Network-based real option models

Building on earlier work to incorporate real option methodologies into network modeling, two models are proposed. The first is the network option design problem, which maximizes the expanded net present value of a network investment as a function of network design variables with the option to defer the committed design investment. The problem is shown to be a generalized version of the network design problem and the multi-period network design problem. A heuristic based on radial basis functions is used to solve the problem for continuous link expansion with congestion effects. The second model is a link investment deferral option set, which decomposes the network investment deferral option into individual, interacting link or project investments. This model is a project selection problem under uncertainty, where each link or project can be deferred such that the expanded net present value is maximized. The option is defined in such a way that a lower bound can be solved using an exact method based on multi-option least squares Monte Carlo simulation. Numerical tests are conducted with the classical Sioux Falls network and compared to earlier published results.     

A proposal of reliability-based design formats for ultimate hull girder strength checks for bulk carriers under combined global and local loadings

The purpose of this paper is to provide a basis for the development of reliability-based design formats for ultimate hull girder strength checks for bulk carriers in hogging conditions under combined global and local loading and to estimate implied safety levels in current rule practices for hull girders. The effect of alternative definitions of characteristic still-water loads on the safety format and, hence, the safety factors is assessed. The effect of systematic (bias) model uncertainties associated with loads and strength on the reliability measures is investigated.     

CFD validation studies for a high-speed foil-assisted semi-planing catamaran

This paper details the CFD validation studies carried out as a prerequisite for multi-fidelity CFD-based design optimization of high-speed passenger-only ferries aimed at reducing far-field wake energy that causes beach erosion. A potential flow program (WARP) and a URANS program (CFDSHIP) were validated using full-scale measurements of resistance, sinkage, trim, and far-field wake train obtained over a wide range of speeds for two high-speed semi-planing foil-assisted catamarans: Spirit (LOA-22 m) and 1060 (LOA-17 m). This study posed a unique combination of challenges for CFD modeling: the foil appended geometry required complicated surface overset grids, the effect of the waterjet and wind resistance had to be modeled, and a method had to be devised to extrapolate the calculated near-field elevation to get the far-field wake train using Havelock sources. A more concentrated effort was applied to the URANS verification and validation which forms the focus of this paper. The results show that URANS is able to accurately predict the resistance and motions for both vessels when coupled with models that account for the propulsors and air resistance. The overall accuracy of URANS for the performance analysis of the foil-assisted, semi-planing catamarans was adequate to warrant its use as a tool for subsequent design and optimization of a new vessel with significantly reduced wakes.     

Common rail injection system iterative learning control based parameter calibration for accurate fuel injection quantity control

This paper presents an accurate engine fuel injection quantity control technique for high pressure common rail (HPCR) injection systems by an iterative learning control (ILC)-based, on-line calibration method. Accurate fuel injection quantity control is of importance in improving engine combustion efficiency and reducing engine-out emissions. Current Diesel engine fuel injection quantity control algorithms are either based on pre-calibrated tables or injector models, which may not adequately handle the effects of disturbances from fuel pressure oscillation in HPCR, rail pressure sensor reading inaccuracy, and the injector aging on injection quantity control. In this paper, by using an exhaust oxygen fraction dynamic model, an on-line parameter calibration method for accurate fuel injection quantity control was developed based on an enhanced iterative learning control (EILC) technique in conjunction with HPCR injection system. A high-fidelity, GT-Power engine model, with parametric uncertainties and measurement disturbances, was utilized to validate such a methodology. Through simulations at different engine operating conditions, the effectiveness of the proposed method in rejecting the effects of uncertainties and disturbance on fuel injection quantity control was demonstrated.     

Sliding-mode observer for urea-selective catalytic reduction (SCR) mid-catalyst ammonia concentration estimation

This paper presents an observer design for SCR mid-catalyst ammonia concentration estimation using tailpipe NO_x and ammonia sensors. Urea-SCR has been popularly used by Diesel engine powered vehicles to reduce NO_x emissions in recent years. It utilizes ammonia, converted from urea injected at upstream of the catalyst, as the reductant to catalytically convert NO_x emissions to nitrogen. To simultaneously achieve high SCR NO_x conversion efficiency and low tailpipe ammonia slip, it is desirable to control the ammonia storage distribution along the SCR catalyst. Such a control method, however, requires a mid-catalyst ammonia sensor. The observer developed in this paper can replace such a mid-catalyst ammonia sensor and be used for SCR catalyst ammonia distribution control as well as serves for fault diagnosis purpose of the mid-catalyst ammonia sensor. The stability of the observer was shown based on the sliding mode approach and analyzed by simulations. Experimental validation of the observer was also conducted based on a medium-duty Diesel engine two-catalyst SCR system setup with emission sensors.     

