Yi Hsuan Hung

Estimation and experimental study of the density and specific heat for alumina nanofluid

This study analyses the density and specific heat of alumina (Al2O3)/water nanofluid to determine the feasibility of relative calculations. The Al2O3/water nanofluid was produced by the direct-synthesis method with cationic chitosan dispersant served as the experimental sample, and was dispersed into three concentrations of 0.5, 1.0 and 1.5 wt.%. This experiment measures the density and specific heat of nanofluid with weight fractions and sample temperatures with a liquid density meter and a differential scanning calorimeter (DSC). To assess the availability of these equations, it then compares the experimental data with the calculated results according to the concepts of mixing theory and statistical mechanism. Comparing the calculated results of density and specific heat with the experimental data, the deviation of density fell within the range of −1.50% to 0.06% and 0.25% to 2.53%, whereas the deviation of specific heat fell within the range of −0.07% to 5.88% and −0.35% to 4.94%, respectively. Calculated results of density and specific heat show a trend of greater deviation with an increased concentration of nanofluid. However, two kinds of density and specific heat of the calculated results fall within an acceptable deviation range in this study.

Performance evaluation on an air-cooled heat exchanger for alumina nanofluid under laminar flow

This study analyzes the characteristics of alumina (Al2O3)/water nanofluid to determine the feasibility of its application in an air-cooled heat exchanger for heat dissipation for PEMFC or electronic chip cooling. The experimental sample was Al2O3/water nanofluid produced by the direct synthesis method at three different concentrations (0.5, 1.0, and 1.5 wt.%). The experiments in this study measured the thermal conductivity and viscosity of nanofluid with weight fractions and sample temperatures (20-60°C), and then used the nanofluid in an actual air-cooled heat exchanger to assess its heat exchange capacity and pressure drop under laminar flow. Experimental results show that the nanofluid has a higher heat exchange capacity than water, and a higher concentration of nanoparticles provides an even better ratio of the heat exchange. The maximum enhanced ratio of heat exchange and pressure drop for all the experimental parameters in this study was about 39% and 5.6%, respectively. In addition to nanoparticle concentration, the temperature and mass flow rates of the working fluid can affect the enhanced ratio of heat exchange and pressure drop of nanofluid. The cross-section aspect ratio of tube in the heat exchanger is another important factor to be taken into consideration.

Application of a recurrent wavelet fuzzy-neural network in the positioning control of a magnetic-bearing mechanism

A new recurrent wavelet fuzzy neural network (RWFNN) controller is proposed.RWFNN is adopted to control the rotor position of a thrust magnetic bearing (TMB).The online learning algorithm of RWFNN is derived using back-propagation method.The adaptive learning rates are performed via improved particle swarm optimization.Numerical simulations show the validity of TMB using the RWFNN controller. A new recurrent wavelet fuzzy neural network (RWFNN) with adaptive learning rates is proposed to control the rotor position on the axial direction of a thrust magnetic bearing (TMB) mechanism in this study. First, the dynamic analysis of the TMB with differential driving mode (DDM) is derived. Because the dynamic characteristics and system parameters of the TMB mechanism are high nonlinear and time-varying, the RWFNN, which integrates wavelet transforms with fuzzy rules, is proposed to achieve precise positioning control of the TMB. For the designed RWFNN controller, the online learning algorithm is derived using back-propagation method. Moreover, since the improper selection of learning rates for the RWFNN will deteriorate the control performance, an improved particle swarm optimization (IPSO) is adopted to adapt the learning rates of the RWFNN on-line. Numerical simulations show the validity of TMB system using the proposed RWFNN controller with IPSO under the occurrence of uncertainties. Display Omitted

Feasibility assessment of thermal management system for green power sources using nanofluid

A thermal management system using alumina (Al2O3)/water as the nanofluid for green power sources was experimentally assessed in this paper. Basic thermal principles and formulas were utilized to evaluate the performance of an air-cooled heat exchanger. The Al2O3/water nanofluid was produced at the concentrations of 0.5, 1.0, and 1.5 wt.%. The testing conditions of this experiments were above three concentrations, five coolant flow rates (0.8, 1.2, 1.6, 2.0, and 2.4 L/min.), and three heating powers (50, 100, and 150W). Firstly, basic properties of nanoparticles were analyzed. Fundamental relationships of the Al2O3/water nanofluid with respect to temperatures and concentrations were measured such as: viscosity, density, and specific heat. Next, an innovative concept named efficiency factor (EF) was proposed to quantitatively evaluate the thermal system performance. Theenhancement of thermal system performance compared with distilled water was then defined as an efficiency factor ratio (REF). The experimental results demonstrated that the efficiency factor ratios were optimal at low flow rate (0.8 L/min.) and low concentration (0.5%). Values of REF were all below 1.0 at high flow rates (1.2-2.4 L/min.). This research points out the direction of optimizing a thermal management system for green energy sources in the near future.

Multiwalled carbon nanotube nanofluids used for heat dissipation in hybrid green energy systems

This study was conducted to characterize carbon nanotube (CNT)/water nanofluids (CNWNFs) and to apply the nanofluids in a heat-dissipation system of dual green energy sources. CNTs were mixed with water in weight fractions of 0.125%, 0.25%, and 0.5% to produce nanofluids. The thermal conductivity, density, viscosity, and specific heat of the nanofluids were measured. An experimental platform consisting of a simulated dual energy source and a microchip controller was established to evaluate the heat-dissipation performance. Two indices, the heat dissipation enhancement ratio and specific heat dissipation enhancement ratio (SHDER), were defined and calculated. The CNWNFs with a CNT concentration of 0.125 wt.% were used because they exhibited the highest SHDER. The steady-state performance was evaluated at 2 flow rates, 11 hybrid flow ratios, and 3 heating ratios for a total power of 1000 W. The transient behavior of the energy sources at preset optimal temperatures was examined, and the CNWNFs exhibited average increases in stability and heat dissipation efficiency of 36.2% and 5%, respectively, compared with water. This nanofluid heat-dissipation system is expected to be integrated with real dual energy sources in the near future.

