Merge pull request #62 from RemDelaporteMathurin/yanns_remarks

Yann's remarks

bibfile.bib

@@ -10566,3 +10566,22 @@ @article{mcnabb_new_1963
 	year = {1963},
 	pages = {618--627},
 }
+
+@article{brown_endfb-viii0_2018,
+	series = {Special {Issue} on {Nuclear} {Reaction} {Data}},
+	title = {{ENDF}/{B}-{VIII}.0: {The} 8th {Major} {Release} of the {Nuclear} {Reaction} {Data} {Library} with {CIELO}-project {Cross} {Sections}, {New} {Standards} and {Thermal} {Scattering} {Data}},
+	volume = {148},
+	issn = {0090-3752},
+	shorttitle = {{ENDF}/{B}-{VIII}.0},
+	url = {https://www.sciencedirect.com/science/article/pii/S0090375218300206},
+	doi = {10.1016/j.nds.2018.02.001},
+	abstract = {We describe the new ENDF/B-VIII.0 evaluated nuclear reaction data library. ENDF/B-VIII.0 fully incorporates the new IAEA standards, includes improved thermal neutron scattering data and uses new evaluated data from the CIELO project for neutron reactions on 1H, 16O, 56Fe, 235U, 238U and 239Pu described in companion papers in the present issue of Nuclear Data Sheets. The evaluations benefit from recent experimental data obtained in the U.S. and Europe, and improvements in theory and simulation. Notable advances include updated evaluated data for light nuclei, structural materials, actinides, fission energy release, prompt fission neutron and γ-ray spectra, thermal neutron scattering data, and charged-particle reactions. Integral validation testing is shown for a wide range of criticality, reaction rate, and neutron transmission benchmarks. In general, integral validation performance of the library is improved relative to the previous ENDF/B-VII.1 library.},
+	language = {en},
+	urldate = {2022-07-14},
+	journal = {Nuclear Data Sheets},
+	author = {Brown, D. A. and Chadwick, M. B. and Capote, R. and Kahler, A. C. and Trkov, A. and Herman, M. W. and Sonzogni, A. A. and Danon, Y. and Carlson, A. D. and Dunn, M. and Smith, D. L. and Hale, G. M. and Arbanas, G. and Arcilla, R. and Bates, C. R. and Beck, B. and Becker, B. and Brown, F. and Casperson, R. J. and Conlin, J. and Cullen, D. E. and Descalle, M. -A. and Firestone, R. and Gaines, T. and Guber, K. H. and Hawari, A. I. and Holmes, J. and Johnson, T. D. and Kawano, T. and Kiedrowski, B. C. and Koning, A. J. and Kopecky, S. and Leal, L. and Lestone, J. P. and Lubitz, C. and Márquez Damián, J. I. and Mattoon, C. M. and McCutchan, E. A. and Mughabghab, S. and Navratil, P. and Neudecker, D. and Nobre, G. P. A. and Noguere, G. and Paris, M. and Pigni, M. T. and Plompen, A. J. and Pritychenko, B. and Pronyaev, V. G. and Roubtsov, D. and Rochman, D. and Romano, P. and Schillebeeckx, P. and Simakov, S. and Sin, M. and Sirakov, I. and Sleaford, B. and Sobes, V. and Soukhovitskii, E. S. and Stetcu, I. and Talou, P. and Thompson, I. and van der Marck, S. and Welser-Sherrill, L. and Wiarda, D. and White, M. and Wormald, J. L. and Wright, R. Q. and Zerkle, M. and Žerovnik, G. and Zhu, Y.},
+	month = feb,
+	year = {2018},
+	pages = {1--142},
+	file = {ScienceDirect Full Text PDF:D\:\\Logiciels\\data_zotero\\storage\\2I9PI5MF\\Brown et al. - 2018 - ENDFB-VIII.0 The 8th Major Release of the Nuclea.pdf:application/pdf;ScienceDirect Snapshot:D\:\\Logiciels\\data_zotero\\storage\\JFGW9MM5\\S0090375218300206.html:text/html},
+}

