Scaling of turbulent flame speed for expanding flames with Markstein diffusion considerations

In this paper we clarify the role of Markstein diffusivity, which is the product of the planar laminar flame speed and the Markstein length, on the turbulent flame speed and its scaling, based on experimental measurements on constant-pressure expanding turbulent flames. Turbulent flame propagation data are presented for premixed flames of mixtures of hydrogen, methane, ethylene, n-butane, and dimethyl ether with air, in near-isotropic turbulence in a dual-chamber, fan-stirred vessel. For each individual fuel-air mixture presented in this work and the recently published iso-octane data from Leeds, normalized turbulent flame speed data of individual fuel-air mixtures approximately follow a Re-T,f(0.5) scaling, for which the average radius is the length scale and thermal diffusivity is the transport property of the turbulence Reynolds number. At a given Re-T,Re-f, it is experimentally observed that the normalized turbulent flame speed decreases with increasing Markstein number, which could be explained by considering Markstein diffusivity as the leading dissipation mechanism for the large wave number flame surface fluctuations. Consequently, by replacing thermal diffusivity with the Markstein diffusivity in the turbulence Reynolds number definition above, it is found that normalized turbulent flame speeds could be scaled by Re-T,M(0.5) irrespective of the fuel, equivalence ratio, pressure, and turbulence intensity for positive Markstein number flames.

Large-area Piezoceramic Coating with IDT Electrodes for Ultrasonic Sensing Applications

In the present paper, the ultrasonic strain sensing performance of large-area piezoceramic coating with Inter Digital Transducer (IDT) electrodes is studied. The piezoceramic coating is prepared using slurry coating technique and the piezoelectric phase is achieved by poling under DC field. To study the sensing performance of the piezoceramic coating with IDT electrodes for strain induced by the guided waves, the piezoceramic coating is fabricated on the surface of a beam specimen at one end and the ultrasonic guided waves are launched with a piezoelectric wafer bonded on another end. Often a wider frequency band of operation is needed for the effective implementation of the sensors in the Structural Health Monitoring (SHM) of various structures, for different types of damages. A wider frequency band of operation is achieved in the present study by considering the variation in the number of IDT electrodes in the contribution of voltage for the induced dynamic strain. In the present work, the fabricated piezoceramic coatings with IDT electrodes have been characterized for dynamic strain sensing applications using guided wave technique at various different frequencies. Strain levels of the launched guided wave are varied by varying the magnitude of the input voltage sent to the actuator. Sensitivity variation with the variation in the strain levels of guided wave is studied for the combination of different number of IDT electrodes. Piezoelectric coefficient e(11) is determined at different frequencies and at different strain levels using the guided wave technique.

The Lovasz θ function, SVMs and finding large dense subgraphs

The Lovasz θ function of a graph, is a fundamental tool in combinatorial optimization and approximation algorithms. Computing θ involves solving a SDP and is extremely expensive even for moderately sized graphs. In this paper we establish that the Lovasz θ function is equivalent to a kernel learning problem related to one class SVM. This interesting connection opens up many opportunities bridging graph theoretic algorithms and machine learning. We show that there exist graphs, which we call SVM−θ graphs, on which the Lovasz θ function can be approximated well by a one-class SVM. This leads to a novel use of SVM techniques to solve algorithmic problems in large graphs e.g. identifying a planted clique of size Θ(n√) in a random graph G(n,12). A classic approach for this problem involves computing the θ function, however it is not scalable due to SDP computation. We show that the random graph with a planted clique is an example of SVM−θ graph, and as a consequence a SVM based approach easily identifies the clique in large graphs and is competitive with the state-of-the-art. Further, we introduce the notion of a ''common orthogonal labeling'' which extends the notion of a ''orthogonal labelling of a single graph (used in defining the θ function) to multiple graphs. The problem of finding the optimal common orthogonal labelling is cast as a Multiple Kernel Learning problem and is used to identify a large common dense region in multiple graphs. The proposed algorithm achieves an order of magnitude scalability compared to the state of the art.