Multi-scale modelling of dilute viscoelastic liquids: Atomistic to mesoscale mapping of polymer solutions

Ravikumar, Bharath; Karathanassis, Ioannis K.; Smith, Timothy; Gavaises, Manolis

doi:10.1016/j.polymer.2023.126360

Cited by 2 publications

(1 citation statement)

References 75 publications

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“…Several technical reviews have been published, which explain how to appropriately derive CG interaction potentials from atomistic models. 33,34) Ravikumar et al 35) reported many-body dissipative particle dynamics (mDPD) simulations of polymer solutions using CG potentials, which preserve the Schmidt number of the atomistic description, defined as the ratio of kinetic viscosity and diffusion coefficient. Fundamentally, CG potentials are state-dependent, making it difficult to simulate across an extended parameter range, since this would require a separate CG potential for each state.…”

Section: Constitutive Modelmentioning

confidence: 99%

Short Review on Machine Learning-Based Multi-Scale Simulation in Rheology

Miyamoto

2024

J. Soc. Rheol., Jpn

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We briefly review the machine-learning (ML) applications for rheological research, particularly on the multi-scale simulation (MSS) techniques for complex fluid flows. For such simulations, it is essential to accurately model the constitutive relation (i.e., the strain-rate and stress relation) of complex fluids. Several past studies have found that ML applications for modeling the constitutive relation can reasonably accelerate the flow predictions from a microscopically resolved description of complex fluids such as polymers and colloidal suspensions. Nevertheless, even when the proposed regression models are consistent with the physical knowledge, several outstanding questions remain to be answered, for example, how to ensure the robustness of the predictions. This review summarizes the methods to obtain constitutive models using ML techniques and those applications based on molecular and phenomenological knowledge. Furthermore, we provide a perspective on how to develop the ML framework starting from a microscopic description.

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Section: Constitutive Modelmentioning

confidence: 99%

Short Review on Machine Learning-Based Multi-Scale Simulation in Rheology

Miyamoto

2024

J. Soc. Rheol., Jpn

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Enhancing Heat Transfer in Immersion Cooling of Battery Packs - Using ALTP Heat Flux Sensors

Weiss,

Karathanassis,

Rueppel

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">The effectiveness of immersion cooling for the thermal management of Electric-Vehicle (EV) batteries is crucially influenced by the thermophysical and rheological properties of the heat-transfer liquid. This study emphasizes upon the design requirements for such a fluid in terms of bulk properties, i.e., high electrical resistivity and thermal conductivity, low viscosity, but also relevant to the rheological properties maximizing the heat transfer rate. Key concepts of the implemented research constitute: (i) the promotion of vortical motion in the laminar flow regime, which, in turn, enhances heat transfer by disrupting boundary layers; (ii) vortex stabilization through the addition of viscoelasticity-inducing agents in the base heat-transfer liquid. To improve cooling efficiency, the primary objective is to maximize the achievable heat transfer rate for minimal pumping losses. Hence, a multi-objective optimization process must be set in place where the optimal coolant rheology is dependent on the geometrical features of the battery module. The overall framework of interdependent research activities comprises: (i) the characterization of viscoelastic flow with the use of Particle Image Velocimetry (PIV) in a flow loop with benchmark geometries; (ii) heat-transfer measurements employing a novel Atom Layer Thermopile (ALTP) sensor and (iii) dedicated computational fluid dynamics (CFD) modelling using the Phan-Thien-Tanner constitutive equation for elastic stresses. While there are tailored designs for efficient heat transfer in immersion-cooling paradigms needed, in this paper we concentrate on heat flux measurements when cooling a bluff body. In this work results on heat transfer in the wake behind a square rod were analyzed and discussed. High-viscosity liquids have higher heat transfer at equivalent Re-number. An improvement of heat-transfer due to viscoelastic flow behavior is indicated for high-viscosity liquids, but the trend must be proven with additional experiments. PIV based flow analysis shows a mismatch between the flow pattern and the heat transfer surface.</div></div>

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Multi-scale modelling of dilute viscoelastic liquids: Atomistic to mesoscale mapping of polymer solutions

Cited by 2 publications

References 75 publications

Short Review on Machine Learning-Based Multi-Scale Simulation in Rheology

Short Review on Machine Learning-Based Multi-Scale Simulation in Rheology

Enhancing Heat Transfer in Immersion Cooling of Battery Packs - Using ALTP Heat Flux Sensors

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