prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the effect of the proximity of superconductivity on the Kondo effect?

Read the following text and answer briefly.

Context 1: \section{Introduction}
\label{sec:Intro}

The exchange interactions control the magnetic order and properties of a vast number of materials
\cite{White2006Dec}
and lead to many fascinating phenomena, such as various types of the Kondo effect 
\cite{Kondo,NozieresBlandin,Pustilnik_Glazman} 
Double quantum dots (DQDs), and in general multi-impurity systems, constitute
a convenient and controllable playground,
where nearly as much different exchange mechanisms compete with each other to
shape the ground state of the system 
\emph{Local exchange} between the spin of a quantum dot (QD)
and the spin of conduction band electrons gives rise to the
Kondo effect \cite{Kondo,Hewson_book}  
\emph{Direct exchange} arriving with an additional side-coupled QD may destroy it or lead to the 
two-stage Kondo screening \cite{Pustilnik_Glazman,Cornaglia,Granger,ZitkoBonca,ZitkoPRB2010,Ferreira} 
In a geometry where the two QDs contact the same lead, conduction band electrons 
mediate the \emph{RKKY exchange} \cite{RK,K,Y}  The RKKY interaction competes
with the Kondo effect and leads to the quantum phase transition of a still debated nature
\cite{Doniach,Jones,Affleck,Bork,Neel,KondoRKKYexp,Hans,Hans2,Fabian} 
Moreover, in DQDs coupled in series also \emph{superexchange} can alter the Kondo physics significantly
\cite{Zitko_2QDEx,Sela} 

Recently, hybrid quantum devices, in which the interplay between various magnetic correlations
with superconductivity (SC) plays an important role, have become an important direction of research
\cite{hybridQDs,SCspintronics}  In particular, chains of magnetic atoms on SC surface have proven 
to contain self-organized Majorana quasi-particles and exotic spin textures
\cite{Braunecker,Klinovaja,Vazifeh,Yazdani},
while hybrid DQD structures have been used to split the Cooper pairs coherently into two entangled 
electrons propagating to separated normal leads \cite{CPS1,CPS2,CPS4,CPS5,CPS9} 
The latter is possible due to non-local (\emph{crossed}) Andreev reflections (CARs),
in which each electron of a Cooper pair tunnels into different QD, and
subsequently to attached lead  Such processes give rise to an exchange mechanism \cite{Yao},
that we henceforth refer to as \emph{the CAR exchange}, which can greatly modify
the low-temperature transport behavior of correlated hybrid nanostructures 

The CAR exchange may be seen as RKKY-like interaction between
two nearby impurities on SC surface \cite{Yao} 
The effect can be understood as a consequence
of spin-dependent hybridization of the Yu-Shiba-Rusinov (YSR)
states \cite{Yu,Shiba,Rusinov} in SC contact,
caused both by the overlap of their wave functions
and their coupling to Cooper-pair condensate 
This process is the most effective when the YSR states 
are close to the middle of the SC gap, {\it e g } in the YSR-screened phase \cite{YSRscreening} 
The mechanism presented here is essentially the same,
yet in the considered regime can be understood
perturbatively without referring to YSR states,
as a consequence of the non-local pairing induced by SC electrode  
In particular, the presence of YSR bound states close to the Fermi level 
is not necessary for significant consequences for the Kondo physics, 
as long as some inter-dot pairing is present  


The proximity of SC induces pairing in QDs \cite{RozhkovArovas,Buitelaar} 
and tends to suppress the Kondo effect if the superconducting energy gap $2\Delta$ 
becomes larger than the relevant Kondo temperature $T_K$ 
\cite{Buitelaar2002Dec,adatomsSC,Kondo_vs_SC1,Kondo_vs_SC2,Zitko_Kondo-Andreev,Zitko_S-QD-N,IW_Sau,YSRscreening} 
Moreover, the strength of SC pairing can greatly affect the Kondo physics in the sub-gap transport regime:
For QDs attached to SC and normal contacts, it can enhance the Kondo effect
\cite{DomanskiIW,KWIW,part1}, while
for DQD-based Cooper pair splitters, it tends to suppress both the $\mathrm{SU}(2)$ and $\mathrm{SU}(4)$ Kondo effects \cite{IW_Kacper} 
Our main result is that the non-local pairing induced by superconducting 
proximity effect, which gives rise to CAR exchange, can be the sole cause of the Kondo screening 
Moreover, relatively small values of coupling to SC, $\GS{}\ll U$, are sufficient for the effect to occur 
This is in contrast to the DQD system considered in Ref ~\cite{part1},
where only one of the quantum dots is proximized, such that 
CAR exchange cannot arise,
and the Kondo physics becomes qualitatively
affected only for $\GS{}\sim U/2$ %


\begin{figure}[bt]
\centering
\includegraphics[width=1\linewidth]{Fig1 png}
\caption{
		 (a) Schematic of the considered system  Left/right (L/R) lead
		 is coupled to the first quantum dot (QD1), while superconductor
		 is attached to both QD1 and QD2 
		 (b)-(d) illustrate an example of direct spin exchange:
		 spin-up electron from the initial state (b) hops to the other QD (c) and spin-down electron 
		 hops back (d)  Note, that the final state is in fact the same singlet state, 
		 only with opposite sign 
		 (e)-(g) show an example of process contributing to crossed Andreev reflection (CAR) exchange 
		 A Cooper pair from SC approaches DQD (e) and two singlets of the same charge 
		 are formed (f), before the Cooper pair is re-emitted (g) 
		 (h)-(j) present an example of RKKY process: an electron scattered off
		 one QD (h) mediates the spin exchange towards the other (i), before it is finally scattered
		 off there, too (j) 
		 }
\label{fig:system}
\end{figure}


In this paper we discuss the CAR-induced Kondo screening in a setup comprising T-shaped DQD
with normal and superconducting contacts, see \fig{system}(a) 
We note that despite quite generic character of CAR exchange,
and its presence in systems containing at least two localized electrons
coupled close to each other to the same SC bath,
to best of our knowledge CAR-induced screening
has hardly been identified in previous studies
\cite{CPS1,CPS2,CPS4,CPS5,CPS9,IW_Kacper,IW_Sau,Zitko_Josephson,Zitko_S2QD,Martinek2017} 
In the system proposed here [\fig{system}(a)], its presence is evident 
Moreover, CAR exchange magnitude can be directly related to the relevant energy scales, such as the Kondo 
temperature, which provides a fingerprint for quantitative experimental verification of our predictions  

The paper is organized as follows  In \Sec{model} we describe the considered system 
and present the model we use to study it  In \Sec{scales} the relevant energy scales are estimated
to make the discussion of main results concerning CAR-induced Kondo effect in \Sec{main} more clear  
Finally, the influence of effects neglected in \Sec{main} are presented in the following sections,
including CAR exchange interplay with RKKY interaction (\Sec{RKKY}), particle-hole asymmetry (\Sec{asym}),
couplings asymmetry (\Sec{x}) and reduced efficiency of CAR coupling (\Sec{coef})  In summary,
the effects discussed in \Sec{main} remain qualitatively valid in all these cases 
The paper is concluded in \Sec{conclusions} 


\section{Model}
\label{sec:model}

The schematic of the considered system is depicted in \fig{system}(a) 
It contains two QDs attached to a common SC lead 
Only one of them (QD1) is directly attached to the left (L) and right (R) normal leads,
while the other dot (QD2) remains coupled only through QD1 
The SC is modeled by the BCS Hamiltonian, 
$H_{\mathrm{S}}=\sum_{\mathbf{k}\sigma}\xi_{\mathbf{k}}a_{\mathbf{k}\sigma}^{\dag}a_{\mathbf{k}\sigma}-\Delta\sum_{\mathbf{k}}(a^\dag_{\mathbf{k}\uparrow}a_{-\mathbf{k}\downarrow}^{\dag}+a_{-\mathbf{k}\downarrow}a_{\mathbf{k}\uparrow})$,
with energy dispersion $\xi_{\mathbf{k}}$, energy gap $2\Delta>0$ and $a_{\mathbf{k}\sigma}$ annihilation operator 
of electron possessing spin $\sigma$ and momentum $\mathbf{k}$  The coupling between
SC and QDs is described by the hopping Hamiltonian 
$H_{\mathrm{TS}}=\sum_{i\mathbf{k}\sigma}v_{\mathrm{S}i}(d^\dagger_{i\sigma}a^{}_{\mathbf{k}\sigma}+h c )$,
with $d^\dagger_{i\sigma}$ creating a spin-$\sigma$ electron at QD$i$  The matrix element 
$v_{\mathrm{S}i}$ and the normalized density of states of SC in normal state, $\rho_{\rm S}$, 
contribute to the coupling of QD$i$ to SC electrode as $\GS{i} = \pi \rho_{\rm S} |v_{{\rm S}i}|^2$  
We focus on the sub-gap regime, therefore, we integrate out SC degrees of freedom lying outside the energy gap \cite{RozhkovArovas} 
This gives rise to the following effective Hamiltonian,
$H_{\mathrm{eff}}=H_{\mathrm{SDQD}}+H_{\rm L}+H_{\rm R}+H_{\rm T}$, 
where 
\begin{eqnarray}
H_{\rm SDQD} 	& = & 
				\sum_{i\sigma} \varepsilon_{i} n_{i\sigma} 
				+\sum_{i} U n_{i\uparrow} n_{i\downarrow} 
				+U' (n_1-1)(n_2-1) 
				\nonumber\\
				&+&\sum_\sigma t(d^\dagger_{1\sigma}d^{}_{2\sigma} + h c ) 
				+J \vec{S}_1\vec{S}_2
				\nonumber\\
				&+&\sum_{i} \!\!\left[ \Gamma_{{\rm S}i} (d^\dagger_{i\uparrow} d^\dagger_{i\downarrow} \!+\! h c )
				+\Gamma_{\rm SX} (d^\dagger_{i\uparrow} d^\dagger_{\bar{i}\downarrow} \!+\! h c ) \right]
	\label{H_DQD} 
\end{eqnarray}
is the Hamiltonian of the SC-proximized DQD
\cite{IW_Kacper,Walldorf2018Feb}, with QD$i$ energy level $\varepsilon_i$,
inter-site (intra-site) Coulomb interactions $U'$ ($U$),
inter-dot hopping $t$, and CAR coupling $\GS{\rm X}$ 
$n_{i\sigma}=d^\dagger_{i\sigma}d^{}_{i\sigma}$ denotes the electron number operator 
at QD$i$, $n_i=n_\uparrow+n_\downarrow$, and $\bar{i}\equiv 3-i$  
Our model is strictly valid in the regime where $\Delta$ is the largest 
energy scale  Nevertheless, all discussed phenomena are
present in a full model for energies smaller than SC gap 
Moreover, by eliminating other consequences of the presence of SC lead,
our model pinpoints the fact that the non-local pairing is 
sufficient for the occurrence of the CAR exchange 
The presence of out-gap states shall result mainly in additional broadening of DQD energy levels,
changing the relevant Kondo temperatures 
We note that the procedure of integrating out out-gap states neglects the 
RKKY interaction mediated by SC lead and other possible indirect exchange mechanisms%
 \footnote{
 Note, that by RKKY interaction we mean only such an effective exchange, 
 which arises due to multiple scattering of a single electron or hole, see \fig{system}(h)-(j) 
 Other mechanisms leading to the total indirect exchange are considered separately 
 In particular, in the large gap limit, exchange described in Ref ~\cite{Yao} is in fact reduced to
 the CAR exchange, and additional antiferromagnetic contribution would arise for finite gap 
 }  
To compensate for this,
we explicitly include the Heisenberg term $ J \vec{S}_1\vec{S}_2$ in
$H_{\rm SDQD}$, with $\vec{S}_i$ denoting the spin operator of QD$i$
and a Heisenberg coupling $J$ substituting the genuine RKKY exchange 

