diff --git a/D_EUROFER/D_EUROFER.ipynb b/D_EUROFER/D_EUROFER.ipynb
index 65edb50..87049c0 100644
--- a/D_EUROFER/D_EUROFER.ipynb
+++ b/D_EUROFER/D_EUROFER.ipynb
@@ -30,38 +30,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        6.1e+05 s    Elapsed time so far: 33.6 s\n",
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        2.7e+05 s    Elapsed time so far: 27.0 s\n",
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        6.1e+05 s    Elapsed time so far: 45.1 s\n",
-      "Defining initial values\n",
-      "Defining variational problem\n",
-      "Defining source terms\n",
-      "Defining boundary conditions\n",
-      "Time stepping...\n",
-      "100.0 %        2.7e+05 s    Elapsed time so far: 28.8 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import festim as F\n",
     "import fenics as f\n",
@@ -72,7 +41,7 @@
     "import json\n",
     "\n",
     "# Monkey patch the C99CodePrinter class\n",
-    "# this is to avoid the bug obersed in https://github.com/festim-dev/FESTIM/issues/813\n",
+    "# this is to avoid the bug observed in https://github.com/festim-dev/FESTIM/issues/813\n",
     "from sympy.printing.c import C99CodePrinter\n",
     "\n",
     "original_print_function = C99CodePrinter._print_Function\n",
@@ -105,10 +74,10 @@
     "L = 0.8e-3  # half thickness, m\n",
     "\n",
     "# EUROFER properties\n",
-    "rho_EFe = 8.59e28  # EUROFER atomic concentration, m^-3\n",
-    "n_IS = rho_EFe  # concentration of interstitial sites, m^-3\n",
-    "n_surf = rho_EFe ** (2 / 3)  # concentration of adsorption sites, m^-2\n",
-    "lambda_lat = rho_EFe ** (-1 / 3)  # Typical lattice spacing, m\n",
+    "n_EFe = 8.59e28  # EUROFER atomic concentration, m^-3\n",
+    "n_IS = 6 * n_EFe  # concentration of interstitial sites, m^-3\n",
+    "n_surf = n_EFe ** (2 / 3)  # concentration of adsorption sites, m^-2\n",
+    "lambda_lat = n_EFe ** (-1 / 3)  # Typical lattice spacing, m\n",
     "\n",
     "D0 = 1.5e-7  # diffusivity pre-factor, m^2 s^-1\n",
     "E_diff = F.kJmol_to_eV(14.470)  # diffusion activation energy, eV\n",
@@ -119,7 +88,7 @@
     "E_diss = 0.4  # energy barrier for D2 dissociation, eV\n",
     "E_rec = 0.63  # energy barrier for D2 recombination, eV\n",
     "E_sol = 0.238  # heat of solution, eV\n",
-    "S0 = 1.5e-6  # solubility pre-factor\n",
+    "S0 = 1.5e-6 * n_EFe  # solubility pre-factor, m^-3 Pa^-0.5\n",
     "Xi0 = 1e-5  # adsorption rate pre-factor\n",
     "chi0 = 1e-7  # recombination rate pre-factor\n",
     "E_sb = (\n",
@@ -127,17 +96,17 @@
     ")  # energy barrier from bulk to surface transition, eV\n",
     "\n",
     "# Trap properties\n",
-    "nu_tr = D0 / lambda_lat**2 / 6  # trapping attempt frequency, s^-1\n",
+    "nu_tr = D0 / lambda_lat**2  # trapping attempt frequency, s^-1\n",
     "nu_dt = 4.0e13  # detrapping attempt frequency, s^-1\n",
     "E_tr = E_diff\n",
     "E_dt_intr = 0.9  # detrapping energy for intrinsic traps, eV\n",
     "E_dt_dpa = 1.08  # detrapping energy for DPA traps, eV\n",
     "\n",
     "# Implantation parameters\n",
-    "Gamma = 9e19  # irradiation flux, m^-2 s^-1\n",
-    "R = -1.0e-10  # implantation range, m\n",
+    "indicent_flux = 9e19  # irradiation flux, m^-2 s^-1\n",
+    "R_impl = -1.0e-10  # implantation range, m\n",
     "sigma = 7.5e-10 / np.sqrt(2)\n",
-    "r = 0.612  # reflection coefficient\n",
+    "refl_coeff = 0.612  # reflection coefficient\n",
     "\n",
     "\n",
     "################### FUNCTIONS ###################\n",
@@ -152,7 +121,7 @@
