Thermophysical Characteristics of the Ferrofluid in  a Vertical Rectangle

Seo, Jae-Hyeong; You, Byoung Hee; Kwen, Sang-Seuk; Lee, Dong-Yeon; Lee, Moo‐Yeon

doi:10.3390/e17020903

Cited by 6 publications

(6 citation statements)

References 20 publications

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“…While we consider a relatively simple strategy to achieve rich exploration, our framework allows the translation of any progress in reward-free RL into training the guide agent. For instance, we could adopt works with the entropy of the state density [23,29,41,25,61,47,52,37,56], or with the adaptive reward functions to explore various skills [12]. Another option to improve exploration is to find a set of diverse policies to the same problem [16,28,59].…”

Section: Discussionmentioning

confidence: 99%

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Reinforcement Learning by Guided Safe Exploration

Yang,

Simão,

Jansen

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them in the real world. However, the real-world target task might be unknown prior to deployment. Reward-free RL trains an agent without the reward to adapt quickly once the reward is revealed. We consider the constrained reward-free setting, where an agent (the guide) learns to explore safely without the reward signal. This agent is trained in a controlled environment, which allows unsafe interactions and still provides the safety signal. After the target task is revealed, safety violations are not allowed anymore. Thus, the guide is leveraged to compose a safe behaviour policy. Drawing from transfer learning, we also regularize a target policy (the student) towards the guide while the student is unreliable and gradually eliminate the influence of the guide as training progresses. The empirical analysis shows that this method can achieve safe transfer learning and helps the student solve the target task faster.

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Section: Discussionmentioning

confidence: 99%

“…We could also consider more sophisticated reward-free exploration strategies such as maximizing the entropy of the state occupancy distribution [41,47,23]. We leave this as future work and focus on using the guide to improve how the student learns.…”

Section: Training the Safe Guidementioning

confidence: 99%

Reinforcement Learning by Guided Safe Exploration

Yang,

Simão,

Jansen

et al. 2023

Frontiers in Artificial Intelligence and Applications

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“…Magnetic flux density due to permanent magnet (12) where, B r is the remanent flux density of the permanent magnet (Neodymium magnet NdFeB), which was placed in the position shown in Fig. 2 and little gap was maintained between the magnet and the heat source.…”

Section: A Continuity Momentum Energy and Magneto-static Equationsmentioning

confidence: 99%

“…Jae-Hyeong Seo et al [12] numerically investigated the thermophysical behaviors of a ferrofluid in a vertical rectangle with the variation of intensity of the magnetic field, viscosity of the ferrofluid and boundary conditions. The flow behavior and heat transfer characteristics of the ferrofluid with the intensity of the magnetic field, viscosities of the ferrofluid and boundary conditions were analyzed through isotherms, velocity profiles and both mean and local Nusselt numbers.…”

Section: Introductionmentioning

confidence: 99%

Performance study of magnetic cooling system using kerosene based ferrofluid under magnetic field effect

Garg¹,

Mehta²,

Kumar³

2015

2015 2nd International Conference on Recent Advances in Engineering &Amp; Computational Sciences (RAECS)

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Thermal management of modern electronic circuitry is important as it governs the reliability and life of any miniaturized electronic circuit. The active cooling techniques are not efficient in removing the high heat load generated by these miniature devices. The passive cooling is the next generation cooling. This paper is an attempt to measure the performance using magnetic cooling passive system in terms of heat transfer coefficient using kerosene based ferrofluid and to evaluate the effect of parameters like temperature and pressure head of kerosene based ferrofluid. COMSOL Multiphysics modeling software has been used for analysis. The results after numerical analysis showed that the convective heat transfer coefficient increased with the use of kerosene based ferrofluid as compared to natural convection coefficient.

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“…Enhanced-free convective heat transfer due to solid-fluid interaction in a square cavity was discussed by Jamesahar et al [28]. The thermal and physical properties of magnetic nanofluids over a vertical channel were experimentally studied by the researchers [29,30]. The effect of heat generation on non-Newtonian flow due to unsteady stretching surface was numerically studied by Megahed [31].…”

Section: Introductionmentioning

confidence: 99%

Numerical exploration of magnetohydrodynamic nanofluid flow suspended with magnetite nanoparticles

Sandeep

Chamkha

Animasaun

2017

J Braz. Soc. Mech. Sci. Eng.

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In general, magnetite (Fe 3 O 4) nanoparticles move randomly within the base fluid. By applying an external magnetic field, the movement of those nanoparticles becomes homogeneous. This is very useful in heat transfer processes. In addition, applied magnetic fields are capable to set the thermal and physical natures of the nanofluids with magnetic properties. The heat and mass transfers of non-Newtonian fluids play a major role in technology and in nature due to its stress relaxation, shear thinning, and thickening properties. With this incentive, we investigate the momentum, thermal, and concentration boundary layer behavior of liquid-film flow of water-based non-Newtonian nanofluids dispensed with magnetite nanoparticles. For this investigation, we propose a mathematical model, which deals with the flow of Jeffrey and Oldroyd-B nanofluids past a stretched plate with transverse magnetic field, space and temperature-dependent heat source/sink, thermophoresis, and Brownian movement effects. Numerical results are carried out by employing the Runge-Kutta and Newton's methods. The influence of pertinent parameters on common profiles (velocity, temperature, and concentration) along with the reduced Nusselt number is presented graphically. It is found that suspending the magnetite nanoparticles effectively enhances the thermal conductivity of the Jeffrey nanofluid when compared with the Oldroyd-B nanofluid.

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Thermophysical Characteristics of the Ferrofluid in a Vertical Rectangle

Cited by 6 publications

References 20 publications

Reinforcement Learning by Guided Safe Exploration

Reinforcement Learning by Guided Safe Exploration

Performance study of magnetic cooling system using kerosene based ferrofluid under magnetic field effect

Numerical exploration of magnetohydrodynamic nanofluid flow suspended with magnetite nanoparticles

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