Yang Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Yang Liu has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Juan Felipe Torres
Australian National University
2 shared publications - Pengpeng Zhao
School of Hydraulic, Energy and Power Engineering, Yangzhou University 2 , Jiangsu 225127, China
2 shared publications Yin Xu
School of Hydraulic, Energy and Power Engineering, Yangzhou University 2 , Jiangsu 225127, China
2 shared publications- Yongling Zhao
ETH Zurich
2 shared publications - Chenyang Liu
School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, China
1 shared publication - Dayong Zhang
School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, China
1 shared publication
Latest Publications
- Open Access
Impact of Blade Ice Coverage on Wind Turbine Power Generation Efficiency: A Combined CFD and Wind Tunnel Study
This study investigates aerodynamic degradation and power loss mechanisms in iced wind turbine blades using a hybrid methodology integrating high-fidelity CFD simulations (ANSYS Fluent, FENSAP-ICE, STAR-CCM+ with SST k-ω turbulence model and shallow-water icing theory) with controlled wind tunnel experiments (10–15 m/s). Three ice accretion types, glaze, mixed, and rime, on NACA0012 airfoils are quantified. Glaze ice at the leading edge induces the most severe degradation, reducing lift by 34.9% and increasing drag by 97.2% at 10 m/s. STAR-CCM+ analyses reveal critical pressure anomalies and ice morphology-dependent flow separation patterns. These findings inform the optimization of anti-icing strategies for cold-climate wind farms.
Open AccessImpact of Blade Ice Coverage on Wind Turbine Power Generation Efficiency: A Combined CFD and Wind Tunnel Study
This study investigates aerodynamic degradation and power loss mechanisms in iced wind turbine blades using a hybrid methodology integrating high-fidelity CFD simulations (ANSYS Fluent, FENSAP-ICE, STAR-CCM+ with SST k-ω turbulence model and shallow-water icing theory) with controlled wind tunnel experiments (10–15 m/s). Three ice accretion types, glaze, mixed, and rime, on NACA0012 airfoils are quantified. Glaze ice at the leading edge induces the most severe degradation, reducing lift by 34.9% and increasing drag by 97.2% at 10 m/s. STAR-CCM+ analyses reveal critical pressure anomalies and ice morphology-dependent flow separation patterns. These findings inform the optimization of anti-icing strategies for cold-climate wind farms.
- 16 May 2020
- Published by Springer Nature in Heat and Mass Transfer
- Vol. 56 (8) , 2545-2556
Experimental investigation of rheological properties of low concentrated TiO2/water and MWCNT-TiO2/water hybrid nanofluids
- 16 May 2020
- Published by Springer Nature in Heat and Mass Transfer
- Vol. 56 (8) , 2545-2556
Experimental investigation of rheological properties of low concentrated TiO2/water and MWCNT-TiO2/water hybrid nanofluids
- Open Access
- 1 October 2019
- Published by AIP Publishing in Physics of Fluids
- Vol. 31 (10) , 109904
Publisher’s Note: “On the selection of perturbations for thermal boundary layer control” [Phys. Fluids 31, 104102 (2019)]
5(e.1)-5(e.4)" should read "as shown in Figs. 5(a)-5(d)." In the caption of Fig. 5, the text "Convective waves are observed
- 1 October 2019
- Published by AIP Publishing in Physics of Fluids
- Vol. 31 (10) , 109904
Open AccessPublisher’s Note: “On the selection of perturbations for thermal boundary layer control” [Phys. Fluids 31, 104102 (2019)]
5(e.1)-5(e.4)" should read "as shown in Figs. 5(a)-5(d)." In the caption of Fig. 5, the text "Convective waves are observed
- Open Access
- 1 October 2019
- Published by AIP Publishing in Physics of Fluids
- Vol. 31 (10) , 104102
On the selection of perturbations for thermal boundary layer control
The convective instability of the natural convection boundary layers of air (Pr = 0.7) in the laminar-to-turbulent transition regime (Ra = 8.7 × 107–1.1 × 109) is investigated by stability analysis in the framework of direct numerical simulations. To understand the spatial and temporal evolution of the convective instability of the thermal boundary layers, small-amplitude random-mode numerical perturbations are first introduced into the boundary condition of the boundary layer flow. The prescribed full spectral perturbations (i.e., white noise) are mostly damped out immediately by a limited upstream boundary layer. A low-frequency band is initially distinct in the upstream near the leading edge but decays spatially as the instability propagates downstream. In contrast, a high-frequency band emerges to finally become the most dominant frequency band in the thermal boundary layer transition regime. To obtain further insights into the nature of the established high-frequency band, single-mode perturbations of various frequencies are then introduced into the boundary layer near the leading edge. It is found that a single-mode perturbation at the peak frequency within the high-frequency band excites the maximum response of the thermal boundary layer, suggesting that the peak frequency is in fact the characteristic frequency or resonance frequency of the thermal boundary layer. The dimensionless form of the dependence of the characteristic frequency on Ra is then found to be fc = 0.07Ra2/3. The single-mode perturbation numerical experiments also revealed the propagation speed of convective instability waves, which was significantly greater than the convection speed of the thermal boundary layer. The smaller the Ra, the larger the difference between the two propagation speeds. A semi-analytical scaling of the wave propagation speed in the form csc ∼ Ra1/2y1/2Pr was derived (y denoting the streamwise location of the boundary layer), providing a predictive correlation that can be used for thermal boundary layer control.
- 1 October 2019
- Published by AIP Publishing in Physics of Fluids
- Vol. 31 (10) , 104102
Open AccessOn the selection of perturbations for thermal boundary layer control
The convective instability of the natural convection boundary layers of air (Pr = 0.7) in the laminar-to-turbulent transition regime (Ra = 8.7 × 107–1.1 × 109) is investigated by stability analysis in the framework of direct numerical simulations. To understand the spatial and temporal evolution of the convective instability of the thermal boundary layers, small-amplitude random-mode numerical perturbations are first introduced into the boundary condition of the boundary layer flow. The prescribed full spectral perturbations (i.e., white noise) are mostly damped out immediately by a limited upstream boundary layer. A low-frequency band is initially distinct in the upstream near the leading edge but decays spatially as the instability propagates downstream. In contrast, a high-frequency band emerges to finally become the most dominant frequency band in the thermal boundary layer transition regime. To obtain further insights into the nature of the established high-frequency band, single-mode perturbations of various frequencies are then introduced into the boundary layer near the leading edge. It is found that a single-mode perturbation at the peak frequency within the high-frequency band excites the maximum response of the thermal boundary layer, suggesting that the peak frequency is in fact the characteristic frequency or resonance frequency of the thermal boundary layer. The dimensionless form of the dependence of the characteristic frequency on Ra is then found to be fc = 0.07Ra2/3. The single-mode perturbation numerical experiments also revealed the propagation speed of convective instability waves, which was significantly greater than the convection speed of the thermal boundary layer. The smaller the Ra, the larger the difference between the two propagation speeds. A semi-analytical scaling of the wave propagation speed in the form csc ∼ Ra1/2y1/2Pr was derived (y denoting the streamwise location of the boundary layer), providing a predictive correlation that can be used for thermal boundary layer control.