2.1. DNS

Direct numerical simulations achieved by a finite-difference method of high-order accuracy in space and time and with shock capturing ability is applied to study the hypersonic compression-ramp flow problem. The three-dimensional (3-D) Navier–Stokes equations for unsteady, compressible flow are employed in a conservative form, and can be written as

(2.1)\begin{equation} \frac{\partial U}{\partial t} + \frac{\partial F}{\partial x} + \frac{\partial G}{\partial y} + \frac{\partial H}{\partial z} = \frac{\partial F_\nu}{\partial x} + \frac{\partial G_\nu}{\partial y} + \frac{\partial H_\nu}{\partial z}, \end{equation}

where $U = (\rho , \rho u, \rho v, \rho w, \rho e)^\textrm {T}$ is the vector of conservative variables, $\rho$ is the density, $u, v$ and $w$ are the flow velocities and $e$ is the total energy per unit mass. The equation system is closed by the perfect gas law relating pressure, density and temperature, as well as Sutherland's law for calculating the viscosity.

In terms of the numerical methods, time integration is performed by an explicit third-order total variation diminishing Runge–Kutta scheme. A weighted, essentially non-oscillatory, fifth-order finite-difference scheme is applied for the discretisation of the inviscid fluxes, based on the work of Jiang & Shu (Reference Jiang and Shu1996). A sixth-order central-difference scheme is used to approximate the viscous fluxes. The DNS solver has been validated and successfully applied to study hypersonic compression-ramp flows (Cao et al. Reference Cao, Klioutchnikov and Olivier2019; Cao, Klioutchnikov & Olivier Reference Cao, Klioutchnikov and Olivier2020; Cao et al. Reference Cao, Hao, Klioutchnikov, Olivier and Wen2021).

As for the boundary conditions, free-stream parameters are prescribed at the upper boundary (blunt leading-edge cases). A zero-gradient extrapolation condition is used for the out-flow boundary. For the no-slip wall, isothermal conditions are specified with the wall temperature being 293 K. Periodic boundary conditions are applied in the spanwise direction.

2.2. GSA

The governing equations (2.1) are linearised by decomposing $U$ into a 2-D base flow $U_\text {2-D}$ and a 3-D small-amplitude perturbation $U'$ as

(2.2)\begin{equation} U(x,y,z,t) = U_\text{2-D}(x,y) + U'(x,y,z,t). \end{equation}

It is also assumed that $U_\text {2-D}$ satisfies the 2-D Navier–Stokes equations. The linearised governing equations are then discretised using a second-order finite-volume method. The inviscid fluxes are evaluated using the modified Steger–Warming scheme (MacCormack Reference MacCormack2014) near discontinuities to eliminate numerical noise and a simple arithmetic average in smooth regions to reduce numerical dissipation, respectively. The viscous fluxes are calculated using a second-order central-difference scheme. Details of the inviscid and viscous fluxes were provided by Sidharth et al. (Reference Sidharth, Dwivedi, Candler and Nichols2018).

In this study, we consider the temporal stability of a spanwise periodic perturbation in the following harmonic-wave form:

(2.3)\begin{equation} U'(x,y,z,t) = \hat{U}(x,y) \exp[-\textrm{i}(\omega_r + \textrm{i}\omega_i) t + \textrm{i} \beta z] + \text{complex conjugate}, \end{equation}

where $\hat {U}$ is the eigenfunction, $\omega _r$ is the angular frequency, $\omega _i$ is the growth rate and $\beta$ is the spanwise wavenumber. The corresponding frequency and spanwise wavelength are defined by $f = \omega _r / 2 {\rm \pi}$ and $\lambda _z = 2 {\rm \pi}/ \beta$, respectively.

Substituting (2.3) into the linearised Navier–Stokes equations results in an eigenvalue problem, which is solved using the implicit restarted Arnoldi method implemented in ARPACK (Sorensen et al. Reference Sorensen, Lehoucq, Yang and Maschhoff1996). A shift-invert approach is adopted to efficiently explore the eigenvalue spectrum for a given $\beta$.

