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Item DEVELOPING HETEROGENEOUS TRAFFIC STATE MEASUREMENT TECHNIQUES BASED ON IMAGE PROCESSING AND MODELING(DEPARTMENT OF CIVIL ENGINEERING, 2016-12) MUNIRUZZAMAN, SHAH MDIn Dhaka city traffic operating condition is predominantly non-lane-based and heterogeneous in nature. Accurate measurement of traffic state is the first and principal task for describing and improving such traffic condition. For this, video-based image processing algorithms are preferred over other alternatives. This research initiates with the development of a novel traffic detection algorithm for detecting non-lane-based heterogeneous traffic. The algorithm uses mainly two different parameters: (1) static background; and (2) threshold parameter. The algorithm achieves high accuracy of detection through small computational effort. The qualitative analysis shows that compared to others the proposed algorithm can detect vehicles accurately. The quantitative analysis of the algorithm shows stable Precision-Recall Relationship. The RMSE values computed from ground truth and estimated traffic parameters show that the proposed algorithm outperforms other state of the art algorithms. Given the difficulty of obtaining variable traffic demands in the real world and the cost and time associated with the field data collection, a controlled environment is needed where the demand can be changed artificially and the overall network performance can be observed. Microscopic simulators (e.g. VISSIM) are capable of providing such controlled environment. In this research a generic calibration tool, VISCAL has been developed, for microscopic simulation parameters in VISSIM environment. The optimization system of the tool is based on three heuristic algorithms: (a) GA; (b) SPSA; (c) SA. VISCAL can be used to calibrate any type (rural, urban etc.) and extent (large, medium etc.) of network. Again macroscopic traffic flow models play an irreplaceable role in real-time traffic state estimation & prediction and represent the traffic states with the help of aggregated variables. The two most frequently used macroscopic models are the first-order cell transmission model (CTM) and the second-order METANET model. In this research a 1st order CTM model was developed for non-lane-based heterogeneous urban traffic condition. At first the nature of the fundamental traffic relationships was systematically investigated based on the traffic data. From regression analysis it was concluded that 3rd degree polynomial structure shows the best fit with vii the measured traffic data. As classical CTM tends to simulate inappropriate flow and speed behavior due to simple and linear consideration of FD, a new model was proposed in this research. Detail investigation shows that, the non-linear FD plays the most important role in developing CTM model, for estimating traffic state accurately in heterogeneous traffic operating condition. Again this research proposes a new second-order macroscopic traffic flow model having the following special features: (1) both the flow and speed dynamics have a normally distributed stochastic term; (2) the FD in the speed dynamics follows Zhang‘s one-parameter polynomial structure and (3) the parameters of the FD are variable over the links. In the model calibration stage, simultaneous optimization of FD parameters and driver-related parameters were conducted. The optimized parameters capture the existing traffic conditions of the respective links quite well. The link-specific FD parameters and the stochastic traffic state influencing terms improve the model performance the most, followed by the Car Following parameter. Proposed model performs most poorly in the absence of FD in the speed dynamics. Thus it can be concluded that FD affects the traffic states very seriously for heterogeneous composition and cannot be dropped off from the speed dynamics for simplicity in control design. The predicted speed, flow and density from the developed macroscopic model were compared with those from a microscopic simulation model, VISSIM. Four levels of traffic demands viz. light, moderate, heavy and excessive demand levels were applied to evaluate the compatibility of the two models. Based on the performance of the models and comparative analysis, the following main conclusions are found: i) The prediction of traffic states from the proposed stochastic METANET-based model is generally consistent with that from VISSIM simulation over the whole range of traffic demand levels used in this research. ii) The MAEs in traffic state estimation of various links do not show any distinct trend with the change of traffic demand levels, thus indicating that the developed macroscopic model performs quite satisfactorily for different traffic demand levels.Item HEAT TRANSFER PERFORMANCE OF COOLING TOWER WITH NANOFLUIDS(Research and Development Wing, MIST, 2025-07) RAHMAN, MD. HABIBURCooling towers are evaporative heat transfer devices in which atmospheric air cools warm water with direct contact between the air and the water by evaporating cooling of water. The main objective of this study is to analysis the cooling tower performance, with induced draft cooling tower and finding out the effect of adding nanofluids of Al Oxide (Al2O3), Zn Oxide (ZnO), and Ti Oxide (Ti2O3) with base fluid as water. This was done by establishing experimental set up supported with computer program and