Combustion phase detection algorithm for four-cylinder controlled autoignition engines using in-cylinder pressure information

For several decades, the primary goal of the automotive industry has been to reduce harmful emissions and improve fuel economy. Gasoline engines are clean and powerful propulsion systems, but have poorer fuel economy than that of diesel engines. However, due to the development of new technologies such as variable valve timing and lift and direct gasoline injection, controlled autoignition (CAI) combustion can be realized. CAI engines combine the advantages of cleaner emissions and lower fuel consumption than conventional spark-ignition gasoline engines. In this study, a cylinder-pressure-based combustion phase detection method for CAI combustion is proposed. This method utilizes a normalized difference pressure (NDP), which is defined as the normalized pressure difference between the firing and motoring in-cylinder pressures. The proposed method was developed and validated with steady-state experimental data from an inline 4 cylinder, 2 L gasoline direct injection (GDI) CAI engine. Because the calculations in the NDP method are faster and simpler than in the conventional combustion phase detection method in CAI engines, this method can be embedded in a real-time controller. Furthermore, the proposed method displayed good accuracy in detecting the combustion phase and thus stabilized CAI combustion. Finally, the detailed experimental results are presented.     

Active controller design for an electromagnetic energy-regenerative suspension

In this paper, with the parameters acquired from measured and tested data, a three-phase mathematical model is applied to the motor component of the developed electromagnetic suspension actuator. A main/inner-loop structure is used for its active control, and the constraints of the control current and energy flow states of actuator are analyzed by simplifying the inner-loop control system. Two different control modes, i.e., Consumptive Full Active (CFA) and Regenerative Semi Active (RSA) modes, which emphasize vibration control of sprung mass and vibration energy regeneration caused by road roughness, respectively, are proposed. Simulations are carried out using different road conditions, and the results demonstrate that the CFA mode can improve vehicle ride comfort by more than 30 percent, despite battery energy consumption; in RSA mode, the ride comfort can be improved by up to 10 percent with the battery charged by regenerated energy.     

Estimation of valve spring surge amplitude using the variable natural frequency and the damping ratio

Internal vibration of the valve spring is a critical factor in determining the dynamic characteristics of high-speed valve train systems. Because precise prediction of the spring surge amplitude is a difficult problem, especially for nonlinear variable-pitch springs, the development stage requires a process of trial and error. In the present study, a new method that considers the variable natural frequency and variable damping ratio is proposed to predict the spring surge amplitude. First, the change in the natural frequency and damping ratio caused by compression is predicted from the initially given pitch curve at the free height. Second, the spring surge amplitude is estimated by solving the wave equation with nonlinear variable coefficients. The surge amplitudes of typical valve springs are also measured using a motoring test rig and are compared with theoretical results predicted by the spring drawing and cam profile data.     

Soot and temperature distribution in a diesel diffusion flame: 3-D CFD simulation and measurement with laser diagnostics

In this study, a 3-D CFD simulation and laser diagnostics were developed to understand the characteristics of soot generation in a diesel diffusion flame. The recently developed RANS (Reynolds-averaged Navier-Stokes equations) hybrid combustion model (Extended Coherent Flame Model — 3 Zones, ECFM-3Z model) was used. This industrial, state-ofthe-art model of the diffusion flame is commonly used in diesel combustion models as well as for propagating (premixed) flame combustion. The simulation results were validated with measurements from a constant volume combustion chamber. The experiment revealed that soot accumulated in the chamber where the temperature decreased. Where the temperature increased rapidly, only a little soot accumulated. The temperature and soot distribution were independently examined using both the two-color method and a 3-D CFD simulation for a turbulent diesel diffusion flame.