3.1. Flow conditions and geometry

The numerically considered flow conditions and compression-ramp geometry are based on those used in the experimental campaign carried out in the hypersonic Aachen Shock Tunnel TH2 at the Shock Wave Laboratory of RWTH Aachen University (Roghelia et al. Reference Roghelia, Chuvakhov, Olivier and Egorov2017a,Reference Roghelia, Olivier, Egorov and Chuvakhovb). A comprehensive study, involving experiments (Roghelia et al. Reference Roghelia, Olivier, Egorov and Chuvakhov2017b) and numerical simulations (Cao et al. Reference Cao, Hao, Klioutchnikov, Olivier and Wen2021) has provided a detailed description for the case of a sharp leading edge. Hence, the same flow conditions as for the sharp leading-edge case are used for the blunt leading-edge cases. The free-stream Mach number ($M_\infty$) and unit Reynolds number ($Re_\infty$) are 7.7 and $4.2 \times 10^6\ \text {m}^{-1}$, respectively. The inflow (air) is assumed to be a calorically perfect gas owing to a relatively low total enthalpy ($h_0 = 1.7$ MJ kg$^{-1}$), that is, the specific heat ratio equals 1.4. The constant wall temperature ($T_w$) is given by 293 K, which corresponds to a wall-to-total temperature ratio of 0.18.

For the compression-ramp geometry, a flat plate of length ($L$) 100 mm is followed by a ramp with a deflection angle of 15$^{\circ }$. The length of the ramp for the blunt leading-edge cases equals 120 mm. The leading-edge radius ranges from 0.15 to 2.5 mm. In particular, 11 cases are considered in the present study, for which the radii are 0.15, 0.3, 0.5, 0.7, 1, 1.3, 1.4, 1.5, 1.7, 2 and 2.5 mm. For convenience, these cases, together with the sharp leading-edge case, are labelled as R0, R015, R03, R05, R07, R1, R13, R14, R15, R17, R2 and R25, respectively.

In terms of mesh distribution, the number of grid points in streamwise ($x$) and wall-normal ($y$) directions is 1600 and 300, respectively. The mesh is clustered at the leading edge and near the wall, yielding a non-dimensional wall distance of $\varDelta y_{wall} / L \approx 9 \times 10^{-5}$ at the separation position for all cases. Note that the boundary-layer flow upstream of separation is laminar. Based on the set-up described above, the time step is set as $3 \times 10^{-9}$ s to ensure that the Courant–Friedrichs–Lewy number remains less than one. It is important to note that the mesh resolution in the $x$–$y$ plane for the blunt leading-edge cases is higher than that for the sharp leading-edge case ($1080\times 240$), which has been shown to be sufficient to capture the flow separation (Cao et al. Reference Cao, Hao, Klioutchnikov, Olivier and Wen2021). Nevertheless, preliminary simulations have been performed to verify the grid independence of our present results.

3.2. Reversal trend of the separation-bubble length

The reversal trend of flow separation caused by the leading-edge bluntness is shown in the following on the basis of the DNS results. Figure 1 presents the Mach number contour for cases R0, R015, R05, R1, R15 and R2. As can be observed, the size of the separation bubble (the blue region) increases when the leading-edge radius is increased from 0 to 0.15 mm. A further growth of the separation bubble is found when the radius is increased to 0.5 mm. However, as the radius rises from 1 to 2 mm, the size of the separation bubble decreases monotonically.

Figure 1. Mach number contour for cases with different leading-edge radii. (a) R = 0; (b) R = 0.15 mm (c) R = 0.5 mm; (d) R = 1 mm; (e) R = 1.5 mm; ( f) R = 2 mm.