The normal leads are treated as reservoirs of noninteracting electrons,
$H_{r}=\sum_{\mathbf{k}\sigma}\varepsilon_{r\mathbf{k}}c^\dagger_{r\mathbf{k}\sigma}c^{}_{r\mathbf{k}\sigma}$,
where $c^{}_{r\mathbf{k}\sigma}$ annihilates an electron of spin 
$\sigma$ and momentum $\mathbf{k}$ in lead $r$ ($r={\rm L,R}$) with the corresponding energy $\varepsilon_{r\mathbf{k}\sigma}$ 
The tunneling Hamiltonian reads,
$H_{\rm T} = \sum_{r\mathbf{k}\sigma} v_{r} (d^\dagger_{1\sigma}c^{}_{r\mathbf{k}\sigma} + h c )$,
giving rise to coupling between lead $r$ and QD$i$ of strength $\Gamma_r = \pi \rho_r |v_r|^2$,
with $\rho_r$ the normalized density of states of lead $r$ and $v_r$ the 
local hopping matrix element, assumed momentum-independent 
We consider a wide-band limit, assuming constant $\Gamma_r=\Gamma/2$
within the cutoff $\pm D = \pm 2U$ around the Fermi level  

For thorough analysis of the CAR exchange mechanism and its consequences
for transport, we determine the linear conductance between the two normal leads from
\begin{equation}
G = \frac{2e^2}{h} \pi \Gamma \int \left[ -\frac{\partial f_T}{\partial\omega} \right] \mathcal{A}(\omega) {\rm d} \omega ,
\label{G}
\end{equation}
where $f_T$ is the Fermi function at temperature $T$,
while $\mathcal{A}(\omega)$ denotes the normalized local spectral density 
of QD1 \cite{fn1} 
Henceforth, unless we state otherwise, we assume a maximal CAR coupling, 
$\GS{\rm X} = \sqrt{\GS{1}\GS{2}}$ \cite{IW_Kacper,Walldorf2018Feb},
$\GS{1}=\GS{2}=\GS{}$ and consider DQD tuned to the particle-hole symmetry point, 
$\varepsilon_1=\varepsilon_2=-U/2$  However, these assumptions are not crucial for the results presented
here, as discussed in Secs ~\ref{sec:asym}-\ref{sec:coef} 

\section{Estimation of relevant energy scales}
\label{sec:scales}

Since we analyze a relatively complex system, let us build up the understanding of its behavior starting
from the case of a QD between two normal-metallic leads, which can be obtained in our 
model by setting $t=\GS{}=J=U'=0$  Then, the conductance as a function of temperature, $G(T)$, grows
below the Kondo temperature $T_K$ and reaches maximum for $T\to 0$, $G(T\!=\!0)=G_{\rm max}$ 
At particle-hole symmetry point, the unitary transmission is achieved, $G_{\rm max}= G_0 = 2e^2/h$;
see short-dashed line in \fig{G-T}(a) 
An experimentally relevant definition of $T_K$ is that at $T=T_K$ 
$G(T)=G_{\rm max}/2$  $T_K$ is exponentially small in 
the local exchange $J_0 = 8\Gamma / (\pi \rho U)$, and is approximated by
$T_K \approx D \exp[-1/(\rho J_0)]$ \cite{Hewson_book} 

The presence of a second side-coupled QD, $t,U'>0$, significantly enriches the physics of the system 
by introducing direct exchange between QDs, see \fig{system}(b-d) 
In general, effective inter-dot exchange can be defined as energy difference between 
the triplet and singlet states of isolated DQD, 
$J^{\mathrm{eff}} = E_{S=1} - E_{\rm GS}$  Unless $U$ becomes very large, superexchange can be neglected
\cite{Zitko_2QDEx} and $J^{\mathrm{eff}}$ is determined by \emph{direct exchange}, $J^{\mathrm{eff}}\approx 4t^2/(U-U')>0$ 
When the hopping $t$ is tuned small \cite{CPS1}, one can expect $J^{\mathrm{eff}}\lesssim T_K$, which 
implies the two-stage Kondo screening \cite{Pustilnik_Glazman,Cornaglia} 
Then, for $T \ll T_K$, the local spectral density of QD1 serves as a band of width $\sim T_K$ for QD2 
The spin of an electron occupying QD2 
experiences the Kondo screening below the associated Kondo temperature
\begin{equation}
T^* = a T_K \exp(- b T_K / J_{\rm eff})
\label{Tstar}
\end{equation}
with $a$ and $b$ constants of order of unity \cite{Pustilnik_Glazman,Cornaglia} 
This is reflected in conductance, which drops to $0$ with lowering $T$, maintaining characteristic 
Fermi-liquid 
$G\sim T^2$ dependence \cite{Cornaglia}; see the curves indicated with squares 
in \fig{G-T}(a)  Similarly to $T_K$, experimentally relevant definition of $T^*$ is that 
$G(T\!=\!T^*) = G_{\rm max}/2$  Even at the particle-hole 
symmetry point $G_{\rm max} < G_0$, because the single-QD strong-coupling fixed point 
is unstable in the presence of QD2 and $G(T)$ does not achieve $G_0$ exactly,
before it starts to decrease 


The proximity of SC gives rise to two further exchange mechanisms that
determine the system's behavior  First of all, the (conventional)
\emph{RKKY interaction} appears, $J \sim \GS{}^2$ \cite{RK,K,Y}  
Moreover, the \emph{CAR exchange} emerges as a consequence of finite $\GS{}$ \cite{Yao}  
It can be understood on the basis 
of perturbation theory as follows  DQD in the inter-dot singlet state may absorb
and re-emit a Cooper pair approaching from SC; see \fig{system}(e)-(g)  As a second-order
process, it reduces the energy of the singlet, which is the ground state of isolated DQD 
A similar process is not possible in the triplet state due to spin conservation 
Therefore, the singlet-triplet energy splitting $J^{\mathrm{eff}}$ is increased (or generated for $t=J=0$)  
More precisely, the leading ($2$nd-order in $t$ and $\GS{}$) terms
in the total exchange are 
\begin{equation}
J^{\mathrm{eff}} 	\approx 	J + \frac{4t^2}{U-U'+\frac{3}{4}J} + \frac{4\GS{}^2}{U+U'+\frac{3}{4}J} 
\label{Jeff}
\end{equation}
Using this estimation, one can predict $T^*$ for finite $\GS{}$, $t$ and $J$ with \eq{Tstar} 
Apparently, from three contributions corresponding to:
(i) RKKY interaction, (ii) direct exchange and (iii) CAR exchange, only the first may bear a negative (ferromagnetic) sign 
The two other contributions always have an anti-ferromagnetic nature 
More accurate expression for $J^{\mathrm{eff}}$ is derived in Appendix~\ref{sec:downfolding}
[see \eq{A_J}] by the Hamiltonian down-folding procedure  The relevant terms differ 
by factors important only for large $\GS{}/U$  
Finally, it seems worth stressing that normal leads are not necessary for CAR exchange to occur 
At least one of them is inevitable for the Kondo screening though, and two symmetrically coupled 
normal leads allow for measurement of the normal conductance 


It is also noteworthy that inter-dot Coulomb interactions
decrease the energy of intermediate states contributing to direct exchange 
[\fig{system}(c)], while increasing the energy of intermediate
states causing the CAR exchange [\fig{system}(f)] 
This results in different dependence of corresponding terms in \eq{Jeff} on $U'$ 
As can be seen in \figs{G-T}(b) and \ref{fig:G-T}(c), it has a significant effect 
on the actual values of $T^*$ 

\begin{figure}
\includegraphics[width=1\linewidth]{Fig2 pdf}
\caption{(a) Linear conductance $G$ as function of $T$ calculated for 
		 $\varepsilon_1=\varepsilon_2=-U/2$, $\Gamma=U/5$, $U'=U/10$ and different situations, 
		 as indicated  The quantity $\xi\equiv\sqrt{\GS{}^2+t^2}$ is fixed 
		 for different curves drawn with the same dashing style 
		 Note the logarithmic scale on both axes 
		 %
		 (b) Points show $T^*/T_K$ calculated by NRG from curves in subfigure (a)  
		 Lines present the fit to \eq{Tstar} with $J^{\mathrm{eff}}$ obtained from \eq{Jeff} 
		 %
		 (c) The same as (b), only for $U'=0$ 
		 %
		 (d) and (e) show the residual conductance $G_{\mathrm{min}} \equiv G(T \!=\! 0)$ as a function of
		 $\GS{}$ for $t=0$ (denoted "CAR") and $t=\GS{}$ (denoted "Both")  
		 Dotted line is a guide for eyes  $U'=U/10$ in (b) and (d) and $U'=0$ in (c) and (e) 
		}
\label{fig:G-T}
\end{figure}

\section{CAR exchange and Kondo effect}
\label{sec:main}

To verify \eqs{Tstar}-(\ref{Jeff}) we calculate $G$ using
accurate full density matrix numerical renormalization group (NRG) technique \cite{WilsonNRG,Weichselbaum,FlexibleDMNRG,fn2} 
We compare $U'=0$ case with experimentally relevant value $U'=U/10$ \cite{Keller2013Dec} 
While for two close adatoms on SC surface RKKY interactions may lead to prominent consequences
\cite{Klinovaja}, the conventional ({\it i e } non-CAR) contribution should 
vanish rapidly when the inter-impurity distance $r$ exceeds a few lattice constants \cite{RKKYrange,SC_RKKY}  
Meanwhile, the CAR exchange may remain significant for $r$ of the order
of coherence length of the SC contact \cite{Yao}  Therefore, we first neglect the conventional RKKY coupling and analyze its consequences in Sec ~\ref{sec:RKKY} 