     "\n",
     "\n",
     "def S(T):\n",
-    "    # solubility\n",
+    "    # solubility m^-3 Pa^-0.5\n",
     "    return S0 * f.exp(-E_sol / F.k_B / T)\n",
     "\n",
     "\n",
@@ -161,20 +130,121 @@
     "\n",
     "\n",
     "def k_bs(T, surf_conc, t):\n",
-    "    return nu_bs * f.exp(-E_bs / F.k_B / T)\n",
+    "    # n_IS / n_EFe is needed to obtain lambda_abs=n_surf/n_EFe in the final\n",
+    "    # expression for the bulk-to-surface flux of atoms as used in TESSIM\n",
+    "    return nu_bs * f.exp(-E_bs / F.k_B / T) * n_IS / n_EFe\n",
     "\n",
     "\n",
     "def k_sb(T, surf_conc, t):\n",
     "    # see eqs. (13-14) in K. Schmid and M. Zibrov 2021 Nucl. Fusion 61 086008\n",
-    "    return k_bs(T, surf_conc, t) * S(T) * n_surf * f.sqrt(chi(T) / Psi(T) / Xi(T))\n",
+    "    K_bs = nu_bs * f.exp(-E_bs / F.k_B / T)\n",
+    "    return K_bs * S(T) * lambda_lat * f.sqrt(chi(T) / Psi(T) / Xi(T))\n",
     "\n",
     "\n",
-    "def norm_flux(X, sigma):\n",
-    "    return 2 / (1 + special.erf(X / np.sqrt(2) / sigma))\n",
+    "def normal_distr(X, sigma):\n",
+    "    return 2 / (1 + special.erf(X / np.sqrt(2) / sigma))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The temperature after the loading phase was fitted to the experimental data with smooth functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def temperature_after_load(t, t_load: float, mod):\n",
+    "    \"\"\"Temperature evolution after the loading phase\n",
+    "\n",
+    "    Args:\n",
+    "        t: sp.Symbol or float or np.NDArray, the absolute time in seconds\n",
+    "        t_load (float): the duration of the loading phase in seconds\n",
+    "        mod: sympy or numpy module\n",
+    "\n",
+    "    Returns:\n",
+    "        appropriate object for the temperature evolution in K\n",
+    "    \"\"\"\n",
+    "    if mod == sp:\n",
+    "        log = log_bis\n",
+    "    else:\n",
+    "        log = mod.log\n",
     "\n",
+    "    a1 = log(mod.cosh(0.005 * (-612700 + (143 * 3600 - t_load) + t)))\n",
+    "    a2 = log(mod.cosh(0.005 * (-607300 + (143 * 3600 - t_load) + t)))\n",
+    "    a3 = log(mod.cosh(0.005 * (-603200 + (143 * 3600 - t_load) + t)))\n",
+    "    a4 = log(mod.cosh(0.005 * (-603200 + (143 * 3600 - t_load) + t)))\n",
+    "    a5 = log(mod.cosh(0.005 * (-602200 + (143 * 3600 - t_load) + t)))\n",
     "\n",
-    "################### MODEL ###################\n",
-    "def run_simulation(t_load, is_dpa, dpa_conc):\n",
+    "    value = (\n",
+    "        293.55\n",
+    "        + 50\n",
+    "        * (\n",
+    "            0\n",
+    "            - 0.05194 * a1\n",
+    "            + 0.05194\n",
+    "            * (\n",
+    "                -3035.806852819440\n",
+    "                - (3035.806852819440 + a1)\n",
+    "                + 2 * (3062.806852819440 + a2)\n",
+    "            )\n",
+    "        )\n",
+    "        + 50\n",
+    "        * (\n",
+    "            0\n",
+    "            - 0.06 * a2\n",
+    "            + 0.06\n",
+    "            * (\n",
+    "                -3020.806852819440\n",
+    "                - (3020.806852819440 + a2)\n",
+    "                + 2 * (3035.806852819440 + a3)\n",
+    "            )\n",
+    "        )\n",
+    "        + 50\n",
+    "        * (\n",
+    "            0\n",
+    "            - 0.04 * a3\n",
+    "            + 0.04\n",
+    "            * (\n",
+    "                -3015.306852819440\n",
+    "                - (3015.306852819440 + a3)\n",