To better illustrate the reversal trend of flow separation, the variation of separation and reattachment positions together with the variation of separation-bubble length ($L_b$), which is defined as the streamwise distance between the separation and reattachment positions, with the leading-edge radius are presented in figure 2. It is seen from figure 2(a) that the separation point reaches its most upstream position when $R = 0.3$ mm. However, the farthest downstream position for the reattachment point occurs for case R07. The combined variation of separation and reattachment positions results in a reversal trend for the separation-bubble length, as shown in figure 2(b). The critical radius for the reversal trend is $R = 0.5$ mm. This reversal trend and the observed critical radius are consistent with experimental measurements (Roghelia et al. Reference Roghelia, Chuvakhov, Olivier and Egorov2017a). In the following, a comprehensive study aiming to reveal the occurrence of three-dimensionality in the compression-ramp flows is conducted.

Figure 2. Variation of (a) the separation (square) and reattachment (diamond) positions and (b) the separation-bubble length with leading-edge radius.

4.1. Global stability analysis with respect to the 2-D base flows

In this section, GSA is performed to reveal the effect of leading-edge bluntness on the intrinsic instability of the compression-ramp flow. The 2-D solutions for different nose radii obtained in § 3 are used as the base flows of GSA. To reduce the computational cost, especially memory usage, the numerical solutions are interpolated on a coarse grid ($600\times 200$). Grid independence was verified by using a finer grid ($800 \times 300$). To facilitate a clear comparison, the GSA results for case R0 obtained in our previous study (Cao et al. Reference Cao, Hao, Klioutchnikov, Olivier and Wen2021) are also presented.

Figure 3 shows the growth rates of the most unstable modes as a function of $\lambda _z$ for cases R0, R05, R1 and R2. In each subfigure, the vertical line indicates the spanwise wavelength where the largest growth rate occurs. The eigenvalue spectra at these wavelengths are plotted in figure 4. For case R0, three branches of unstable modes were identified. Here, the leading stationary and oscillatory unstable modes are denoted by modes 1 and 2, with their growth rates peaking at $\lambda _z / L = 0.066$ and 0.070, respectively. The third branch is also an oscillatory mode, which is insignificant because of its low growth rate. When the leading-edge bluntness is increased to 0.5 mm, i.e. the critical radius, the flow system is substantially destabilised, a feature highlighted by the coexistence of multiple unstable modes (see figure 4b). The largest growth rate is increased by a factor of 2 with the corresponding wavelength shifted to $\lambda _z / L = 0.105$. In addition, two further stationary unstable modes can be observed. For case R1, the flow system is stabilised compared with case R05. The wavelength corresponding to the largest growth rate is reduced to $\lambda _z / L =$ 0.084. For case R2, only one stationary unstable mode can be identified. Its low growth rate indicates that the flow system is marginally unstable. Although not shown here, further increasing the nose radius monotonically stabilises the compression-ramp flow until the system becomes globally stable. It is also interesting to note that the non-dimensional frequency of the leading oscillatory mode (mode 2) is largely unchanged, which is approximately 0.2 for different nose radii.

Figure 3. Growth rates of the most unstable modes as a function of spanwise wavelength for cases (a) R0; (b) R05; (c) R1 and (d) R2. The vertical dash-dotted lines correspond to $\lambda _z / L = 0.066, 0.105, 0.084$ and 0.180, respectively.

Figure 4. Eigenvalue spectra for cases (a) R0 at $\lambda _z / L = 0.066$; (b) R05 at $\lambda _z / L = 0.105$; (c) R1 at $\lambda _z / L = 0.084$; (d) R2 at $\lambda _z / L = 0.180$.

By comparing the growth rates of the most unstable modes (mode 1) for cases R0, R05, R1 and R2, which are $\omega _i L / U_\infty = 0.51$, 1.04, 0.82 and 0.05, respectively, it is clear that the growth rate exhibits a reversal trend. In other words, the amplification rate of 3-D perturbations initially increases and then decreases with increasing leading-edge radius. In summary, the GSA results demonstrate a reversal trend for the intrinsic instability of the considered compression-ramp flows.