The main results are presented in \fig{G-T}(a), showing the temperature dependence of $G$
for different circumstances  
For reference, results for $\GS{}=0$ are shown, exhibiting 
the two-stage Kondo effect caused by \emph{direct} exchange mechanism 
As can be seen in \figs{G-T}(b) and \ref{fig:G-T}(c), an excellent agreement of $T^*$ found from NRG calculations and \eq{Tstar} 
is obtained with $a=0 42$ and $b=1 51$, the same for both $U'=0$ and $U'=U/10$  Note, 
however, that $J^{\mathrm{eff}}$ is different in these cases, cf  \eq{Jeff},
and $U'$ leads to increase of $T^*$ 

Furthermore, for $t=0$ and $\GS{}>0$ the two-stage Kondo effect caused solely by the \emph{CAR
exchange} is present; see \fig{G-T}(a) 
Experimentally, this situation
corresponds to a distance between the two QDs smaller than the superconducting coherence length,
but large enough for the exponentially suppressed direct hopping to be negligible 
While intuitively one could expect pairing to compete with any kind of magnetic ordering,
the Kondo screening induced by CAR exchange is a beautiful example of a superconductivity
in fact leading to magnetic order, namely the formation of the Kondo singlet 
This CAR-exchange-mediated Kondo screening is our main finding 
For such screening, \eq{Tstar} is still fulfilled with very similar 
parameters, $a=0 37$ ($a=0 35$) and $b=1 51$ ($b=1 50$) for $U'=0$ ($U'=U/10$),
correspondingly; see \figs{G-T}(b-c) 
Moreover, as follows from \eq{Jeff}, $U'$ reduces CAR exchange, and therefore diminishes $T^*$ 
For the same values of $J^{\mathrm{eff}}$, the dependence of $G(T)$ for $t=0$ and $\GS{}>0$ is hardly different 
from the one for $\GS{}=0$ and $t>0$ for $T\geq T^*$ (results not shown) 
However, $G(T)$ saturates at residual value $G_{\mathrm{min}}$ as $T\to 0$ only for finite
$\GS{}$, which at particle-hole symmetry makes $G_{\mathrm{min}}$
the hallmark of SC proximity and the corresponding CAR exchange processes 
From numerical results, one can estimate it as
\begin{equation}
G_{\mathrm{min}} = \frac{e^2}{h} \cdot c \, \frac{\GS{}^2}{U^2} 
	\qquad {\scriptstyle (\GS{1}=\GS{2}=\GS{})} ,
\label{Gmin}
\end{equation}
with $c\approx 2 25$, barely depending on $U'$ and getting smaller for $t>0$  
This is illustrated in \figs{G-T}(d-e), where the dotted line corresponds to \eq{Gmin} with $c=2 25$  

Lastly, in \fig{G-T}(a) we also present the curves obtained for $t=\GS{}$ chosen such, 
that the quantity $\xi=\sqrt{t^2+\GS{}^2}$ remains the same 
in all the cases 
This is to illustrate what happens when \emph{both} (direct and CAR) exchange interactions are
present  \fig{G-T}(c) clearly shows that $T^*$ remains practically unaltered for $U'=0$ 
The comparison with \fig{G-T}(b) proves that in this case it practically does not depend 
on $U'$  The enhancement of direct exchange is compensated by the decrease of the CAR one  
On the contrary, $G_{\mathrm{min}}$ decreases for larger $t$ below the estimation given by Eq ~(\ref{Gmin}), 
as can be seen in \figs{G-T}(d-e)  

While analyzing the results concerning $G_{\mathrm{min}}(\GS{})$ plotted in \figs{G-T}(d-e) 
one needs to keep in mind that $G_{\mathrm{min}}$ is obtained at deeply cryogenic conditions  To illustrate
this better, $G(\GS{})$ obtained for $t=0$ and $T=10^{-6}U$ is plotted with solid line 
in \fig{3}  Clearly, for weak $\GS{}$ the system exhibits rather conventional (single-stage)
Kondo effect with $G=G_{\mathrm{max}}\approx 2e^2/h$, while QD2 is effectively decoupled ($G_{\mathrm{max}}<2e^2/h$
in the proximity of SC lead \cite{KWIW})  Only for larger values of $\GS{}$
the CAR exchange is strong enough, such that $T^*>T$ and the dependence $G(\GS{})$ continuously 
approaches the $T=0$ limit estimated by \eq{Gmin} and presented in \figs{G-T}(d-e) 

\section{CAR-RKKY competition}
\label{sec:RKKY}

\begin{figure}
\includegraphics[width=0 98\linewidth]{Fig3 pdf}
\caption{Linear conductance $G$ vs  $\GS{}$ calculated
		 for $t=0$, $\Gamma=U/5$, $U'=U/10$, finite $T=10^{-6}U$
		 and different values of RKKY coupling $J$, as indicated  
		 Inset shows QD1 spectral function $\mathcal{A}(\omega)$ as a function of energy $\omega$
		 for points on $J=-0 1U$ curve, indicated with corresponding symbols 
		}
\label{fig:3}
\end{figure}

Let us now discuss the effects introduced by the conventional RKKY interaction 
We choose $t=0$ for the sake of simplicity and
analyze a wide range of $\GS{}$, starting from the case of anti-ferromagnetic 
RKKY interaction ($J>0$)  Large $J>0$ leads to the formation of a molecular singlet in the 
nanostructure  This suppresses the conductance, unless $\GS{}$ becomes of the order of $U/2$, 
when the excited states of DQD are all close to the ground state  This is illustrated 
by double-dotted line in \fig{3} 
Smaller value of $J>0$ causes less dramatic consequences, namely it just increases $J^{\mathrm{eff}}$ according
to \eq{Jeff}, leading to enhancement of $T^*$, cf  \eq{Tstar}  This is presented with
dot-dashed line in \fig{3} 

The situation changes qualitatively for ferromagnetic RKKY coupling, $J<0$ 
Then, RKKY exchange and CAR exchange have opposite signs and compete with each other 
Depending on their magnitudes and temperature, one
of the following scenarios may happen 

For $J^{\mathrm{eff}} > 0$, {\it i e } large enough $\GS{}$, and $T<T^*$, the system is in the 
singlet state due to the two-stage Kondo screening of DQD spins  $G(T\!=\!0)$ is reduced 
to $G_{\mathrm{min}}$, which tends to increase for large negative $J$; see dashed lines in \fig{3}  
In the inset to \fig{3}, the spectral density of QD1 representative for this regime is plotted 
as curve indicated by triangle  It corresponds to a point on the $J=-0 1U$ curve in the main 
plot, also indicated by triangle  The dip in $\mathcal{A}(\omega)$ has width of order of $T^*$ 

For finite $T$, there is always a range of sufficiently small $|J^{\mathrm{eff}}|$, where QD2 becomes effectively
decoupled, and, provided $T<T_K$, $G$ reaches $G_{\mathrm{max}}$ due to conventional Kondo effect 
at QD1  This is the case for sufficiently small $\GS{}$ for $J=0$ or $J=-0 01U$, and in the narrow
range of $\GS{}$ around the point indicated by a circle in \fig{3} for $J=-0 1U$ (for $J=0 01U$, 
the considered $T$ is close to $T^*$ and $G$ does not reach $G_{\rm max}$)  The conventional Kondo effect manifests itself with 
a characteristic peak in $\mathcal{A}(\omega)$, as illustrated in the inset in \fig{3} with line denoted by circle 

Finally, large enough $J^{\mathrm{eff}} < 0$ and low $T$, give rise to an effective ferromagnetic coupling of DQDs 
spins into triplet state  Consequently, the underscreened Kondo effect occurs 
\cite{Mattis,NozieresBlandin} for weak $\GS{}$ and, {\it e g }, $J=-0 1U$; 
see the point indicated by square in \fig{3} 
This leads to $G=G_{\mathrm{max}}$ and a peak in $\mathcal{A}(\omega)$, whose shape is significantly different from the
Kondo peak, cf  the curve denoted by square in the inset in \fig{3}  



\section{Effects of detuning from the particle-hole symmetry point}
\label{sec:asym}

\begin{figure}
\includegraphics[width=0 98\linewidth]{Fig4 pdf}
\caption{
         (a) Linear conductance between the normal leads $G$ as a function of temperature $T$
         for parameters corresponding to \fig{G-T}(a) with $\xi=U/10$, and additional curves for finite 
         detuning from particle-hole symmetry point, $\delta_1=-\delta_2$, 
         and two values of $\xi=\sqrt{t^2+\GS{}^2}$, as indicated in the figure 
         (b) $G_{\mathrm{min}} \equiv G(T \!=\! 0)$ as a function of QD1 detuning $\delta_1$ for different
         exchange mechanisms, $\xi=U/10$ and $\delta_2=\pm\delta_1$ (as indicated) 
		}
\label{fig:asym}
\end{figure}

At PHS $G_{\mathrm{min}}=G(T \!=\! 0)=0$ in the absence of superconducting lead, making $G_{\mathrm{min}} > 0$ a hallmark
of SC-induced two-stage Kondo effect  However, outside of PHS point $G_{\mathrm{min}} > 0$ even in the case of 
the two-stage Kondo effect caused by the direct exchange  
Exact PHS conditions are hardly possible in real systems, and the fine-tuning of the QD energy
levels to PHS point is limited to some finite accuracy 
Therefore, there may appear a question, if the results obtained at PHS are of any importance for the
realistic setups  As we show below --- they are,
in a reasonable range of detunings $\delta_i=\varepsilon_i +U/2$ 

In \fig{asym}(a) we present the $G(T)$ dependence in and outside the PHS, corresponding to 
parameters of \fig{G-T}(a)  
Clearly, for considered small values of $\delta_1=\delta_2=\delta$, 
$G_{\mathrm{min}}<10^{-3}e^2/h$ for direct exchange only, while $G_{\mathrm{min}}$ in the presence of a superconductor is 
significantly increased and close to the PHS value  Furthermore, for $|\delta_1| \sim |\delta_2| 
\sim \delta$, the residual conductance caused by the lack of PHS, $G_{\mathrm{min}} \approx e^2/h \cdot (\delta/U)^2$,
which is a rapidly decreasing function in the vicinity of PHS point, as illustrated in \fig{asym}(b)
with lines denoted by a square  Evidently, in the regime $|\delta_i| < 0 01U$ the residual conductance
caused by SC is orders of magnitude larger, leading to the plateau in $G_{\mathrm{min}}(\delta_1)$ dependence,
visible in \fig{asym}(b) 
Taking into account that the realistic values of $U$ in the semiconductor quantum dots are rather 
large, this condition seems to be realizable by fine-tuning of QD gate voltages 

