Lattice Boltzmann Method for Flow and Heat Transfer in Microgeometries

Recent technological developments have made it possible to design various microdevices where fluid flow and heat transfer are involved. For the proper design of such systems, the governing physics needs to be investigated. Due to the difficulty to study complex geometries in micro scales using experimental techniques, computational tools are developed to analyze and simulate flow and heat transfer in microgeometries. However, conventional numerical methods using the Navier-Stokes equations fail to predict some aspects of microflows such as nonlinear pressure distribution, increase mass flow rate, slip flow and temperature jump at the solid boundaries. This necessitates the development of new computational methods which depend on the kinetic theory that are both accurate and computationally efficient. In this study, lattice Boltzmann method (LBM) was used to investigate the flow and heat transfer in micro sized geometries. The LBM depends on the Boltzmann equation which is valid in the whole rarefaction regime that can be observed in micro flows. Results were obtained for isothermal channel flows at Knudsen numbers higher than 0.01 at different pressure ratios. LBM solutions for micro-Couette and micro-Poiseuille flow were found to be in good agreement with the analytical solutions valid in the slip flow regime (0.01 < Kn < 0.1) and direct simulation Monte Carlo solutions that are valid in the transition regime (0.1 < Kn < 10) for pressure distribution and velocity field. The isothermal LBM was further extended to simulate flows including heat transfer. The method was first validated for continuum channel flows with and without constrictions by comparing the thermal LBM results against accurate solutions obtained from analytical equations and finite element method. Finally, the capability of thermal LBM was improved by adding the effect of rarefaction and the method was used to analyze the behavior of gas flow in microchannels. The major finding of this research is that, the newly developed particle-based method described here can be used as an alternative numerical tool in order to study non-continuum effects observed in micro-electro-mechanical-systems (MEMS).

Particle and gaseous emissions from compressed natural gas and ultralow sulphur diesel-fuelled buses at four steady engine loads

Exhaust emissions from thirteen compressed natural gas (CNG) and nine ultralow sulphur diesel in-service transport buses were monitored on a chassis dynamometer. Measurements were carried out at idle and at three steady engine loads of 25%, 50% and 100% of maximum power at a fixed speed of 60 kmph. Emission factors were estimated for particle mass and number, carbon dioxide and oxides of nitrogen for two types of CNG buses (Scania and MAN, compatible with Euro 2 and 3 emission standards, respectively) and two types of diesel buses (Volvo Pre-Euro/Euro1 and Mercedez OC500 Euro3). All emission factors increased with load. The median particle mass emission factor for the CNG buses was less than 1% of that from the diesel buses at all loads. However, the particle number emission factors did not show a statistically significant difference between buses operating on the two types of fuel. In this paper, for the very first time, particle number emission factors are presented at four steady state engine loads for CNG buses. Median values ranged from the order of 1012 particles min-1 at idle to 1015 particles km-1 at full power. Most of the particles observed in the CNG emissions were in the nanoparticle size range and likely to be composed of volatile organic compounds The CO2 emission factors were about 20% to 30% greater for the diesel buses over the CNG buses, while the oxides of nitrogen emission factors did not show any difference due to the large variation between buses.

Electron beam evaporation of tungsten oxide films for gas sensors

Pure and Iron incorporated nanostructured Tungsten Oxide (WO3) thin films were investigated for gas sensing applications using noise spectroscopy. The WO3 sensor was able to detect lower concentrations (1 ppm-10 ppm) of NH3, CO, CH4 and Acetaldehyde gases at higher operating temperatures between 100oC to 250oC. The response of the WO3 sensor to NH3, CH4 and Acetaldehyde at lower temperatures (50oC-100oC) was significant when the sensor was photo-activated using blue-light emitting diode (Blue-LED). The WO3 with Fe (WO3:Fe) was found to show some response to Acetaldehyde gas only at relatively higher operating temperature (250oC) and gas concentration of 10 ppm.

Thin film deposition and characterization of pure and iron-doped electron-beam evaporated tungsten oxide for gas sensors

Pure Tungsten Oxide (WO3) and Iron-doped (10 at%) Tungsten Oxide (WO3:Fe) nanostructured thin films were prepared using a dual crucible Electron Beam Evaporation techniques. The films were deposited at room temperature in high vacuum condition on glass substrate and post-heat treated at 300 oC for 1 hour. From the study of X-ray diffraction and Raman the characteristics of the as-deposited WO3 and WO3:Fe films indicated non-crystalline nature. The surface roughness of all the films showed in the order of 2.5 nm as observed using Atomic Force Microscopy (AFM). X-Ray Photoelectron Spectroscopy (XPS) analysis revealed tungsten oxide films with stoichiometry close to WO3. The addition of Fe to WO3 produced a smaller particle size and lower porosity as observed using Transmission Electron Microscopy (TEM). A slight difference in optical band gap energies of 3.22 eV and 3.12 eV were found between the as-deposited WO3 and WO3:Fe films, respectively. However, the difference in the band gap energies of the annealed films were significantly higher having values of 3.12 eV and 2.61 eV for the WO3 and WO3:Fe films, respectively. The heat treated samples were investigated for gas sensing applications using noise spectroscopy and doping of Fe to WO3 reduced the sensitivity to certain gasses. Detailed study of the WO3 and WO3:Fe films gas sensing properties is the subject of another paper.

Electron beam evaporation of tungsten oxide films for gas sensors

Pure and Iron incorporated nanostructured Tungsten Oxide (WO3) thin films were investigated for gas sensing applications using noise spectroscopy. The WO3 sensor was able to detect lower concentrations (1 ppm-10 ppm) of NH3, CO, CH4 and Acetaldehyde gases at operating temperatures between 100 degrees celcius to 250 degrees celcius. The iron doped Tungsten Oxide sensor (WO3:Fe) showed some response to Acetaldehyde gas at relatively higher operating temperature (250 degrees celcius) and gas concentration of 10 ppm. The sensitivity of the WO3 sensor towards NH3, CH4 and Acetaldehyde at lower operating temperatures (50 degrees celcius - 100 degrees celcius) was significant when the sensor was photo-activated using blue-light emitting diode (Blue-LED). From the results, photo-activated WO3 thin film that operates at room temperature appeared to be a promising gas sensor. The overall results indicated that the WO3 sensor exhibited reproducibility for the detection of various gases and the WO3:Fe indicated some response towards Acetaldehyde gas.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.