Enclosure enhancement of flight performance

We use a potential flow solver to investigate the aerodynamic aspects of flapping flights in enclosed spaces. The enclosure effects are simulated by the method of images. Our study complements previous aerodynamic analyses which considered only the near-ground flight. The present results show that flying in the proximity of an enclosure affects the aerodynamic performance of flapping wings in terms of lift and thrust generation and power consumption. It leads to higher flight efficiency and more than 5% increase of the generation of lift and thrust. © 2014 The Chinese Society of Theoretical and Applied Mechanics

ASSESSMENT AND PERFORMANCE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR GAS SENSING E-NOSE SYSTEMS

E-noses that combine machine learning and gas sensor arrays (GSAs) are widely used for the detection and identification of various gases. GSAs produce signals that provide vital information about the exposed gases for the machine learning algorithms, rendering them indispensable within the smart-gas sensing arena. In this work, we present a detailed assessment of several machine learning techniques employed for the detection of gases and estimation of their concentrations. The modeling and predictive analysis conducted in this paper are based on kNN, ANN, Decision Trees, Random Forests, SVM and other ensembling-based techniques. Predictive models are implemented and tested on three different MoX gas sensor-based experimental datasets as reported in the literature. The assessment includes a delineated analysis of the different models’ performance followed by a detailed comparison against results found in the literature. It highlights factors that play a pivotal role in machine learning for gas sensing and sheds light on the predictive capability of different machine learning approaches applied on experimental GSA datasets

PyFly: A fast, portable aerodynamics simulator

We present a fast, user-friendly implementation of a potential flow solver based on the unsteady vortex lattice method (UVLM), namely PyFly. UVLM computes the aerodynamic loads applied on lifting surfaces while capturing the unsteady effects such as the added mass forces, the growth of bound circulation, and the wake while assuming that the flow separation location is known a priori. This method is based on discretizing the body surface into a lattice of vortex rings and relies on the Biot–Savart law to construct the velocity field at every point in the simulated domain. We introduce the pointwise approximation approach to simulate the interactions of the far-field vortices to overcome the computational burden associated with the classical implementation of UVLM. The computational framework uses the Python programming language to provide an easy to handle user interface while the computational kernels are written in Fortran. The mixed language approach enables high performance regarding solution time and great flexibility concerning easiness of code adaptation to different system configurations and applications. The computational tool predicts the unsteady aerodynamic behavior of multiple moving bodies (e.g., flapping wings, rotating blades, suspension bridges) subject to incoming air. The aerodynamic simulator can also deal with enclosure effects, multi-body interactions, and B-spline representation of body shapes. We simulate different aerodynamic problems to illustrate the usefulness and effectiveness of PyFly

Global-local nonlinear model reduction for flows in heterogeneous porous media

In this paper, we combine discrete empirical interpolation techniques, global mode decomposition methods, and local multiscale methods, such as the Generalized Multiscale Finite Element Method (GMsFEM), to reduce the computational complexity associated with nonlinear flows in highly-heterogeneous porous media. To solve the nonlinear governing equations, we employ the GMsFEM to represent the solution on a coarse grid with multiscale basis functions and apply proper orthogonal decomposition on a coarse grid. Computing the GMsFEM solution involves calculating the residual and the Jacobian on a fine grid. As such, we use local and global empirical interpolation concepts to circumvent performing these computations on the fine grid. The resulting reduced-order approach significantly reduces the flow problem size while accurately capturing the behavior of fully-resolved solutions. We consider several numerical examples of nonlinear multiscale partial differential equations that are numerically integrated using fully-implicit time marching schemes to demonstrate the capability of the proposed model reduction approach to speed up simulations of nonlinear flows in high-contrast porous media

Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

A thermosensitive electromechanical model for detecting biological particles

Miniature electromechanical systems form a class of bioMEMS that can provide appropriate sensitivity. In this research, a thermo-electro-mechanical model is presented to detect biological particles in the microscale. Identification in the model is based on analyzing pull-in instability parameters and frequency shifts. Here, governing equations are derived via the extended Hamilton’s principle. The coupled effects of system parameters such as surface layer energy, electric field correction, and material properties are incorporated in this thermosensitive model. Afterward, the accuracy of the present model and obtained results are validated with experimental, analytical, and numerical data for several cases. Performing a parametric study reveals that mechanical properties of biosensors can significantly affect the detection sensitivity of actuated ultra-small detectors and should be taken into account. Furthermore, it is shown that the number or dimension of deposited particles on the sensing zone can be estimated by investigating the changes in the threshold voltage, electrode deflection, and frequency shifts. The present analysis is likely to provide pertinent guidelines to design thermal switches and miniature detectors with the desired performance. The developed biosensor is more appropriate to detect and characterize viruses in samples with different temperatures

Flapping wings in line formation flight: A computational analysis

The current understanding of the aerodynamics of birds in formation flights is mostly based on field observations. The interpretation of these observations is usually made using simplified aerodynamic models. Here, we investigate the aerodynamic aspects of formation flights. We use a potential flow solver based on the unsteady vortex lattice method (UVLM) to simulate the flow over flapping wings flying in grouping arrangements and in proximity of each other. UVLM has the capability to capture unsteady effects associated with the wake. We demonstrate the importance of properly capturing these effects to assess aerodynamic performance of flapping wings in formation flight. Simulations show that flying in line formation at adequate spacing enables significant increase in the lift and thrust and reduces power consumption. This is mainly due to the interaction between the trailing birds and the previously-shed wake vorticity from the leading bird. Moreover, enlarging the group of birds flying in formation further improves the aerodynamic performance for each bird in the flock. Therefore, birds get significant benefit of such organised patterns to minimise power consumption while traveling over long distances without stop and feeding. This justifies formation flight as being beneficial for bird evolution without regard to potential social benefits, such as, visual and communication factors for group protection and predator evasion. © 2014 Royal Aeronautical Society