chapters/chapter2/verification_and_validation/comparison_with_tmap7.tex

@@ -50,7 +50,8 @@
     \end{align}
     \labeq{code comparison BCs}
 \end{subequations}
-with $\varphi_\mathrm{imp} = \SI{5e23}{m^{-2}.s^{-1}}$ the implanted particle flux, $R_p = \SI{1.25}{nm}$ the implantation depth, $\mathbf{n}$ the normal vector and $K_\mathrm{CuCrZr} = 2.9 \times 10^{-14}\cdot \exp{(-1.92/(k_B\cdot T))}$ the recombination coefficient of the CuCrZr (in vacuum) expressed in \si{m^4.s^{-1}} \sidecite{anderl_deuterium_1999}.
+% $\varphi_\mathrm{imp} = \SI{5e23}{m^{-2}.s^{-1}}$ the implanted particle flux, $R_p = \SI{1.25}{nm}$ the implantation depth, $\mathbf{n}$ the normal vector and 
+with $K_{r,\mathrm{CuCrZr}} = 2.9 \times 10^{-14}\cdot \exp{(-1.92/(k_B\cdot T))}$ the recombination coefficient of the CuCrZr (in vacuum) expressed in \si{m^4.s^{-1}} \sidecite{anderl_deuterium_1999}.
 
 The Dirichlet boundary condition on $\Gamma_\mathrm{top}$ for the hydrogen transport corresponds to a flux balance between the implanted flux and the flux that is retro-desorbed at the surface (see \refsec{triangle model}).
 The temperature profile in TMAP7 was fixed on the temperature profile produced by FESTIM (see \reffig{temperature}).

chapters/chapter3/monoblocks.tex

@@ -44,7 +44,7 @@ \section{Model description}\labsec{model description}
     \end{align}
     \labeq{bc thermal monoblock}
 \end{subequations}
-where $\varphi_\mathrm{heat} = \SI{10}{MW.m^{-2}}$, $T_\mathrm{coolant} = \SI{323}{K}$ and $h = \SI{70000}{W.m^{-2}.K^{-1}}$.
+where $T_\mathrm{coolant} = \SI{323}{K}$ and $h = \SI{70000}{W.m^{-2}.K^{-1}}$.
 
 \begin{subequations}
     \begin{align}
@@ -54,14 +54,14 @@ \section{Model description}\labsec{model description}
     \end{align}
     \labeq{bc H transport monoblock}
 \end{subequations}
-where $\varphi_\mathrm{imp} = \SI{1.6e22}{m^{-2}.s^{-1}}$, $R_p = \SI{1}{nm}$, and $K_\mathrm{r, \, CuCrZr} = 2.9 \times 10^{-14}\cdot \exp{(-1.92/(k_B\cdot T))}$ the recombination coefficient of the copper alloy (in vacuum) expressed in \si{m^4.s^{-1}} \sidecite{anderl_deuterium_1999}.
+where $K_\mathrm{r, \, CuCrZr} = 2.9 \times 10^{-14}\cdot \exp{(-1.92/(k_B\cdot T))}$ the recombination coefficient of the copper alloy (in vacuum) expressed in \si{m^4.s^{-1}} \sidecite{anderl_deuterium_1999}.
 
