Muhammed A. Hassan

Solar radiation is an essential input in the design and operation of many engineering systems. However, access to high-resolution data (hourly or sub-hourly) is usually limited, especially in developing countries, either due to its unavailability or expensive costs. A novel data-driven approach is proposed to predict the hourly global irradiation profiles from the cheaper and more likely available records of daily global irradiation. The proposed approach is based on a prior categorization of hourly observations using the K-means clustering algorithm, followed by non-parametric function approximation using the multi-layered perceptron artificial neural network. This approach is applied to measured data (130,000 data points) at six locations in the North African Sahara, and the developed models are benchmarked against all existing parametric models in the literature. The artificial neural network-based models outperformed all existing models, with maximum and minimum coefficients of determination of 0.960 and 0.930, respectively. The non-parametric models also captured the true asymmetric profiles of hourly irradiation with enhanced distributions of the residuals. Hence, the suggested models can be used to generate synthetic hourly data for multiple applications, most notably for building energy simulations and scheduling the operation of power generation systems.

The quality of decisions in the renewable energy sector is as good as the quality of available data. This makes data quality a cornerstone in renewable energy system planning, designing, operation, and assessment. Unfortunately, such data is not always available and usually is cost-prohibitive. One solution for this issue is using the data of few representative days (RDs) instead of the full year for reduced costs of the data itself and the system simulations. A new framework is proposed in this study to distinguish these RDs based on meteorological features. The new framework represents an end-to-end pipeline, starting with measurements, data curing, feature extraction, clustering, and representative year construction. The analysis showed that increasing the number of RDs indeed improves the representativeness of the reconstructed year with disagreement indices as low as 1.041. Including system-irrelevant meteorological parameters was found to increase the disagreement index between original data and reconstructed year from 0.206 to 0.989. The proposed autoencoder feature extraction approach outperformed the conventional statistical one, especially for shallow autoencoders, where the disagreement index was reduced from 1.564 to 1.001. Finally, a brief case study of a standard solar water heating system was performed using TRNSYS v18 software to verify the proposed approach, where the absolute percentage deviation in the annual solar fraction was found to be only 0.278%. This study takes the first steps towards offering decision-makers, designers, and modelers a framework that provides high-quality and high-resolution data compatible with the elevating measurements and simulation cost.

The driving patterns in developing countries, especially in metropolitan cities, are significantly different from those in developed countries or less-populated cities. However, most of the vehicles driven in these cities are tested using driving cycles that do not match those special driving characteristics, typically resulting in an underestimation or overestimation of the emission rates. In this study, a first driving cycle of passenger cars with gasoline engines is proposed for Greater Cairo, Egypt, based on a very large and diverse dataset of high-resolution onboard measurements, recorded using 87 cars driven in 10 distinctive areas in Greater Cairo. Two global driving cycles have been developed based on the commonly used K-means and the newly suggested K-medoids clustering techniques. The developed cycle based on the K-medoids algorithm, which was found more representative of the collected data, is a time series of speed, $\sim$1500 s, covering a distance of $\sim$5.4 km, with an average speed of 12.480 km/h, where 16.3% of the total duration spent in the idling mode. The superiority of the developed cycle over two commonly used cycles, namely the American transient FTP-75 cycle and the European modal ECE cycle, in terms of estimated fuel consumption and emission rates, is reported.

The ever-growing interest in developing compact and more effective heat exchangers necessitates the investigation of combined passive solutions. Despite the diverse related literature, there are no studies on smooth conical tubes (convergent and divergent tubes) fitted with twisted tape inserts. This study analyzes and optimizes the absolute and relative thermal and hydraulic performances of tube heat exchangers, with and without twisted tape inserts, based on Nusselt number and friction factor, by adjusting the tube's diameter ratio and the operational Reynolds number. A consolidated framework of computational fluid dynamics simulations, data-driven multilayered perceptron-based modeling, and gradient-free genetic dual-objective optimization is employed. The results showed that conventional straight tubes are the most favorable in terms of hydraulic performance, with a maximum friction factor of only 0.042. Convergent tubes are the most effective in terms of thermal performance, with Nusselt numbers up to 475.9. Divergent tubes do not show potentials for heat transfer enhancement unless equipped with a tape insert. Twisted tapes effectively improve the thermal performances of all system configurations but also drastically increase the friction factor. Compared to a baseline design of an empty straight tube, the thermal performance can be improved by up to 74.8%. Almost all Pareto frontier solutions belonged to convergent tubes of different configurations. The selected moderate non-dominated solution (assuming equal importance of thermal and hydraulic performances) corresponds to a Nusselt number of 402.9 and a friction factor of 0.130 for a tape-fitted convergent tube with a diameter ratio of 0.445, operating at a Reynolds number of 39,854. In terms of relative performance, the moderate solution corresponds to a Nusselt number ratio of 1.535 and a friction factor ratio of 6.157 using a tape-fitted convergent tube with a diameter ratio of 0.385, operating at a Reynolds number of 31,254. Overall, convergent tubes are recommended as a simple way for boosting the heat transfer rate and the proposed models can be used as flexible tools for selecting the operating conditions based on the designer's preference.

