# HG changeset patch # User vlabarre <vincent.labarre@oca.eu> # Date 1679676003 -3600 # Fri Mar 24 17:40:03 2023 +0100 # Node ID 398f2535b2869eb5f9b9b251160342c718355767 # Parent b8964b32fb327eda1d70e5e49e2a241f88b8a50e improvements (in progress) diff --git a/2022strat_turb_polo/input/article.tex b/2022strat_turb_polo/input/article.tex --- a/2022strat_turb_polo/input/article.tex +++ b/2022strat_turb_polo/input/article.tex @@ -1,17 +1,14 @@ -\documentclass[% - aps, - superscriptaddress, - longbibliography, - 12pt, - onecolumn, - a4paper, - % preprint, - % reprint, - linenumbers, - showpacs, - showkeys, - % draft, - amsfonts, amssymb, amsmath]{revtex4-2} +\documentclass[ +aps, +prb, +superscriptaddress, +reprint, +onecolumn, +amsfonts, +amssymb, +amsmath, +]{revtex4-2} + % The result seems nicer with revtex4-2, but % Debian 9 (Stretch) does not have revtex4-2 @@ -121,6 +118,8 @@ \newcommand{\thk}{\theta_{\kk}} \newcommand{\thf}{\theta_f} \newcommand{\ok}{\omega_{\kk}} +\newcommand{\kb}{k_{\rm b}} +\newcommand{\ko}{k_{\rm O}} \newcommand{\todo}[1]{\textcolor{red}{TODO: #1}} @@ -172,7 +171,7 @@ We present a dataset of numerical simulations of stratified turbulence under the Oberbeck-Boussinesq approximation with Schmidt number equal to unity and periodic boundary conditions. An anisotropic time correlated forcing acting on the poloidal component of the velocity field is used in order to excite internal gravity waves. We vary the viscosity and the \bv frequency in order to obtain flows at different horizontal Froude $F_h$ and the -buoyancy Reynolds number $\R$. The datas are availabe at \href{https://mycore.core-cloud.net/index.php/apps/files/?dir=/2022strat-turb-polo&fileid=2553105195}{https://mycore.core-cloud.net/index.php/apps/files/?dir=/2022strat-turb-polo\&fileid=2553105195}. Isotropy and mixing coefficients allows to distinguish between 5 regimes in the $F_h-\R$ parameters space. The spectral energy budgets are analyzed for simulations in each regime. +buoyancy Reynolds number $\R$. The datas are availabe at \href{https://mycore.core-cloud.net/index.php/apps/files/?dir=/2022strat-turb-polo&fileid=2553105195}{https://mycore.core-cloud.net/index.php/apps/files/?dir=/2022strat-turb-polo\&fileid=2553105195}. Isotropy and mixing coefficients allows to distinguish between 5 regimes in the $F_h-\R$ parameters space. The spectral energy budgets are analyzed for simulations in each regime. \todo{improve abstract: add results...} \end{abstract} %---------------------------------------------------------------------------------------- @@ -194,7 +193,7 @@ \section{Results} \label{sec:res} -%\input{results.tex} +\input{results.tex} \section{Conclusions and perspectives} \label{sec:conclusion} diff --git a/2022strat_turb_polo/input/biblio.bib b/2022strat_turb_polo/input/biblio.bib --- a/2022strat_turb_polo/input/biblio.bib +++ b/2022strat_turb_polo/input/biblio.bib @@ -475,6 +475,20 @@ publisher={Cambridge University Press} } +@article{Bartello1995-geostrophic, + author = "Peter Bartello", + title = "Geostrophic Adjustment and Inverse Cascades in Rotating Stratified Turbulence", + journal = "Journal of Atmospheric Sciences", + year = "1995", + publisher = "American Meteorological Society", + address = "Boston MA, USA", + volume = "52", + number = "24", + doi = "https://doi.org/10.1175/1520-0469(1995)052<4410:GAAICI>2.0.CO;2", + pages= "4410 - 4428", + url = "https://journals.ametsoc.org/view/journals/atsc/52/24/1520-0469_1995_052_4410_gaaici_2_0_co_2.xml" +} + @article{Waite-Bartello2004, title={Stratified turbulence dominated by vortical motion}, volume={517}, diff --git a/2022strat_turb_polo/input/intro.tex b/2022strat_turb_polo/input/intro.tex --- a/2022strat_turb_polo/input/intro.tex +++ b/2022strat_turb_polo/input/intro.tex @@ -51,7 +51,7 @@ Having comprehensive open datasets of stratified turbulence would allow to investigate various aspect of these flows and allow simpler comparison to litterature. The goal of this manuscript