Development of a hardware in-the-loop platform for plug-in hybrid electric vehicles

To evaluate the system dynamics and verify the control strategy for a plug-in hybrid electric vehicle (PHEV), this paper studied the techniques for developing a real-time PHEV simulator and a hardware-in-the-loop (HIL) platform. For PHEV modeling, a graphical program was constructed. Subsystems of a PHEV include: driving cycles, drivers behavior, the SI engine, the traction motor, the high-power lithium battery, the Integrated Starter Generator (ISG), the transmission, the longitudinal vehicle dynamics, etc. have been introduced and combined to form a complete PHEV model. Next, to verify the strategy of the Vehicle Control Unit (VCU), a HIL environment needs to be established. A host PC is utilized to download real-time models and supervising the chosen parameters. A target simulator operates the model in real time due to its superior computational efficiency. A rapid-prototyping controller executes the control strategy online to examine the accuracy and detect logical errors. Ultimately, the real-time PHEV dynamics as well as the VCU control strategy are able to be simulated in this PHEV HIL platform online.

Optimal designs and experimental verification for a hybrid energy storage system

This paper studies the optimal sizing and the experimental verification of a vehicle-used hybrid energy storage system (HESS). To determine the optimized combination that maximizes the traveling distance at an FTP driving pattern, an exhaustive search method was employed. Under constraints of cost, vehicle acceleration, and HESS gross weight, the optimal sizing can be derived. Next, for the purpose of investigating the transient behavior of HESS, equivalent circuits of the lithium battery set and ultracapacitors are proposed. Parameters of all components in circuits can be identified via Nyquist empirical formula and experimental data. An HESS testing platform was established for performance verification. It consists of an HESS, a battery control unit (BMS), power circuits, and an DC Electronic Load. With a supervisory computer, control variables can be adjusted online, while testing data can be retrieved. The results show that using HESS, system performance and battery cycle life will be improved.

Speed Control of Vane-Type Air Motor Servo System Using Proportional-Integral-Derivative-Based Fuzzy Neural Network

A novel proportional-integral-derivative-based fuzzy neural network (PID-based FNN) controller is proposed in this study to control the speed of a vane-type air motor (VAM) servo system for tracking periodic speed command. First, the structure and operating principles of the VAM servo system are introduced. Then, the dynamics of the VAM servo system is analyzed to derive the second-order state equation of the VAM. Moreover, due to the dynamic characteristics and system parameters of the VAM servo system are highly nonlinear and time-varying, a PID-based FNN controller, which integrates conventional proportional-integral-derivative neural network (PIDNN) control with fuzzy rules, is proposed to achieve precise speed control of VAM servo system under the occurrences of the inherent nonlinearities and external disturbances. The network structure and its on-line learning algorithm using delta adaptation law are described in detail. Meanwhile, the convergence analysis of the speed tracking error is given using the discrete-type Lyapunov function. To enhance the control performance of the proposed intelligent control approach, a 32-bit floating-point digital signal processor (DSP), TMS320F28335, is adopted for the implementation of the proposed control system. Finally, experimental results are illustrated to show the validity and advantages of the proposed PID-based FNN controller for VAM servo system.

Bond graph modelling of fuel cell and engine hybrid electric scooters

This paper presents the modelling technique and dynamic simulation results of fuel cell and engine Hybrid Electric Scooters (HES) using the bond graph approach. The first example is on a fuel cell-battery hybrid scooter, in which the zero pollution fuel cell is the major powerplant, also acts as the battery charger. The second case study is on a typical engine-motor-regenerator-battery HES. It mainly consists of a brush-less direct current motor/regenerator and a 4-stroke direct injection spark ignition engine. They are interconnected in parallel by a continuously variable transmission. Detailed differential equation sets as well as simulation results are presented. A fuzzy auto-pilot control rule was employed to simulate the vehicle performance under typical driving pattern conditions.

Development of a Thermal Management System for Energy Sources of an Electric Vehicle

This study investigated a novel high-efficiency hybrid thermal management system (HTMS) for green energy sources of electric vehicles (EVs). The system consists of two coolant flow paths, an air-cooled heat exchanger, a proportional valve, and a coolant pump for managing the optimal temperatures of dual heat sources. An experimental platform was established to assess the system. The mechanical elements (coolant and cooling system components) and electrical elements (actuators, sensors, and a data logger) were correctly combined. A system microchip controller and a rule-based algorithm were designed and integrated in the HTMS. A novel performance index, the specific heat dissipation index, was developed to enable quantitatively evaluating the HTMS. In the test, the heating powers of the dual source were: 200/800 W and 500/500 W; the coolant flow rates were: 3, 5, and 7 L/min; the voltages of the proportional valve varied from 0.6 to 3 V. The results of the steady-state and transient-response demonstrated that optimal temperatures of the heat sources could be achieved through proper control and system design of this novel HTMS. The proposed HTMS demonstrated the potential for academic and industrial contributions as well as use in future EVs.