Tribology is derived from 'tribos' (Greek) = rubbing & 'ology' (Greek) = the study of. It is the study of interacting surfaces. Whenever two surfaces interact, there is friction which results in wear. In most engineering applications, friction has detrimental effects and thus needs to be reduced by lubricating the surfaces. While in certain engineering systems, friction is necessary (e.g. interaction of shoe with ground) for proper working. Thus, tribology is the study of interacting surfaces with respect to friction, wear and lubrication.

Doctoral research at the Particle Flow and Tribology Lab (PFTL)

Advisor: Prof. C. F. Higgs III

Thesis committee: Prof. Jack Beuth, Prof. Anthony Rollett, and Prof. Shelley Anna

The title of my PhD thesis is "Tribosurface Interactions involving Particulate Media with DEM-calibrated Properties: Experiments and Modeling"

While tribology involves the study of friction, wear, and lubrication of interacting surfaces, the tribosurfaces are the pair of surfaces in sliding contact with a fluid (or particulate) media between them. The ubiquitous nature of tribology is evident from the usage of its principles in all aspects of life, such as the friction promoting behavior of shoes on slippery water-lubricated walkways and tires on roadways to the wear of fingernails during filing or engine walls during operations. These tribosurface interfaces, due to the small length scales, are difficult to model for contact mechanics, fluid mechanics and particle dynamics, be it via theory, experiments or computations. Also, there is no simple constitutive law for a tribosurface with a particulate media. Thus, when trying to model such a tribosurface, there is a need to calibrate the particulate media against one or more property characterizing experiments. Such a calibrated media, which is the “virtual avatar” of the real particulate media, can then be used to provide predictions about its behavior in engineering applications. This thesis proposes and attempts to validate an approach that leverages experiments and modeling, which comprises of physics-based modeling and machine learning enabled surrogate modeling, to study particulate media in two key particle matrix industries: metal powder-bed additive manufacturing and energy resource rock drilling.

Publications:

- Desai, P., & Higgs III, C. F., Spreading Process Maps for Powder-Bed Additive Manufacturing Derived from Physics Model-Based Machine Learning, Metals 9(11), (2019): 1176.

- Desai, P. S., Mehta, A., Dougherty, P. S. M., & Higgs III, C. F., A rheometry based calibration of a first-order DEM model to generate virtual avatars of metal Additive Manufacturing (AM) powders, Powder Technology 342 (2019): 441-456.

- Higgs III, C. F., & Desai, P. S., Carnegie Mellon University & William Marsh Rice University, Machine learning enabled model for predicting the spreading process in powder-bed three-dimensional printing, Patent pending (2017)

- Zhang, W., Mehta, A., Desai, P. S., & Higgs III, C. F., Machine Learning Enabled Powder Spreading Process Map for Metal Additive Manufacturing(AM), in Proceedings of SFF Symposium, Austin, TX, Aug, 2017, pp. 1235–1249.

Design of power efficient hydrostatic thrust bearings for large telescopes

Project Partner: Mr. Akash Mehta

Lubrication theory based modeling of levitation sub-system for a typical Hyperloop pod using air bearings

Project Partner: Mr. Akash Mehta