 \begin{table*}
     \centering
     \begin{tabular}{p{1.7cm}  R{3cm}  R{3cm}  R{1.8cm}  R{1cm} R{1.8cm}  R{1cm}}
          & \multicolumn{2}{c}{Thermal properties} & \multicolumn{4}{c}{Hydrogen transport properties}\\
         \hline
-        Material & $\rho \cdot C_p \newline(\si{J.K^{-1}.m^{-3}})$ & $\lambda \newline(\si{W.m^{-1}.K^{-1}})$ & $D_0 \newline(\si{m^2.s^{-1}})$ & $E_\mathrm{diff} \newline(\si{eV})$ & $S_0 \newline(\si{m^{-3}.Pa^{-0.5}})$ & $E_\mathrm{S} \newline(\si{eV})$\\
+        Material & $\rho \cdot C_p \newline(\si{J.K^{-1}.m^{-3}})$ & $\lambda \newline(\si{W.m^{-1}.K^{-1}})$ & $D_0 \newline(\si{m^2.s^{-1}})$ & $E_D \newline(\si{eV})$ & $S_0 \newline(\si{m^{-3}.Pa^{-0.5}})$ & $E_\mathrm{S} \newline(\si{eV})$\\
         \hline
         \\
         W \cite{frauenfelder_solution_1969,fernandez_hydrogen_2015}& %
@@ -173,7 +173,7 @@ \subsection{Thermal behaviour}\labsec{monoblock thermal behaviour}
     \caption{Thermal behaviour of the monoblock.}
 \end{figure*}
 
-The average surface temperature $T_\mathrm{surface}$ therefore increases linearly with the heat load and can be modelled by \refeq{thermal behaviour law} (see \reffig{surface temperature as a function of heat flux}).
+The average surface temperature $T_\mathrm{surface}$ therefore increases linearly with the heat load and can be fitted by \refeq{thermal behaviour law} (see \reffig{surface temperature as a function of heat flux}).
 \begin{equation}
     T_\mathrm{surface} = 1.1 \times 10^{-4} \cdot \varphi_\mathrm{heat} + T_\mathrm{coolant}
     \labeq{thermal behaviour law}
@@ -249,10 +249,10 @@ \subsection{Influence of cycling}\labsec{influence of cycling}
 Simulating these transient cycles would require stepsizes of $\approx \SI{10}{s}$ in order to capture the ramp-up and ramp-down phases.
 Simulating one cycle would therefore require more than 60 steps (excluding the resting phase).
 
-On the other hand, FESTIM has an adaptive stepsize feature allowing the stepsize to increase (resp. decrease) when steps are solved in less (resp. more) than 5 Newton iterations.
+On the other hand, FESTIM has an adaptive stepsize feature allowing the stepsize to increase (resp. decrease) when steps are solved in less (resp. more) than five Newton iterations.
 Therefore, if a continuous plasma exposure was simulated, the adaptive stepsize would allow the stepsize to increase up to thousands of seconds, reducing a lot the simulation time.
 
-To verify the validity of this approximation, 1D simulations were run with plasma cycles or continuous exposure.
+To verify the validity of the continuous exposure approximation, 1D simulations were run with plasma cycles or continuous exposure.
 For the cycled simulation, both the heat flux $\varphi_\mathrm{heat}$ and the particle flux $\varphi_\mathrm{imp}$ were varied from zero during the resting phases to their nominal values during the plateau phase (see \reffig{plasma cycle}).
 
 Two cases were run:

chapters/chapter3/monoblocks/interface_conditions.tex

@@ -28,7 +28,7 @@
         \includegraphics[width=\linewidth]{Figures/Chapter3/monoblocks/interface_condition/iter case/retention_chemical_pot.pdf}
         \caption{Retention (continuity of chemical potential).}
     \end{subfigure}
-    \caption{Concentration fields at $t=\SI{2e7}{s}$, $\varphi_\mathrm{heat} = \SI{7}{MW.m^{-2}}$.}
+    \caption{Influence of interface conditions on concentration fields at $t=\SI{2e7}{s}$, $\varphi_\mathrm{heat} = \SI{7}{MW.m^{-2}}$.}
     \labfig{concentration fields w/wo chemical potential}
 \end{figure}
 
@@ -44,7 +44,7 @@
 For the case at \SI{6}{MW.m^{-2}}, differences start to appear after \SI{3e6}{s} (\SI{7e5}{s} at \SI{7}{MW.m^{-2}}).
 After \SI{2e7}{s} of continuous exposure, the absolute difference at \SI{6}{MW.m^{-2}} was 25 \% and 55 \% at \SI{7}{MW.m^{-2}}.
 