Solar radiation is an essential input in the design and operation of many engineering systems. However, access to high-resolution data (hourly or sub-hourly) is usually limited, especially in developing countries, either due to its unavailability or expensive costs. A novel data-driven approach is proposed to predict the hourly global irradiation profiles from the cheaper and more likely available records of daily global irradiation. The proposed approach is based on a prior categorization of hourly observations using the K-means clustering algorithm, followed by non-parametric function approximation using the multi-layered perceptron artificial neural network. This approach is applied to measured data (130,000 data points) at six locations in the North African Sahara, and the developed models are benchmarked against all existing parametric models in the literature. The artificial neural network-based models outperformed all existing models, with maximum and minimum coefficients of determination of 0.960 and 0.930, respectively. The non-parametric models also captured the true asymmetric profiles of hourly irradiation with enhanced distributions of the residuals. Hence, the suggested models can be used to generate synthetic hourly data for multiple applications, most notably for building energy simulations and scheduling the operation of power generation systems.

The quality of decisions in the renewable energy sector is as good as the quality of available data. This makes data quality a cornerstone in renewable energy system planning, designing, operation, and assessment. Unfortunately, such data is not always available and usually is cost-prohibitive. One solution for this issue is using the data of few representative days (RDs) instead of the full year for reduced costs of the data itself and the system simulations. A new framework is proposed in this study to distinguish these RDs based on meteorological features. The new framework represents an end-to-end pipeline, starting with measurements, data curing, feature extraction, clustering, and representative year construction. The analysis showed that increasing the number of RDs indeed improves the representativeness of the reconstructed year with disagreement indices as low as 1.041. Including system-irrelevant meteorological parameters was found to increase the disagreement index between original data and reconstructed year from 0.206 to 0.989. The proposed autoencoder feature extraction approach outperformed the conventional statistical one, especially for shallow autoencoders, where the disagreement index was reduced from 1.564 to 1.001. Finally, a brief case study of a standard solar water heating system was performed using TRNSYS v18 software to verify the proposed approach, where the absolute percentage deviation in the annual solar fraction was found to be only 0.278%. This study takes the first steps towards offering decision-makers, designers, and modelers a framework that provides high-quality and high-resolution data compatible with the elevating measurements and simulation cost.

Sleeping pods are special confined enclosures used as quiet spaces for short naps (e.g., in libraries and airports) or as cheap accommodations in metropolitan cities. This study aims to analyze the thermal environment in such pods when operating with radiant cooling panels, as potential energy saving (high-temperature cooling) systems, using a validated computational model. The results show that the pod can be maintained at category A (predicted percent dissatisfied of nearly 5%) by maintaining a high supply air temperature (25 °C), a relatively high panel temperature (19 °C), and using an untreated panel surface (emissivity of 0.7). Most of the considered operational settings showed no signs of local thermal discomfort, where the draft risk is below 10%, the radiant temperature asymmetry is lower than 10 °C, and all pod surfaces are safely touchable. Besides, no condensation risks are encountered. The cooling capacity of the panel ranges between 24.4 and 65.65 W/m2 and the radiation heat share ranges between 23.2 and 41.0%. The cooling capacity was found to increase by decreasing the panel's temperature, increasing the panel's emissivity, and increasing the supply air temperature. When switched on, the internal heat dissipation appliances increased the operative temperature by 2.6%, decreased the draft risk by 11.6%, and increased the cooling capacity by 13.9%, but the pod's environment only moved from category A to the beginning of category B.