Lastly, let us point out that while in the presence of only one exchange mechanism, \emph{CAR} or
\emph{direct}, $G_{\mathrm{min}}(\delta_1)$ dependencies depicted in \fig{asym}(b) are symmetrical with respect
to sign change of $\delta_1$, for \emph{both} exchange mechanisms the dependence is non-symmetric  

\section{Effects of asymmetry of couplings to superconductor}
\label{sec:x}

\begin{figure}
\includegraphics[width=0 98\linewidth]{Fig5 pdf}
\caption{
		 (a) Linear conductance between the normal leads, $G$, as a function of temperature, $T$,
		 for parameters corresponding to \fig{G-T}(a) with $\xi=U/10$, for different values 
		 of asymmetry coefficient $x$ [see \eq{xGS}], in the presence of \emph{CAR} exchange only 
		 %
		 (b) The second-stage Kondo temperature $T^*$ normalized by $T_K$ as a function of $x$, 
		 calculated with the aid of NRG (points) and a fit to \eq{Tstar} (lines) 
		 with $J^{\mathrm{eff}}$ from \eq{Jeff} 
		 %
		 (c) The zero-temperature conductance $G_{\mathrm{min}}$ as a function of QD1 coupling to SC lead, $\GS{1}$,
		 compiled from data obtained at different circumstances (as indicated in the legend)
		 for different $x$  Dotted line corresponds to \eq{Gmin2} with $c=2 25$ 
		}
\label{fig:x}
\end{figure}

Similarly to PHS, the ideal symmetry in the coupling between respective QDs and SC lead is hardly possible
in experimental reality  As shown below, it does not introduce any qualitatively new features 
On the other hand, it decreases the second stage Kondo temperature, which is already small, therefore,
quantitative estimation of this decrease may be important for potential experimental approaches 
To analyze the effects of $\GS{1}\neq\GS{2}$, we introduce the asymmetry parameter $x$ and extend
the definition of $\GS{}$,
\beq
x = \frac{\GS{1}-\GS{2}}{\GS{1}+\GS{2}}, \quad \GS{} = \frac{\GS{1}+\GS{2}}{2} 
\label{xGS}
 \end{equation} 
Note, that even for a fixed $\GS{}$, the actual CAR coupling $\GS{\rm X}=\GS{}\sqrt{1-x^2}$ decreases
with increasing $|x|$, which is a main mechanism leading to a decrease of $T^*$ outside the $x=0$ point
visible in \figs{x}(a) and (b)  To illustrate this, the curves corresponding to \emph{both} exchange
mechanisms were calculated using $x$-dependent $t=\GS{\rm X}$ instead of $t=\xi/\sqrt{2}$  
Therefore, $\xi$ was generalized for $x\neq 0$ by setting $\xi=\sqrt{t^2(1-x^2)^{-1}+\GS{}^2}$ 
Clearly, in \fig{x}(b) the curves for different exchange mechanisms are very similar and differ mainly 
by a constant factor, resulting from different influence of $U'$; see \Sec{scales}  
The magnitude of $T^*$ changes is quite large, exceeding an order of magnitude for $x=\pm 0 5$ 
and $\xi=U/20$  Moreover, $T^* \to 0$ for $x\to\pm 1$  Consequently, for strongly asymmetric
devices one cannot hope to observe the second stage of Kondo screening 

A careful observer can note that the $T^*(x)$ dependency is not symmetrical; note for example different 
$T^*$ for $x=\pm 0 5$ in \fig{x}(a)  This is caused by the dependence of the first stage Kondo temperature
$T_K$ on $\GS{1}$ \cite{part1,DomanskiIW},
\beq
\widetilde{T}_K(\GS{1}) = T_K \cdot \exp\!\left( \frac{\pi}{2} \frac{\GS{1}^2}{\Gamma U}\right) 
 \end{equation} 
Here, $T_K$ is, as earlier, defined in the absence of SC, while $\widetilde{T}_K$ is a function 
of $\GS{1}$, such that $G(\widetilde{T}_K) = G_{\rm max}(\GS{1})/2$ in the absence of QD2  
As $\widetilde{T}_K$ grows for increasing $\GS{1}$ (or $x$), $T^*$ decreases according to \eq{Tstar}  
Its $\GS{}$ dependence can be accounted for by small changes in the coefficients $a$ and $b$ in \eq{Tstar}, 
as long as $x$ is kept constant  

To close the discussion of $T^*(x)$ dependence let us point out, that in \eq{A_J} 
there appears a correction to \eq{Jeff} for $x\neq 0$  However, it is very small due to additional
factor $\GS{}^2/U^2$ in the leading order  Its influence on curves plotted in \fig{x}(b) is hardly visible 

In turn, let us examine the $x$ dependence of the $T=0$ conductance $G_{\mathrm{min}}$  As can be seen 
in \fig{x}(a), it monotonically increases with $x$, as it crosses $x=0$ point  In fact, \eq{Gmin}
can be generalized to
\beq
G_{\mathrm{min}} = \frac{e^2}{h} \cdot c \, \frac{\GS{1}^2}{U^2} ,
\label{Gmin2}
 \end{equation} 
with $c\approx 2 25$ (indicated by a dotted line in \fig{x}(c))  Note that $G_{\mathrm{min}}$ is proportional to 
$\GS{1}^2=(x+1)^2 \GS{}^2$, instead of simply $\GS{}$, cf  \eq{Gmin}  The values of $G_{\mathrm{min}}$ obtained
from all analyzed $G(T)$ dependencies for different $x$ have been compiled in \fig{x}(c) 
It is evident, that \eq{Gmin2} is approximately fulfilled for all the considered cases 

Finally, it seems noteworthy that the normal-lead coupling asymmetry, 
$\Gamma_{\rm L}\neq \Gamma_{\rm R}$, is irrelevant for the results except for a constant factor
diminishing the conductance $G$ \cite{KWIWJB-asym} 



\section{The role of CAR efficiency}
\label{sec:coef}

\begin{figure}[tb]
\includegraphics[width=0 98\linewidth]{Fig6 pdf}
\caption{Linear conductance between the normal leads
		 $G$ as a function of coupling to SC lead, $\GS{}$, for indicated values of RKKY exchange $J$
		 and the efficiency of CAR processes reduced by factor (a) $\mathcal{C}=0 9$ and (b) $\mathcal{C}=0 5$ 
		 Other parameters as in \fig{3} 
		 Insets: QD1 local spectral density $\mathcal{A}(\omega)$ as a function of energy $\omega$
		 for points on $J=-0 1U$ curve, indicated with corresponding symbols 
		} 
\label{fig:C}
\end{figure}

Up to this point we assumed $\GS{\rm X} = \sqrt{\GS{1}\GS{2}}$, which is valid when the two 
quantum dots are much closer to each other than the coherence length in the superconductor 
This does not have to be the case in real setups, yet relaxing this assumption does not 
introduce qualitative changes  Nevertheless, the model cannot be extended to inter-dot 
distances much larger than the coherence length, where $\GS{\rm X}\to 0$ 

To quantitatively analyze the consequences of less effective Andreev coupling we define the 
CAR efficiency as $\mathcal{C} \equiv \GS{\rm X} / \sqrt{\GS{1}\GS{2}}$ and analyze $\mathcal{C} < 1$
in the wide range of $\GS{1}=\GS{2}=\GS{}$ and other parameters corresponding to \fig{3}  
The results are presented in \fig{C} 

Clearly, decreasing $\mathcal{C}$ from $\mathcal{C}=1$ causes diminishing of $\GS{\rm X}$, and consequently of CAR 
exchange  For a change as small as $\mathcal{C}=0 9$, the consequences reduce to some shift of the 
conventional Kondo regime, compare \fig{C}(a) with \fig{3}  Stronger suppression of CAR may, 
however, increase the SC coupling necessary to observe the second stage of Kondo screening caused
by CAR outside the experimentally achievable range, see \fig{C}(b)  Moreover, the reduced $T^*$
leads to narrowing of the related local spectral density dip, while the
increased critical $\GS{}$ necessary for the observation of the second stage of screening leads to the
shallowing of the dip  This is visible especially in the inset in \fig{C}(b) 


\section{Conclusions}
\label{sec:conclusions}

The CAR exchange mechanism is present in any system comprising at least
two QDs or magnetic impurities coupled to the same superconducting contact
in a way allowing for crossed Andreev reflections 
In the considered setup, comprised of two quantum dots in a T-shaped geometry 
with respect to normal leads and proximized by superconductor,
it leads to the two-stage Kondo
screening even in the absence of other exchange mechanisms 
This CAR induced exchange screening is characterized by a residual 
low-temperature conductance at particle-hole symmetric case 
We have also shown that the competition between CAR exchange and RKKY
interaction may result in completely different Kondo screening scenarios 

The presented results bring further insight into the low-temperature
behavior of hybrid coupled quantum dot systems, which hopefully could be verified
with the present-day experimental techniques 
Moreover, non-local pairing is present also in bulk systems such as non-$s$-wave superconductors 
The question if an analogue of discussed CAR exchange may play a role there
seems intriguing in the context of tendencies of many strongly correlated materials
to possess superconducting and anti-ferromagnetic phases 


\begin{acknowledgments}
This work was supported by the National Science Centre in Poland through project no 
2015/19/N/ST3/01030 
We thank J  Barna\'{s} and T  Maier for valuable discussions 
\end{acknowledgments}








ground_truth: It tends to suppress the Kondo effect.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How do the runtimes and iteration counts of NFPA and FPSA compare to GMRES and DSA in the numerical experiments?

Read the following text and answer briefly.