-This time of appearance of differences is identical to the time required for the hydrogen to migrate up to the W/Cu interface.
+This time of appearance of differences corresponds to the time required for the hydrogen to migrate up to the W/Cu interface.
 This is explained by the high solubility ratio between W, Cu and CuCrZr leading to a higher concentration of mobile particles in CuCrZr (see \reffig{concentration fields w/wo chemical potential}) and therefore a higher trapping rate.
 Since the trap density in Cu is low, the global inventory is not affected by it.
 
@@ -59,7 +59,7 @@
         \includegraphics[width=\linewidth]{Figures/Chapter3/monoblocks/interface_condition/retention_concentration_short_exposure.pdf}
         \caption{continuity of mobile concentration.}
     \end{subfigure}
-    \caption{Retention fields at $t=\SI{6.1e4}{s}$.}
+    \caption{Influence of interface conditions on retention fields at $t=\SI{6.1e4}{s}$.}
     \labfig{retention fields w/wo chemical pot short exposure}
 \end{figure}
 
@@ -69,5 +69,4 @@
 Moreover, outgassing flux through the cooling pipe greatly depends on the boundary condition imposed at the cooling surface.
 Therefore, in order to assess the impact of interface conditions on the outgassing flux through the cooling pipe, uncertainties must first be lifted regarding the recombination process occurring on surfaces in contact with water.
 
-Since this work is motivated by the estimation of the divertor inventory, the concentration continuity assumption is therefore valid.
-Moreover, only a few monoblocks are exposed to high heat fluxes and most of the divertor is at the coolant temperature (this will be explained further in \refch{Divertor inventory estimation}).
+Since this work is motivated by the estimation of the divertor inventory, the concentration continuity assumption is therefore valid since only a few monoblocks are exposed to high heat fluxes and most of the divertor is at the coolant temperature (this will be explained further in \refch{Divertor inventory estimation}).

chapters/chapter3/monoblocks/parametric_study.tex

@@ -1,5 +1,5 @@
 Monoblocks in a fusion reactor will be exposed to a wide range of exposure conditions (heat and particle fluxes) and their behaviour in terms of hydrogen transport will change based on these conditions.
-In ITER, these fluxes can reach $\approx$ \SI{10}{MW.m^{-2}} and $\approx$ \SI{e24}{H.m^{-2}.s^{-1}}.
+In ITER, these fluxes can reach $\approx$ \SI{10}{MW.m^{-2}} and $\approx$ \SI{e24}{H.m^{-2}.s^{-1}} (see \reffig{divertor exposure conditions}).
 The distribution of these fluxes depend on many operation parameters.
 
 One way of simulating a whole divertor would be to simulate each and every monoblock for a given scenario along one Plasma-Facing Unit.

chapters/chapter4/divertor.tex

@@ -5,7 +5,7 @@ \chapter{Divertor inventory estimation}\label{Chapter4}\labch{Chapter4}
 
 % \section{Introduction}
 
-This Chapter focusses on the estimation of the \gls{H} \gls{inventory} in the \glspl{divertor} of \gls{west} and \gls{iter}.
+This Chapter focusses on the estimation of the \gls{H} \gls{inventory} in the \glspl{divertor} of \acrshort{west} and \gls{iter}.
 This estimation relies on the \gls{monoblock} behaviour law computed in \refch{Chapter3}.
 This behaviour law allows rapid evaluations of the \glspl{monoblock} \gls{H} \gls{inventory} for any exposure condition.
 Inputs are taken from \gls{soledge}-EIRENE \cite{bufferand_three-dimensional_2019} and \gls{solps} \cite{kaveeva_solps-iter_2020} plasma simulations.
@@ -22,7 +22,7 @@ \subsection{Plasma simulations}
 \begin{figure}[h!]
     \centering
     \includegraphics[width=0.95\linewidth]{Figures/Chapter4/coordinates.pdf}
-    \caption{Geometry of \gls{west} and \gls{iter} \glspl{divertor}.}
+    \caption{Poloidal cross section of \gls{west} and \gls{iter} showing the \glspl{divertor} in red.}
     \labfig{reactors}
 \end{figure}
 This Section describes the parameters of the plasma simulations.
@@ -74,8 +74,15 @@ \subsubsection{\gls{solps} runs}
 
