An adsorbed gas estimation model for shale gas reservoirs via statistical learning

Shale gas plays an important role in reducing pollution and adjusting the structure of world energy. Gas content estimation is particularly significant in shale gas resource evaluation. There exist various estimation methods, such as first principle methods and empirical models. However, resource evaluation presents many challenges, especially the insufficient accuracy of existing models and the high cost resulting from time-consuming adsorption experiments. In this research, a low-cost and high-accuracy model based on geological parameters is constructed through statistical learning methods to estimate adsorbed shale gas conten

Noise Generation by Airfoils and Rotors with Porous and Serrated Trailing Edges

Trailing-edge (TE) noise is an important noise source for airfoil applications that operate near populated areas. This thesis aims to develop novel (porous, serrated, and porous-serrated) geometries for the TE noise control of airfoils/rotors and investigate their noise generation mechanisms. First, the acoustic absorption of ten additively manufactured porous specimens is characterised to facilitate the design of porous TE geometries. The aeroacoustic performance and near-wake characteristics of eleven novel TE designs are then measured at various velocities in UNSW Anechoic Wind Tunnel. Their noise attenuation performance on laminar-transitional boundary layer TE (LBL-TE) and turbulent boundary layer TE (TBL-TE) noise are evaluated. Fluctuating velocity results indicate that the proposed designs influence LBL-TE noise generation by altering the flow characteristics around the TE. A TBL-TE noise intensity factor is proposed to relate near-wake flow statistics to TBL-TE noise generation, showing good consistency with the measured TBL-TE noise level. A high-frequency-broadband noise increase is observed for all porous TE designs. Moreover, the aeroacoustic performance of three sets of rotor blades with integrated novel TEs is evaluated at various pitch angles and RPMs on UNSW Rotor rig. Compared with serrated blades, porous blades show better low-frequency noise attenuation. At frequencies where the porous structures have good acoustic absorption, porous blades can effectively control TE noise at all operating conditions, indicating the acoustic absorption may contribute to TE noise attenuation by altering the acoustic scattering efficiency. In addition, Large-Eddy Simulations (LES) are performed on a porous and a reference airfoil. Ffowcs-Williams and Hawkings (FWH) acoustic analogy results of porous airfoil capture the high-frequency excessive noise and agree well with single microphone measurements. Flow simulation results reveal that the TBL-TE noise reduction for porous TE is mainly due to an attenuation of convection velocity and spanwise correlation, and the excessive noise is originated from the interaction of the permeated turbulent flow and pore geometries. Finally, a wind turbine noise prediction model based on a noise scaling function is proposed. It accurately predicts the noise spectra and overall noise levels of a full-scale wind turbine using the aerodynamic and acoustic data of lab-scale airfoil models

Crystal Structure Transformation and Dielectric Properties of Polymer Composites Incorporating Zinc Oxide Nanorods

Zinc oxide (ZnO) nanorods were synthesized using a modified wet chemical method. Poly(vinylidene fluoride-co-hexafluoropropylene), P(VDF-HFP), nanocomposites with different ZnO nanorods loadings were prepared via a solution blend route. Field emission scanning electron microscopic (FE-SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) were used to investigate the structure and morphology of the nanocomposites. XRD and FTIR data indicate that the incorporation of ZnO nanorods promote the crystalline structure transformation of P(VDF-HFP). As the content of ZnO nanorods increases, the β phase structure increases while the α phase decreases. In addition, the dielectric properties of the P(VDF-HFP) and its composites were systematically studied

Shoulder muscle activation pattern recognition based on sEMG and machine learning algorithms

BACKGROUND AND OBJECTIVE: Surface electromyography (sEMG) has been used for robotic rehabilitation engineering for volitional control of hand prostheses or elbow exoskeleton, however, using sEMG for volitional control of an upper limb exoskeleton has not been perfectly developed. The long-term goal of our study is to process shoulder muscle bio-electrical signals for rehabilitative robotic assistive device motion control. The purposes of this study included: 1) to test the feasibility of machine learning algorithms in shoulder motion pattern recognition using sEMG signals from shoulder and upper limb muscles, 2) to investigate the influence of motion speed, individual variability, EMG recording device, and the amount of EMG datasets on the shoulder motion pattern recognition accuracy. METHODS: A novel convolutional neural network (CNN) structure was constructed to process EMG signals from 12 muscles for the pattern recognition of upper arm motions including resting, drinking, backward-forward motion, and abduction motion. The accuracy of the CNN models for pattern recognition under different motion speeds, among individuals, and by EMG recording devices was statistically analyzed using ANOVA, GLM Univariate analysis, and Chi-square tests. The influence of EMG dataset number used for CNN model training on recognition accuracy was studied by gradually increasing dataset number until the highest accuracy was obtained. RESULTS: Results showed that the accuracy of the normal speed CNN model in motion pattern recognition was 97.57% for normal speed motions and 97.07% for fast speed motions. The accuracy of the cross-subjects CNN model in motion pattern recognition was 79.64%. The accuracy of the cross-device CNN model in motion pattern recognition was 88.93% for normal speed motion and 80.87% for mixed speed. There was a statistical difference in pattern recognition accuracy between different CNN models. CONCLUSION: The EMG signals of shoulder and upper arm muscles from the upper limb motions can be processed using CNN algorithms to recognize the identical motions of the upper limb including drinking, forward/backward, abduction, and resting. A simple CNN model trained by EMG datasets of a designated motion speed accurately detected the motion patterns of the same motion speed, yielding the highest accuracy compared with other mixed CNN models for various speeds of motion pattern recognition. Increase of the number of EMG datasets for CNN model training improved the pattern recognition accuracy

Laser-based defect characterization and removal process for manufacturing fused silica optic with high ultraviolet laser damage threshold

Residual processing defects during the contact processing processes greatly reduce the anti-ultraviolet (UV) laser damage performance of fused silica optics, which significantly limited development of high-energy laser systems. In this study, we demonstrate the manufacturing of fused silica optics with a high damage threshold using a CO2 laser process chain. Based on theoretical and experimental studies, the proposed uniform layer-by-layer laser ablation technique can be used to characterize the subsurface mechanical damage in three-dimensional full aperture. Longitudinal ablation resolutions ranging from nanometers to micrometers can be realized; the minimum longitudinal resolution is < 5 nm. This technique can also be used as a crack-free grinding tool to completely remove subsurface mechanical damage, and as a cleaning tool to effectively clean surface/subsurface contamination. Through effective control of defects in the entire chain, the laser-induced damage thresholds of samples fabricated by the CO2 laser process chain were 41% (0% probability) and 65.7% (100% probability) higher than those of samples fabricated using the conventional process chain. This laser-based defect characterization and removal process provides a new tool to guide optimization of the conventional finishing process and represents a new direction for fabrication of highly damage-resistant fused silica optics for high-energy laser applications

Assessment of gene order computing methods for Alzheimer’s disease

This article was originally published by BMC Medical Genomics in 2013. doi:10.1186/1755-8794-6-S1-S8Background: Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation. Methods: Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified. Results: Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data. Conclusion: Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods.The work was supported by the BWH Radiology and MGH Psychiatry research funds (to X. Huang) and the Technology Innovation fund (No. 09zz028) of Key Developing Program from Education Department of Sichuan Province, ChinaPearson distanc