Context 2: \section{Introduction}\label{sec1}
\setcounter{equation}{0} 

Transport problems with highly forward-peaked scattering are prevalent in a variety of areas, including astrophysics, medical physics, and plasma physics \cite{HGK,aristova,multiphysics} 
For these problems, solutions of the transport equation converge slowly when using conventional methods such as source iteration (SI) \cite{adamslarsen} and the generalized minimal residual method (GMRES) \cite{gmres} 
Moreover, diffusion-based acceleration techniques like diffusion synthetic acceleration (DSA) \cite{alcouffe} and nonlinear diffusion acceleration (NDA) \cite{smithetall} are generally inefficient when tackling these problems, as they only accelerate up to the first moment of the angular flux \cite{JapanFPSA} 
In fact, higher-order moments carry important information in problems with highly forward-peaked scattering and can be used to further accelerate convergence \cite{japanDiss} 

This paper focuses on solution methods for the monoenergetic, steady-state transport equation in homogeneous slab geometry 
Under these conditions, the transport equation is given by
\begin{subequations}\label[pluraleq]{eq1}
\begin{equation}
\label{t1}
\mu\frac{\partial}{\partial x} \psi(x,\mu) + \sigma_t \psi(x,\mu) = \int_{-1}^{1} d\mu' \sigma_s(\mu,\mu') \psi(x,\mu') + Q(x, \mu), \,\,\, x\in [0, X],-1\leq\mu\leq 1  ,\\
\end{equation}
with boundary conditions
\begin{align}
\label{t2}
\psi(0,\mu) &= \psi_L(\mu), \quad \mu > 0,\\
\label{t3}
\psi(X,\mu) &= \psi_R(\mu), \quad \mu < 0 
\end{align}
\end{subequations}
Here, $\psi(x,\mu)$ represents the angular flux at position $x$ and direction $\mu$, $\sigma_t$ is the macroscopic total cross section, $\sigma_s(\mu,\mu')$ is the differential scattering cross section, and $Q$ is an internal source 

New innovations have paved the way to better solve this equation in systems with highly forward-peaked scattering 
For instance, work has been done on modified $P_L$ equations and modified scattering cross section moments to accelerate convergence of anisotropic neutron transport problems \cite{khattab} 
In order to speed up the convergence of radiative transfer in clouds, a quasi-diffusion method has been developed \cite{aristova} 
In addition, the DSA-multigrid method was developed to solve problems in electron transport more efficiently \cite{trucksin} 

One of the most recent convergence methods developed is Fokker-Planck Synthetic Acceleration (FPSA) \cite{JapanFPSA,japanDiss} 
FPSA accelerates up to $N$ moments of the angular flux and has shown significant improvement in the convergence rate for the types of problems described above 
The method returns a speed-up of several orders of magnitude with respect to wall-clock time when compared to DSA  \cite{JapanFPSA} 

In this paper, we introduce a new acceleration technique, called \textit{Nonlinear Fokker-Planck Acceleration} (NFPA) 
This  method  returns  a  modified  Fokker-Planck (FP) equation  that  preserves  the  angular moments of the flux given by the transport  equation 
This preservation of moments is particularly appealing for applications to multiphysics problems \cite{multiphysics}, in which the coupling between the transport physics and the other physics can be done through the (lower-order) FP equation 
To our knowledge, this is the first implementation of a numerical method that returns a Fokker-Planck-like equation that is discretely consistent with the linear Boltzmann equation 

This paper is organized as follows 
\Cref{sec2} starts with a brief description of FPSA 
Then, we derive the NFPA scheme 
In \cref{sec3}, we discuss the discretization schemes used in this work and present numerical results 
These are compared against standard acceleration techniques 
We conclude with a discussion in \cref{sec4} 

\section{Fokker-Planck Acceleration}\label{sec2}
\setcounter{equation}{0} 
In this section we briefly outline the theory behind FPSA, describe NFPA for monoenergetic, steady-state transport problems in slab geometry, and present the numerical methodology behind NFPA 
The theory given here can be easily extended to higher-dimensional problems 
Moreover, extending the method to energy-dependence shall not lead to significant additional theoretical difficulties 

To solve the transport problem given by \cref{eq1} we approximate the in-scattering term in \cref{t1} with a Legendre moment expansion:
\begin{equation}
\label{transport1}
\mu\frac{\partial}{\partial x} \psi(x,\mu) + \sigma_t \psi(x,\mu) = \sum_{l=0}^L \frac{(2l+1)}{2} P_l(\mu) \sigma_{s,l} \phi_l(x) + Q(x, \mu),
\end{equation}
with 
\begin{equation}
\label{transport2}
\phi_l(x) =  \int_{-1}^{1} d\mu P_l(\mu) \psi(x,\mu) 
\end{equation}
Here, $\phi_l$ is the $l^{th}$ Legendre moment of the angular flux, $ \sigma_{s,l}$ is the $l^{th}$ Legendre coefficient of the differential scattering cross section,  and $P_l$ is the $l^{th}$-order Legendre polynomial 
For simplicity, we will drop the notation $(x,\mu)$ in the remainder of this section 

The solution to \cref{transport1} converges asymptotically to the solution of the following Fokker-Planck equation in the forward-peaked limit \cite{pomraning1}:
\begin{equation}
\label{fp1}
\mu\frac{\partial \psi}{\partial x} + \sigma_a \psi = \frac{\sigma_{tr}}{2}\frac{\partial }{\partial \mu} (1-\mu^2) \frac{\partial \psi}{\partial \mu} + Q\,,
\end{equation}
where $\sigma_{tr}= \sigma_{s,0} -\sigma_{s,1}$ is the momentum transfer cross section and $\sigma_a = \sigma_t-\sigma_{s,0}$ is the macroscopic absorption cross section 

Source Iteration \cite{adamslarsen} is generally used to solve \cref{transport1}, which can be rewritten in operator notation:
\begin{equation}
\label{si1}
\mathcal{L} \psi^{m+1} = \mathcal{S} \psi^{m} + Q\,,
\end{equation}
where 
\begin{equation}
\mathcal{L} = \mu \frac{\partial}{\partial x} + \sigma_t,
   \quad
\mathcal{S} = \sum_{l=0}^L \frac{(2l+1)}{2} P_l(\mu) \sigma_{s,l} \int_{-1}^{1}d\mu P_l(\mu) ,
\label{trans1}
\end{equation}
and $m$ is the iteration index 
This equation is solved iteratively until a tolerance criterion is met  The FP approximation shown in \cref{fp1} can be used to accelerate the convergence of \cref{transport1} 

\subsection{FPSA: Fokker-Planck Synthetic Acceleration}\label{FPSA}

In the FPSA scheme \cite{JapanFPSA,japanDiss}, the FP approximation is used as a preconditioner to synthetically accelerate convergence when solving \cref{transport1} (cf  \cite{adamslarsen} for a detailed description of synthetic acceleration) 
When solving \cref{si1}, the angular flux at each iteration $m$ has an error associated with it 
FPSA systematically follows a predict, correct, iterate scheme 
A transport sweep, one iteration in \cref{si1}, is made for a prediction 
The FP approximation is used to correct the error in the prediction, and this iteration is performed until a convergence criterion is met 
The equations used are:
\begin{subequations}
\label{fpsaeq}
\begin{align}
\label{predict}
\mathrm{Predict}&: \mathcal{L} \psi^{m+\frac{1}{2}} = \mathcal{S} \psi^{m} + Q\,,\\
\label{correct}
\mathrm{Correct}&: \psi^{m+1} =  \psi^{m+\frac{1}{2}} + \mathcal{P}^{-1} \mathcal{S} \left( \psi^{m+\frac{1}{2}} -  \psi^{m}\right),
\end{align}
\end{subequations}
where we define $\mathcal{P}$ as
\begin{equation}
\label{FPSAsi1}
\mathcal{P} = \mathcal{A}-\mathcal{F} =\underbrace{\left(\mu\frac{\partial}{\partial x} + \sigma_a\right)}_\mathcal{A} - \underbrace{\left(\frac{\sigma_{tr}}{2}\frac{\partial }{\partial \mu} (1-\mu^2) \frac{\partial }{\partial \mu}\right)}_\mathcal{F},
\end{equation}
In this synthetic acceleration method, the FP approximation is used to correct the error in each iteration of the high-order (HO) equation (\ref{predict})  
Therefore, there is no consistency between the angular moments of the flux in the HO and low-order (LO) equations 

\subsection{NFPA: Nonlinear Fokker-Planck Acceleration}\label{NFPA}

Similar to FPSA, NFPA uses the FP approximation to accelerate the convergence of the solution 
We introduce the additive term $\hat{D}_F$ to \cref{fp1}, obtaining the modified FP equation
\begin{equation}
\label{mfp1}
\mu\frac{\partial \psi}{\partial x} + \sigma_a \psi = \frac{\sigma_{tr}}{2}\frac{\partial }{\partial \mu} (1-\mu^2) \frac{\partial \psi}{\partial \mu} + \hat{D}_F + Q\, 
\end{equation}
The role of $\hat{D}_F$ is to force the transport and modified FP equations to be consistent 
Subtracting \cref{mfp1} from \cref{transport1} and rearranging, we obtain the consistency term
\begin{equation}
\label{dfp}
\hat{D}_F = \sum_{l=0}^L \frac{(2l+1)}{2} P_l \sigma_l \phi_l - \frac{\sigma_{tr}}{2}\frac{\partial}{\partial \mu} (1-\mu^2) \frac{\partial \psi}{\partial \mu} - \sigma_{s,0} \psi\, 
\end{equation}

The NFPA method is given by the following equations:
\begin{subequations}\label[pluraleq]{holocons}
\begin{align}
\label{HO1}
\text{HO}&: \mu\frac{\partial \psi_{HO}}{\partial x} + \sigma_t \psi_{HO} = \sum_{l=0}^L \frac{(2l+1)}{2} P_l \sigma_l \phi_{l, LO} + Q\,,\\
\label{LO11}
\text{LO}&: \mu\frac{\partial \psi_{LO}}{\partial x} + \sigma_a \psi_{LO} = \frac{\sigma_{tr}}{2}\frac{\partial }{\partial \mu} (1-\mu^2) \frac{\partial \psi_{LO}}{\partial \mu} + \hat{D}_F + Q\,,\\
\label{con1}
\text{Consistency term}&: \hat{D}_F = \sum_{l=0}^L \frac{(2l+1)}{2} P_l \sigma_l \phi_{l, HO}^m - \frac{\sigma_{tr}}{2}\frac{\partial }{\partial \mu} (1-\mu^2) \frac{\partial \psi_{HO}}{\partial \mu} - \sigma_{s,0} \psi_{HO}\,,
\end{align}
\end{subequations}
where $\psi_{HO}$ is the angular flux obtained from the HO equation and $\psi_{LO}$ is the angular flux obtained from the LO equation 
The nonlinear HOLO-plus-consistency system given by \cref{holocons} can be solved using any nonlinear solution technique \cite{kelley}  Note that the NFPA scheme returns a FP equation that is consistent with HO transport  
Moreover, this modified FP equation accounts for large-angle scattering which the standard FP equation does not  
The LO equation (\ref{fp1}) can then be integrated into multiphysics models in a similar fashion to standard HOLO schemes \cite{patelFBR}  To solve the HOLO-plus-consistency system above, we use Picard iteration \cite{kelley}:
\begin{subequations}
\begin{align}
\label{H1}
\text{Transport Sweep for HO}&:
\mathcal{L} \psi_{HO}^{k+1} = \mathcal{S} \psi_{LO}^{k} + Q, \\
\label{L1}
\text{Evaluate Consistency Term}&: \hat{D}_F^{k+1} = \left(\mathcal{S} - \mathcal{F} - \sigma_{s,0}\mathcal{I}\right) \psi_{HO}^{k+1}, \\
\label{c1}
\text{Solve LO Equation}&: \psi_{LO}^{k+1} = \mathcal{P}^{-1} \left(\hat{D}_F^{k+1} + Q\right), 
\end{align}
\end{subequations}
where $\mathcal{L}$ and $\mathcal{S}$ are given in \cref{trans1}, $\mathcal{P}$ and $\mathcal{F}$ are given in \cref{FPSAsi1}, $\mathcal{I}$ is the identity operator, and $k$ is the iteration index 
Iteration is done until a convergence criterion is met 

