Krishna Kumar is an Assistant Professor at the Civil, Architecture, and Environmental Engineering at the University of Texas at Austin. Krishna completed his Ph.D. from the University of Cambridge in January 2015 on multi-scale multiphase modeling of granular flows and was supervised by Professor Kenichi Soga. Krishna’s work involves developing exascale micro and macro-scale numerical methods: Material Point Method, Lattice Boltzmann - Discrete Element coupling, Finite Element Method, and Lattice Element method. His work in high-performance computing in geomechanics provides insights into the mechanics of natural hazards such as landslides. Krishna uses Machine Learning (ML) to model multi-scale problems in geomechanics. Krishna is a Software Sustainability Institute Fellow, UK, and has developed many open-source research codes. Krishna also builds large-scale graph networks and agent-based models for simulating the resilience of city-scale infrastructure systems.
Abram, G., Solis, A., Liang, Y., and Kumar, K.;
IEEE Computing in Science & Engineering
Visualizing regional-scale landslides is the key to conveying the threat of natural hazards to stakeholders and policymakers. Traditional visualization techniques are restricted to post-processing a limited subset of simulation data and are not scalable to rendering exascale models with billions of particles. In-situ visualization is a technique of rendering simulation data in real-time, i.e., rendering visuals in tandem while the simulation is running. In this study, we develop a scalable N:M interface architecture to visualize regional-scale landslides. We demonstrate the scalability of the architecture by simulating the long runout of the 2014 Oso landslide using the Material Point Method coupled with the Galaxy ray tracing engine rendering 4.2 million material points as spheres. In-situ visualization has an amortized runtime increase of 2\% compared to non-visualized simulations. The developed approach can achieve in-situ visualization of regional-scale landslides with billions of particles with minimal impact on the simulation process.
Wang, Q., Hosseini, R., Kumar, K.;
Proceedings of the 20th International Conference on Soil Mechanics and Geotechnical Engineering, Sydney 2021
In nature, submarine slope failures usually carry thousands of cubic-meters of sediments across extremely long distances and cause tsunamis and damages to offshore structures. This paper uses the granular column collapse experiment to investigate the effect of slope angle on the runout behavior of submarine granular landslides for different initial volumes. A two-dimensional coupled lattice Boltzman and discrete element method (LBM-DEM) approach is adopted for numerically modeling the granular column collapse. Columns with four different slope angles and six different volumes are modelled under both dry and submerged conditions. The effects of hydrodynamic interactions, including the generation of excess pore pressures, hydroplaning, and drag forces and formation of turbulent vortices, are used to explain the difference in the runout behavior of the submerged columns compared to the dry columns. The results show that at any given slope angle, there is a threshold volume above which the submerged columns have a larger final runout compared to their dry counterpart, and this threshold volume decreases with slope angle.
Wang, Q., Hosseini, R., Kumar, K.;
GeoCongress 2021, Dallas, USA
Submarine landslides transport thousands of cubic meters of sediment across continental shelves even at slopes as low as 1° and can cause significant casualty and damage to infrastructure. The run-out mechanism in a submarine landslide is affected by factors such as the initial packing density, permeability, slope angle, and initial volume. While past studies have focused on the influence of density, permeability, and slope angle on the granular column collapse, the impact of volume on the run-out characteristics has not been investigated. This study aims to understand how the initial volume affects the run-out using a two-dimensional coupled lattice Boltzman and discrete element (LBM-DEM) method. The coupled LBM-DEM approach allows simulating fluid flow at the pore-scale resolution to understand the grain-scale mechanisms driving the complex continuum-scale response in the granular column collapse. For submerged granular column collapse, the run-out mechanism is heavily influenced by the interaction between the grains and the surrounding fluid. The development of negative pore pressures during shearing and hydrodynamic drag forces inhibit the flow. On the other hand, entrainment of water resulting in hydroplaning enhances the flow. With an increase in volume, the interaction between the grains and the surrounding fluid varies, causing changes in the run-out behavior. For smaller volumes, the forces inhibiting the underwater flow predominates, resulting in shorter run-outs than their dry counterparts. At large volumes, hydroplaning results in larger run-out than the dry cases, despite the inhibiting effects of drag forces and negative pore pressures.
Ridge, A., Konaklieva, S., Bradley, S., McMahon, R. A., Kumar, K.;
IEEE Workshop on Emerging Technologies - Wireless Power (WoW), San Diego, USA, 1-4 June 2021
When power electronics are deployed under the road surface as part of a wireless system it is important to know that their packaging provides adequate heat extraction as well as the required environmental protection – often conflicting requirements. Presently very little can be found in wireless charging standards and literature on the topic of thermal modelling for in-ground components. Yet, this is a topic of great practical significance especially for in-road systems. Traditional cooling methods are not readily applicable underground. This paper uses finite element thermal modelling to investigate the cooling of a representative medium-power in-road wireless system, housed in a sealed ground assembly (GA) chamber and installed to UK requirements (HAUC). The paper quantitatively compares design options and provides practical recommendations for in-road installation thermal management.
