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Global technological advancements drive daily energy consumption, generating additional carbon-induced climate challenges. Modifying process parameters, optimizing design, and employing high-performance working fluids are among the techniques offered by researchers for improving the thermal efficiency of heating and cooling systems. This study investigates the heat transfer enhancement of hybrid "Al2O3-Cu/water" nanofluids flowing in a two-dimensional channel with semicircle ribs. The novelty of this research is in employing semicircle ribs combined with hybrid nanofluids in turbulent flow regimes. A computer modeling approach using a finite volume approach with k-ω shear stress transport turbulence model was used in these simulations. Six cases with varying rib step heights and pitch gaps, with Re numbers ranging from 10,000 to 25,000, were explored for various volume concentrations of hybrid nanofluids Al2O3-Cu/water (0.33%, 0.75%, 1%, and 2%). The simulation results showed that the presence of ribs enhanced the heat transfer in the passage. The Nusselt number increased when the solid volume fraction of "Al2O3-Cu/water" hybrid nanofluids and the Re number increased. The Nu number reached its maximum value at a 2 percent solid volume fraction for a Reynolds number of 25,000. The local pressure coefficient also improved as the Re number and volume concentration of "Al2O3-Cu/water" hybrid nanofluids increased. The creation of recirculation zones after and before each rib was observed in the velocity and temperature contours. A higher number of ribs was also shown to result in a larger number of recirculation zones, increasing the thermal performance. [ABSTRACT FROM AUTHOR]

Nanofluids are recommended to improve heat transfer in cooling and heating systems, resulting in significant benefits. This paper numerically investigates turbulent heat transfer and Al2O3-nanofluid flow over a vertical double forward-facing step. A two dimensional with three different cases of vertical DFFS is conducted using K-ε model based on finite volume method for volume fraction of nanofluids varied for 1%, 2%,3% and Reynolds number changed from 10000 to 40000. With increasing Reynolds number, there is an increase in local coefficients of heat transfer, with the highest coefficient of heat transfer detected at Re=40000. For volume fractions of Al2O3= 3% and Reynolds numbers of 40000, the effects of step height on surface coefficients of heat transfer are described. In addition, the findings have discovered that as the volume fraction of Al2O3 nanofluids has increased, the coefficient of heat transfer has increased as well, with the maximum coefficient of heat transfer occurring at a volume fraction of Al2O3 nanofluids of 3%. Furthermore, the first step-case 2 local coefficient of heat transfer has been higher than the first step-cases 1 and 3. Increased Re number causes a sharp drop in local static pressure at the first and at the second steps. Due to the recirculation flow, there has been a reduction in velocity profile near the first and second steps, indicating an increase in heat transfer rate. Moreover, velocity counters are shown in order to demonstrate how Reynolds number affects the size of the recirculation zone. In addition, the turbulence kinetic energy counter has been shown in order to demonstrate how to achieve thermal efficiency in the second step in all the cases. [ABSTRACT FROM AUTHOR]

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Demand response (DR) program can shift peak time load to off-peak time, thereby reducing greenhouse gas emissions and allowing energy conservation. In this study, the home energy management scheduling controller of the residential DR strategy is proposed using the hybrid lightning search algorithm (LSA)-based artificial neural network (ANN) to predict the optimal ON/OFF status for home appliances. Consequently, the scheduled operation of several appliances is improved in terms of cost savings. In the proposed approach, a set of the most common residential appliances are modeled, and their activation is controlled by the hybrid LSA-ANN based home energy management scheduling controller. Four appliances, namely, air conditioner, water heater, refrigerator, and washing machine (WM), are developed by Matlab/Simulink according to customer preferences and priority of appliances. The ANN controller has to be tuned properly using suitable learning rate value and number of nodes in the hidden layers to schedule the appliances optimally. Given that finding proper ANN tuning parameters is difficult, the LSA optimization is hybridized with ANN to improve the ANN performances by selecting the optimum values of neurons in each hidden layer and learning rate. Therefore, the ON/OFF estimation accuracy byANNcan be improved. Results of the hybrid LSA-ANN are compared with those of hybrid particle swarm optimization (PSO) based ANN to validate the developed algorithm. Results show that the hybrid LSA-ANN outperforms the hybrid PSO based ANN. The proposed scheduling algorithm can significantly reduce the peak-hour energy consumption during the DR event by up to 9.7138% considering four appliances per 7-h period. [ABSTRACT FROM AUTHOR]

The primitive controller used in the early version for HVAC systems, like the on-off (Bang- Bang) controller, is inefficient, inaccurate, instable, and suffers from high-level mechanical wear. On the other hand, other controllers like PID, compensator, and cascade controllers overcome these disadvantages but when an offset response (inaccurate response) occurs, power consumption will increase. In order to acquire better performance in the central air-conditioning system, a hybrid PIDcascade control is investigated in this paper and compared to the traditional PID, industrial PID (Ziegler- Nichols tuning) and compensator controllers in simulation and experiments. The output of the system is predicted through indoor and outdoor disturbances. Based on the mathematical model of air-conditioning space, the simulations in this paper have found that the proposed hybrid PID-cascade controller has the capability of self-adapting to system changes and results in faster response and better performance. [ABSTRACT FROM AUTHOR]

Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950–2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172–2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: 'Wet and cold' (WC) and 'Wet' (W) representing wetter zones, and 'Wet and hot' (WH) and 'Dry and hot' (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8–94.6%), MSE (57.0–99.5%), NRMSE (42.7–92.1%), PBIAS (100–108%), MD (23.0–85.3%), KGE (21.1–78.1%), and VE (5.1–9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts. • Borneo was classified into several climate zones based on cluster analysis. • Fuzzy C-Mean clustering method can better classify the climate zone of Borneo. • Selection of CMIP6 models was done by employing relative importance metrics. • EC-Earth3-Veg was selected for future climate zones under SSP2-4.5 and SSP5-8.5. • The hotter climate zones, namely, WH and DH, are expected to expand in the future. [ABSTRACT FROM AUTHOR]