The ever-growing interest in developing compact and more effective heat exchangers necessitates the investigation of combined passive solutions. Despite the diverse related literature, there are no studies on smooth conical tubes (convergent and divergent tubes) fitted with twisted tape inserts. This study analyzes and optimizes the absolute and relative thermal and hydraulic performances of tube heat exchangers, with and without twisted tape inserts, based on Nusselt number and friction factor, by adjusting the tube’s diameter ratio and the operational Reynolds number. A consolidated framework of computational fluid dynamics simulations, data-driven multilayered perceptron-based modeling, and gradient-free genetic dual-objective optimization is employed. The results showed that conventional straight tubes are the most favorable in terms of hydraulic performance, with a maximum friction factor of only 0.042. Convergent tubes are the most effective in terms of thermal performance, with Nusselt numbers up to 475.9. Divergent tubes do not show potentials for heat transfer enhancement unless equipped with a tape insert. Twisted tapes effectively improve the thermal performances of all system configurations but also drastically increase the friction factor. Compared to a baseline design of an empty straight tube, the thermal performance can be improved by up to 74.8%. Almost all Pareto frontier solutions belonged to convergent tubes of different configurations. The selected moderate non-dominated solution (assuming equal importance of thermal and hydraulic performances) corresponds to a Nusselt number of 402.9 and a friction factor of 0.130 for a tape-fitted convergent tube with a diameter ratio of 0.445, operating at a Reynolds number of 39,854. In terms of relative performance, the moderate solution corresponds to a Nusselt number ratio of 1.535 and a friction factor ratio of 6.157 using a tape-fitted convergent tube with a diameter ratio of 0.385, operating at a Reynolds number of 31,254. Overall, convergent tubes are recommended as a simple way for boosting the heat transfer rate and the proposed models can be used as flexible tools for selecting the operating conditions based on the designer’s preference.

The driving patterns in developing countries, especially in metropolitan cities, are significantly different from those in developed countries or less-populated cities. However, most of the vehicles driven in these cities are tested using driving cycles that do not match those special driving characteristics, typically resulting in an underestimation or overestimation of the emission rates. In this study, a first driving cycle of passenger cars with gasoline engines is proposed for Greater Cairo, Egypt, based on a very large and diverse dataset of high-resolution onboard measurements, recorded using 87 cars driven in 10 distinctive areas in Greater Cairo. Two global driving cycles have been developed based on the commonly used K-means and the newly suggested K-medoids clustering techniques. The developed cycle based on the K-medoids algorithm, which was found more representative of the collected data, is a time series of speed, ~1500 s, covering a distance of ~5.4 km, with an average speed of 12.480 km/h, where 16.3% of the total duration spent in the idling mode. The superiority of the developed cycle over two commonly used cycles, namely the American transient FTP-75 cycle and the European modal ECE cycle, in terms of estimated fuel consumption and emission rates, is reported.

Sensible thermal energy storage is the most common solution for small-to-medium solar process heat systems. Hence, it is vital to enhance the thermal stratification and energy utilization of stratified tanks. In this study, four process heat systems are dynamically simulated: 1) S0 with a single outlet port to the load, 2) S1 with a single port and a thermostatic mixing valve, 3) M0 with multiple ports, and 4) M1 with multiple ports and a mixing valve. Two novel models are proposed for the compound parabolic collectors and the stratified tanks. System M1, with annual system efficiency of 47.3% and solar fraction of 0.57, was found to be the best-performing one, followed by S1, M0, and S0. System M1 also reduced the annual energy consumption of the backup heater in S0 by 16.45%. The thermostatic mixing valve reduced the thermocline thickness. The incorporation of both mixing valve and multiple ports delayed the startup of the heater, reduced the system's thermal losses, and slightly improved the collectors' efficiencies. The proposed tank model can be a viable tool for solar energy designers to optimize the number and relative positions of the inlet and outlet ports for maximized system efficiency and solar coverage.

Accurate monitoring and operation of solar power systems require high-resolution solar radiation measurements and precise separation models. This study aims to improve the accuracy of classic diffuse fraction-clearness index piecewise separation models by applying data-driven classifications of sky conditions. This is achieved through a novel outlier-insensitive clustering algorithm and shape prescriptive modeling, applied to 1-, 10-, 30-, and 60-min ground measurements from 4 different locations in the MENA region. This study shows that classifications of sky conditions are not uniform among the selected locations even though all stations fall in the arid desert climate category. This highlights the importance of extracting the sky conditions from measurements rather than using available classifications in the literature. The selection of the number of clusters has to undergo optimization. The number of clusters is also a function of the time resolution. One of the selected locations shows four optimal clusters for 1-min data and six clusters for 60-min data. All developed piecewise separation models show high accuracy and stability with the mean bias errors approaching zero values and the mean absolute errors ranging between 8.7 and 11.8%. The models also outperform existing ones and have good generalization capabilities under the same climate classification.

