(2kb) Unraveling the Multi-Scale Dynamics of Soft Materials: A Path to Sustainable Engineering and Environmental Applications

Research Interests: My research elicits fundamental understanding of the mechanics and flow of soft matter using multi-scale modeling, simulations, and experiments at the intersection of engineering and physics to address current limitations in three areas: energy storage technologies, advanced manufacturing, and environmental sciences. Specifically, I focus on understanding and designing dynamically evolving mechanical properties of Particulate Complex Fluids (PCFs) and soft materials with an aim to: 1) understand why do these materials behave the way they do? 2) quantitatively model their complex behavior and 3) innovate design strategies incorporating the predictions of these models. My expertise in numerical modeling, simulations, theory, and rheometry as well as my prowess in fluid mechanics, transport phenomenon, applied mathematics, colloidal science, polymer physics, and technology transfer uniquely position me to answer these fundamental questions and leverage this understanding in inventing novel technologies.

Abstract: Particulate complex fluids (PCFs) and soft materials have become ubiquitous in engineering, manufacturing, and technology, finding applications in diverse fields such as energy, advanced manufacturing, consumer products, and biotechnology [1, 2]. The ability of PCFs to exhibit dynamic functionality is vital for optimizing their performance in these applications. However, the existing design approaches primarily focus on static mechanical properties, overlooking the dynamic evolution of PCFs under varying thermodynamic, chemical, and kinematic conditions. My research bridges this gap by developing a design strategy that connects the adaptable microstructure of PCFs to their global mechanical behavior, addressing the multi-physics complexity inherent in these materials.

For example, a crucial function of PCFs lies in their ability to effectively transport suspended particles. Whether it is hydraulic fracking fluids delivering proppants or biomass slurries carrying organic waste particles, maintaining particle suspension, and preventing sedimentation is paramount [3]. Furthermore, preventing delayed gravitational collapse in highly filled PCFs, as observed in tailings dam construction, is essential to avoid catastrophic events like mudslides and dam-breaks, which have severe environmental and economic consequences [4, 5]. Developing predictive and data-driven models rooted in fundamental mechanometric principles is crucial for ensuring the safety and sustainability of manufacturing and mining operations.

While empirical observations have traditionally guided PCF design, the multidimensional parameter space encompassing fluid viscoelasticity, particle shapes and size distributions, flow-unsteadiness, particle volume fraction, and many-body effects makes it impossible to fully explore with empirical models alone. To overcome these limitations, a robust and accurately calibrated simulation tool complementing experiments is essential. By leveraging simulation data, advanced Machine Learning (ML) and Deep Learning (DL) techniques can be employed to develop models that extend beyond the limitations of empirical approaches. ML/DL models have demonstrated superior predictive capabilities and offer a promising avenue for tackling the complexity of PCFs [6].

To this end, I have developed two highly optimized and scalable computational tools based on 1) molecular dynamics like coarse-grain approach [7], and 2) finite volume/element methods [8,9] capable of accurately predicting the flow behavior of PCFs across a broad range of length scales, conditions, incorporating particle volume fractions and diverse carrier fluid rheologies. My simulations, validated against experimental data, showcase the efficacy of this approach in accurately predicting 1) the intriguing non-Newtonian rheology of dense suspensions [10-12], and 2) dynamics of microorganisms in complex fluids [8]. The predictive models developed in (1) have been utilized to design an optimized metal pastes for conducting line printing in solar cells [13]. On the other hand, the insights obtained in (2) reveal the role of hydrodynamic interactions in accumulation of microscopic marine plants and microplastic pollutants in thin but vast horizontal layers in oceans [14].

Currently, the focus is on unraveling and quantifying the role of microstructural changes in depletion gels, which showcase thixotropy or time dependent rheological behavior. Such microstructural changes are commonplace in flow electrodes in redox batteries, which have potential to fulfil grid sized energy requirements [15]. In addition, other direction of my research is to design efficient fluids for liquid cooling of electric vehicle batteries by leveraging the elasto-inertial instabilities in viscoelastic fluids [16]. In the future, my research will integrate ML/DL methodologies with direct numerical simulations, creating a meta-model that accurately predicts hydrodynamic interactions between particles of various shapes in complex fluids [17] – a problem, which has been found to be theoretically intractable. This ML/DL integration will significantly reduce the computational time opening doors to explore the micromechanics of high concentration PCFs while resolving the flow at the same time, which is currently computationally highly expensive. This research endeavors to establish a comprehensive framework for designing PCFs with tailored functionality and improved performance across a range of engineering applications. By combining advanced simulation tools with ML/DL techniques, we aim to unlock the full potential of PCFs, enabling safer, more sustainable, and more efficient engineering solutions.

