@article{saini_narsipur_gopalarathnam_2021, title={Leading-edge flow sensing for detection of vortex shedding from airfoils in unsteady flows}, volume={33}, ISSN={["1089-7666"]}, DOI={10.1063/5.0060600}, abstractNote={Sensing of vortex shedding in unsteady airfoil flows can be beneficial in controlling and positively harnessing their effects for increased aerodynamic performance. The time variation of the leading-edge suction parameter (LESP), which is a non-dimensional measure of the leading-edge suction force, is shown to be useful in deducing the various events related to vortex shedding from unsteady airfoils. The recently developed leading-edge flow sensing (LEFS) technique, which uses a few pressures in the airfoil leading-edge region for deducing the aerodynamic state of an airfoil, is adapted to deduce the variation of LESP during an unsteady motion in incompressible flow. For this purpose, the flow over the airfoil is divided into an outer-region flow over the chord, modeled using thin airfoil theory, and an inner-region flow over the leading edge, modeled as a flow past a parabola. By matching these two flows, relations are derived for calculating the LESP from a few pressures at the leading edge. By studying the variations of the LEFS outputs and the calculated LESP for various unsteady motions, guidelines are presented for detecting events related to vortex shedding: initiation, pinch-off, and termination. Computational and experimental results for additional unsteady motions confirm the effectiveness of the LEFS as a sensing technique for events associated with vortex shedding on unsteady airfoils.}, number={8}, journal={PHYSICS OF FLUIDS}, author={Saini, Aditya and Narsipur, Shreyas and Gopalarathnam, Ashok}, year={2021}, month={Aug} }
 @article{saini_gopalarathnam_2018, title={Leading-Edge Flow Sensing for Aerodynamic Parameter Estimation}, volume={56}, ISSN={["1533-385X"]}, DOI={10.2514/1.J057327}, abstractNote={The identification of inflow air-data quantities such as the airspeed, angle of attack, and local lift coefficient on various sections of a wing or rotor blade is beneficial for load monitoring, aerodynamic diagnostics, and control on devices ranging from air vehicles to wind turbines. Real-time measurement of aerodynamic parameters during flight provides the ability to enhance aircraft operating capabilities while preventing dangerous stall situations. This Paper presents a novel leading-edge flow sensing algorithm for the determination of the air-data parameters using discrete surface pressures measured at a few ports in the vicinity of the leading edge of a wing or blade section. The approach approximates the leading-edge region of the airfoil as a parabola and uses pressure distribution from the exact potential-flow solution for the parabola to fit the pressures measured from the ports. Pressures sensed at five discrete locations near the leading edge of an airfoil are given as input to the algorithm to solve for the model parabola-flow problem using a simple nonlinear regression. The algorithm directly computes the inflow velocity and the stagnation-point location. A look-up table approach is adopted to deduce the section angle of attack and lift coefficient. The performance of the algorithm is assessed using experimental data in the literature for airfoils under different flow conditions. The results show good correlation between the actual and predicted aerodynamic quantities within the prestall regime, even for rotating blade sections.}, number={12}, journal={AIAA JOURNAL}, author={Saini, Aditya and Gopalarathnam, Ashok}, year={2018}, month={Dec}, pages={4706–4718} }
 @article{kim_saini_kim_gopalarathnam_zhu_palmieri_wohl_jiang_2017, title={Piezoelectric Floating Element Shear Stress Sensor for the Wind Tunnel Flow Measurement}, volume={64}, ISSN={["1557-9948"]}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000407208000050&KeyUID=WOS:000407208000050}, DOI={10.1109/tie.2016.2630670}, abstractNote={A piezoelectric sensor with a floating element was developed for direct measurement of flow induced shear stress. The piezoelectric sensor was designed to detect the pure shear stress while suppressing the effect of normal stress generated from the vortex lift-up by applying opposite poling vectors to the piezoelectric elements. During the calibration stage, the prototyped sensor showed a high sensitivity to shear stress (91.3 ± 2.1 pC/Pa) due to the high piezoelectric coefficients (}, number={9}, journal={IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS}, author={Kim, Taeyang and Saini, Aditya and Kim, Jinwook and Gopalarathnam, Ashok and Zhu, Yong and Palmieri, Frank L. and Wohl, Christopher J. and Jiang, Xiaoning}, year={2017}, month={Sep}, pages={7304–7312} }
 @misc{kim_saini_kim_gopalarathnam_zhu_palmieri_wohl_jiang_2016, title={A piezoelectric shear stress sensor}, volume={9803}, ISSN={0277-786X}, url={http://dx.doi.org/10.1117/12.2219185}, DOI={10.1117/12.2219185}, abstractNote={In this paper, a piezoelectric sensor with a floating element was developed for shear stress measurement. The piezoelectric sensor was designed to detect the pure shear stress, suppressing effects of normal stress components, by applying opposite poling vectors to the piezoelectric elements. The sensor was first calibrated in the lab by applying shear forces where it demonstrated high sensitivity to shear stress (91.3 ± 2.1 pC/Pa) due to the high piezoelectric coefficients of 0.67Pb(Mg<sub>1∕3</sub>Nb<sub>2∕3</sub>)O<sub>3</sub>-0.33PbTiO<sub>3</sub> (PMN-33%PT, d<sub>31</sub>=-1330 pC/N). The sensor also exhibited negligible sensitivity to normal stress (less than 1.2 pC/Pa) because of the electromechanical symmetry of the device. The usable frequency range of the sensor is up to 800 Hz.}, journal={SPIE Proceedings}, publisher={SPIE}, author={Kim, Taeyang and Saini, Aditya and Kim, Jinwook and Gopalarathnam, Ashok and Zhu, Yong and Palmieri, Frank L. and Wohl, Christopher J. and Jiang, Xiaoning}, editor={Lynch, Jerome P.Editor}, year={2016}, month={Apr}, pages={98032S} }