This study investigates the performance enhancement of parabolic trough concentrators (PTCs) with transparent insulation material shell (TIMS) of different diameters and thicknesses integrated into evacuated and non-evacuated heat collection elements (HCEs). The results show that non-evacuated TIMS-HCEs have higher energy and exergy efficiencies by up to 62.4 and 63.2%, compared to conventional evacuated HCEs, and by up to 109.2 and 110.9%, compared to conventional non-evacuated HCEs. At high flow rates and low fluid temperatures, the energetic performance of a conventional evacuated HCE is only 6.3% higher than that of the modified non-evacuated one. The modified design also enhances the circumferential temperature uniformity and shows a marginal drop in efficiency when the vacuum is lost. By simulating the PTC performance during four typical days, and without vacuum, the proposed design increased the daily useful heat gain by up to 1.36 and 5.64 kWh, compared to conventional evacuated and non-evacuated HCEs, respectively. Hence, it is proposed as a low-tech alternative to HCE evacuation and as a method of boosting the performance of PTCs operating at low flow rates and high temperatures.

Boosting the optical and thermal efficiencies of parabolic trough concentrators is gaining renewal global interest for improving the overall concentrating solar power plant efficiency and reducing the specific costs of power generation. Using internal radiation heat shields in the annular space of the heat collection element is an attractive passive solution that is not well-addressed in the literature. This study is a first attempt to analyze and map both energetic and exergetic performances of parabolic trough concentrators in terms of the configuration of the radiation heat shield and the operating conditions of the concentrator. A 3D model based on Monte-Carlo ray tracing and computational fluid dynamics is developed, validated, and used to examine 420 combinations of design and operating parameters. The proposed design outperformed the conventional one in the whole spectrum of operating conditions, except for a narrow range of low operating temperatures and high flow rates. Floating radiation heat shields with small diameters and large shading angles enhanced the temperature uniformity of heat collection element and showed the highest enhancement ratios of both energy and exergy efficiencies, which were up to 15.4 and 14.4%, respectively. A radiation heat shield with a diameter of 75 mm and a shading angle of 150° was found the best performing configuration for most operating conditions. Increasing the shield's emissivity (0.06) to that of the absorber tube (0.14) reduced the energy and exergy efficiencies by up to 33.75 and 29.06%, respectively. The effectiveness of the modified design was more pronounced at lower solar irradiance levels. The enhancements of energy and exergy efficiency decreased by 85.76 and 86.40% as the irradiance increased from 200 to 1000 W/m2. However, the modified design was still more efficient at all considered values of emissivity and solar irradiance.

Molten salts are typically used as energy storage media in concentrating solar power systems for their lower costs and environmental impact. This study aims to map and optimize the performance of parabolic trough concentrators (PTCs) working with molten salt-based nanofluids (MSNFs) as heat transfer media at high temperatures. The thermal, hydraulic, energetic, and exergetic performances were analyzed and optimized using a unique framework of Monte Carlo optical simulations, computational fluid dynamics, data-drive support vector regression, particle swarm optimization, and decision-making techniques. Three molten salts (Solar Salt, Hitec, and Hitec XL) and three nanoparticle types (Al2O3, CuO, and SiO2) were investigated in a broad range of volumetric concentrations (0.0–4.0%), operating Reynolds numbers (4 × 103 to 40 × 103), and temperatures (535–805 K). The results showed a maximum energy efficiency of 69.1%, achieved when using SiO2-Hitec nanofluid (1.0%) at a Reynolds number of 40 × 104 and temperature of 535 K. The maximum achieved exergy efficiency was 70.48%, obtained using pure Hitec at a Reynolds number of 40 × 104 and temperature of 535 K. The maximum possible enhancements in energy and exergy efficiencies in the covered range are 17.0 and 42.0%, respectively. The optimal combination of energy and exergy efficiencies are ∼73.1 and 69.0%, obtained using CuO-Hitec nanofluid at temperature, Reynolds number, and concentration of 535 K, 39912.98, and 0.019%, respectively. The optimum combination of percentage enhancements in energy and exergy efficiencies are 0.465 and 7.182%, respectively, which corresponds to CuO-Hitec nanofluid operating at 805 K, 32025.4, and 0.092%, respectively.