The insights gained from this research have wide-ranging implications for engineering applications. By optimizing the manufacturing and end-use processes, we can improve the transport and handling of complex slurries in energy and consumer products industry, develop impact-resistant materials for enhanced safety, prevent failure of trailing dams in mining operations, and enhance battery cooling for improved performance. The investigation of particles and active matter in complex fluids, such as the stratified fluid in oceans or biological fluids, holds promise for understanding and modeling their effects on ecology and the environment. The fusion of the tool I have developed with advanced ML/DL techniques unlocks a powerful potential for creating precise and resilient predictive continuum models capable of capturing the intricate multi-physics and multiscale complexity exhibited by soft materials.

Teaching Interests: My teaching and mentoring experiences, as well as my exposure to diverse teaching environments at MIT, Purdue, and IIT Bombay (India), have adequately prepared me for teaching a variety of courses such as Transport Phenomena, Fluid Mechanics, Mass/Heat Transfer, Thermodynamics, Complex Fluid Mechanics, Rheology, Computational Fluid Dynamics, and Numerical Methods at both the undergraduate and graduate levels. Through my broad academic training and strong graduate and undergraduate GPAs, I am also well-equipped and open to teaching courses not directly related to my research expertise. In addition to teaching traditional courses, I propose to develop two courses targeted towards senior undergraduate and graduate students. The first course, Advanced Topics in Complex Fluids, will explore both the fundamental physics and the novel and emerging applications, in both research and industrial settings, of a range of materials that fall under the category of complex fluids. The second course, Colloids: Fundamentals to Applications, will be a more research-oriented course aimed at providing a survey of the current state-of-the-art in colloid science and soft matter.

References:

[1] Fernandes, C., Faroughi, S. A., Ribeiro, R., Isabel, A., & McKinley, G. H. Engineering with Computers, 1-27. (2022).

[2] Shaqfeh, E. S. AIChE Journal, 65(5), e16575. (2019).

[3] Thomas, L., Tang, H., Kalyon, D. M., Aktas, S., et al. Journal of Petroleum Science and Engineering, 173, 793-803. (2019).

[4] “Mudslides in Brazil kill at least 94 people,” (16 February 2022). The New York Times; "Barragem de rejeitos da Vale se rompe e causa destruição em Brumadinho (MG)" [Vale's tailings dam collapses and causes destruction in Brumadinho, Minas Gerais], (25 January 2019). Correio Braziliense (in Brazilian Portuguese); "Dam burst at mining site devastates Brazilian town," (6 November 2015). Al Jazeera English. AFP and Reuters.

[5] Raj K. Singhal, ed. (2000). Environmental issues and management of waste in energy and mineral production. Proceedings of the Sixth International Conference on Environmental Issues and Management of Waste in Energy and Mineral Production: SWEMP 2000; Calgary, Alberta, Canada, May 30 – June 2, 2000. Rotterdam: Balkema. pp. 257–260.

[6] Lennon, Kyle R., Gareth H. McKinley, and James W. Swan. "Scientific machine learning for modeling and simulating complex fluids." Proceedings of the National Academy of Sciences 120.27 (2023): e2304669120.

[7] More, R. V., and Arezoo M. Ardekani. Journal of Rheology 64.1 (2020): 67-80.

[8] More, R. V., and Arezoo M. Ardekani. Journal of Fluid Mechanics 905 (2020): A9.

[9] More, R. V., et al. Journal of Fluid Mechanics 929 (2021): A7.

[10] More, R. V., and Arezoo M. Ardekani. Physical Review E 103.6 (2021): 062610.

[11] More, R. V., and A. M. Ardekani. Journal of Rheology 64.5 (2020): 1107-1120.

[12] More, R. V., and A. M. Ardekani. Journal of Rheology 64.2 (2020): 283-297.

[13] More, R. V., and Arezoo M. Ardekani. Annual Review of Fluid Mechanics 55 (2023).

[14] Hilali, M. M., Pal, S., More, R. V., Saive, R., & Ardekani, A. M. (2022). Advanced Energy and Sustainability Research, 3(1), 2100145.

[15] Duduta, M., et al. Advanced Energy Materials 1.4 (2011): 511-516.

[16] More, R. V., et al. Soft Matter 19.22 (2023): 4073-4087.

[17] More, R. V., Extreme Science & Engineering Discovery Environment (XSEDE) research allocation (2022) for 200k Computational hours. (PI). Jan – Dec 2023.