Browsing by Subject "Dynamic Modeling"
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Item Dynamic Modeling and Cascaded Control for a Multi-Evaporator Supermarket Refrigeration System(2012-09-27) Gupta, Ankush 1986-The survey from US Department of Energy showed that about one-third of energy consumption in US is due to air conditioning and refrigeration systems. This significant usage of electricity in the HVAC industry has prompted researchers to develop dynamic models for the HVAC components, which leads to implementation of better control and optimization techniques. In this research, efforts are made to model a multi-evaporator system. A novel dynamic modeling technique is proposed based on moving boundary method, which can be generalized for any number of evaporators in a vapor compression cycle. The models were validated experimentally on a commercial supermarket refrigeration unit. Simulation results showed that the models capture the major dynamics of the system in both the steady state and transient external disturbances. Furthermore the use of MEMS (microelectromechanical) based Silicon Expansion Valves (SEVs) have reportedly shown power savings as compared to the Thermal Expansion Valves (TEVs). Experimental tests were conducted on a supermarket refrigeration unit fitted with the MEMS valves to explain the cause of these potential energy savings. In this study an advanced cascaded control algorithm was also designed to control the MEMS valves. The performance of the cascaded control architecture was compared with the standard Thermal Expansion Valves (TEVs) and a commercially available Microstaq (MS) Superheat Controller (SHC). The results reveal that the significant efficiency gains derived on the SEVs are due to better superheat regulation, tighter superheat control and superior cooling effects in shorter time period which reduces the total run-time of the compressor. It was also observed that the duty cycle was least for the cascaded control algorithm. The reduction in duty cycle indicates early shut-off for the compressor resulting in maximum power savings for the cascaded control, followed by the Microstaq controller and then the Thermal Expansion Valves.Item Dynamic Modeling and Wavelet-Based Multi-Parametric Tuning and Validation for HVAC Systems(2014-07-10) Liang, ShuangshuangDynamic Heating, Ventilation, and Air-Conditioning (HVAC) system models are used for the purpose of control design, fault detection and diagnosis, system analysis, design and optimization. Therefore, ensuring the accuracy and reliability of the dynamic models is important before their application. Parameter tuning and model validation is a crucial way to improve the accuracy and reliability of the dynamic models. Traditional parameter tuning and validation methods are generally time-consuming, inaccurate and can only handle a limited number of tuning parameters. This is especially true for multiple-input-multiple-output (MIMO) models due to their intrinsic complexity. This dissertation proposes a new automatic parameter tuning and validation approach to address this problem. In this approach, a fast and accurate model is derived using linearization. Discrete-time convolution is then applied on this linearized model to generate the model outputs. These outputs and data are then processed through wavelet decomposition, and the corresponding wavelet coefficients obtained from it are used to establish the objective function. Wavelets are advantageous in capturing the dynamic information hidden in the time series. The objective function is then optimized iteratively using a hybrid method consisting of a global search genetic algorithm (GA) and a local gradient search method. In order to prove the feasibility and robustness of the proposed approach, it is applied on different dynamic models. These models include an HVAC system model with moving boundary (MB) heat exchanger models, a heat pump model with finite control volume (FCV) heat exchanger models, and a lumped parameter residential conditioned space model. These models generally have a large number of parameters which need tuning. The proposed method is proved to be efficient in tuning single data set, and can also tune the models using multiple experimental or field data sets with different operating conditions. The tuned parameters are further cross-validated using other data sets with different operating conditions. The results also indicate the proposed method can effectively tune the model using both static and transient data simultaneously.