Biomimetic optimization research on wind noise reduction of an asymmetric cross-section bar

Zhang et al. SpringerPlus (2016) 5:1221 DOI 10.1186/s40064-016-2857-2 Open Access RESEARCH Biomimetic optimization research on wind noise reduction of an asymmetric cross‑section bar Yingchao Zhang1,2* , Weijiang Meng1, Bing Fan1 and Wenhui Tang1 *Correspondence: 1 State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China Full list of author information is available at the end of the article Abstract Background: In this paper, we used the principle of biomimetics to design twodimensional and three-dimensional bar sections, and used computational fluid dynamics software to numerically simulate and analyse the aerodynamic noise, to reduce drag and noise. Methods: We used the principle of biomimetics to design the cross-section of a bar. An owl wing shape was used for the initial design of the section geometry; then the feathered form of an owl wing, the v-shaped micro-grooves of a shark’s skin, the tubercles of a humpback whale’s flipper, and the stripy surface of a scallop’s shell were used to inspire surface features, added to the initial section and three-dimensional shape. Results: Through computational aeroacoustic simulations, we obtained the aerodynamic characteristics and the noise levels of the models. These biomimetic models dramatically decreased noise levels. Keywords: Biomimetics, Asymmetric section geometry, Non-smooth characteristics, Computational aeroacoustics, Noise level Background In 1952, British scholar James Lighthill used “acoustic analogy” to derive Lighthill’s equation from the Navier–Stokes equations; Lighthill’s equation describes the noise of moving airflow (Lighthill 1952). In 1969, Ffowcs-Williams and Hawkings expanded the Curle analogy to consider the effects of a moving solid boundary on sound, and introduced the Ffowcs-Williams–Hawkings (FW–H) equation (Williams and Hawkings 1969). In 1998, Cox and Brentner studied vortex shedding and noise radiation around a cylinder (Cox et al. 1998), and found two-dimensional (2D) computational fluid dynamics (CFD) noise prediction to be fast and convenient. In 2002, Myunghan and Jeonghan studied the aerodynamic noise of a rack beam that featured an asymmetric section bar (Myunghan et al. 2002). In 2004, Perot and Auger studied the noise generated by low-Mach-number flows around a cylinder and a wall-mounted half-cylinder (Pérot et al. 2004), and verified the resolution growth of the FW–H equation by using a higher order time algorithm. In 2012, Cox and Rumsey studied the computation of sound generated by viscous flow over a circular cylinder (Cox et al. 2012). In 2003, Yu Chao was able to predict 2D parallel shear layer sound, by using the integral method (Yu and Li 2003); they showed that their © 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Zhang et al. SpringerPlus (2016) 5:1221 Page 2 of 12 integral solution agreed well with a computational aeroacoustics numerical solution. In 2008, Sun Shaoming and Ren Luquan studied the noise reduction mechanism of the non-smooth leading edge of an owl’s wing (Ren et al. 2008); they combined biomimetics with aerodynamic noise optimization. In 2011, Shi Lei, Zhang Chengchun produced a noise reduction optimization of the NACA0018 aerofoil model (Shi et al. 2011), and found that a saw tooth leading and trailing edge could effectively reduce noise at high speeds. In 2014, Zhang Chengchun and Wang Wenqiang studied the aerodynamic noise reduction of a cylindrical rod with a biomimetic wavy surface. They found that the aerodynamic noise of the circular cylinder was reduced (Zhang et al. 2014). This paper adds biomimetic features to the design of an asymmetric bar and explores the drag and noise reduction effects of various surface features. To ensure the accuracy of the simulation results, we constructed a three-dimensional (3D) model for verification. Methods The commercial CFD software StarCCM+ by CD-Adapco was used for the 2D and 3D aeroacoustic simulations (Zhang 2011). The K-Omega-SST turbulence model was used in the transient 2D simulations. By using the FW–H model, we could predict the middle and far field noise sources and observe the noise reduction effect of each design scheme. For the computational methods, we used a FW–H acoustic model; this assumes that the boundary surface can penetrate the flow, creating a discontinuity. The FW–H control equation can be written as:   2 2 D∞ ∂ ∂ ∂2 2 ∂ [Tij H (f )] − [Fi δ(f )] + [Qδ(f )] − c [(ρ − ρ )H(f )] = ∞ ∞ 2 2 ∂xi ∂xj ∂xi ∂t Dt ∂xi 2 Tij = ρ(ui − Ui∞ )(uj − Uj∞ ) + (p − c∞ (ρ − ρ∞ ))δij − τij (1) (2) where:    ∂f  Fi = − ρ ui − 2Ui∞ uj + ρ∞ Ui∞ Uj∞ + pδij − τij ∂xj   ∂f Q = ρui − ρUi∞ ∂xi (3) (4) Equation 1 has a clear physical meaning, the three right-hand parts of the equation represent the main types of acoustic radiation source. The first represents the turbulent shear stress of the fluid itself, and takes the form of a quadrupole. The second represents the divergence of an unsteady force on an interface, and takes the form of a dipole. The third includes the quality of unsteady flow entering into the fluid, its action is unrelated to a monopole. Through the FW–H method, time and space were discredited by an appropriately small time step and a relatively fine mesh, enabling second-order prediction of far field noise. Zhang et al. SpringerPlus (2016) 5:1221 In the 3D simulations, the K-Omega-SST turbulence model was first used for steady simulation, and the model of Curle and Proudman was used to predict noise sources. We then used the K-Omega-SST detached eddy simulation (DES) model for transient simulation. After getting the pressure from each monitoring point, we obtained the sound pressure level values through a fast Fourier transform. Thus we could compare the advantages and disadvantages of each design scheme. We use the finite volume method as the spatial discrete control equation, the Computational domain is divided into volume meshes, and there is no overlap of a control volume around each volume mesh, the spatial discrete equations can be solved from each control volume integral. We use implicit time discretization scheme, each unknown discrete equations are coupled together. After determining the time step, we should solve the coupled linear equations of each time step. 2D models In 2003, Pietro Catalano and Meng Wang numerically simulated the flow around a circular cylinder at high Reynolds numbers (Catalano et al. 2003), and established the associated drag coefficient; this paper provides good validation data for our simulation results. We ran a steady simulation (...truncated)