Scientific outputs what we'll be remembered for

The TMNT-Lab does not maintain a separate list of publications; please consult the PI's lists below. Software and datasets produced by the lab are listed further down this page.

PI's publication lists:

Personal page:

• https://christov.tmnt-lab.org/publications.html, which indirectly includes all TMNT-Lab publications as well, starting from ca. 2016.

Google Scholar:

• https://scholar.google.com/citations?hl=en&user=GFWvWNkAAAAJ&view_op=list_works&sortby=pubdate

Software and data:

Computational lecturebooks:

• intermediate-fluid-mechanics ( doi:10.5281/zenodo.22969555): Jupyter-based computational lecturebook for ME 50900 – Intermediate Fluid Mechanics, including exact unsteady solutions, Stokes' problems, and start-up flows (I. C. Christov).
• nonlinear-wave-mechanics: Jupyter-based computational lecturebook for Nonlinear Wave Mechanics, including dispersion, solitons, and breathers in the Korteweg–de Vries, nonlinear Schrödinger, sine–Gordon, and kinks (I. C. Christov).

Research software:

• soft-hydraulic-ale-fsi ( doi:10.5281/zenodo.22970054): arbitrary Lagrangian–Eulerian fluid–structure interaction solver for soft hydraulics (I. C. Christov).
• viscoelastic-startup ( doi:10.5281/zenodo.22930189): verification and reproducibility of exact solutions for start-up flows of viscoelastic fluids (I. C. Christov).
• Rheology_UQ ( doi:10.5281/zenodo.15616034): hierarchical Bayesian inference for uncertainty quantification of thermal grease rheology (P. P. Nagrani, PhD 2025).
• Rheology_PINNs ( doi:10.5281/zenodo.7813788): physics-informed neural networks for stress buildup and relaxation in thermal greases (P. P. Nagrani, PhD 2025).
• PINN_shear_migration_of_particles: physics-informed neural networks for shear migration of particles in viscous flow (D. Lu, PhD 2021).
• HydraulicConductivityofShapedFractures ( doi:10.5281/zenodo.3934415): hydraulic conductivity of a shaped fracture with permeable walls (D. Lu, PhD 2021, with F. Municchi).
• InstabilityRegimesInAngledHeleShawCells: interfacial instability regimes in angled Hele-Shaw cells (D. Lu, PhD 2021, with F. Municchi).
• long_wave_eq_ferrofluid_thin_film: long-wave equation for a confined ferrofluid interface (Z. Yu, PhD 2023).
• twoFluidsNBSuspensionFoam ( doi:10.5281/zenodo.3558676): OpenFOAM two-fluid solver for shear-dominated suspension flows (F. Municchi, postdoc 2017–2018).

Datasets:

Data supporting TMNT-Lab papers are archived in the Purdue University Research Repository:

• doi:10.4231/NJ7B-5N87: FEniCS codes and Jupyter notebook for “Flow in a porous non-axisymmetric annular conduit: Coupling wall compliance and peristalsis” (N. Surianarayanan).
• doi:10.4231/BWA1-E343: ALE-FSI simulation scripts, data, and MATLAB post-processing for “Theory and simulation of elastoinertial rectification of oscillatory flows in two-dimensional deformable rectangular channels” (U. M. Rade and S. D. Pande).
• doi:10.4231/VHB3-EN13: ANSYS Fluent cases, data, and scripts for “Correlations for the interphase drag in the two-fluid model of gas–liquid flows through packed-bed reactors” (P. P. Nagrani, PhD 2025).
• doi:10.4231/8AJD-2483: ANSYS Fluent cases, geometry, data, and scripts for “Hydrodynamics of bubble flow through a porous medium with applications to packed bed reactors” (P. P. Nagrani, PhD 2025).
• doi:10.4231/SW73-D037: MATLAB scripts and SimVascular case files for “Oscillatory flows in compliant conduits at arbitrary Womersley number” (S. D. Pande, MSME 2023, and X. Wang, PhD 2022).
• doi:10.4231/35CV-PN42: Jupyter notebook and SimVascular case files for “Flow rate–pressure drop relations for new configurations of slender compliant tubes arising in microfluidics experiments” (X. Wang, PhD 2022, and S. D. Pande, MSME 2023).
• doi:10.4231/SX6D-1370: Simulation codes and data for “Reduced modeling and global instability of finite-Reynolds-number flow in compliant rectangular channels” (X. Wang, PhD 2022).
• doi:10.4231/37PY-K896: Jupyter notebook for “Reduced models of unidirectional flows in compliant rectangular ducts at finite Reynolds number” (X. Wang, PhD 2022).