of a full system in DT
- Enhancing the model's credibility which is carried out by V&V as an essential initialservice in DT
- Providing quick access to asset data or information and a reduction in manual data transfer effort
Does implementing V&V using DT improve access to data and reduce the effort of data exchange?
- To conduct a code and calculation verification
- To validate simulations against experiments
- To demonstrate easy access to assetinformation or data
1 : Texas Tech University, Department of Mechanical Engineering, 2 : Shippensburg University of Pennsylvania, Departmentof Mechanical and Civil Engineering, 3 : University of Campinas, Department of Mechanical Engineering
Prediction of the remaining useful life of a battery with limited data and no temporal identifier
Camilo Lopez-Salazar^1, Stephen Ekwaro-Osire^1, Shweta Dabetwa^r2, and Fisseha Alemayehu^3
- Li-ion batteries are ubiquitous
- Unavailability of battery data makes it difficult to perform prognostics (deep learning)
- Is it possible to predict the remaining use fullife (RUL) of batteries having only three data points without a temporal identifier?
- High unpredictability of battery state of healthparameters and experimental data unavailability
- Learning methods can handle complex regression tasks but they require large amounts of high-quality data
- There is a literature gap on predicting the RUL of batteries without temporal information
- By estimating RUL of batteries on an ongoing basis, adaptative actions can be made during the mission to extend the maintenance-free operation window
Can the remaining useful life(RUL) of a battery be predictedwith no temporal identifier?
1 Dept. of Mechanical Engineering, Texas Tech University, 2 Dept. of Mechanical Engineering, University of MassachusettsLowell, 3 College of Engineering, West Texas A&M University
Can entropy be used to predict the incipient failure of systems?
Nazir L. Gandur^1, Camilo A. Lopez-Salazar^1, Stephen Ekwaro-Osire^1
- Entropy is used to quantify dynamic complexity from an arbitrary time series
- Incipient failure (IF) is an imperfection in the condition of an item from which a degraded or critical failure can be expected to result if corrective action is not taken
- Remaining useful life (RUL) is an estimate of the amount of time that an item is estimated to be able to function before replacement
- Data can be used to better predict IF and RUL
- Avoid catastrophic failure
Can Multi-scale Divesirty Entropy be used to develop a framework to predict the IF of a system?
1 Dept. of Mechanical Engineering, Texas Tech University,
S. Ekwaro-Osire, N. Gandur, and C.A. Lopez-Salazar, “Incipient Fault Point Detection Based on Multiscale Diversity Entropy,” Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, In Press.
Yasar Yanik, Stephen Ekwaro-Osire, João Paulo Dias, Edgard Haenisch Porto, Diogo Stuani Alves, Tiago Henrique Machado, Gregory Bregion Daniel, Helio Fiori de Castro and Katia Lucchesi Cavalca. " Verification and Validation of Rotating Machinery using Digital Twin." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 8, no. 2 (2023): 221072
C. Yüce, O. Gecgel, O. Doğan, S. Dabetwar, O.C. Kalay, Y. Yanik, E. Karpat,F. Karpat, and S. Ekwaro-Osire*, “Prognostics and Health Management of Wind Energy Infrastructure Systems,” Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, Vol. 8, No. 2,020801, Jun 2022.
O. Gecgel, S. Ekwaro-Osire*, U. Gulbulak, and T.S. Morais, “Deep Convolutional Neural Network Framework for Diagnostics of Planetary Gearboxes Under Dynamic Loading With Feature-Level Data Fusion,” Journal of Vibration and Acoustics, Vol. 144, No. 3, 031003, Jun 2022.
S. Dabetwar, S. Ekwaro-Osire*, and J.P. Dias, “Fatigue Damage Diagnostics of Composites Using Data Fusion and Data Augmentation with Deep Neural Networks,” Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, Vol. 5, No. 2, 021004, May 2022.
S. Dabetwar, S. Ekwaro-Osire*, J.P. Dias, G.R. Hübner, C.M. Franchi, and H. Pinheiro, “Mass Imbalance Diagnostics in Wind Turbines using Deep Learning with Data Augmentation,” Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, doi.org/10.1115/1.4054420, Apr 28, 2022.
