Optimization and performance analysis of a solar concentrated photovoltaic-thermoelectric (CPV-TE) hybrid system

This work presents, for the first time, a statistical model to forecast the electrical efficiency of concentrated photovoltaic-thermoelectric system (CPV-TE). The main objective of this work is to analyze the impact of the input factors (product of solar radiation and optical concentration, external load resistance, leg height of TE and ambient temperature) most affecting the electrical efficiency of CPV-TE system. An innovative and integrated approach based on a multi-physics numerical model coupling radiative, conductive and convective heat transfers Seebeck and photoelectrical conversion physical phenomena inside the CPV-TE collector and a response surface methodology (RSM) model was developed. COMSOL 5.4 Multiphysics software is used to perform the three-dimensional numerical study based on finite element method. Furthermore, results from the numerical model is then analysed using the statistical tool, response surface methodology. The analysis of variance (ANOVA) is conducted to develop the quadratic regression model and examine the statistical significance of each input factor. The results reveal that the obtained determination coefficient Image 1 for electrical efficiency is 0.9945. An excellent fitting is achieved between forecast values obtained from the statistical model and the numerical data provided by the three-dimensional numerical model. The influence of the parameters in order of importance on the electrical efficiency are respectively: product of solar radiation and optical concentration, the height legs of TE, external electrical resistance load, and ambient temperature. A simple polynomial statistical model is created in this work to predict and maximize the electrical efficiency from the solar CPV-TE system based on the four investigated input parameters. The maximum electrical efficiency of the proposed CPVTE (17.448%) is obtained for optimum operating parameters at 229.698 W/m2 value of product of solar radiation and optical concentration, 303.353 K value of ambient temperature, 2.681Ω value of resistance electrical load and at 3.083 mm value of height of TE module

Managing University of Sharjah Setting and Infrastructure Towards a Sustainable and Livable Campus

This paper describes the setting and infrastructure management at the University of Sharjah (UoS) as a continuous effort towards a livable and sustainable campus. The UoS has been participating in the UI GreenMetric World Universities Ranking (UIGWUR) since 2017 to measure its performance in the field of sustainability for continuous improvement. During the last three years, the UoS has succeeded in being among the best 150 universities in the SI category by achieving 70% of the score. However, the UoS managed to get 75% of the total score in this KPI and 100% in the open space per person KPI ratio. To become one of the leading universities, the Landscape and Building Management Sustainability Circle (LBMSC) at the Sustainability Office has analyzed the KPIs and suggested an action plan for continuous improvement. Two KPIs can be improved: sustainability efforts and the total area covered in plants. The UoS shall increase the sustainability efforts and budget and increase the internal and external planting in the coming years. For some KPIs, it cannot be applied to desert regions. It is recommended that the UIGWUR revisit its KPIs and make them more flexible and applicable worldwide. Furthermore, for the open space ratio to the total area KPI, it is recommended to revisit the distribution of the points to have fair comparison. Action plans to improve the sustainability and livability of the campus have also been addressed.Keyword: Setting and infrastructure, LBMSC, livable campus, open area, forest, water absorption, green area, sustainability efforts/budget, and GreenMetri

Water Conservation and Management Practices at the University of Sharjah to Achieve Sustainability Excellence

The University of Sharjah is a leading educational and research institution in the Gulf region. To stimulate the different aspects of sustainability in education and research as well as to ensure the implementation of sustainability concepts throughout the University campus operations, the concept of sustainability circles is implemented. The University being in hot-arid-zone and mostly surrounded by desert terrain relies on unconventional water conservation programs and initiatives such as the use of innovation & technology, reuse and recycling of water, and awareness campaigns. In line with such programs, the use of potable water is limited for hygiene purposes and wastewater generated within the University is reused after treatment to irrigate the vast green spaces through the most efficient irrigation water application systems. Examples of water conservation practices include use of efficient water devices, reuse of treated greywater for toilet flushing at a selected location, water quality monitoring, preservation to conserve water for its intended use, promoting waterless car wash on the campus grounds etc. On-campus water is also conserved through disseminating knowledge and awareness to the University community and beyond through various sustainability related programs and initiatives organized by Sustainability Office for water conservation and environmental protection

Rib suppression in frontal chest radiographs: A blind source separation approach

Chest radiographs play an important role in the diagnosis of lung cancer. Detection of pulmonary nodules in chest radiographs forms the basis of early detection. Due to its sparse bone structure and overlapping of the nodule with ribs and clavicles the nodule is difficult to detect in conventional chest radiographs. We present a technique based on Independent Component Analysis (ICA) for the suppression of posterior ribs and clavicles which will en-hance the visibility of the nodules and aid the radiologist in diagnosis. 1

Central authority controlled air traffic flow management: An optimization approach.

