Mubashir Ali Siddiqui

Performance Model and Sensitivity Analysis for a Solar Thermoelectric Generator

In this paper, a regression model for evaluating the performance of solar concentrated thermoelectric generators (SCTEGs) is established and the significance of contributing parameters is discussed in detail. The model is based on several natural, design and operational parameters of the system, including the thermoelectric generator (TEG) module and its intrinsic material properties, the connected electrical load, concentrator attributes, heat transfer coefficients, solar flux, and ambient temperature. The model is developed by fitting a response curve, using the least-squares method, to the results. The sample points for the model were obtained by simulating a thermodynamic model, also developed in this paper, over a range of values of input variables. These samples were generated employing the Latin hypercube sampling (LHS) technique using a realistic distribution of parameters. The coefficient of determination was found to be 99.2%. The proposed model is validated by comparing the predicted results with those in the published literature. In addition, based on the elasticity for parameters in the model, sensitivity analysis was performed and the effects of parameters on the performance of SCTEGs are discussed in detail. This research will contribute to the design and performance evaluation of any SCTEG system for a variety of applications.

Critical Concentration Ratio for Solar Thermoelectric Generators

A correlation for determining the critical concentration ratio (CCR) of solar concentrated thermoelectric generators (SCTEGs) has been established, and the significance of the contributing parameters is discussed in detail. For any SCTEG, higher concentration ratio leads to higher temperatures at the hot side of modules. However, the maximum value of this temperature for safe operation is limited by the material properties of the modules and should be considered as an important design constraint. Taking into account this limitation, the CCR can be defined as the maximum concentration ratio usable for a particular SCTEG. The established correlation is based on factors associated with the material and geometric properties of modules, thermal characteristics of the receiver, installation site attributes, and thermal and electrical operating conditions. To reduce the number of terms in the correlation, these factors are combined to form dimensionless groups by applying the Buckingham Pi theorem. A correlation model containing these groups is proposed and fit to a dataset obtained by simulating a thermodynamic (physical) model over sampled values acquired by applying the Latin hypercube sampling (LHS) technique over a realistic distribution of factors. The coefficient of determination and relative error are found to be 97% and ±20%, respectively. The correlation is validated by comparing the predicted results with literature values. In addition, the significance and effects of the Pi groups on the CCR are evaluated and thoroughly discussed. This study will lead to a wide range of opportunities regarding design and optimization of SCTEGs.

A novel method for determining sky view factor for isotropic diffuse radiations for a collector in obstacles-free or urban sites

A novel method for computing sky view factor for isotropic diffuse radiations on tilted collectors located in obstacle free and urban sites has been introduced in this paper. The proposed method involves disintegrating the hemispherical sky vault around collector into small portions (sky elements). Collector is a flat receiving surface, positioned at a particular tilt angle and facing a specific azimuthal orientation. The three-dimensional celestial vault is transformed into a two-dimensional plane. This plane is converted into a computer graphic raster image for computing. Each pixel of raster image represents a sky element. Color (gray shade) of every pixel describes specific weight which depends upon sky elements solid angle and incidence angle with respect to collector. Sky view factor is evaluated by taking ratio of the sum of color codes of all the pixels for tilted collector to that of horizontal collector. Sky view factors at different tilt angles for obstacle free sites are evaluated and results...

Solar energy potential estimation by calculating sun illumination hours and sky view factor on building rooftops using digital elevation model

The detailed and precise estimation of solar energy is a major requirement for solar applications on building roof tops. These estimations help in sustainable development, energy policy making, and renewable energy consumption. In this paper, a methodology was devised to estimate the solar radiation components. This methodology is easily accessible using a simple measuring tape to form a digital elevation model with a cell size of 1 × 1 sq. ft. Liu and Jordans model [Liu and Jordan, “The interrelationship and characteristic distribution of direct, diffuse and total solar radiation,” Sol. Energy. 4(3), 1–19 (1960)] was modified for the urban environment and programmed in MATLAB to estimate solar potential. To validate the developed algorithm and modified model, the roof-top area of the postgraduate laboratories of the Mechanical Engineering Department (NED University) was selected as a case study. Measurements were carried out on different days, which showed good agreement with the proposed work. Within t...

Adaptive sequential experimentation based on revised simplex search

This paper presents an effective experimentation strategy for expensive industrial experiments. In these experiments, there is no prior knowledge about the behaviour of the system under testing and the cost of running experiments is high and the total testing budget is limited. Adaptive sequential experimentation strategy is needed which is able to explore and reach the best design space quickly. Our experimentation strategy is based on revised simplex search method that combines the advantages of simplex search method and adaptive one-factor-at-a-time method and utilises response surface modelling to predict the results of subsequent experiments. Six experimental datasets are used to test our new adaptive sequential experimentation strategy. The results show that our strategy is able to explore the neighbourhood of the best design space and reach the best experimental point significantly faster than the existing approach.

Split-plot experiments with time trends in easy-to-change factors

Split-plot experiments involve two types of factors, hard-to-change factors (HTC) and easy-to-change factors (ETC). In regular split-plot experiments, the experimental sequence of ETC factor levels should be randomised. However in many practical applications, the run sequence of ETC factors can only be changed in one direction. This restriction in the randomisation of ETC factors will create many serially correlated response observation subsequences, and regular analysis procedure will no longer be valid due to non independence of experimental residuals. In this paper, an integrated factorial and time series model is developed to analyse this kind of split-plot experiment with time trend in ETC factors.

Adaptive sequential experimentation technique for 3³ factorial designs based on revised simplex search

This paper is an extension to the work carried out in the field of sequential experimentation strategy by Siddiqui and Yang (2009). The methodology presented in this paper deals with expensive industrial experiments under the constraint of limited testing budget. This research focuses on involving three factors, each being at three levels. Another constraint appropriately assumed for these experiments is that of inadequate prior knowledge of the system, i.e., the behaviour of the system is not very well known to the experimenter. The aim of this research is to explore high quality parameter space in a minimum number of experimental runs in such situations. The explained experimentation strategy uses adaptive one factor at a time method, simplex downhill method, and response surface method.

Effective data analysis methods for incomplete two-level factorial experiments

Designing and analysing two-level factorial or fractional factorial experiments require all experimental runs should be completed. However, in many actual industrial experiments, a portion of experimental runs cannot be completed due to either infeasibility or budget limitation, resulting in incomplete experiments. In this paper, two effective methods to estimate the factorial effects in incomplete 2 level factorial or fractional factorial experiments are developed. In the first method, each traditional factorial main effect or interaction effect is decomposed as the average of many individual elementary effects. Under this model, missing runs in factorial experiments only affect some individual elementary effects; the main effects or interactions can still be estimated by the partial average of the remaining individual elementary effects. The second method is based on estimation of missing runs by using response surface model on incomplete experimental data set. These two methods are compared with other two existing methods for incomplete factorial experiments on eight testing problems and the results show our two methods perform well.