Describing evolution of preferential flow paths during pressurized injection of chemically

reactive fluid into subsurface using non-equilibrium thermodynamics

Thursday August 21th 2025 at 14:00 

Wolfson Building of Mechanical Engineering, Room 206

:Abstract

Pressurized injection of chemically reactive fluid into underground rocks is ubiquitous in geophysical applications such as subsurface carbon sequestration, hydraulic fracturing and wastewater disposal, leading to flow-induced deformation of the rock, accompanied by chemical weathering. Both these processes, acting individually or coupled together, lead to alteration of transport properties of the rock and facilitate emergence and intensification of preferential flow paths, characterized by gradients of hydrodynamic pressure and solute concentration. Since these paths dominate the multiscale transport dynamics, control the nature of poromechanical and reaction-transport coupling and determine the overall efficiency of the process, understanding their role in subsurface transport following pressurized fluid injection is essential to ensure safety, efficiency and control throughout the process.

As a case study, we consider pressurized injection of acidic fluid into calcite porous rock leading to poromechanical deformation of the rock, accompanied by a reversible dissolution-precipitation reaction, simulated numerically using in-house created models. We apply non-equilibrium thermodynamic framework to analyze the ensuing complex interaction between flow-induced deformation, chemical reaction and transport in this geophysical scenario. To this end, we identify the entropy generation sources, attributed to pertinent dissipative processes and show a clear correlation between the emergence and intensification of preferential flow paths and the accompanying dissipative dynamics, where the evolution of emerging paths leads to a decrease in the free-energy dissipation in the system. This indicates that the emergence of preferential flow paths in geophysical systems represents an energetically-preferred state of the system and can be considered a manifestation of the minimum energy dissipation principle. The developed concepts may assist in determining optimal conditions for pressurized fluid injection into subsurface, as well as in geophysical dating.

Bio:

Evgeny Shavelzon is a Ph.D candidate in Environmental Engineering at the Technion – Israel Institute of Technology under the advision of Dr. Yaniv Edery (Porous Media Visualized lab). His current research topics include numerical modeling of nonequilibrium processes in heterogeneous porous media in application to Geophysics and application of nonequilibrium thermodynamic framework for their characterization. Coming from Aerospace / Mechanical Engineering field, Evgeny’s previous research focused on developing numerical methods for solution of PDE. His goal during Ph.D is to apply and expand this previously acquired knowledge to modeling and understanding complex subsurface transport processes. Following the publication of his first Ph.D paper, Evgeny has received the Jacobs award for an outstanding research paper. Besides academic research, Evgeny has a significant experience in the industry as a Computational Mechanics analyst working on development and implementation of numerical methods in the field of Fluid mechanics, Poroelasticity, Chemical kinetics, Structural mechanics, Heat transfer, and Optics.

Tensile Forces in Virology: Mechanical Drivers of Genome Release and Host Cell Transformation During Infection

Wednesday July 7th 2025 at 14:00 

Wolfson Building of Mechanical Engineering, Zoom

Abstract:

Viruses are nanoscale machines that exploit extreme mechanical forces to drive infection. In this talk, I will explore how tensile and compressive forces regulate the herpesvirus life cycle—from genome packaging under pressure to nuclear remodeling during replication. Inside the viral capsid, the DNA is so tightly packed that it generates internal pressures exceeding tens of atmospheres. This immense pressure acts as the driving force for rapid genome ejection into the host nucleus, with a force comparable to a biological bullet. Using a multidisciplinary platform combining X-ray and neutron scattering with bio-atomic force microscopy (BioAFM), we have quantified these forces and visualized how viral DNA physically transforms the host cell nucleus. Our findings uncover a new mechanical layer of viral replication and suggest strategies for antiviral design that exploit the physical vulnerabilities of the viral life cycle.

Bio:

Alex Evilevitch is a professor at the Faculty of Medicine, Lund University, and an internationally renowned researcher with a distinguished background in interdisciplinary research at the intersection of biophysics, virology, and physical chemistry. He earned his PhD in Physical Chemistry from Lund University in 2001 and pursued a STINT postdoctoral fellowship at UCLA between 2002 and 2003. His academic journey includes tenured faculty appointments at Lund University (Sweden), Carnegie Mellon University (USA), and the University of Illinois at Urbana-Champaign (USA).

Evilevitch's work has significantly advanced the understanding of viral genome packaging and infectivity, with a particular focus on capsid mechanics and the internal pressure and confinement forces acting on viral genomes. His research reveals how these physical parameters drive genome ejection into host cells and how infection leads to mechanical transformations within the host nucleus and chromatin architecture, influencing the outcome of viral replication. His translational research addresses key challenges in herpes virology and gene therapy, leading to the development of non-resistance-based antiviral therapies and improved viral vector production methods, for which he holds several U.S. patents.

He has received numerous international awards, including the Hebert Newby McCoy Award for the most important contribution in the field of chemistry at UCLA and the Hagberg Prize in Biochemistry from the Swedish Royal Academy of Sciences.

פרטים יפורסמו בהמשך 

https://Intuitive.zoom.us/j/96079672856?pwd=voqmtJqqGpz3BXC1UlYBnX05P475gu.1
 

Electrical Engineering Systems Seminar

Speaker: Moshe Yelisevitch

M.Sc. student under the supervision of Prof. Haim Yelisevitch

Wednesday, 25^th June 2025, at 15:00

Conditional Inverse Sampling for the Design of Antennas in Complex Environments

Abstract

The design of compact, high-performance antennas remains a formidable challenge due to the intricate relationship between structural geometry, material, and electromagnetic behavior. Traditional design approaches rely on iterative tuning and brute-force search, often requiring extensive electromagnetic (EM) simulations that are computationally expensive and time-consuming. Furthermore, real-world constraints such as environmental interactions, fabrication limitations, and nonlinear geometry-performance dependencies make it difficult to generate antennas that are both optimal and physically realizable. To address these challenges, we propose a novel Conditional Neural Inverse Transform Sampler (C-NITS) framework for inverse antenna design. Unlike conventional optimization-based approaches, our method learns to map desired electromagnetic characteristics, including reflection coefficient and radiation pattern, to a distribution of feasible antenna geometries, directly generating solutions that satisfy both performance and manufacturability constraints. Our approach extends the Neural Inverse Transform Sampler (NITS) to a conditional formulation, enabling controllable sampling based on environmental parameters such as substrate properties and nearby obstructions. By leveraging a learned inverse model and a fast surrogate simulation network, our framework efficiently explores high-dimensional design spaces without exhaustive full-wave EM simulations. A key feature of our method is its ability to generate diverse, multiple antenna solutions, rather than converging to a single optimal design. This enables engineers to explore multiple feasible configurations that satisfy design objectives while considering fabrication and environmental constraints. Furthermore, we incorporate a structured selection mechanism that filters generated designs to ensure real-world feasibility criteria. Our experiments demonstrate that C-NITS significantly outperforms traditional optimization techniques and existing deep-learning-based inverse design models in terms of EM similarity - both in numerical accuracy (e.g., pixel-wise comparisons) and structural similarity - as well as in engineering-relevant performance metrics. By combining conditional generative modeling with physics-aware constraints, our framework advances the state of automated antenna design, making it more adaptable and scalable for real-world applications.

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום בצ'ט של שם מלא + מספר ת.ז.