Computational Modeling of Airborne Noise Demonstrated Via Benchmarks, Supersonic Jet, and Railway Barrier

In the last several years, there has been a growing demand for mobility to cope with the increasing population. All kinds of transportation have responded to this demand by expanding their networks and introducing new ideas. Rail transportation introduced the idea of high-speed trains and air transportation introduced the idea of high-speed civil transport (HSCT). In this expanding world, the noise legislation is felt to inhibit these plans. Accurate computational methods for noise prediction are in great demand. In the current research, two computational methods are developed to predict noise propagation in air. The first method is based on the finite differencing technique on generalized curvilinear coordinates and it is used to solve linear and nonlinear Euler equations. The dispersion-relation-preserving scheme is adopted for spatial discretization. For temporal integration, either the dispersion-relation-preserving scheme or the low-dispersion-and-dissipation Runge-Kutta scheme is used. Both characteristic and asymptotic nonreflective boundary conditions are studied. Ghost points are employed to satisfy the wall boundary condition. A number of benchmark problems are solved to validate different components of the present method. These include initial pulse in free space, initial pulse reflected from a flat or curved wall, time-periodic train of waves reflected from a flat wall, and oscillatory sink flow. The computed results are compared with the analytical solutions and good agreements are obtained. Using the method developed, the noise of Mach 2.1, perfectly expanded, two-dimensional supersonic jet is computed. The Reynolds-averaged Navier-Stokes equations are solved for the jet mean flow. The instability waves, which are used to excite the jet, are obtained from the solution of the compressible Rayleigh equation. Then, the linearized Euler equations are solved for jet noise. To improve computational efficiency, flow-adapted grid and a multi-block time integration technique are developed. The computations are compared with the experimental results for both the mean flow and the jet noise. Good agreement is obtained. The method proved to be fast and efficient. The second computational method is based on the boundary element technique. The Helmholtz equation is solved for the sound field around a railway noise barrier. Linear elements are used to discretize the barrier surface. Frequency-dependent grids are employed for efficiency. The train noise is represented by a point source located above the nearest rail. The source parameters are estimated from a typical field measurement of train noise spectrum. Both elevated and ground-level train decks are considered. The performance of the noise barrier at low and high frequencies is investigated. Moreover, A-weighted sound pressure levels are calculated. The computed results are successfully compared with field measurements

Evaluation of Orifice Flow Meter Accuracy under Pulsation Conditions

Orifice meter is a flow measuring device which is widely used in various industrial applications. Although the device gives accurate measurement during steady flow, measurement errors related to square root and sampling errors are unavoidable if pulsations exist. This research investigatesand improves the performance of an orifice plate flow meter under pulsation effects. A simple model for the pulsating flow through an orifice meter is presented. Square root error (SRE) is estimated. Sampling errors (SE) are reduced by proper selection of the averaging tim

Dynamic thermal model for proton-exchange membrane fuel cell

In this paper, a mathematical model is developed to simulate the transient phenomena in a polymer electrolyte membrane fuel cell (PEMFC) system. Large transient changes are expected for practical application such as transportation vehicles due to acceleration and deceleration. Simple models are usually unable to capture these transient dynamics. For control purposes, a fuel cell model must include the dynamics of flow and pressure in the anode and cathode channels and mass/heat transfer transients. The proposed model can predict the transient response of cell voltage, temperature of the cell, hydrogen/oxygen out flow rates and cathode and anode channel pressures under sudden change in load current. It is implemented in SIMULINK environment. The model is tested by simulating a transportation-size fuel cell with 85 kW maximum power output. Results for maximum power and multi-step input current that simulate start up-shut down cycle are shown. The predicted power, pressure and temperature are matching the published data for the fuel cell. The model will be very useful for the optimal design and real-time control of PEM fuel cell systems in practical automotive or stationary applications

Optimization of driving mode switching strategy for a multimode plug-in hybrid electric vehicle

Hybrid electric vehicles have become increasingly popular recently. Switching from internal combustion engine to battery as a clean source of energy is considered as a solution to reduce city pollution due to vehicle emissions. PHEV is a viable balance between the two sources of energy to achieve higher fuel economy with lower emissions. For a multimode PHEV, the car switches among three operation modes; namely electric mode, series mode, and parallel mode to maximize fuel economy based on the driving conditions. In this work, minimization of fuel consumption is used to optimize the mode switching strategy for a PHEV. The study is conducted using a reference vehicle that resembles 2014 Honda Accord Plug-in Hybrid vehicle. Global optimization with constraints using pattern search method is utilized. Starting from a switching strategy with 〖MPG〗_e = 30, optimization increased fuel economy to 〖MPG〗_e = 51.4 for a combined cycle (FTW75 and HWFET). Optimization proved to be a feasible method to improve mode switching strateg

Power management controller for hybrid electric vehicle using fuzzy logic

This paper presenting a study on hybrid electric vehicle (HEV), using backward facing approach simulation or QSS approach and fuzzy logic power management controller for HEV. The software being used for modelling of HEV and fuzzy logic power management controller is MATLAB/Simulink. A comparison study was completed to investigate fuzzy logic power management controller capability compared to optimal ideal controller optimized by dynamic programming. It was concluded that fuzzy logic controller shows excellent performance as HEV final battery SOC lies within 2.8% margin of that dynamic programming. Then, a comparison study was completed after addition of supercapacitor set to this HEV against battery only supply. After fuzzy logic PMC modified to include supercapacitors addition, it was observed that fuel economy improved by 54.34% from 57.6 mpg to 88.9 mpg, and total energy consumption reduced by 27.27%