varying the quality of circulating fluids by adding together at different ratio. Recently large numbers of experiments have been carried out to evaluate the effect of nanofluid in enhancement of the heat transfer rate in various heat exchangers. The heat transfer enhancement using nanofluid mainly depends on type of nanoparticles, size of nanoparticles, shape of nanoparticles, and type of base fluid and concentration of nanoparticles in the base fluid. Therefore, this study deals with several experimental investigation of the thermal performance of a prototype mechanical wet cooling tower with a counter flow arrangement. Different volume concentrations ranging from 0.18 to 0.50 vol. % of stable Al Oxide (Al2O3), Zn Oxide (ZnO), and Ti Oxide (Ti2O3) nanoparticles of 80, 35, and 70 nm diameter were considered. Water was taken as a base fluid, and the experiment was carried out at 60, 70, and 80 °C, respectively, in laboratory conditions. The study revealed that an increase in the volume concentration of the nanofluids increased the cooling range, cooling efficiency, convective heat transfer coefficient, tower characteristic called number of transfer unit (NTU), and effectiveness of the cooling tower compared with water at the same mass flow rate and inlet temperature. However, increasing the volume concentration increased the viscosity of the nanofluids, leading to an increase in friction factor. From the result it has been obtained a comparative study on terms of tower characteristics (kav/L), water to air flow ratio (L/G), efficiency, range, percentage of make-up water and evaporation heat loss are presented in graphical form. The graph shows that the performance of cooling tower is affected by the type of cooling tower and the quality of circulating fluids. The graphical analysis shows the cooling tower characteristics and efficiency decreases with an increase in (L/G), the induced draft cooling tower shows better performance than natural draft cooling tower. It is revealed that at higher volume concentration of nanofluid, cooling range would increase by 29% at low flow rates which is also vi accompanied by the heat lost by water, and thereby, average increase in efficiency is 27% at temperature 80°C. This happens more significantly with Al2O3 and ZnO nanoparticles when they are added in base fluid with different ratios. For instance, for 0.18% volume concentration of ZnO, at an inlet water temperature of 66.4 °C and water/air (L/G) flow ratio of 1.93, the cooling range increased by 3.62%, cooling efficiency increased by 33.3%, and NTU increased by 50.5% compared with fresh water (FW). Prediction of thermal performance is necessary for thermo-fluid engineering applications as well as manufacturing industries. Hence, the validation of the developed mathematical model in this study has been carried out by making comparison of the measured and predicted thermal performance. This study presents an intelligent approach based on fuzzy expert system (FES) of a cooling tower. FES links between volumetric concentration (VC), mass flow rate of liquid to air ratio (L/G) and flow rate (FR) and cooling efficiency (CE) and range (CR). To validate the mathematical model, the thermal performance in terms of cooling efficiency (CE) and cooling range (CR), L/G and liquid flow rate are measured on the developed cooling tower and compared with the predicted ones. Values are obtained from experiments on an induced draft cooling tower with nanofluids in different VC of 0%, 0.06%, 0.18% and 0.30%, respectively. The efficiency of nanofluids slightly increases about 5.04–8.82% with the growth of VC related to water at the highest value of L/G. Whereas, the efficiency significantly increased about 11–50% with the increase of VC at the lowest value of L/G. FES model has been developed for the prediction of cooling efficiency and range with nanofluid in different VC. For instance, in case of CE, the mean of measured (experiment) and predicted (FES) values have been found as 15.84% and 15.90%, respectively. Similarly, for CR, the values have been found as 3.30°C and 3.28°C, respectively. The correlation coefficients of cooling efficiency and cooling range are found as 0.961, and 0.997, respectively. The mean relative error of measured and predicted values from the FES model on cooling efficiency and cooling range are found as 9.0 % and 5.87 %, respectively. For all parameters, the relative error of predicted values are found to be less than the acceptable limits of 10 %. The goodness of fit of the prediction values from the FES model on cooling efficiency and cooling range are found as 0.960 and 0.987, respectively, which are found to be close to 1.0 as projected. The results indicate that there is less variability of the measured data and predicted data vii of the counter flow induced draft cooling tower with water and with nanofluids in different volume concentration. It also indicates that the predicted data over the measured data has a good agreement and thus substantiates the validity of the mathematical model. In this study, according to assessment principles of predicted performance of the developed fuzzy expert system based intelligent model has been found to be valid. It is an innovative adaption leading to technological capacity building which has a remarkable contribution to enhance the machine life, better-efficient outcome and friendly to the aquatic and environmental system.