is to present such a dataset where both $F_h$ and $\R$ are varied. The particularity of this dataset is that it is -designed to excite the kinetic part of internal gravity waves. It contains averaged quantities, spatial spectra, and spatiotemporal spectra. We present simulations in which the vortical +designed to excite the kinetic part of internal gravity waves. It contains various integral quantities, spatial spectra, and spatiotemporal spectra. We present simulations in which the vortical modes are forced in a similar way in a separate study \todo{Quote the manuscript with toroidal forcing}. The remaining of the manuscript is as follows. In section \ref{sec:num}, we present the numerical setup, and the methodology used to contruct the dataset. In \ref{sec:res}, we present our results and compare them to previous studies. In a first subsection \ref{sec:res-isotropy}, we study the large and small scale isotropy coefficients that allows to distinguish between 5 different turbulent regimes. A second subsection \ref{sec:res-mixing} is dedicated to the study of the evolution of the mixing coefficient, and component of energies as a function of $F_h$ in the limit of large $\R$. In a third subsection \ref{sec:res-spectral}, we analyse the spectral energy budget in different regimes. In section \ref{sec:conclusion}, we give concluding remarks. diff --git a/2022strat_turb_polo/input/methods.tex b/2022strat_turb_polo/input/methods.tex --- a/2022strat_turb_polo/input/methods.tex +++ b/2022strat_turb_polo/input/methods.tex @@ -23,13 +23,13 @@ In spectral space, the equations of motion reads \begin{align} - (\p_t + \nu k^2 + \nu_4 k^4) \vvk - \bar{\bar{P}}_{\kk} \cdot \bbk \eez &= -i \bar{\bar{P}}_{\kk} \cdot \widehat{\vv \cdot \bnabla \vv} + \ffk \\ + (\p_t + \nu k^2 + \nu_4 k^4) \vvk - \bar{\bar{P}} \cdot \bbk \eez &= -i \bar{\bar{P}} \cdot \widehat{\vv \cdot \bnabla \vv} + \ffk \\ (\p_t + \kappa k^2 + \kappa_4 k^4) \bbk + N^2 \hat{v}_{z \kk} &= -i \widehat{\vv \cdot \bnabla b}. \end{align} -\noindent where $\kk = (k_x, k_y, k_z)$, $k_n \in \delta k_n \mathbb{Z}$ with $\delta k_n = 2\pi /L_n$, $\widehat{(\cdot)}$ stands for the Fourier transform, and $\bar{\bar{P}}_{\kk} \equiv \bar{\bar{\mathbb{I}}} - \kk \otimes \kk /k^2$ is the projection operator onto the plane orthogonal to $\kk$. \\ +\noindent where $\kk = (k_x, k_y, k_z)$, $k_n \in \delta k_n \mathbb{Z}$ with $\delta k_n = 2\pi /L_n$, $\widehat{(\cdot)}$ stands for the Fourier transform, and $\bar{\bar{P}} \equiv \bar{\bar{\mathbb{I}}} - \kk \otimes \kk /k^2$ is the projection operator onto the plane orthogonal to $\kk$. \\ -The Craya-Herring decomposition \cite{Craya1958} constitutes a geometrical decomposition of a divergent free velocity field in the Fourier space that is commonly used in the study of stratified and rotating flows. Namely, the Fourier transform of +The Craya-Herring decomposition \cite{Craya1958} constitutes a geometrical decomposition of a divergent free velocity field in the Fourier space that is commonly used in the study of stratified and rotating flows (see e.g. \cite{Laval-McWilliams-Dubrulle2003, Maffioli2017spectra}). Namely, the Fourier transform of the velocity field can be written \begin{equation} \vvk = \begin{cases} \vvp \eep + \vvt \eet ~~~~ \text{if} ~ |\kk_h| \neq 0, \\ @@ -46,17 +46,15 @@ \end{equation} -\noindent $\vvp$ is the poloidal component, $\vvt$ is the toroidal component, and $\vvs = \hat{v}_x \eex + \hat{v}_y \eey$ for $|\kk_h|=0$ represents the shear component. The poloidal velocity encompass horizontally divergent modes and the vertical velocity, the toroidal velocity corresponds to vertical vorticity, while shear modes corresponds to horizontally invariant vertical shear. The linear inviscid modes of equations of motions are the internal gravity waves and the vortical modes \cite{Staquet-Sommeria2002}. Since the linear modes decomposition breaks down for $|k_h| = 0$, there remains additional velocity degrees of freedom which