A Low Order Control-Oriented Model of Liquid Water Transport in PEM Fuel Cell

In this work, an accurate and computationally fast model for liquid water transport within a proton exchange membrane fuel cell (PEMFC) electrode is developed by lumping the space-dependence of the relevant variables. Capillarity is considered as the main transport mechanism within the gas diffusion layer (GDL). The novelty of the model lies in the simulation of the water transport at the interface between gas diffusion layer and gas flow channel (GFC). This is achieved with a phenomenological description of the process that allows its simulation with relative simplicity. Moreover, a detailed two-dimensional visualization of such interface is achieved via geometric simulation of water droplets formation, growth, coalescence and detachment on the surface of the GDL. The accomplishment of reduced computational time and good accuracy makes the model suitable for control strategy implementation to ensure PEM fuel cells operation within optimal electrode water content. Furthermore, the model is useful for optimization analysis oriented to both PEMFC design and balance of plant.

Thermodynamic Performance Optimization of Reciprocating Internal Combustion (IC) Engines

Reciprocating IC engines are traditionally modeled as operating on air standard cycles that approximate indicator diagrams obtained in experiments on real engines. These indicator diagrams can best be approximated by the dual cycle for both gasoline and diesel engines. Analysis of air standard cycles unfortunately fails to capture second law effects such as exergy destruction due to the irreversibility of combustion. Indeed, a complete thermodynamic study of any process requires application of both the first and second laws of thermodynamics. This article gives a combined first and second law analysis of reciprocating IC engines in general with optimization of performance as primary goal. A practical dual-like cycle is assumed for the operation of a typical reciprocating IC engine and process efficiencies are assigned to allow for irreversibilities in the compression and expansion processes. The combustion process is modeled instead of being replaced simply by a heat input process to air as is common in air standard cycle analysis. The study shows that performance of the engine can indeed be optimized on the basis of geometrical design parameters such as the compression ratio as well as the air-fuel ratio used for the combustion.

Study of a Fuel Supply Pump With a Piezoelectric Effect for Micro Solid Oxide Fuel Cells

A novel design for an ethanol injection system has been proposed, which consists of the fuel injector, two valves, one pump chamber, and one piezoelectric device (central vibration). The system uses a micro-diaphragm pump with a piezoelectric device for the micro solid oxide fuel cells (SOFC), which operate at a low temperature (550 to 600 °C) and are supplied by Enerage Inc. The diameters of the pump chamber are 31 mm and 23mm, and the depths of the chamber are 1 mm and 2 mm. When the piezoelectric device actuates for changing pump chamber volume, the valves will be opened/closed, and the ethanol will be delivered into SOFC system due to its pressure variation. The dimensions of the injector chamber, vibration frequencies of piezoelectric (PZT) device, input voltages, and valve thickness and shape, are used as important parameters for the performance of the novel ethanol injection system. The experimental results show that the ethanol flow rate can reach 170ml/min at a piezoelectric device frequency of 75Hz. In addition, the ethanol flow rate is higher than the water flow rate.

Bootstrap Statistical Method to Attain the Average Nusselt Number Correlation for Milli Channels and Micro Channels Heat Sink Using Aluminum Oxide (Al2O3)/Water Nanofluid

The Bootstrap Statistical method is applied for estimating the accuracy of the convective heat transfer non linear correlation of AL2O3 nanofluid working as cooling fluid. The flow experiment considers laminar and turbulent regimen through an array of aluminum microchannels and millichannels heat sink, taking into account the Volume Fractions, Reynolds, Peclet and Prandtl numbers. The β’s parameters are estimated with nonlinear least square approach. StatGraphics® was used, considering the Gauss-Newton algorithm with Levenberg-Marquardt modifications for global convergence. Correlation for Nusselt number is presented and suggestions for future experimentation are presented in order to improve the accuracy of the regression.