4.2. Verification by DNS

In the following, DNS is performed to verify the intrinsic instability revealed by the GSA. For the 3-D simulations, the width of the physical domain equals 50 mm for the blunt leading-edge cases (R05, R1 and R2). Note that the spanwise width of the physical domain for the sharp leading-edge case was 100 mm (Cao et al. Reference Cao, Hao, Klioutchnikov, Olivier and Wen2021), whereas in experiments models of 200 mm width were used for both sharp and blunt leading-edge cases (Roghelia et al. Reference Roghelia, Chuvakhov, Olivier and Egorov2017a). As shown by Cao et al. (Reference Cao, Hao, Klioutchnikov, Olivier and Wen2021), when the spanwise width was reduced from 100 to 30 mm, both saturated flows exhibit similar surface heat-flux streaks and almost identical wall parameters (pressure, Stanton number). This is expected because a periodic boundary condition is used in the spanwise direction. Therefore, a width of 50 mm is chosen for the blunt leading-edge cases in order to reduce the computational costs. In the spanwise direction ($z$), 300 grid points are placed with a constant spacing $\varDelta z / L = 1.67 \times 10^{-3}$. This resolution is slightly higher than that for the sharp leading-edge case ($\varDelta z / L = 2.08 \times 10^{-3}$).

The initial 3-D flow fields are generated by duplicating the 2-D solutions discussed in § 3, in the spanwise direction. For case R0, initial values for the spanwise velocity (randomised between $-0.01 \leq w/U_\infty \leq 0.01$) are introduced on the first twenty grid points normal to the wall between $0.5 < x/L < 1.5$ (Cao et al. Reference Cao, Hao, Klioutchnikov, Olivier and Wen2021), while the initial spanwise velocity is set to zero for the blunt leading-edge cases. It is demonstrated in Appendix that the introduction of initial randomised spanwise velocity has no remarkable influence on the occurrence of the unstable modes, and that the growth of instability waves can arise from the extremely low-level perturbations provided by numerical round-off error. No external disturbances are introduced at the inflow or at the wall, which enables the examination of intrinsic instabilities in the fluid-dynamic system.

To capture the growth of three-dimensionality, we consider the temporal evolution of the absolute value of spanwise velocity at a streamwise position, which is defined as

(4.1)\begin{equation} \bar{w} = \sqrt{\frac{1}{N_y N_z} \sum_{j=1}^{N_y} \sum_{k=1}^{N_z} (w/U_\infty)_{j,k}^2}, \end{equation}

where $N_y$ and $N_z$ denote the number of grid points in wall-normal and spanwise directions, respectively. Figure 5 shows the temporal history of $\bar {w}$ at $x/L = 1.04$ for case R0, at $x/L = 1.13$ for case R05, at $x/L = 1.16$ for case R1 and at $x/L = 1.05$ for case R2. These streamwise positions were chosen because $\bar {w}$ is largest here for all streamwise positions. It should be mentioned that these positions are located in the separated region as the instability core lies inside the separation bubble (Sidharth et al. Reference Sidharth, Dwivedi, Candler and Nichols2018; Cao et al. Reference Cao, Hao, Klioutchnikov, Olivier and Wen2021). The slope of the dotted lines shown in figure 5 corresponds to the growth rate of the most unstable mode identified by GSA (i.e. mode 1 in figure 4a–c).

Figure 5. Temporal evolution of the absolute spanwise velocity (4.1) at a streamwise position for cases R0, R05, R1 and R2. The slope of the dotted lines represents the growth rates of the most unstable modes predicted by GSA (mode 1 in figure 4a–c).