corresponds to the shear modes. It is now well known that the poloidal velocity is involved in waves (together with buoyancy), while the toroidal velocity corresponds to vortical modes. This decomposition is now common in studies of stratified turbulence \cite{RileyLelong2000, Laval-McWilliams-Dubrulle2003, Maffioli2020waves, Lam-Delache-Godeferd2020waves-eddies, Lam-Delache-Godeferd2021mixing}. \\ +\noindent $\vvp$ is the poloidal component, $\vvt$ is the toroidal component, and $\vvs = \hat{v}_x \eex + \hat{v}_y \eey$ for $|\kk_h|=0$ is the shear component. Physically, the poloidal velocity encompass horizontally divergent modes and the vertical velocity, the toroidal velocity corresponds to vertical vorticity, while shear modes corresponds to horizontally invariant vertical shear. In the absence of rotation, the linear inviscid modes of the equations of motions \cite{Bartello1995-geostrophic} are the internal gravity waves modes $a^{(\pm)} \sim \vvp \mp i \bbk/N$, the vortical modes $a^{(0)} \sim \vvt$, and shear modes $\left\{\left. (\vvk, \bbk) ~\right|~ |\kk_h|=0 \right\}$. \\ -For this dataset, we were motivated by forcing internal gravity waves modes -\cite{Waite2006stratified}. One important characteristic of those waves is that their dispersion relation is anisotropic. More precisely, the pulsation of internal gravity waves is given by +For this dataset, we were motivated by forcing internal gravity waves modes. One important characteristic of those waves is that their dispersion relation is anisotropic. More precisely, the pulsation of internal gravity waves is \begin{equation} - \ok = N \sin \thk + \ok = N \sin \thk, \end{equation} where $\thk$ is the angle between the wavevector $\kk$ and the stratification axis $\eez$. This implies that, for a given \bv frequency, $\thk$ fully determines the wave pulsation. Consequently, in order to force internal waves at a given temporal scale, we have to fix the direction of the forced modes. Then, we choose a velocity -forcing $\ffk$ acting only on $\vvp$ at large scales and small angles $\left\{ \kk ~ | ~ 5 \leq -|\kk|/\delta k_h \leq 20, ~ |\sin \thk - 0.3| \leq 0.05 \right\}$. The forcing is time -correlated with a correlation time corresponding to the period of the forced waves $T_c = 2\pi / 0.3N$. It is computed in spectral space such that the kinetic energy injection rate $P_K$ is constant and equal to unity. The forcing scheme is described by the algorithm \ref{algorithm:forcing}. \\ +forcing $\ffk$ acting only on $\vvp$ at small $|\kk|$ and small angles $\left\{ \kk ~ | ~ 5 \leq +|\kk|/\delta k_h \leq 20, ~ |\sin \thk - 0.3| \leq 0.05 \right\}$ such that large slow waves are excited. The correlation time of the forcing is fixed to the period of the forced waves $T_c = 2\pi / 0.3N$. The forcing is computed in spectral space such that the kinetic energy injection rate $P_K$ is constant and equal to unity. More precisely, the forcing scheme is described by the algorithm: \\ \begin{algorithm}[H] $t=0$: generate two random numbers $f_{0\kk}, f_{1\kk} \sim \mathcal{P}$ $\forall \kk$ in the forcing region; $t_0 = 0$; \\ @@ -67,16 +65,23 @@ $f_{0 \kk} \rightarrow f_{1 \kk}$; \\ genererate $f_{1\kk} \sim \mathcal{P}$ }{} - $\ffk = \left\{ f_{0\kk} - \dfrac{(f_{1\kk} - f_{0\kk})}{2} \left[ \cos\left( \dfrac{\pi(t-t_0)}{T_c} \right) + 1 \right] \right\} ~ \eep$ \\ + $\ffk = \left\{f_{0\kk} - \dfrac{(f_{1\kk} - f_{0\kk})}{2} \left[\cos\left(\dfrac{\pi(t-t_0)}{T_c} \right)+ 1 \right]\right\}~ \eep$ \\ $\ffk$ are normalized to ensure $P_K = 1$ } - \caption{Forcing scheme. $\mathcal{P}$ is the probability law which has for distribution $p_X(x) = \left\{ - \frac{1}{4} ~~ \text{if} ~ Re(x) \in [-1:1] ~ \text{and} ~ Im(x) \in [-1:1], 0 ~~ \text{otherwise} \right\}$. $\delta t$ is the time increment at each time step. \label{algorithm:forcing}} + %\caption{} \end{algorithm} +\noindent where $\mathcal{P}$ is the probability law which has for distribution +\begin{equation} +p_X(x) = \begin{cases} +\frac{1}{4} ~~ \text{if} ~ Re(x) \in[-1:1] ~ \text{and} ~ Im(x) \in[-1:1] \\ +0 ~~ \text{otherwise} +\end{cases} +\end{equation} +and $\delta t$ is the time increment at each time step. \\ -Some Fourier modes are removed from the dynamics because they cause numerical and physical problems and/or are not consistent with experiments in which the flow is bounded with walls. (i) All modes with wavenumber modulus larger than $\kmax = 0.8 (n_x/2) \delta -k_x$ are truncating to limit aliasing. (ii) +Some Fourier modes are removed from the dynamics because they cause numerical and physical problems and/or are not consistent with experiments in which the flow is bounded with walls. (i) All modes with wavenumber modulus larger than $\kmax = 0.8 (n_h/2) \delta +k_h$ are truncating to limit aliasing \todo{Give a good reason to not take 2/3 coefficient for dealiasing?}. (ii) All shear modes are truncated. If we do not truncate them, they tend to grow very slowly so it is longer to reach a statistically steady state \cite{Smith2002generation, Laval-McWilliams-Dubrulle2003}. Finally, (iii) vertically invariant vertical velocity @@ -89,47 +94,43 @@ \subsection{Control parameters and overview of the simulations} In our simulations, we observe that the time needed to reach -a statistically steady state increases with $N$. In fact, we observe that the energy -tends to concentrate at large horizontal scales ($k_h /\delta k_x =1, 2, ...$). In this state, the time +a statistically steady state increases with $N$. In this state, the time averaged total energy dissipation rate $\eps = \epsA + \epsK$ is equal to the kinetic -energy injection rate $P_K = 1$. The main physical input +energy injection rate $P_K = 1$, and we observe that the energy +is accumulated at large horizontal scales ($k_x/\delta k_x, k_y/\delta k_y =1, 2, ...$). The main physical control parameters are the \bv frequency $N$ and the viscosity $\nu$. Since both forcing length and -energy injection rate are in practice equal or close to 1, we can take as input parameters the +energy injection rate are equal or close to 1, we can take as input parameters the \bv frequency and an input buoyancy Reynolds number $\R_i = 1/ \nu N^2$. \\ To decrease the computational cost, we combined two strategies. -Firstly, the aspect ratio $L_z/L_h$ of the numerical domain is varied depending only on the value of $N$, going from $1/2$ for $N<20$ to $1/8$ for $N \geq 80$. The vertical resolution $n_z$ is fixed in order to have an homogeneous mesh in physical space, i.e. $n_h/L_h = n_z/L_z$. -Secondly, we used coarse simulations to develop the large scales present in the steady +(i) The aspect ratio $L_z/L_h$ of the numerical domain is varied depending only on the value of $N$, going from $1/2$ for $N<20$ to $1/8$ for $N \geq 80$. The vertical resolution $n_z$ is fixed in order to have an homogeneous mesh in physical space, i.e. $n_h/L_h = n_z/L_z$. +(ii) We used coarse simulations to develop the large scales present in the steady state. Indeed, because the time scales of the flows studied here are very long, -simulating the transiant states can be computationally demanding. In order to +simulating the transient states can be computationally demanding. In order to reach the steady state in a reasonable computational time, we start all the simulations at a reduced -horizontal resolution $n_h=320$, and we increase the resolution step by step when a -sufficiently stationary state has been reached. When such a state is observed, +horizontal resolution $n_h=320$, and we increase the resolution step by step when a stationary state has been reached. When such a state is observed, specific outputs are turned on and the simulation is ran further for several units time -in order to produce substantial data to analyze, before increasing the resolution again -if needed. \\ +in order to produce substantial data to analyze, before increasing the resolution if needed. \\ For some couples $(N,\ \R_i)$ with quite large $N$ and $\R_i$, the required resolution for proper -DNS (i.e. without hyperviscosity and hyperdifusivity) becomes too large. Consequently, we used the fourth-order hyperviscosity and +DNS (i.e. without hyperviscosity and hyperdiffusivity) becomes too large. Consequently, we used the fourth-order hyperviscosity and hyperdifusivity terms for most of the simulations. Here, $\nu_4 (= \kappa_4)$ is left as a free parameter and adapted to the resolution of simulations in order to ensure that dissipative scales are resolved, avoiding numerical issues. -We use the measure of the turbulent kinetic dissipations $\epsKK$ and $\epsKKKK$ based +We use the measure of the turbulent kinetic dissipation rates $\epsKK$ and $\epsKKKK$ based on both viscosities, and the ratio $\epsKK/\epsK$ where $\epsK=\epsKK+\epsKKKK$, as an indicator of how close the simulations we perform are to proper DNS. The product of the maximal wavevector $\kmax$ with the Kolmogorov scale $\eta \equiv (\nu^3 / \epsKK)^{1/4}$ is -also computed to estimate que quality of the simulation. For a set of -physical parameters, the needed hyperviscosity decreases when the resolution is -increased, the ratio $\epsKK/\epsK$ grows towards unity, and $\kmax \eta$ increases -(eventually beyond unity). \\ +also computed to estimate que quality of the simulations. For a set of input parameters, the needed hyperviscosity decreases when the resolution is +increased so the ratio $\epsKK/\epsK$ grows towards unity and $\kmax \eta$ increases +(eventually beyond unity). Table \ref{table-methods-1couple} shows quantities computed for the simulations performed for the couple $(N, \R_i) = (40, 20)$. \\ %% Method: simulations 1 couple (N, R_i) \input{../tmp/table_methods_1couple.tex} -Table \ref{table-methods-1couple} shows quantities computed for the simulations -performed for the couple $(N, \R_i) = (40, 20)$. The turbulent non-dimensional numbers + The turbulent non-dimensional numbers are computed in the statistically stationary state as \begin{equation} @@ -137,25 +138,25 @@ \R = \epsK / (\nu N^2), ~~~~ \R_4 = \epsK{U_h}^2 / (\nu_4 N ^ 4), \end{equation} -\noindent where $\epsK$ is the mean kinetic energy dissipation and $U_h$ is the rms horizontal velocity. We also compute the buoyancy and Ozmidov lengthscales $L_b = U_h/N$ and $L_O = \sqrt{\epsK/N^3}$. We define the associated wavectors as $k_b = 1/L_b$ and $k_O = 1/L_O$. \\ +\noindent where $U_h$ is the rms horizontal velocity. We also compute the buoyancy and Ozmidov wavectors $\kb = N/U_h$ and $\ko = \sqrt{N^3/\epsK}$. \\ Figure \ref{fig:method-N40-R20} shows the energies signals for simulations at different resolutions for the couple $(N, \R_i) = (40, 20)$ and the averaged quantities vs $\kmax\eta$ for the same simulations. The coarse simulations with $N \geq 20$ where runned for $2 N$ simulation times at resolution $n_h=320$, and then restarted for $N/2$ -simulations times at resolution $n_h=640$. We observed that the flow takes some time to +simulations times at resolution $n_h=640$. We observe that the flow takes some time to adjust when changing the resolution, but this time remains small before the total simulation time. As expected, the ratio $\epsKK / \epsK$ approaches unity when $\kmax \eta$ increases and the dimensionless quantities tend to constant values, meaning that hyperviscosity and hyperdiffusivity don't affect these quantities for sufficiently large resolutions (typically when the criteria $\kmax \eta \sim 1$ is attained). Remarkably, global quantities like -$\Gamma$, $I_{velo}$ and $I_{diss}$ (see section \ref{sec:res} for definitions) are already well +$\Gamma=\epsA/\epsK$, $I_{velo}$ and $I_{diss}$ (see equations (\ref{eq:Ivelo}) and (\ref{eq:Idiss}) for definitions) are already well estimated for $\kmax \eta \simeq 0.3$. This convergence with the resolution is also visible on the kinetic energy spectra, as illustrated on figure \ref{fig:method-N40-Ri20-spectra}. \\ -\begin{figure}[H] +\begin{figure} \centerline{ \includegraphics[width=0.48\textwidth]{% ../tmp/fig_E_vs_time_N40_Ri20} @@ -167,7 +168,7 @@ \label{fig:method-N40-R20}} \end{figure} -\begin{figure}[H] +\begin{figure} \centerline{ \includegraphics[width=0.98\textwidth]{% ../tmp/fig_spectra_1couple} @@ -187,14 +188,9 @@ each couple $(N, \R_i)$. Most important parameters and dimensionless numbers for these simulations are summarized in table~\ref{table-better-simuls}. In this table, $Re_{\lambda} \equiv U_h \lambda / \nu$ is the Reynolds number based on the Taylor microscale $\lambda \equiv \sqrt{U_h^2 \nu / \epsK}$. Figure \ref{fig:method-resolution-hyperdiffusivity} allows to see how our simulations fill the -$(F_h, \R)$ parameter space, and appreciate which ones are close to DNS. Litterature's simulations are also reported for comparison. We see that many of our simulations verify the criteria $\kmax \eta \geq 1$, except for very low $F_h$ and/or high $\R$. The simulations were performed on local clusters for resolutions up to $n_h = 640$: the Mesocentre SIGAMM -machine, hosted by Observatoire de la Cote d'Azur, and the Université Côte d'Azur's -Center for High-Performance Computing. For larger resolutions ($n_h \geq 1280$), we -used the cluster Jean-Zay of the french national center IDRIS. +$(F_h, \R)$ parameter space, and appreciate which ones are close to DNS. Litterature's simulations are also reported for comparison. We see that many of our simulations verify the criteria $\kmax \eta \geq 1$, except for low $F_h$ and/or high $\R$. - - -\begin{figure}[H] +\begin{figure} \centerline{ \includegraphics[width=0.48\textwidth]{% ../tmp/fig_kmaxeta_vs_FhR} diff --git a/2022strat_turb_polo/input/results.tex b/2022strat_turb_polo/input/results.tex --- a/2022strat_turb_polo/input/results.tex +++ b/2022strat_turb_polo/input/results.tex @@ -1,5 +1,7 @@ +\todo{Continue here} + Our dataset can be used to do several parametric studies. In this section, we perform such an analysis to -identify different regimes in the $(F_h, \R)$ parameters-space. These regimes differ in particular in +identify different regimes in the $(F_h, \R)$ parameters-space, and we analyse the properties of the different regimes. These regimes differ in particular in their isotropy and mixing properties. For all the regimes, we also present the spectral energy budget. diff --git a/2022strat_turb_polo/py/save_spectra_kh_kz.py b/2022strat_turb_polo/py/save_spectra_kh_kz.py --- a/2022strat_turb_polo/py/save_spectra_kh_kz.py +++ b/2022strat_turb_polo/py/save_spectra_kh_kz.py @@ -20,7 +20,7 @@ from fluidsim import load -cm = matplotlib.cm.get_cmap("jet", 100) +cm = matplotlib.cm.get_cmap("inferno", 100) keys = ["potential", "poloidal", "toroidal", "kinetic", "total"] diff --git a/2022strat_turb_polo/py/save_spectra_spatiotemporal_1couple_1nh.py b/2022strat_turb_polo/py/save_spectra_spatiotemporal_1couple_1nh.py --- a/2022strat_turb_polo/py/save_spectra_spatiotemporal_1couple_1nh.py +++ b/2022strat_turb_polo/py/save_spectra_spatiotemporal_1couple_1nh.py @@ -31,7 +31,7 @@ #keys = ["potential", "kinetic", "total"] key = "potential" -cm = matplotlib.cm.get_cmap("jet", 100) +cm = matplotlib.cm.get_cmap("inferno", 100) ############################ N = 40 diff --git a/2022strat_turb_polo/py/save_spectra_spatiotemporal_kh_kz.py b/2022strat_turb_polo/py/save_spectra_spatiotemporal_kh_kz.py --- a/2022strat_turb_polo/py/save_spectra_spatiotemporal_kh_kz.py +++ b/2022strat_turb_polo/py/save_spectra_spatiotemporal_kh_kz.py @@ -31,7 +31,7 @@ #keys = ["potential", "kinetic", "total"] key = "potential" -cm = matplotlib.cm.get_cmap("jet", 100) +cm = matplotlib.cm.get_cmap("inferno", 100) ############################ N = 40 diff --git a/2022strat_turb_polo/py/util.py b/2022strat_turb_polo/py/util.py --- a/2022strat_turb_polo/py/util.py +++ b/2022strat_turb_polo/py/util.py @@ -178,7 +178,7 @@ ax=ax, ) pc = ax.collections[-1] - pc.set_cmap("jet") + pc.set_cmap("inferno") if c is not None: plt.colorbar(pc, ax=ax)