Stochastic Modeling and Uncertainty Analysis With Multi-Objective Optimization Strategies for the Synthesis/Design and Operation/Control of a PEMFC Fuel Processing Subsystem

A stochastic modeling and uncertainty analysis methodology for energy system synthesis/design is proposed in this paper and applied to the development of the fuel processing subsystem (FPS) of a proton exchange membrane fuel cell (PEMFC) system. The FPS consists of a steam methane reformer, both high and low temperature water-gas shift reactors, a CO preferential oxidation reactor, a steam generator, a combustor, and several heat exchangers. For each component of the system, detailed thermodynamic, geometric, chemical kinetic, and cost models are developed and integrated into an overall model for the subsystem. Conventionally, in energy system synthesis/design, such models are treated deterministically, using a specific set of non-probabilistic input variable values that produce a specific set of non-probabilistic output variable values. Even though these input values, which include the specific load profile (i.e. electrical, thermal, and/or aerodynamic) for which the system or subsystem is synthesized/designed, can have significant uncertainties that inevitably propagate through the system to the outputs, such deterministic approaches are unable to quantify these uncertainties and their effect on the final synthesis/design and operation/control. This deficiency can, of course, be overcome by treating the inputs and outputs probabilistically. The difficulty with doing this, particularly when large-scale dynamic optimization with a large number of degrees of freedom is being used to determine the optimal synthesis/design and operation/control of the system, is that the traditional probabilistic approaches (e.g., Monte Carlo Method) are so computationally intensive that combined with large-scale optimization it renders the problem computationally intractable. This difficulty can be overcome by the use of approximate approaches such as the response sensitivity analysis (RSA) method based on Taylor series expansion. In this study, RSA is employed and developed by the authors for use with dynamic energy system optimization. Load profile and cost models are treated as probabilistic input values and uncertainties in output results investigated. The results for the uncertainty analysis applied to the optimization of the FPS synthesis/design and operation/control are compared with those found using a Monte Carlo approach with good results. In this paper, the FPS synthesis/design and operation optimization is treated as a multi-objective optimization problem to minimize the capital cost and operating cost simultaneously, and uncertainty effects on the optimization are assessed by taking uncertainties into account in the objectives and constraints. Optimization results show that there is little effect on the objective (the operating cost and capital cost), while the constraints (e.g., that on the CO concentration) can be significantly affected during the synthesis/design and operation/control optimization.

Thermodynamic Estimate of Minimal Dissipation for Heat Exchange System

In this paper we consider heat exchange system from point of view of Finite-time thermodynamics. At first time the novel estimate of the minimal entropy production in a general-type heat exchange system with given heat load and fixed heat exchange surface is derived. The corresponding optimal distribution of heat exchange surface and optimal contact temperatures are also obtained. It is proven that if a heat flow is proportional to the difference of contacting flows’ temperatures then dissipation in a multi-flow heat exchanger is minimal only if the ratio of contact temperatures of any two flows at any point inside heat exchanger is the same and the temperatures of all heating flows leaving exchanger are also the same. Our result based on those assumptions: 1. heat transfer law is linear (17); 2. summary exchange surface is given; 3. heat load is given; 4. input tempretures for all flows are given; 5. water equivalents for all flows are given.

Analysis of Inventory Management in Reverse Supply Chain Using Stochastic Dynamic Programming Model

Reverse logistics is a critical topic that has captured the attention of government, private entities and researchers in recent years. This increase in the concern was driven by current set of government regulations, increase of public awareness, and the attractive economic opportunities. Also, environmentalists have always demanded Original Equipment Manufacturers (OEMs) to be more involved and be responsible of their products at the end of its life cycle. However, the uncertainty in quality of items returned, and its quantity discourage OEMs from participating in such programs. Because of the unique problems associated and the complex nature of the reverse logistics activities, numerous studies have been carried out in this field. One of those crucial areas is inventory management of End-of-Life (EOL) products. The take back program could possibly bring financial burden to OEM if it is not managed well. Thus, an efficient yet cost effective system should be implemented to appropriately manage the overwhelming number of returns. Previously, we have analyzed the problem based on the assumption that the number of core products returned and disassembled parts and subassemblies are known in advance. In this paper, we introduce a probabilistic approach where different quality levels of for every component disassembled are considered and different probabilities of these qualities given the quality of the returned product. The model utilizes a multi-period stochastic dynamic programming in a disassembly line context to solve the problem, and generate the best option that will maximize the system total profit. A numerical example is given to illustrate the approach. Finally, directions for future research are suggested.