The main advantage of setting up the LO equation in this fashion is that the stiffness matrix for LO needs to be setup and inverted \textit{only once}, just as with FPSA \cite{JapanFPSA, japanDiss}  This has a large impact on the method's performance 
A flowchart of this algorithm is shown in \cref{Nalgorithm} 

\begin{figure}[H]
\centering
\begin{tikzpicture}[node distance = 3cm, auto]
   
    \node [block] (init) {Initial guess of flux moments};
    \node [cloud_HO, right of=init, node distance=4cm] (HOm) {HO};
    \node [cloud_LO, below of=HOm, node distance=2cm] (LOm) {LO};
    \node [HO, below of=init] (transport) {One sweep in transport equation};
    \node [decision, below of=transport,node distance=4cm] (decide) {Flux moments converged?};
    \node [LO, left of=decide, node distance=4cm] (dterm) {Solve for consistency term};
    \node [LO, left of=dterm, node distance=3cm] (MFP) {Solve for FP angular flux};
    \node [LO, above of=MFP, node distance=4cm] (moments) {Convert angular flux to moments};
    \node [block, right of=decide, node distance=4cm] (stop) {Stop};
   
    \path [line] (init) -- (transport);
    \path [line] (transport) -- (decide);
    \path [line] (decide) -- node {no} (dterm);
    \path [line] (dterm) -- (MFP);
    \path [line] (MFP) -- (moments);
    \path [line] (moments) -- (transport);
    \path [line] (decide) -- node {yes}(stop);
\end{tikzpicture}
\caption{NFPA algorithm}
\label{Nalgorithm}
\end{figure}

\section{Numerical Experiments}\label{sec3}

In \cref{sec31} we describe the discretization methods used to implement the algorithms 
In \cref{sec32} we provide numerical results for 2 different choices of source $Q$ and boundary conditions 
For each choice we solve the problem using 3 different scattering kernels, applying 3 different choices of parameters for each kernel 
We provide NFPA numerical results for these 18 cases and compare them against those obtained from FPSA and other standard methods 

All numerical experiments were performed using MATLAB 
Runtime was tracked using the tic-toc functionality \cite{matlab17}, with
only the solver runtime being taken into consideration in the comparisons 
A 2017 MacBook Pro with a 2 8 GHz Quad-Core Intel Core i7 and 16 GB of RAM was used for all simulations 


\subsection{Discretization}\label{sec31}

The Transport and FP equations were discretized using linear discontinuous finite element discretization in space \cite{mpd1}, and discrete ordinates (S$_N$) in angle \cite{landm} 
The Fokker-Planck operator $\mathcal{F}$ was discretized using moment preserving discretization (MPD) \cite{mpd1} 
Details of the derivation of the linear discontinuous finite element discretization can be seen in \cite{japanDiss,martin} 
The finite element discretization for the  Fokker-Planck equation follows the same derivation 

A brief review for the angular discretization used for the FP equation is given below 
First, we use Gauss-Legendre quadrature to discretize the FP equation in angle:
\begin{equation}
\mu_n\frac{\partial \psi_n(x)}{\partial x} + \sigma_a \psi_n(x) - \frac{\sigma_{tr}}{2}\nabla^2_n \psi_n(x) =  Q_n(x),
\end{equation}
for $n=1,  ,N$ 
Here, $\nabla^2_n$ term is the discrete form of the angular Laplacian operator evaluated at angle $n$ 

The MPD scheme is then shown as
\begin{equation}
\nabla^2_n \psi_n = M \psi_n = V^{-1} L V \psi_n,
\end{equation}
where $M$ is the MPD discretized operator defined by
\begin{subequations}
\begin{equation}
V_{i,j} = P_{i-1}(\mu_j)w_j,
\end{equation}
and 
\begin{equation}
L_{i,j} = -i(i-1),
\end{equation}
\end{subequations}
for $i,j=1,   ,N$ 
Here, $P_l(\mu_j)$ are the Legendre polynomials evaluated at each angle $\mu_j$ and $w_j$ are the respective weights 
$M$ is defined as a (N x N) operator for a vector of $N$ angular fluxes $ \psi(x)$, at spatial location $x$  

In summary, if we write the FP equation as
\begin{equation}
\mathcal{H} \frac{\partial \psi}{\partial x}(x) + \sigma_a \psi(x) - \mathcal{F} \psi(x) =  Q(x),
\end{equation}
then $\mathcal{H}$ is Diag$(\mu_n)$ for $n=1,   ,N$, $Q(x)$ is a vector of source terms $Q_n(x)$, and $\mathcal{F}$ is represented by $\frac{\sigma_{tr}}{2}M$ 


\subsection{Numerical Results}\label{sec32}

It is shown that for slowly converging problems, typical convergence methods like $L_\infty$ suffer from false convergence \cite{adamslarsen} 
To work around this issue, the criterion is modified to use information about the current and previous iteration:
\begin{equation}
\label{falseconverge}
\frac{|| \phi^{m}_0(x) - \phi^{m-1}_0(x) ||_2}{1-\frac{|| \phi^{m+1}_0(x) - \phi^{m}_0(x) ||_2}{|| \phi^{m}_0(x) - \phi^{m-1}_0(x) ||_2}} < 10^{-8} 
\end{equation}

Two problems were tested using 200 spatial cells, $X$ = 400, $\sigma_a = 0$, $L$ = 15, and $N$ = 16 
Problem 1 has vacuum boundaries and a homogeneous isotropic source $Q$ for $0 < x < X$ 
Problem 2 has no internal source and an incoming beam at the left boundary  The source and boundary conditions used are shown in \cref{parameters}  
\begin{table}[H]
\begin{center}
\scalebox{0 9}{
\begin{tabular}{c | c | c} \hline 
& Problem 1 & Problem 2 \\ \hline \hline
Q(x) & 0 5 & 0 \\
$\psi_L$ & 0 & $\delta(\mu - \mu_N)$  \\
$\psi_R$ & 0 & 0 \\
\end{tabular}}
\end{center}
\caption{Problem Parameters}
\label{parameters} 
\end{table} 
We consider three scattering kernels in this paper: Screened Rutherford \cite{pomraning1}, Exponential \cite{pomraning2}, and Henyey-Greenstein \cite{HGK} 
Three cases for each kernel were tested 
The results obtained with NFPA are compared with those obtained using GMRES, DSA, and FPSA with the MPD scheme 

\subsubsection{SRK: Screened Rutherford Kernel}

The Screened Rutherford Kernel \cite{pomraning1, JapanFPSA} is a widely used scattering kernel for modeling scattering behavior of electrons \cite{SRK} 
The kernel depends on the parameter $\eta$, such that
\begin{equation}
\sigma^{SRK}_{s,l} = \sigma_s  \int_{-1}^{1} d\mu P_l(\mu) \frac{\eta (\eta+1)}{(1+2\eta-\mu)^2} 
\end{equation}
The SRK has a valid FP limit as $\eta$ approaches 0 \cite{patelFBR}  Three different values of $\eta$ were used to generate the scattering kernels shown in \cref{SRK} 
GMRES, DSA, FPSA, and NFPA all converged to the same solution for problems 1 and 2  \Cref{SRK_plots} shows the solutions for SRK with $\eta = 10^{-7}$ 
\begin{figure}[t]
\begin{center}
  \includegraphics[scale=0 1,angle=0]{SRK jpg}
  \caption{Screened Rutherford Kernels}
  \label{SRK}
\end{center}
\end{figure}
\begin{figure}[H]
    \centering
    \subfloat[Problem 1]{{\includegraphics[width=7cm]{s7_iso jpg} }}
    \qquad
    \subfloat[Problem 2]{{\includegraphics[width=7cm]{s7_beam jpg} }}
    \caption{Results for SRK Problems with $\eta = 10^{-7}$}
    \label{SRK_plots}
\end{figure}

\begin{table}[H]
\begin{center}
\scalebox{0 8}{
\begin{tabular}{c || c || c || c} \hline 
Parameter & Solver & Runtime (s) & Iterations \\ \hline \hline
\multirow{4}{*}{$\eta = 10^{-5}$} & GMRES & 98 8 & 12 \\
& DSA & 2380 & 53585 \\
& FPSA & 1 21 & 26 \\
& NFPA & 1 39 & 26 \\ \hline 
\multirow{4}{*}{$\eta = 10^{-6}$} & GMRES & 208 & 84 \\
& DSA & 3040 & 69156 \\
& FPSA & 0 747 & 16 \\
& NFPA & 0 857 & 16 \\ \hline 
\multirow{4}{*}{$\eta = 10^{-7}$} & GMRES & 174 & 124 \\
& DSA & 3270 & 73940 \\
& FPSA & 0 475 & 10 \\
& NFPA & 0 542 & 10 \\ \hline
\end{tabular}}
\end{center}
\caption{Runtime and Iteration Counts for Problem 1 with SRK}
\label{SRKresults1} 
\end{table}
\begin{table}[H]
\begin{center}
\scalebox{0 8}{
\begin{tabular}{c || c || c || c} \hline 
Parameter & Solver & Runtime (s) & Iterations \\ \hline \hline
\multirow{4}{*}{$\eta = 10^{-5}$} & GMRES & 52 4 & 187 \\
& DSA & 1107 & 25072 \\
& FPSA & 0 953 & 20 \\
& NFPA & 1 14 & 20 \\ \hline 
\multirow{4}{*}{$\eta = 10^{-6}$} & GMRES & 108 & 71 \\
& DSA & 1434 & 32562  \\
& FPSA & 0 730 & 14 \\
& NFPA & 0 857 & 14 \\ \hline 
\multirow{4}{*}{$\eta = 10^{-7}$} & GMRES & 94 1 & 185 \\
& DSA & 1470 & 33246 \\
& FPSA & 0 438 & 8 \\
& NFPA & 0 484 & 8 \\ \hline  
\end{tabular}}
\end{center}
\caption{Runtime and Iteration Counts for Problem 2 with SRK}
\label{SRKresults2} 
\end{table}