+    "                + 2.00003 * (3020.806852819440 + a4)\n",
+    "            )\n",
+    "        )\n",
+    "        + 50\n",
+    "        * (\n",
+    "            0\n",
+    "            - 0.00339 * a4\n",
+    "            + 0.00339\n",
+    "            * (\n",
+    "                -3010.30685\n",
+    "                - (3010.306852819440 + a4)\n",
+    "                + 2.00009 * (3015.306852819440 + a5)\n",
+    "            )\n",
+    "        )\n",
+    "        + 76.45 * 0.5 * (1 - mod.tanh(0.002 * (t - 515800 + (143 * 3600 - t_load))))\n",
+    "    )\n",
+    "    return value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now define a function that will run a FESTIM model for different cases:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(t_load: float, is_dpa: bool, dpa_conc: float):\n",
     "\n",
     "    def J_vs(T, surf_conc, t):\n",
     "\n",
@@ -189,7 +259,7 @@
     "\n",
     "        Omega_loss = 1.4e5\n",
     "        J_loss = (\n",
-    "            (surf_conc / n_surf) * Omega_loss * Gamma * (1 - r)\n",
+    "            (surf_conc / n_surf) * Omega_loss * indicent_flux * (1 - refl_coeff)\n",
     "        )  # ad hoc flux for fit\n",
     "\n",
     "        J_net = (J_diss - J_loss) * cond - J_rec\n",
@@ -256,7 +326,7 @@
     "        E_k=E_tr,\n",
     "        p_0=nu_dt,\n",
     "        E_p=E_dt_intr,\n",
-    "        density=1e-5 * rho_EFe,\n",
+    "        density=1e-5 * n_EFe,\n",
     "        materials=EFe_model.materials[0],\n",
     "    )\n",
     "    trap_dpa = F.Trap(\n",
@@ -264,7 +334,7 @@
     "        E_k=E_tr,\n",
     "        p_0=nu_dt,\n",
     "        E_p=E_dt_dpa,\n",
-    "        density=(0.5 * dpa_conc * (1 - sp.tanh((F.x - 3.3e-6) / 0.01e-6))) * rho_EFe,\n",
+    "        density=(0.5 * dpa_conc * (1 - sp.tanh((F.x - 3.3e-6) / 0.01e-6))) * n_EFe,\n",
     "        materials=EFe_model.materials[0],\n",
     "    )\n",
     "\n",
@@ -274,76 +344,21 @@
     "\n",
     "    EFe_model.sources = [\n",
     "        F.ImplantationFlux(\n",
-    "            flux=Gamma\n",
-    "            * (1 - r)\n",
-    "            * norm_flux(R, sigma)\n",
+    "            flux=indicent_flux\n",
+    "            * (1 - refl_coeff)\n",
+    "            * normal_distr(R_impl, sigma)\n",
     "            * 0.5\n",
     "            * (1 - 1 * sp.tanh(0.002 * (F.t - t_load))),\n",
-    "            imp_depth=R,\n",
+    "            imp_depth=R_impl,\n",
     "            width=sigma,\n",
     "            volume=1,\n",
     "        )\n",
     "    ]\n",
     "\n",
-    "    a1 = log_bis(sp.cosh(0.005 * (-612700 + (143 * 3600 - t_load) + F.t)))\n",
-    "    a2 = log_bis(sp.cosh(0.005 * (-607300 + (143 * 3600 - t_load) + F.t)))\n",
-    "    a3 = log_bis(sp.cosh(0.005 * (-603200 + (143 * 3600 - t_load) + F.t)))\n",
-    "    a4 = log_bis(sp.cosh(0.005 * (-603200 + (143 * 3600 - t_load) + F.t)))\n",
-    "    a5 = log_bis(sp.cosh(0.005 * (-602200 + (143 * 3600 - t_load) + F.t)))\n",
-    "\n",
-    "    fun = (\n",
-    "        293.55\n",
-    "        + 50\n",
-    "        * (\n",
-    "            0\n",
-    "            - 0.05194 * a1\n",
-    "            + 0.05194\n",
-    "            * (\n",
-    "                -3035.806852819440\n",
-    "                - (3035.806852819440 + a1)\n",
-    "                + 2 * (3062.806852819440 + a2)\n",
-    "            )\n",
-    "        )\n",
-    "        + 50\n",
-    "        * (\n",
-    "            0\n",
-    "            - 0.06 * a2\n",
-    "            + 0.06\n",