 \begin{figure}[h!]
     \centering
-    \includegraphics[width=\linewidth]{Figures/Chapter4/example.pdf}
-    \caption{Method of \gls{divertor} \gls{H} \gls{inventory} estimation based on the surface concentration, the surface temperature and the behaviour law obtained in \refch{Chapter3}.}
+    \begin{overpic}[width=\linewidth]{Figures/Chapter4/example.pdf}
+        % \linethickness{2pt}
+        \thicklines
+        \put(55,36){\color{black}\vector(-1, 0){30}}
+        \put(25,75){\color{black}\vector(0, -1){30}}
+        \put(35,45){\color{black}\vector(1, 1){30}}
+    \end{overpic}
+
+    \caption{Method of WEST \gls{divertor} \gls{H} \gls{inventory} estimation based on the surface concentration, the surface temperature and the behaviour law obtained in \refch{Chapter3}.}
     \labfig{behaviour law example}
 \end{figure}
 
@@ -112,7 +119,7 @@ \subsection{Estimation of exposure conditions}
 \begin{equation}
     c_\mathrm{surface, \, i} = \frac{R_{p, \mathrm{i}} \ \varphi_\mathrm{imp, \,i}}{D(T_\mathrm{surface})}
 \end{equation}
-where $R_{p, i}$ is the implantation depth in \si{m}, $\varphi_{\mathrm{imp}, \,i}$ is the implanted particles flux in \si{m^{-2}.s^{-1}} and $D$ is the \gls{H} diffusion coefficient in \si{m^{2}.s^{-1}}.
+where $R_{p, i}$ is the implantation depth in \si{m}, $\varphi_{\mathrm{imp}, \,i}$ is the implanted particles flux in \si{m^{-2}.s^{-1}} and $D$ is the \gls{H} diffusion coefficient in \si{m^{2}.s^{-1}} (see \refsec{triangle model}).
 
 Finally, the implanted flux can be expressed as:
 \begin{equation}
@@ -137,22 +144,24 @@ \subsection{Estimation of exposure conditions}
 
 All of these steps have been automated and packaged into a tool called divHretention.
 divHretention can directly interpret \gls{solps}/\gls{soledge} data and produce a distribution of \gls{monoblock} \gls{inventory} as in \reffig{behaviour law example}.
+\marginnote{
 The source-code of the tool is under version control and openly available via GitHub under a MIT licence \cite{delaporte-mathurin_irfmdivhretention_2021}.
 The divHretention python package is distributed via PyPi \cite{delaporte-mathurin_divhretention_nodate}.
 Moreover, all the results obtained in this Chapter can be reproduced with the scripts available at \url{https://github.com/RemDelaporteMathurin/divHretention-Nucl.Fusion-2021}.
+}
 
-\section{\gls{iter} results}
+\section{ITER results}
 
 \begin{figure*}[h!]
     \captionsetup[subfigure]{format=plain,singlelinecheck=true}  % needed to center the subcaptions
     \centering
-    \begin{subfigure}{0.42\linewidth}
+    \begin{subfigure}{0.40\linewidth}
         \includegraphics[width=\linewidth]{Figures/Chapter4/ITER/inventory_along_inner_divertor.pdf}
-        \caption{IVT.}
+        \caption{Inner Vertical Target.}
     \end{subfigure}%
     \begin{subfigure}{0.58\linewidth}
         \includegraphics[width=\linewidth]{Figures/Chapter4/ITER/inventory_along_outer_divertor.pdf}
-        \caption{OVT.}
+        \caption{Outer Vertical Target.}
         \labfig{distrib outer target}
     \end{subfigure}
     \caption{Surface temperature, surface concentration and \gls{inventory} per unit thickness along the \gls{iter} \gls{divertor} with neutral pressures varying from \SI{2}{Pa} to \SI{11}{Pa}. The area corresponds to the 95\% confidence interval.}
@@ -221,7 +230,7 @@ \section{\gls{iter} results}
 