Tool failure detection is a crucial task for continuous safe machining operations. In this study, a novel approach is proposed to develop an accurate and simple tool condition classification model (TCCM) for early failure detection during machining processes. Signals from current, vibration, and acoustic emission sensors were preprocessed and used for feature extraction in both time and frequency domains, leading to a total of 152 features. Next, a feature reduction was carried out based on relative importance, computed using a fully-grown random forest, which reduced the number of features to 15. To find out the best combination of relevant signal features, a total of 32,767 optimized support vector classifiers were developed. The comparison between different candidate models was based on both accuracy and complexity. The results showed that a classification accuracy up to 0.911 is attainable for a process-independent classification model using only current sensors. Besides, developing an ensemble of material-dependent models showed a good potential for improvement, recording a classification accuracy up to 0.958 while using features extracted only from the current sensors. The novelty in the present study is in its focus on developing a single sensor-based high-accuracy TCCM. This opens the door for wider utilization of such technology, especially that all existing studies focused on increasing the accuracy using multi-sensor TCCMs, which increases the cost of this technology and makes it inaccessible, especially for small and medium enterprises.

Accurate and credible ultra-short-term photovoltaic (PV) power production prediction is very important in short-term resource planning, electric power dispatching, and operational security for the solar power system. This study proposes a novel approach of using genetically optimized non-linear auto-regressive recurrent neural networks (NARX) for ultra-short-term forecasting of PV power output. Hence, the high prediction accuracy of static multi-layered perceptron neural networks can be extended to dynamic (time-series) models with a more stable learning process. Exogenous models with different commonly available meteorological input parameters are developed and tested at five different locations in Algeria and Australia, as case studies of the arid desert climate. The prediction capabilities of the models are quantified as functions of the forecasting horizon (5, 15, 30, and 60 min) and the number of meteorological inputs using various statistical measures. It was found that the proposed models offer very good estimates of output power, with relative root mean square errors ranging between ∼10 and ∼20% and coefficients of determination higher than 91%, while improving the accuracy of corresponding endogenous models by up to 22.3% by only considering the day number and local time as external variables. Unlike the persistent model, the proposed NARX-GA models perform better as the forecasting horizon narrows down, with improvements of up to 58.4%.

Accurate monitoring and operation of solar power systems require high-resolution solar radiation measurements and precise separation models. This study aims to improve the accuracy of classic diffuse fraction-clearness index piecewise separation models by applying data-driven classifications of sky conditions. This is achieved through a novel outlier-insensitive clustering algorithm and shape prescriptive modeling, applied to 1-, 10-, 30-, and 60-min ground measurements from 4 different locations in the MENA region. This study shows that classifications of sky conditions are not uniform among the selected locations even though all stations fall in the arid desert climate category. This highlights the importance of extracting the sky conditions from measurements rather than using available classifications in the literature. The selection of the number of clusters has to undergo optimization. The number of clusters is also a function of the time resolution. One of the selected locations shows four optimal clusters for 1-min data and six clusters for 60-min data. All developed piecewise separation models show high accuracy and stability with the mean bias errors approaching zero values and the mean absolute errors ranging between 8.7 and 11.8%. The models also outperform existing ones and have good generalization capabilities under the same climate classification.

This study investigates the performance enhancement of parabolic trough concentrators (PTCs) with transparent insulation material shell (TIMS) of different diameters and thicknesses integrated into evacuated and non-evacuated heat collection elements (HCEs). The results show that non-evacuated TIMS-HCEs have higher energy and exergy efficiencies by up to 62.4 and 63.2%, compared to conventional evacuated HCEs, and by up to 109.2 and 110.9%, compared to conventional non-evacuated HCEs. At high flow rates and low fluid temperatures, the energetic performance of a conventional evacuated HCE is only 6.3% higher than that of the modified non-evacuated one. The modified design also enhances the circumferential temperature uniformity and shows a marginal drop in efficiency when the vacuum is lost. By simulating the PTC performance during four typical days, and without vacuum, the proposed design increased the daily useful heat gain by up to 1.36 and 5.64 kWh, compared to conventional evacuated and non-evacuated HCEs, respectively. Hence, it is proposed as a low-tech alternative to HCE evacuation and as a method of boosting the performance of PTCs operating at low flow rates and high temperatures.