-    "            * (\n",
-    "                -3020.806852819440\n",
-    "                - (3020.806852819440 + a2)\n",
-    "                + 2 * (3035.806852819440 + a3)\n",
-    "            )\n",
-    "        )\n",
-    "        + 50\n",
-    "        * (\n",
-    "            0\n",
-    "            - 0.04 * a3\n",
-    "            + 0.04\n",
-    "            * (\n",
-    "                -3015.306852819440\n",
-    "                - (3015.306852819440 + a3)\n",
-    "                + 2.00003 * (3020.806852819440 + a4)\n",
-    "            )\n",
-    "        )\n",
-    "        + 50\n",
-    "        * (\n",
-    "            0\n",
-    "            - 0.00339 * a4\n",
-    "            + 0.00339\n",
-    "            * (\n",
-    "                -3010.30685\n",
-    "                - (3010.306852819440 + a4)\n",
-    "                + 2.00009 * (3015.306852819440 + a5)\n",
-    "            )\n",
-    "        )\n",
-    "        + 76.45 * 0.5 * (1 - sp.tanh(0.002 * (F.t - 515800 + (143 * 3600 - t_load))))\n",
-    "    )\n",
-    "\n",
     "    EFe_model.T = F.Temperature(\n",
     "        value=sp.Piecewise(\n",
     "            (T_load, F.t <= t_load),\n",
-    "            (fun, True),\n",
+    "            (temperature_after_load(F.t, t_load, mod=sp), True),\n",
     "        )\n",
     "    )  # This temperature function is defined based on the TESSIM model\n",
     "\n",
@@ -388,9 +403,53 @@
     "    EFe_model.initialise()\n",
     "    EFe_model.run()\n",
     "\n",
-    "    return derived_quantities\n",
-    "\n",
-    "\n",
+    "    return derived_quantities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now run the FESTIM model for the four different cases:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        6.1e+05 s    Elapsed time so far: 33.1 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        2.7e+05 s    Elapsed time so far: 27.1 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        6.1e+05 s    Elapsed time so far: 46.0 s\n",
+      "Defining initial values\n",
+      "Defining variational problem\n",
+      "Defining source terms\n",
+      "Defining boundary conditions\n",
+      "Time stepping...\n",
+      "100.0 %        2.7e+05 s    Elapsed time so far: 28.7 s\n"
+     ]
+    }
+   ],
+   "source": [
     "params = [\n",
     "    {\n",
     "        \"t_load\": 143 * 3600,\n",
@@ -441,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -479,10 +538,9 @@
     "for i, prms in enumerate(params):\n",
     "\n",
     "    T, FESTIM_flux = total_flux(results[i])\n",
-    "    exp_label = prms[\"exp_data\"]\n",
     "\n",
     "    # Experimental data\n",
-    "    exp_data = json.load(open(f\"./reference_data/{exp_label}.json\"))\n",
+    "    exp_data = json.load(open(f\"./reference_data/{prms['exp_data']}.json\"))\n",
     "\n",
     "    ax[i].plot(T, FESTIM_flux / 1e17, lw=2, label=\"FESTIM\")\n",
     "\n",
@@ -523,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -547,13 +605,13 @@
     "    exp_label = prms[\"exp_data\"]\n",
     "\n",
     "    # TESSIM data\n",
-    "    data = json.load(open(f\"./reference_data/{exp_label}.json\"))\n",
+    "    TESSIM_data = json.load(open(f\"./reference_data/{prms['exp_data']}.json\"))\n",
     "\n",
     "    ax[i].plot(T, FESTIM_flux / 1e17, lw=2, label=\"FESTIM\")\n",
     "\n",
     "    ax[i].plot(\n",
-    "        np.array(data[\"temptab\"]),\n",
-    "        np.array(data[\"simflux\"]) / 1e5,\n",
+    "        np.array(TESSIM_data[\"temptab\"]),\n",