 
 % local inventories
-The inventory at the inner and outer \glspl{strike point} globally increases with the \gls{divertor} neutral pressure (see \reffig{local inventory neutral pressure}).
+The inventory at the inner \gls{strike point} is constant from \SI{4}{Pa} whereas the inventory at the outer \gls{strike point} globally increases with the \gls{divertor} neutral pressure (see \reffig{local inventory neutral pressure}).
 The contribution of ions to the surface concentration at the inner strike point is around 50 \% and tends to decrease with increasing neutral pressure (see \reffig{ion contribution neutral pressure}).
 At low \gls{divertor} neutral pressure, the contribution of ions at the outer strike point is around 90 \% and tends to decrease with increasing neutral pressure.
 This can be explained by the fact that in both inner and outer targets, the integrated flux of ions decreases with increasing neutral pressure whereas the integrated flux of atoms increases, leading to a greater proportion of neutral particles.
@@ -233,7 +242,7 @@ \section{\gls{iter} results}
 Past 300 discharges, the additional \gls{inventory} per discharge decreases with the number of discharges.
 The maximum is around \SI{5}{mg/discharge} between 30 and 100 discharges.
 
-\section{\gls{west} results}
+\section{WEST results}
 
 All the computations have been made for very long exposure times (\SI{e7}{s}) in order to better visualise trends.
 Even though cycling can have an effect on \gls{H} outgassing at the \gls{monoblock} plasma facing surface, it was shown in \refsec{influence of cycling} that the evolution of the \gls{monoblock} \gls{inventory} with the fluence was not affected.

chapters/chapter5/He_transport_in_PFCs/Influence_on_H_transport.tex

@@ -124,9 +124,9 @@ \subsection{Results}
     \item Initial state: the sample has some pre-existing defects %(proof from PAS that there are pre-existing defects before He implantation)
     \item \gls{He} implantation: all pre-existing defects are saturated with \gls{He} and bubbles are formed
     \item 1st \gls{D} implantation: \gls{D} can only be trapped around bubbles since defects are saturated with \gls{He}
-    \item 1st \gls{tds} (up to \SI{1250}{K}): \gls{D} is detrapped from bubbles is desorbed (\SI{550}{K} peak), \gls{He} dissociates from pre-existing defects 
+    \item 1st \gls{tds} (up to \SI{1250}{K}): \gls{D} is detrapped from bubbles (\SI{550}{K} peak), \gls{He} dissociates from pre-existing defects 
     \item 2nd \gls{D} implantation: \gls{D} is trapped around bubbles and in the non-saturated defects
-    \item 2nd \gls{tds} (up to \SI{1350}{K}): \gls{D} is detrapped from bubbles is desorbed (\SI{550}{K} peak) and from non-saturated defects (peaks 400K, 450K and 500K) + \gls{He} trapped in deeper traps dissociate (because the \gls{tds} goes to higher temperatures)
+    \item 2nd \gls{tds} (up to \SI{1350}{K}): \gls{D} is detrapped from bubbles (\SI{550}{K} peak) and from non-saturated defects (peaks 400K, 450K and 500K) + \gls{He} trapped in deeper traps dissociate (because the \gls{tds} goes to higher temperatures)
     \item 3rd to 5th \gls{D} implantations: \gls{D} is trapped around bubbles and in pre-existing defects
     \item 3rd to 5th \gls{tds}: \gls{D} is detrapped from bubbles and pre-existing defects (now more available than at the 2nd \gls{tds})
 \end{itemize}