The results of all solvers are shown in \cref{SRKresults1,SRKresults2} 
We see that NFPA and FPSA tremendously outperform GMRES and DSA in runtime for all cases 
FPSA is a simpler method than NFPA, requiring less calculations per iteration; therefore, it is expected that it outperforms NFPA in runtime 
We see a reduction in runtime and iterations for FPSA and NFPA as the FP limit is approached, with DSA and GMRES requiring many more iterations by comparison as $\eta$ approaches 0 

An advantage that NFPA offers is that the angular moments of the flux in the LO equation will remain consistent with those of the transport equation even as a problem becomes less forward-peaked 
On the other hand, the moments found using only the FP equation and source iteration lose accuracy 
To illustrate this, Problem 1 was tested using different Screened Rutherford Kernels with increasing $\eta$ parameters 
The percent errors (relative to the transport solution) for the scalar flux obtained with the LO equation and with the standard FP equation at the center of the slab are shown in \cref{momcomp} 
It can be seen that the percent relative errors in the scalar flux of the FP solution is orders of magnitude larger than the error produced using the LO equation 
The same trend can be seen when using the exponential and Henyey-Greenstein kernels  

\begin{figure}[H]
\begin{center}
  \includegraphics[scale=0 15,angle=0]{relerrorlog jpg}
  \caption{Log Scale of $\%$ Relative Error vs $\eta$ for Problem 1 at the Center of the Slab with SRK}
  \label{momcomp}
\end{center}
\end{figure}

\subsubsection{EK: Exponential Kernel}

The exponential kernel \cite{pomraning2, JapanFPSA} is a fictitious kernel made for problems that have a valid Fokker-Planck limit \cite{pomraning1} 
The zero$^{\text{th}}$ moment, $\sigma^{EK}_{s,0}$, is chosen arbitrarily; we define $\sigma^{EK}_{s,0}$ as the same zero$^{\text{th}}$ moment from the SRK 
The $\Delta$ parameter determines the kernel: the first and second moments are given by 
\begin{subequations}
\begin{align}
\sigma^{EK}_{s,1} &= \sigma^{EK}_{s,0} (1-\Delta),\\
\sigma^{EK}_{s,2} &= \sigma^{EK}_{s,0} (1-3\Delta+3\Delta^2),
\end{align}
and the relationship for $l\geq 3$ is
\begin{equation}
\sigma^{EK}_{s,l} = \sigma^{EK}_{s,l-2} - \Delta(2l+1) \sigma^{EK}_{s,l-1} 
\end{equation}
\end{subequations}
As $\Delta$ is reduced, the scattering kernel becomes more forward-peaked 

The EK has a valid FP limit as $\Delta$ approaches 0 \cite{patelFBR} 
Three different values of $\Delta$ were used to generate the scattering kernels shown in \cref{EXP} 
The generated scattering kernels are shown in \cref{EXP} 
GMRES, DSA, FPSA, and NFPA all converged to the same solution for problems 1 and 2 
\Cref{EK_plots} shows the solutions for EK with $\Delta = 10^{-7}$ 
\begin{figure}[t]
\begin{center}
  \includegraphics[scale=0 1,angle=0]{EXP jpg}
  \caption{Exponential Kernels}
  \label{EXP}
\end{center}
\end{figure}
\begin{figure}[H]
    \centering
    \subfloat[Problem 1]{{\includegraphics[width=7cm]{dta7_iso jpg} }}
    \qquad
    \subfloat[Problem 2]{{\includegraphics[width=7cm]{dta7_beam jpg} }}
    \caption{Results for EK Problems with $\Delta = 10^{-7}$}
    \label{EK_plots}
\end{figure}

The runtimes and iterations for GMRES, DSA, FPSA, and NFPA are shown in \cref{Expresults1,Expresults2} 
We see a similar trend with the EK as seen with SRK 
Smaller $\Delta$ values lead to a reduction in runtime and iterations for NFPA and FPSA, which greatly outperform DSA and GMRES in both categories 

\begin{table}[h]
\begin{center}
\scalebox{0 8}{
\begin{tabular}{c || c || c || c} \hline 
Parameter & Solver & Runtime (s) & Iterations \\ \hline \hline
\multirow{4}{*}{$\Delta = 10^{-5}$} & GMRES & 196 & 142 \\
& DSA & 3110 & 70140 \\
& FPSA & 0 514 & 11 \\ 
& NFPA & 0 630 & 11 \\\hline 
\multirow{4}{*}{$\Delta = 10^{-6}$} & GMRES & 156 & 132 \\
& DSA & 3120 & 70758 \\
& FPSA & 0 388 & 7 \\ 
& NFPA & 0 393 & 7 \\ \hline 
\multirow{4}{*}{$\Delta = 10^{-7}$} & GMRES & 81 & 127 \\
& DSA & 3120 & 70851  \\
& FPSA & 0 292 & 6 \\ 
& NFPA & 0 318 & 6 \\ \hline
\end{tabular}}
\end{center}
\caption{Runtime and Iteration Counts for Problem 1 with EK}
\label{Expresults1} 
\end{table}
\begin{table}[h]
\begin{center}
\scalebox{0 8}{
\begin{tabular}{c || c || c || c} \hline 
Parameter & Solver & Runtime (s) & Iterations \\ \hline \hline
\multirow{4}{*}{$\Delta = 10^{-5}$} & GMRES & 110 & 73 \\
& DSA & 1455 & 33033 \\
& FPSA & 0 492 & 10 \\ 
& NFPA & 0 613 & 10 \\ \hline 
\multirow{4}{*}{$\Delta = 10^{-6}$} & GMRES & 82 7 & 79 \\
& DSA & 1470 & 33309 \\
& FPSA & 0 358 & 7 \\ 
& NFPA & 0 431 & 7 \\ \hline 
\multirow{4}{*}{$\Delta = 10^{-7}$} & GMRES & 56 8 & 90 \\
& DSA & 1470 & 33339 \\
& FPSA & 0 273 & 5 \\ 
& NFPA & 0 319 & 5 \\ \hline  
\end{tabular}}
\end{center}
\caption{Runtime and Iteration Counts for Problem 2 with EK}
\label{Expresults2} 
\end{table}

\subsubsection{HGK: Henyey-Greenstein Kernel}

The Henyey-Greenstein Kernel \cite{HGK,JapanFPSA} is most commonly used in light transport in clouds 
It relies on the anisotropy factor $g$, such that
\begin{equation}
\sigma^{HGK}_{s,l} = \sigma_s g^l 
\end{equation}
As $g$ goes from zero to unity, the scattering shifts from isotropic to highly anisotropic 
\begin{figure}[H]
\begin{center}
  \includegraphics[scale=0 1,angle=0]{HGK jpg}
  \caption{Henyey-Greenstein Kernels}
  \label{HGK}
\end{center}
\end{figure}
\begin{figure}[H]
    \centering
    \subfloat[Problem 1]{{\includegraphics[width=7cm]{g099_iso jpg} }}
    \qquad
    \subfloat[Problem 2]{{\includegraphics[width=7cm]{g099_beam jpg} }}
    \caption{Results for HGK Problems with $g = 0 99$}
    \label{HGK_plots}
\end{figure}


The HGK does not have a valid FP limit \cite{patelFBR} 
The three kernels tested are shown in \cref{HGK} 
GMRES, DSA, FPSA, and NFPA all converged to the same solution for problems 1 and 2 
\Cref{HGK_plots} shows the solutions for HGK with $g = 0 99$ 
The results of each solver are shown in \cref{HGKresults1,HGKresults2}  
\begin{table}[h]
\begin{center}
\scalebox{0 8}{
\begin{tabular}{c || c || c || c} \hline 
Parameter & Solver & Runtime (s) & Iterations \\ \hline \hline
\multirow{4}{*}{$g=0 9$} & GMRES & 9 88 & 76 \\
& DSA & 24 5 & 554 \\
& FPSA & 1 50 & 32 \\ 
& NFPA & 1 39 & 27 \\ \hline 
\multirow{4}{*}{$g=0 95$} & GMRES & 12 2 & 131 \\
& DSA & 47 7 & 1083 \\
& FPSA & 1 75 & 38 \\ 
& NFPA & 1 83 & 35 \\ \hline 
\multirow{4}{*}{$g=0 99$} & GMRES & 40 0 & 27 \\
& DSA & 243 & 5530  \\
& FPSA & 3 38 & 74 \\ 
& NFPA & 3 93 & 73 \\ \hline
\end{tabular}}
\end{center}
\caption{Runtime and Iteration Counts for Problem 1 with HGK}
\label{HGKresults1} 
\end{table}
\begin{table}[h]
\begin{center}
\scalebox{0 8}{
\begin{tabular}{c || c || c || c} \hline 
Parameter & Solver & Runtime (s) & Iterations \\ \hline \hline
\multirow{4}{*}{$g=0 9$} & GMRES & 24 3 & 135 \\
& DSA & 14 8 & 336  \\
& FPSA & 1 15 & 23 \\ 
& NFPA & 1 35 & 24 \\ \hline 
\multirow{4}{*}{$g=0 95$} & GMRES & 31 3 & 107 \\
& DSA & 29 7 & 675 \\
& FPSA & 1 56 & 32 \\ 
& NFPA & 1 90 & 33 \\ \hline 
\multirow{4}{*}{$g=0 99$} & GMRES & 41 4 & 126 \\
& DSA & 146 & 3345 \\
& FPSA & 3 31 & 67 \\ 
& NFPA & 3 99 & 67 \\ \hline  
\end{tabular}}
\end{center}
\caption{Runtime and Iteration Counts for Problem 2 with HGK}
\label{HGKresults2} 
\end{table}

Here we see that NFPA and FPSA do not perform as well compared to their results for the SRK and EK 
Contrary to what happened in those cases, both solvers require more time and iterations as the problem becomes more anisotropic 
This is somewhat expected, due to HGK not having a valid Fokker-Planck limit 
However, both NFPA and FPSA continue to greatly outperform GMRES and DSA 
Moreover, NFPA outperforms FPSA in iteration count for problem 1 


\section{Discussion}\label{sec4}

This paper introduced the Nonlinear Fokker-Planck Acceleration technique for steady-state, monoenergetic transport in homogeneous slab geometry 
To our knowledge, this is the first nonlinear HOLO method that accelerates \textit{all $L$ moments} of the angular flux 
Upon convergence, the LO and HO models are consistent; in other words, the (lower-order) modified Fokker-Planck equation \textit{preserves the same angular moments} of the flux obtained with the (higher-order) transport equation 

NFPA was tested on a homogeneous medium with an isotropic internal source with vacuum boundaries, and in a homogeneous medium with no internal source and an incoming beam boundary 
For both problems, three different scattering kernels were used 
The runtime and iterations of NFPA and FPSA were shown to be similar 
They both vastly outperformed DSA and GMRES for all cases by orders of magnitude 
However, NFPA has the feature of preserving the angular moments of the flux in both the HO and LO equations, which offers the advantage of integrating the LO model into multiphysics models  

In the future, we intend to test NFPA capabilities for a variety of multiphysics problems and analyze its performance 
To apply NFPA to more realistic problems, it needs to be extended to include time and energy dependence  
Additionally, the method needs to be adapted to address geometries with higher-order spatial dimensions 
Finally, for the NFPA method to become mathematically ``complete", a full convergence examination using Fourier analysis must be performed 
However, this is beyond the scope of this paper and must be left for future work 

\section*{Acknowledgements}

The authors acknowledge support under award number NRC-HQ-84-15-G-0024 from the Nuclear Regulatory Commission 
The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the view of the U S  Nuclear Regulatory Commission 

J ~K  Patel would like to thank Dr ~James Warsa for his wonderful transport class at UNM, as well as his synthetic acceleration codes 
The authors would also like to thank Dr ~Anil Prinja for discussions involving Fokker-Planck acceleration 







ground_truth: NFPA and FPSA greatly outperform GMRES and DSA.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How does the specific-heat ratio affect the average motion of the bubble?