For cases R0, R05 and R1, after an initial adaption, $\bar {w}$ experiences an exponential growth, and the growth rates are close to those predicted by GSA. Subsequent to the linear growth stage, a nonlinear saturation takes place until the quasi-steady state is achieved. It should be mentioned that the relatively long adaption period for case R05 may be attributed to the large separation-bubble size and the multiple recirculating vortices inside the primary bubble induced by the secondary separation near the corner (Gai & Khraibut Reference Gai and Khraibut2019).

The previous GSA revealed that the growth rate of the global mode for case R2 is close to zero. As seen in figure 5, the magnitude of $\bar {w}$ remains at a very low level corresponding to the numerical noise. In other words, the flow field for case R2 remains stable during the simulated time period. Although the simulation is run up to $tU_\infty /L = 32$, and a further simulation is not considered owing to the limitation of computing resources, the stabilising effect for large leading-edge radii is in line with experimental results. As mentioned by Roghelia et al. (Reference Roghelia, Chuvakhov, Olivier and Egorov2017a), surface heat-flux streaks were not visible for large leading-edge radii.

Figure 6 shows instantaneous spanwise velocity fields ($w/U_\infty$) in the $x$–$y$ and $z$–$y$ planes for cases R0, R05 and R1. The corresponding time instants are $tU_\infty /L = 9$, 17 and 10, respectively, which are located at the exponential growth stage (see figure 5). Note that for case R0 (figure 6b), only half the spanwise length is shown in order to be consistent with figure 6(d,f). It is apparent that the onset of three-dimensionality lies inside the separation bubble, and that the flow fields are characterised by a spanwise periodicity, with the dominant spanwise wavelengths being $\lambda _z / L \approx 0.067$, 0.1 and 0.083, respectively. These wavelengths coincide with those of the most unstable modes identified by GSA (see figure 3). It should be mentioned that the instability waves do not exhibit a strict uniformity, i.e. the wavelength of each wave is not identical, especially for case R05. The reasons are twofold. Firstly, the spanwise width of the physical domain does not fit in with the whole-number multiples of the wavelength of the most unstable global mode revealed by the GSA (i.e. $L_z/\lambda _z$ is not an integer). As shown in Appendix, letting $L_z/\lambda _z = 4$, a better match in the wavelength is achieved between GSA and DNS. However, a similar non-uniformity is still present for cases A1 and A2 (see Appendix). Secondly, and more importantly, as uncovered by the GSA, for cases R0, R05 and R1, there exist several unstable modes whose growth rates peak at different wavelengths. Although the mode with the largest growth rate dominates the growth of perturbations, other modes can also be present and affect the spanwise periodicity. As shown in Cao et al. (Reference Cao, Hao, Klioutchnikov, Olivier and Wen2021) (case R0), the footprint of mode 2 could be identified in the exponential growth stage. In the present simulations, a fixed but relatively large spanwise width is used allowing the competition or interaction of different unstable modes, which also facilitates the later discussion for the saturated flows.

Figure 6. Instantaneous distribution of spanwise velocity for case R0 at $tU_\infty /L = 9$ in (a) $x$–$y$ plane at $z/L = 0.24$ and (b) $z$–$y$ plane at $x/L = 1.04$; for case R05 at $tU_\infty /L = 17$ in (c) $x$–$y$ plane at $z/L = 0.35$ and (d) $z$–$y$ plane at $x/L = 1.13$; for case R1 at $tU_\infty /L = 10$ in (e) $x$–$y$ plane at $z/L = 0.13$ and (f) $z$–$y$ plane at $x/L = 1.16$. The solid circles highlight the separation and reattachment positions. Flow fields are obtained from DNS data.

In general, the matching growth rates and spanwise wavelengths between GSA and DNS results provide firm support for the fact that the occurrence of three-dimensionality in the compression-ramp flows is triggered by the instabilities intrinsic to the flow system. In the following, we focus on the leading-edge bluntness effects on the surface heat transfer with respect to the saturated flow.