Related papers: Parametric Dynamic Mode Decomposition with multi-l…
Accurate prediction of physical fields is critical in various engineering applications, including thermal management in electronic systems, airfoil shape optimization in aerospace, and flow field control in hypersonic vehicles. This study…
Two data-driven modal analysis approaches, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), are applied to analyze the unsteady flow obtained by solving the Reynolds-averaged Navier-Stokes (RANS) equations in a…
The use of deep learning methods for modeling fluid flow has drawn a lot of attention in the past few years. In situations where conventional numerical approaches can be computationally expensive, these techniques have shown promise in…
Nanofluids are known to have significantly different thermal properties relative to the corresponding conventional fluids. Heat transfer at the solid-fluid interface affects the thermal properties of nanofluids. The current work helps in…
We present a data-efficient, multiscale framework for predicting the density profiles of confined fluids at the nanoscale. While accurate density estimates require prohibitively long timescales that are inaccessible by ab initio molecular…
We present parameter-interpolated dynamic mode decomposition (piDMD), a parametric reduced-order modeling framework that embeds known parameter-affine structure directly into the DMD regression step. Unlike existing parametric DMD methods…
Based on both linear and non-linear estimations of work hardening rate versus strain curves, two mathematical models have been developed to predict the flow curves under hot working conditions up to the peak. The models were tested for a…
The present study examines evaporative cooling and the resulting deposition patterns of a sessile $Al_2O_3$-based nanofluid droplet on a hydrophobic glass substrate at different temperatures. Evaporation predominantly occurs in the pinned…
Liquid water exhibits several important anomalous properties in the vicinity of the melting temperature ($T_{\mathrm{m}}$) of ice Ih, including a higher density than ice and a density maximum at 4~$^{\circ}$C. Experimentally, an isotope…
The simulation of atmospheric flows by means of traditional discretization methods remains computationally intensive, hindering the achievement of high forecasting accuracy in short time frames. In this paper, we apply three reduced order…
Computational fluid dynamics (CFD) is a powerful tool for modeling turbulent flow and is commonly used for urban microclimate simulations. However, traditional CFD methods are computationally intensive, requiring substantial hardware…
We use parsimonious diffusion maps (PDMs) to discover the latent dynamics of high-fidelity Navier-Stokes simulations with a focus on the 2D fluidic pinball problem. By varying the Reynolds number, different flow regimes emerge, ranging from…
In this contribution, we validate a physical model based on a transient temperature equation (including latent heat) w.r.t. the experimental set AMB2018-02 provided within the additive manufacturing benchmark series, established at the…
Knowledge of thermal properties is essential to design and evaluate thermal systems and processes using nanofluids. This paper presents different analytical models to predict thermal conductivity and viscosity. The efforts have been made to…
The interaction of multiple fluids through a heterogeneous pore space leads to complex pore-scale flow dynamics, such as intermittent pathway flow. The non-local nature of these dynamics, and the size of the 4D datasets acquired to capture…
Linear temperature dependence of transport coefficients in metals is often ascribed to non-Fermi-liquid physics. Here we demonstrate the $T$-linear behavior of nonlocal conductivity in a clean 2D electron fluid, where carrier collisions…
The thermal conductivity and stability of nanofluids have posed the biggest challenges to their adoption as coolants in thermal applications in industries such as electronic equipment, heat exchangers, and solar technologies. In this paper,…
Dynamic Mode Decomposition (DMD) is a data-driven and model-free decomposition technique. It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data. Conceptually, DMD performs a…
Hydrofluoroolefins are considered the most promising next-generation refrigerants due to their extremely low global warming potential values, which can effectively mitigate the global warming effect. However, the lack of reliable…
The dispersion stability and thermal conductivity of propylene glycol based nanofluids containing Al2O3 and TiO2 nanoparticles were studied in the temperature range of 20 to 80 {\deg}C. Nanofluids with different concentrations of…