Resonant sensors based on micro- and nano-electro mechanical systems (M/NEMS) are ubiquitous in many sensing applications due to their outstanding performance capabilities, which are directly proportional to the quality factor (Q) of the devices. We address here a recurrent question in the field: do dynamical techniques that modify the effective Q (namely parametric pumping and direct drive velocity feedback) affect the performance of said sensors? We develop analytical models of both cases, while remaining in the linear regime, and introduce noise in the system from two separate sources: thermomechanical and amplifier (read-out) noise. We observe that parametric pumping enhances the quality factor in the amplitude response, but worsens it in the phase response on the resonator. In the case of feedback, we find that Q is enhanced in both cases. Then, we establish a solution for the noisy problem with direct drive and parametric pumping simultaneously. We also find that, in the case when thermomechanical noise dominates, no benefit can be obtained from either artificial Q-enhancement technique. However, in the case when amplifier noise dominates, we surprisingly observe that a significant advantage can only be achieved using parametric pumping in the squeezing region.
Abstract. With respect to the interval neutrosophic Multi-Attribute Decision-Making (MADM) problems, the MADM method is developed based on some interval neutrosophic aggregation operators. Firstly, the Induced Generalized Interval Neutrosophic Hybrid Arithmetic Averaging (IGINHAA) operator and the Induced Generalized Interval Neutrosophic Hybrid Geometric Mean (IGINHGM) operator are proposed, which can weight all the input arguments and their ordered positions. Further, regarding the situation where the input elements are interdependent, the Induced Generalized Interval Neutrosophic Shapley Hybrid Arithmetic Averaging (IGINSHAA) operator and the Induced Generalized Interval Neutrosophic Shapley Hybrid Geometric Mean (IGINSHGM) operator are proposed, which are extensions of IGINHAA and IGINHGM operators, respectively, and some properties of these given operators are investigated. Furthermore, the interval neutrosophic cross entropy, which is an extension of single-valued neutrosophic cross entropy, is de ned, and the models based on the interval neutrosophic cross entropy and generalized Shapley function are respectively constructed to determine the optimal fuzzy measures on the attribute and ordered sets. Finally, an approach to interval neutrosophic MADM with interactive conditions and incomplete known weight information is proposed based on these given operators, and a practical example is shown to verify the practicality and feasibility of the new approach.
A series of experiments were carried out to investigate unsteady behavior of the flow field as well as the boundary layer of an airfoil oscillating in plunging‐type motion in a subsonic wind tunnel. The measurements involved surface‐mounted hot films complimented with surface pressure. In addition, wind tunnel wall pressure distribution was acquired to furnish a baseline for the wall interference corrections. The airfoil is the section of a 660‐kW wind turbine blade. The experiments were conducted at a Reynolds number of 0.42 million, and over two reduced frequencies of k = 0.06 and 0.085, at prestall, nearstall, and poststall regions. The unsteady aerodynamic loads were calculated from the surface pressure measurements, 64 ports, along the chord for both upper and lower surfaces of the model. The plunging displacements were transformed into the equivalent angle of attack. The surface hot‐film measurements provided information about the boundary‐layer events. The boundary‐layer transition occurred via a laminar separation bubble. Variations of the surface pressure coefficients and aerodynamic loads with the equivalent angle of attack showed strong sensitivity to the reduced frequency and the mean angles of attack. The wall pressure distribution was affected by the model oscillation especially the region underneath the model.
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