Thermoeconomic Optimization of COOLCEP-S: A Novel Zero-CO2-Emission Power Cycle Using LNG (Liquified Natural Gas) Coldness

This paper presents a thermoeconomic optimization of a novel zero-CO2 and other emissions and high efficiency power and refrigeration cogeneration system, COOLCEP-S† which uses the liquefied natural gas (LNG) coldness during its revaporization. It was predicted that at the turbine inlet temperature (TIT) of 900°C, the energy efficiency of the COOLCEP-S system reaches 59%. The thermoeconomic optimization determines the specific cost, the cost of electricity, and the system payback period. The optimization started by performing a thermodynamic sensitivity analysis, which has shown that for a fixed TIT and pressure ratio, the pinch point temperature difference in the recuperator, ΔTp1, and that in the condenser, ΔTp2, are the most significant unconstrained variables to have a significant effect on the thermal performance of this novel cycle. The thermoeconomic analysis of the cycle (with fixed net power output of 20 MW and plant life of 40 years) shows that the payback period with the revenue from electricity and CO2 mitigation was ∼5.9 years, and would be reduced to ∼3.1 years when there is a market for the refrigeration byproduct. The capital investment cost of the economically optimized plant is estimated to be about $1,000/kWe, and the cost of electricity is estimated to be 0.34–0.37 CNY/kWh (∼0.04 $/kWh). These values are much lower than those of conventional coal power plants being installed at this time in China, which, in contrast to COOLCEP-S, do produce CO2 emissions at that.

A New Approach in Approximating Thermodynamics Properties in Compressed Liquid Region

Currently it is a common practice to use saturated liquid properties to approximate thermodynamics properties of fluids in the compressed liquid region. In this practice it is assumed that specific volume, internal energy, and entropy of fluids in the compressed liquid region are functions of temperature only and pressure practically has very little or no effect on these properties. Therefore, these properties at a given temperature and pressure are approximated by the saturated liquid properties at the given temperature. In the current literature the approximation formula given for enthalpy in the compressed liquid region is expressed as h(T, p) = hf (T) + vf (T) [p – psat (T)], where the aim of the second term on the right hand side of the equation is to improve the accuracy of the approximation, when pressure is much greater than the saturation pressure. However, in a recent study of thermodynamic properties of water, Kostic has shown that the second term in the equation improves the accuracy of the approximation of the enthalpy only at temperatures below 100 °C. In fact, he has shown that the second term increases the error when the formula is used to approximate the enthalpy of water in the compressed liquid region at intermediate and high temperatures. Kostic’s investigation is expanded in this paper to include substances other than water. The study shows that in many situations pressure has a bigger influence on the internal energy than it does on enthalpy of fluids in the compressed liquids. This paper demonstrates that the current practice of approximating properties of fluids in the compressed liquid region is not accurate at all range of temperatures and pressures. It establishes the range of pressures and temperatures for which the current approximation method could be used with reasonable accuracies. It also proposes a new scheme for the approximation of thermodynamic properties in the compressed liquid region.

Using Support Vector Regression in the Study of Product Form Images

In this paper, Support Vector Regression (SVR) training models using three different kernels: polynomial, Radial Basis Function (RBF), and mixed kernels, are constructed to demonstrate the training performance of unarranged data obtained from 32 virtual 3-D computer models. The 32 samples used as input data for training the three SVR models are represented by the coordination value sets of points extracted from 3-D models built by the 3-D software according to the shapes of 32 actual hairdryer products. To train the SVR model, an adjective (streamline) is used to evaluate all the 32 samples by 37 subjects. Then the scores of all the subjects are averaged to be the target values of the training models. In addition, a technique called k-fold cross-validation (C-V) is used to find the optimal parameter combination for optimizing the SVR models. The performance of the SVR using these three kernels to estimate the product image values is determined by the values of the Root Mean Square Error (RMSE). The results show that the optimal SVR model using the polynomial kernel performed better than the one using the RBF kernel. However, it is important to note that the mixed kernel had the best performance of the three. It is also shown in this study that the single RBF has a local characteristic and cannot process the broadly distributed data well. It can, however, be used to improve the power of the SVR by combining with the polynomial kernel.