Hosseini, R., Kumar, K., Delenne, J.Y.;
Powders and Grains 2021, Buenos Aires, Argentina
The soil water retention curve (SWRC) is the most commonly used relationship in the study of unsaturated soil. In this paper, the effect of porosity on the SWRC is investigated by numerically modeling unsaturated soil using the Shan-Chen multiphase Lattice Boltzmann Method. The shape of simulated SWRCs are compared against that predicted by the van Genuchten model, demonstrating a good fit except at low degrees of saturation. The simulated SWRCs show an increase in the air-entry value as porosity decreases.
Grunstra, N. D. S.;
Proceedings of the National Academy of Sciences of the United States of America (PNAS)
Compared to most other primates, humans are characterized by a tight fit between the maternal birth canal and the fetal head, leading to a relatively high risk of neonatal and maternal mortality and morbidities. Obstetric selection is thought to favor a spacious birth canal, whereas the source for opposing selection is frequently assumed to relate to bipedal locomotion. An alternative, yet under-investigated, hypothesis is that a more expansive birth canal suspends the soft tissue of the pelvic floor across a larger area, which is disadvantageous for continence and support of the weight of the inner organs and fetus. To test this “pelvic floor hypothesis” we generated a finite element model of the human female pelvic floor and varied its radial size and thickness while keeping all else constant. This allowed us to study the effect of pelvic geometry on pelvic floor deflection (i.e., the amount of bending from the original position) and tissue stresses and stretches. Deflection grew disproportionately fast with increasing radial size, and stresses and stretches also increased. By contrast, an increase in thickness increased pelvic floor stiffness - i.e. the resistance to deformation - which reduced deflection but was unable to fully compensate for the effect of increasing radial size. Moreover, larger thicknesses increase the intra-abdominal pressure necessary for childbirth. Our results support the pelvic floor hypothesis and evince functional trade-offs affecting not only the size of the birth canal but also the thickness and stiffness of the pelvic floor.
Vantassel, J. P.;
Cox, B. R.;
Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamen-tally change engineering site characterization by enabling the recovery of high resolution 2D/3D maps ofsubsurface stiffness. Yet, the accuracy of FWI remains quite sensitive to the choice of the initial start-ing model due to the complexity and non-uniqueness of the inverse problem. In response, we presentthe novel application of convolutional neural networks (CNNs) to transform an experimental seismicwavefield acquired using a linear array of surface sensors directly into a robust starting model for 2DFWI. We begin by describing three key steps used for developing the CNN, which include: selectionof a network architecture, development of a suitable training set, and performance of network training.The ability of the trained CNN to predict a suitable starting model for 2D FWI was compared againstother commonly used starting models for a classic near-surface imaging problem; the identification of anundulating, two-layer, soil-bedrock interface. The CNN developed during this study was able to predictcomplex 2D subsurface images of the testing set directly from their seismic wavefields with an averagemean absolute percent error of 6%. When compared to other common approaches, the CNN approachwas able to produce starting models with smaller seismic image and waveform misfits, both before andafter FWI. The ability of the CNN to generalize to subsurface models which were dissimilar to the onesupon which it was trained was assessed using a more complex, three-layered model. While the predictiveability of the CNN was slightly reduced, it was still able to achieve seismic image and waveform misfitscomparable to the other commonly used starting models. This study demonstrates that CNNs have greatpotential as a tool for developing good starting models for FWI, which are critical for producing accurateFWI results.
A new thixotropic model is developed integrating the Papanastasiou-Bingham model with thixotropy equations to simulate the flow behaviour of Tremie Concrete in the Material Point Method framework. The effect of thixotropy on the rheological behaviour of fresh concrete is investigated by comparing field measurements with numerical simulations. The comparison yields new insights into a critical and often overlooked behaviour of concrete. A parametric study is performed to understand the effect of model parameters and rest-time on the shear stress response of fresh concrete. The Material Point Method with the Papanastasiou-Bingham model reproduces slump-flow measurements observed in the field. The novel model revealed a decline in concrete workability during the Slump-flow test after a period of rest due to thixotropy, which the physical version of the test fails to capture. This reduction in workability significantly affects the flow behaviour and the effective use of fresh concrete in construction operation.
Transportmetrica A: Transport Science
In Geotechnical Design and Practice
Delenne, J. Y.;
Journal of Hydrodynamics, Ser. B, 29(4), 529‑541.
Granular Matter, 19(4), 85.
Science China Technological Sciences, 58(12), 2139‑2152.
Subramanian, R. M.;
Indian Geotechnical Journal, 45(1), 43‑50.
Delenne, J. Y.;
The European Physical Journal E, 38(5), 47.
ASCE Geotechnical Special Publication, GSP 227, 710‑721.
Int. J. of Geotechnical Earthquake Engineering, 1, 1, 1–24.
5th International conference on recent advances in geotechnical earthquake engineering and soil dynamics, May 24‑29, San Diego, California
Kumar, K., and Bominathan, A.;
Indian Geotechnical Conference, IIT Bombay. Paper No. 392T11
Kumar, K., and Bominathan, A.;
14th Symposium on Earthquake Engineering Indian Institute of Technology, Roorkee