N.N. Kulkarni, S. Ekwaro-Osire, and P. Egan*, “Fabrication, Mechanics, and Reliability Analysis for 3D Printed Lattice Designs,” Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, Vol. 8, No. 1, 011107, Mar 2022.  E. Asres*, T. Ghebrab, and S. Ekwaro-Osire, “Framework for design of sustainable flexible pavement,” Infrastructures, Vol. 7, No. 1, 6, Jan 2022.
A. Nispel, S. Ekwaro-Osire*, J.P. Dias, and A. Cunha Jr., “Uncertainty Quantification for Fatigue Life of Offshore Wind Turbine Structure,”Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, Vol. 7, No. 4, 040901, Dec 2021.
O. Gecgel, J.P. Dias, S. Ekwaro-Osire, D.S. Alves, T.H. Machado, G.B. Daniel, H.F. de Castro, and K.L. Cavalca*, “Simulation-Driven Deep Learning Approach for Wear Diagnostics in Hydrodynamic Journal Bearings,” Journal of Tribology, Vol. 143, No. 8, 084501, Aug 2021.
S. Dabetwar, S. Ekwaro-Osire*, and J.P. Dias, “Damage Classification of Composites Based on Analysis of Lamb Wave Signals Using Machine Learning,” Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, Vol. 5, No. 3, 011002, Mar 2021.
A. Bhuiyan*, N. Shamim, and S. Ekwaro-Osire, “Magnetic Resonance Image (MRI) Based Computational Modeling for Anterior Cruciate Ligament Response at Low Knee Flexion Angle,” ASME Journal of Engineering and Science in Medical Diagnostics and Therapy, Vol. 4, No.1, 011001, Feb 2021.
P. Chillakanti, S. Ekwaro-Osire*, and A. Ertas, “Evaluation of Technology Platforms for use in Transdisciplinary Research,” Education Sciences, Vol.11, No. 1, 23, Jan 2021.  J. Yang, Y. Zeng*, S. Ekwaro-Osire, A. Nispel, and H. Ge, “Environment-Based Life Cycle Decomposition (eLCD): Adaptation of EBD to Sustainable Design,” Journal of Integrated Design & Process Science, Vol. 24, No. 2, pp. 5-28, 2020.
O. Doğan, F. Karpat*, O. Kopmaz, and S. Ekwaro-Osire, “Influences of Gear Design Parameters on Dynamic Tooth Loads and Time-Varying Mesh Stiffness of Involute Spur Gears,” Sadhana, Vol. 45, 258, Oct 2020.
G. Wanki, S. Ekwaro-Osire*, J.P. Dias, and A. Cunha Jr., “Uncertainty Quantification with Sparsely Characterized Parameters: An Example Applied to Femoral Stem Mechanics,” Journal of Verification, Validation and Uncertainty Quantification, Vol. 5, No. 3, 031005, Sep 2020.
S. Denard*, A. Ertas, S. Mengel, and S. Ekwaro-Osire, “Development Cycle Modeling: Process Risk,” Applied Sciences, Vol. 10, No. 15, 5082, Jul 2020.
S. Denard*, A. Ertas, S. Mengel, and S. Ekwaro-Osire, “Development Cycle Modeling: Resource Estimation,” Applied Sciences, Vol. 10, No. 14, 5013, Jul 2020.
D.S. Alves, G.B. Daniel, H.F. de Castro, T.H. Machado, K.L. Cavalca, O. Gecgel, J.P. Dias, and S. Ekwaro-Osire*, “Uncertainty Quantification in Deep Convolutional Neural Network Diagnostics of Journal Bearings with Ovalization Fault,” Mechanism and Machine Theory, Vol. 149, 103835, Feb 2020.  J.J. Muhammed*, P.W. Jayawickrama, and S. Ekwaro-Osire, “Uncertainty Analysis in Prediction of Settlements for Spatial Prefabricated Vertical Drains Improved Soft Soil Sites,” Geosciences, Vol. 10, No. 2, 42, Jan 2020.
Product Design & Development Lab
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