+    "        np.array(TESSIM_data[\"simflux\"]) / 1e5,\n",
     "        label=f\"TESSIM\",\n",
     "        marker=\"o\",\n",
     "        ls=\"dashed\",\n",
@@ -580,20 +638,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 748.031x354.331 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -609,14 +656,6 @@
     "    return T, total_flux\n",
     "\n",
     "\n",
-    "def remove_spines(axs):\n",
-    "    if hasattr(axs, \"__iter__\"):\n",
-    "        for axis in axs.flatten():\n",
-    "            axis.spines[[\"right\", \"top\"]].set_visible(False)\n",
-    "    else:\n",
-    "        axs.spines[[\"right\", \"top\"]].set_visible(False)\n",
-    "\n",
-    "\n",
     "mpl_params = {\n",
     "    \"text.usetex\": True,\n",
     "    \"text.latex.preamble\": \"\\n\".join(\n",
@@ -628,12 +667,32 @@
     "    ),\n",
     "    \"font.size\": 8,\n",
     "    \"font.family\": \"Times New Roman\",\n",
+    "    \"axes.spines.right\": False,\n",
+    "    \"axes.spines.top\": False,\n",
     "}\n",
     "\n",
     "plt.rcParams.update(mpl_params)\n",
     "\n",
-    "mm2inch = 0.1 / 2.54\n",
-    "\n",
+    "mm2inch = 0.1 / 2.54"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 748.031x354.331 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
     "fig, axs = plt.subplots(\n",
     "    2, 4, figsize=(190 * mm2inch, 90 * mm2inch), sharex=True, sharey=True\n",
     ")\n",
@@ -689,7 +748,6 @@
     "axs[0][0].set_xticks([300, 400, 500, 600, 700, 800])\n",
     "fig.supxlabel(\"Temperature, K\", fontsize=8)\n",
     "fig.supylabel(r\"Flux, $10^{17}$ \\si{m^{-2} s^{-1}}\", x=0.06, fontsize=8)\n",
-    "remove_spines(axs)\n",
     "\n",
     "# plt.savefig(\"./D_EUROFER.pdf\", dpi=1000, bbox_inches=\"tight\", pad_inches=0.01)\n",
     "plt.show()"
@@ -697,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -720,124 +778,29 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
     "from pypalettes import load_cmap\n",
     "\n",
     "cmap = load_cmap(\"Sunset2\", reverse=False)\n",
     "\n",
-    "\n",
-    "def remove_spines(axs):\n",
-    "    if hasattr(axs, \"__iter__\"):\n",
-    "        for axis in axs.flatten():\n",
-    "            axis.spines[[\"right\", \"top\"]].set_visible(False)\n",
-    "    else:\n",
-    "        axs.spines[[\"right\", \"top\"]].set_visible(False)\n",
-    "\n",
-    "\n",
-    "mpl_params = {\n",
-    "    \"text.usetex\": True,\n",
-    "    \"text.latex.preamble\": \"\\n\".join(\n",
-    "        [\n",
-    "            r\"\\usepackage{bm}\",\n",
-    "            r\"\\usepackage{siunitx}\",\n",
-    "            r\"\\sisetup{detect-all}\",\n",
-    "        ]\n",
-    "    ),\n",
-    "    \"font.size\": 8,\n",
-    "    \"font.family\": \"Times New Roman\",\n",
-    "}\n",
-    "\n",
-    "plt.rcParams.update(mpl_params)\n",
-    "\n",
-    "mm2inch = 0.1 / 2.54\n",
-    "\n",
     "fig, axs = plt.subplots(2, 1, figsize=(90 * mm2inch, 90 * mm2inch), sharex=True)\n",
     "\n",
-    "T_load = 370  # D loading temperature, K\n",
-    "T_storage = 290  # temperature after cooling phase, K\n",
-    "ramp = 3 / 60  # TDS heating rate, K/s\n",
-    "t_cool = 1000  # colling duration, s\n",
-    "t_storage = 24 * 3600  # storage time, s\n",
-    "t_TDS = (800 - T_storage) / ramp  # TDS duration (up to 800 K), s\n",
-    "t_load = 143 * 3600  # laoding time, s\n",
-    "\n",
-    "# Implantation parameters\n",
-    "Gamma = 9e19  # irradiation flux, m^-2 s^-1\n",
-    "\n",
-    "\n",
-    "def T(t):\n",