Despite various planning efforts, airspace capacity can sometimes be exceeded, typically due to disruptive events. Air traffic flow management (ATFM) is the process of managing flights in this situation. In this paper, we present an ATFM model that accounts for different rerouting options (path rerouting and diversion) and pre-existing en-route flights. The model proposes having a central authority to control all decisions, which is then compared with current practice. We also consider inter-flight and inter-airline fairness measures in the network. We use an exact approach to solve small-to-medium-sized instances, and we propose a modified fix-and-relax heuristic to solve large-sized instances. Allowing a central authority to control all decisions increases network efficiency compared to the case where the ATFM authority and airlines control decisions independently. Our experiments show that including different rerouting options in ATFM can help reduce delays by up to 8% and cancellations by up to 23%. Moreover, ground delay cost has much more impact on network decisions than air delay cost, and network decisions are insensitive to changes in diversion cost. Furthermore, the analysis of the trade-off between total network cost and overtaking cost shows that adding costs for overtaking can significantly improve fairness at only a small increase in total system cost. A balanced total cost per flight among airlines can be achieved at a small increase in the network cost (0.2 to 3.0%) when imposing airline fairness. In conclusion, the comprehensiveness of the model makes it useful for analyzing a wide range of alternatives for efficient ATF

Fuzzy time-frequency defect classifier for NDT applications

In this paper, a customized classifier is presented for the industry-practiced Nondestructive Evaluation (NDE) protocols using a Hybrid-Fuzzy Inference System (FIS) to classify the and characterize the defects commonly present in the steel pipes used in the gas/petroleum industry. The presented system is hybrid in the sense that it utilizes both soft computing through Fuzzy set theory, as well as conventional parametric analysis through Time-Frequency (TF) methods. Various TF transforms have been tested and the most suitable one for this application, Multiform Tiltable Exponential Distribution (MTED), is presented here. Four defining states are considered in the paper; Slag, Porosity, Crack, and Lack-of-Fusion, representing the four most critical types of defects present in welds on the pipes. The necessary features are calculated using the TF coefficients and are then supplied to the Fuzzy Inference system as input to be used in the classification. The resulting system has shown excellent defect classification with very low Misclassification and False Alarm rates.Scopu

Defect deconvolution using 3rd order statistics for Ultrasonic Nondestructive Testing

Ultrasonic nondestructive testing (NDT) is primarily based upon the detection and classification of a defect in the field of industrial materials. This information is useful in making administrative decisions in terms of maintenance and replacement. The technique presented in this paper utilizes the concept of defect induction as a convolution process between the clean sample and the defect signature. Hence, to identify the type of defect a deconvolution approach can be useful. Due to several similarities between the ultrasonic echoes and the usual modulated sinusoids, a motivation is present to use 2nd and higher order statistics for completely defining the waveform. Such a definition, when compared with standard defects, will provide useful insight in terms of defect classifications and understanding

Defect Deconvolution using 4th Order Statistics for Ultrasonic Nondestructive Testing

Classification of defects using ultrasonic nondestructive testing (NDT) is primarily done in the field of industrial materials to provide useful information in order to assist in making administrative decisions in terms of maintenance and replacement. The technique presented in this paper utilizes the concept of defect induction as a convolution process between the clean sample and the defect signature. Hence, to identify the type of defect a deconvolution approach can be useful. Due to several similarities between the ultrasonic echoes and the usual modulated sinusoids, a motivation is present to use 4th order statistics for completely defining the waveform. Such a definition, when compared with standard defects, will provide useful insight in terms of defect classifications and understanding