Search

To deal with inverse nonlinear heat transfer problems in a two-dimensional heat conduction problem, this paper presents a hybrid approach combining fuzzy logic and the extended Kalman filter (EKF). The proposed algorithm has been applied to the real-time reconstruction of the temperature field, time-varying convective heat transfer coefficient, and time-varying boundary heat flux using temperature data from a set of sensors. The effects of the covariance of the initial estimation error, the time step of data acquisition by the sensors, and the number of installed sensors on the precision and stability of the estimation results has been investigated by numerical experiments. To compare and validate the results, all these parameters were investigated using the EKF method. The results show that the adaptive fuzzy extended Kalman filter (FEKF) method for estimating temperature, convection coefficient, and heat flux at the boundaries is precise and stable. The comparison shows that the performance of the FEKF method in parameter estimation is better than the EKF method, and the highest increase in precision and stability of the results is related to the convective heat transfer coefficient. [ABSTRACT FROM AUTHOR]

This paper presents a model of heat and moisture transfer through multilayer firefighter protective clothing, which for the first time has taken into account all the major heat and moisture transport mechanisms including conductive and convective heat transfer, two-flux radiative heat transfer, moisture bulk flow, moisture absorption/desorption by fibers, moisture diffusion and phase change. The model was solved by Crank–Nicolson scheme of the finite difference method. Numerical analysis showed that our model can accurately predict the second-degree burn time with an error ranging from 0.37% to 3.55% for different types of clothing assemblies. With this model, the distributions of heat and moisture within the protective clothing system are elucidated and the relative importance of key clothing parameters on thermal protection better understood. It revealed that the thickness of the moisture barrier layer has the greatest influence on the skin burn time. This study is significant in providing a theoretical framework for optimizing the design of thermal protective clothing. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive investigation of heat transfer enhancement and pressure drop in a Coiled Flow Reverser (CFR) with a twisted tape insert. Both laminar (10 < Re < 2000) and turbulent (12,000 < Re < 18,000) regimes have been studied using both experimental and numerical methods. The effects of twist ratio of twisted tape, inlet Reynolds number, and perforating the twisted tapes have been investigated. The study found that the best performance in the laminar regime is achieved with a twisted tape with a twist ratio of 15.73 and no holes on it at a Reynolds number of 2000. In the turbulent regime, the same twisted tape performs the best at a Reynolds number of 17,000. The highest Thermal Performance Ratio (TPR) in laminar regime is 1.55 and in turbulent regime is 0.99. In comparison to the straight pipe, all types of CFRs show better performance in heat transfer enhancement. Also, as the Reynolds number increases, the friction coefficient decreases in all configurations. The study reveals that the twisted tape insert enhances heat transfer performance while increasing pressure drop. This study provides valuable insights into the design and optimization of heat exchangers with CFR. The findings can be used to improve the efficiency and performance of various industrial processes, such as refrigeration systems, air conditioning, and power plants. • Fluid flow and heat transfer in a coiled flow reverser (CFR) were studied. • Effects of tube insert on the heat transfer enhancement of CFR were investigated. • Effects of perforation of tube insert on heat transfer enhancement of CFR were examined. • The effects of tube insert at laminar flow are more than turbulent flow. • Perforation has no significant effects on the thermal performance ratio. [ABSTRACT FROM AUTHOR]

Devices utilizing high voltage direct current (HVDC) necessitate effective heat dissipation using heat sinks to ensure stable and dependable operation. This paper introduces a novel spiral grooved cooling path heat sink concept, which uses a combination of propylene glycol and water as a coolant. A numerical analysis thoroughly explores how modifications to the cooling path structure can affect fluid dynamics and heat transfer characteristics. A series of simulations are conducted to scrutinize the performance of these innovative geometric modifications according to Friction factor, Nusselt number and Thermal performance factor. The heat sink's performance is also examined in the context of the working fluid's flow rate. Results show that the novel spiral grooved cooling path heat sink design exhibits the best thermal performance compared to a plain cooling path heat sink. Among the spiral grooved cooling path designs, SGCP 2 shows the highest thermal performance factor, indicating higher convective heat transfer rates. The thermal performance factor of SGCP 2 is 2.6–5.0% higher than SGCP 1 and 1.3–1.4% higher than SGCP 3. The proposed numerical approach provides a comprehensive understanding of cooling path configurations for heat sinks used for the purpose of cooling HVDC devices. [Display omitted] • A novel heat sink is presented for the cooling purpose of HVDC devices. • Experimental results are well matched with simulation results. • SGCP heat sinks exhibit higher heat transfer rate as compared to PCP heat sink. • Among SGCP heat sinks, SGCP 2 shows enhanced thermal performance factor. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has gradually emerged as a novel and effective method for intelligent control of conjugate heat transfer. Through proper training, DRL agent usually can find a better control strategy than the one optimized manually. For numerous numerical simulation-based investigations, the promising results have only been obtained on 2-dimensional models due to the heavy burden of data acquisition. This paper proposes a novel strategy of transferring the control policy learned in 2-dimensional environment into a new 3-dimensional environment. PDD-DQN (Prioritized Dueling Double Deep Q-Networks) algorithm is used to recognize the underlying relationship between the forced fluid flow and heat transfer performance to produce a control strategy. The DRL controller is first trained on a simple 2-dimensional cavity with one heat source, and the DRL controller is able to reduce the maximum temperature to 315 K which is 2 K lower than the manually optimized control strategy; then the control strategy is transferred to 3-dimensional models where the maximum temperature is further reduced to 308 K; and compared to training the DRL agent directly on 3-dimensional model, the policy-transfer strategy requires only 10% computational expenditure. The proposed strategy is then applied to a more complex testbed with multiple heat sources for further investigating its ability; in both 2D and 3D environment, the DRL controller can cool the maximum temperature to be 307 K which is 8 K lower than the manually optimized control policy; and the computational cost drop to be 3%. The generalization ability of the trained DRL agent in inter-dimensional geometries is confirmed. For the cases with huge grid size, the policy-transfer strategy can economize exponential computational cost, which is of great significance for applying DRL methods to practical thermal control problems. [ABSTRACT FROM AUTHOR]