-    "    \"\"\"This function is defined based on the TESSIM model\"\"\"\n",
-    "\n",
-    "    a1 = lambda t: np.log(np.cosh(0.005 * (-612700 + (143 * 3600 - t_load) + t)))\n",
-    "    a2 = lambda t: np.log(np.cosh(0.005 * (-607300 + (143 * 3600 - t_load) + t)))\n",
-    "    a3 = lambda t: np.log(np.cosh(0.005 * (-603200 + (143 * 3600 - t_load) + t)))\n",
-    "    a4 = lambda t: np.log(np.cosh(0.005 * (-603200 + (143 * 3600 - t_load) + t)))\n",
-    "    a5 = lambda t: np.log(np.cosh(0.005 * (-602200 + (143 * 3600 - t_load) + t)))\n",
-    "\n",
-    "    fun = lambda t: (\n",
-    "        293.55\n",
-    "        + 50\n",
-    "        * (\n",
-    "            0\n",
-    "            - 0.05194 * a1(t)\n",
-    "            + 0.05194\n",
-    "            * (\n",
-    "                -3035.806852819440\n",
-    "                - (3035.806852819440 + a1(t))\n",
-    "                + 2 * (3062.806852819440 + a2(t))\n",
-    "            )\n",
-    "        )\n",
-    "        + 50\n",
-    "        * (\n",
-    "            0\n",
-    "            - 0.06 * a2(t)\n",
-    "            + 0.06\n",
-    "            * (\n",
-    "                -3020.806852819440\n",
-    "                - (3020.806852819440 + a2(t))\n",
-    "                + 2 * (3035.806852819440 + a3(t))\n",
-    "            )\n",
-    "        )\n",
-    "        + 50\n",
-    "        * (\n",
-    "            0\n",
-    "            - 0.04 * a3(t)\n",
-    "            + 0.04\n",
-    "            * (\n",
-    "                -3015.306852819440\n",
-    "                - (3015.306852819440 + a3(t))\n",
-    "                + 2.00003 * (3020.806852819440 + a4(t))\n",
-    "            )\n",
-    "        )\n",
-    "        + 50\n",
-    "        * (\n",
-    "            0\n",
-    "            - 0.00339 * a4(t)\n",
-    "            + 0.00339\n",
-    "            * (\n",
-    "                -3010.30685\n",
-    "                - (3010.306852819440 + a4(t))\n",
-    "                + 2.00009 * (3015.306852819440 + a5(t))\n",
-    "            )\n",
-    "        )\n",
-    "        + 76.45 * 0.5 * (1 - np.tanh(0.002 * (t - 515800 + (143 * 3600 - t_load))))\n",
-    "    )\n",
-    "\n",
-    "    T = np.piecewise(t, [t <= t_load, t > t_load], [lambda t: T_load, lambda t: fun(t)])\n",
-    "    return T\n",
-    "\n",
     "\n",
     "def Flux(t):\n",
     "    \"\"\"This function is defined based on the TESSIM model\"\"\"\n",
-    "\n",
-    "    Flux = Gamma * 0.5 * (1 - 1 * np.tanh(0.002 * (t - t_load)))\n",
+    "    Flux = indicent_flux * 0.5 * (1 - 1 * np.tanh(0.002 * (t - t_load)))\n",
     "    return Flux\n",
     "\n",
     "\n",
+    "t_load = params[0][\"t_load\"]\n",
+    "\n",
     "t = np.linspace(0, t_load + t_storage + t_cool + t_TDS, num=20000)\n",
+    "T = np.zeros_like(t)\n",
+    "T[np.where(t <= t_load)] = T_load\n",
+    "T[np.where(t > t_load)] = temperature_after_load(\n",
+    "    t[np.where(t > t_load)], t_load, mod=np\n",
+    ")\n",
     "\n",
-    "axs[0].plot(t / 3600, T(t), color=cmap(0), lw=1.5)\n",
+    "axs[0].plot(t / 3600, T, color=cmap(0), lw=1.5)\n",
     "axs[0].set_ylim(250, 950)\n",
     "axs[0].set_yticks([i for i in range(300, 1050, 150)])\n",
     "axs[0].set_ylabel(\"Temperature, K\")\n",
@@ -851,7 +814,6 @@
     "axs[1].set_ylim(0, 10 + 2.5 / 3)\n",
     "axs[1].set_xlim(0, 175)\n",
     "\n",
-    "remove_spines(axs)\n",
     "fig.align_ylabels()\n",
     "\n",
     "# plt.savefig(\"./D_EUROFER_TFlux.pdf\", dpi=1000, bbox_inches=\"tight\", pad_inches=0.01)\n",