Read the following text and answer briefly.

Context 3: Paper Info

Title: Specific-heat ratio effects on the interaction between shock wave and heavy-cylindrical bubble: based on discrete Boltzmann method
Publish Date: May 29, 2023
Author List: Yanbiao Gan (from School of Liberal Arts and Sciences, Hebei Key Laboratory of Trans-Media Aerial Underwater Vehicle, North China Institute of Aerospace Engineering), Yudong Zhang (from School of Mechanics and Safety Engineering, Zhengzhou University)

Figure

Figure 1: Research orientation and tasks of DBM 
Figure 2: Sketch of D2V16 model The numbers in the figure represent the index i in Eq  (3) 
Figure 3: The computational configuration of the shock-bubble interaction 
In the figure, results from odd rows are experimental, and the even rows indicate DBM simulation results The typical wave patterns and bubble's main characteristic structures are marked out in the figures Numbers in the pictures represent the time in µs Schlieren images of DBM results are calculated from the density gradient formula, i e , |∇ρ|/|∇ρ| max , with |∇ρ| = (∂ ρ/∂ x) 2 + (∂ ρ/∂ y)2  At t = 0µs, the incident shock wave impacts the upstream interface, and subsequently generates a transmitted shock (TS) propagating downstream in the bubble and a reflected shock wave moving upward in ambient gas The incident shock wave travels downstream contin-
The definitions and the corresponding physical meanings of the common TNE quantities in DBM, where the operator ∑ ix,iy indicates integrating over all the fluid units and multiply the unit area dxdy From a certain perspective, the TNE strength is increasing; While from a different perspective, the TNE strength, on the other hand, may be decreasing It is one of the concrete manifestations of the complexity of non-equilibrium flow behavior 
Figure 4: Snapshots of schlieren images of the interaction between a shock wave and a heavy-cylindrical bubble The odd rows represent experimental results from Ref  [31] with permission, and the even rows are DBM simulation results The typical wave patterns and the bubble's main characteristic structure are marked out in the figures Numbers in the picture represent the time in µs 
Figure 5: The temporal variations of length and width of the bubble The symbols represent DBM results and the lines are experimental The definition of the length and the width of the bubble can be seen in the illustration Experimental results are obtained from Fig  12, in Ref  [31] with permission 
Figure 6: Density contours and particle tracer images at three different moments (i e , t = 0 07,t = 0 11, and t = 0 16) with various specific-heat ratios The odd rows represent density contours, and the even rows are tracer particle images 
Figure 9: Vorticity contours at t = 0 134, with various specific-heat ratios The arrows in the vorticity image point out the apparent difference between case γ = 1 4 and case γ = 1 09 
Figure 11: Density contours (first row) and mixing degree M (second row) at several typical moments 
Figure 13: (a) Temporal evolution of D * 3,1 and D * 4,2  (b) Temporal evolution of D * 2 and D * 3  Lines with different colors represent the cases with various specificheat ratios 
The development of schemes for checking TNE state, extracting TNE information and describing corresponding TNE effects in DBM 

abstract

Specific-heat ratio effects on the interaction between a planar shock wave and a two-dimensional heavy-cylindrical bubble are studied by the discrete Boltzmann method  Snapshots of schlieren images and evolutions of characteristic scales, being consistent with experiments, are obtained  The specific-heat ratio effects on some relevant dynamic behaviors such as the bubble shape, deformation process, average motion, vortex motion, mixing degree of the fluid system are carefully studied, as well as the related Thermodynamic Non-Equilibriums (TNE) behaviors including the TNE strength, entropy production rate of the system 
Specifically, it is found that the influence of specific-heat ratio on the entropy production contributed by non-organized energy flux (NOEF) is more significant than that caused by non-organized momentum flux (NOMF)  Effects of specific-heat ratio on entropy production caused by NOMF and NOEF are contrary 
The effects of specific-heat ratio on various TNE quantities show interesting differences  These differences consistently show the complexity of TNE flows which is still far from clear understanding 

Introduction

The applications of shock-accelerated inhomogeneous flows (SAIFs) are of significant value in biomedicine, energy utilization, and astrophysics fields, including but not limited to scenarios such as the impact of shock waves on kidney stones, the interaction between shock waves with foams, the impacting of detonation wave with burning flames in supersonic combustion systems, the formation of supernova remnants, etc  
Shock-bubble interaction (SBI) is one of the most fundamental problems in the research of SAIFs  Its applications and academic research are interdisciplinary  Generally, there are two kinds of problems encountered in SBI research: (i) The geometry of shock waves, the shape of material interfaces, and the structure of container are complex in the actual scene 
They will result in various wave patterns and significantly affect the flow morphology and bubble's evolution  (ii) There usually exist multi-physics coupling problem in the engineering application of SBI  Such as the supersonic combustion machines  When the shock waves passing through the reactants, it may lead to phase transition and chemical reactions, making the flow morphology more complex and inducing small structure (or fast-changing pattern)  
In an underwater explosion experiment, the interaction between shock waves and bubbles may refer to the cavitation and annihilation effects  The other scene is the inertial confinement fusion (ICF), in which the laser ablation, electron heat conduction, self-generated electromagnetic field, radiation, and many other factors may complicate the investigation of hydrodynamic instabilities  
Commonly, research on SBI mainly includes three methods: theoretical derivation, experiment, and numerical simulation  As a fundamental research method, theoretical research can provide a clear understanding of physical processes  In 1960, Rudinger et al  developed a theory that permits computing the response of bubbles to accelerations  
In order to describe the formation and evolution processes of vortex structure quantitatively, many scholars have developed circulation models   However, theoretical works provide limited information  Meanwhile, in the late stage of SBI evolution, the bubble deformation and flow morphology dominated by the developed Richtmyer-Meshkov instability (RMI) and Kelvin-Helmholtz instability (KHI) are difficult to be predicted accu-rately 
As the research method closest to engineering application, the experimental results are often regarded as standard results to verify the rationality and accuracy of theoretical and numerical works  To study the SBI process accurately, the scholars have made a series of improvements to experimental equipment or technique, including the generation techniques of different types of shock waves, interface formation methods, schlieren facilities, and image recognition techniques  
Among these, two of important and valuable works are performed by Ding et al   Based on the soap film technique, they formed kinds of initial interfaces with different curvatures through the wire-restriction method and captured the wave patterns and interface evolution with high-speed schlieren photography  
Other works, such as evolutions of a spherical gas interface under reshock conditions , developments of a membrane-less SF 6 gas cylinder under reshock conditions , and interactions of a cylindrical converging shock wave with an initially perturbed gaseous interface , are also performed by many other scholars 
However, we know that the experimental studies mainly depend on the experimental platform  When studying some complex and demanding condition problems, it takes much work to build the experimental platform  In this situation, numerical simulation research becomes an option  Generally, there are three kinds of physical modeling methods (or models) for SBI numerical research, i e , the macroscopic, mesoscopic, and microscopic modeling methods 
Most of the existing numerical researches on SBI are related to the macroscopic modeling methods (such as the Euler and Navier-Stokes (NS) models) based on the continuous hypothesis (or equilibrium and nearequilibrium hypothesis)   For example, presented the computational results on the evolution of the shock-accelerated heavy bubbles through the multi-fluid Eulerian equation  
There also exist a few SBI works based on the mesoscopic modeling method, such as the Direct Simulation Monte Carlo method   The microscopic modeling methods such as the Molecular dynamics (MD) simulation, is capable of capturing much more flow behaviors but restricted to smaller spatiotemporal scales because of its huge computing costs 
In the numerical research on SBI, three points need to be concerned  (i) Investigation of kinetic modeling that describes the non-continuity/non-equilibrium flows  Most of the current researches are based on macroscopic models  However, there exist abundant small structure (and fast-changing patterns) behaviors and effects such as the shock wave, boundary layer, material defects, etc 
For cases with small structures, the mean free path of molecules cannot be ignored compared to the characteristic length, i e , the non-continuity (discreteness) of the system is pronounced, which challenge the rationality and physical function of the macroscopic models based on the continuity hypothesis 
For cases with fast-changing patterns, the system dose not have enough time to relax to the thermodynamic equilibrium state, i e , the system may significantly deviate from the thermodynamic equilibrium state  Therefore, the rational-ity and physical function of the macroscopic models based on the hypothesis of thermodynamic equilibrium (or near thermodynamic equilibrium) will be challenged 
(ii) Improvement of method that describes the evolution characteristics of bubbles and flows morphology  Most of the studies describe bubble characteristics and flows morphology from a macroscopic view  The mesoscopic characteristics such as the kinetic effects which help understand the kinetic process, are rarely to be studied 
(iii) Further studies of effects of specific-heat ratio on SBI process  The specific-heat ratio is an essential index for studying the compressibility of the gas  Research from Igra et al  has shown that the differences in the specific-heat ratio of bubbles would cause various wave patterns and pressure distribution inside the bubbles during the interaction process  
Besides, many works on hydrodynamic instability have also demonstrated the importance of investigating the specific-heat ratio effect   Among these, Chen et al  investigated the specific-heat ratio effects on temperature gradient and the TNE characteristics of compressible Rayleigh-Taylor (RT) system  
For the above three points, in this work we apply the recently proposed discrete Boltzmann method (DBM)   The Lattice Boltzmann Method (LBM) research has two complementary branches   One aims to work as a kind of new scheme for numerical solving various partial differential equation(s)  The other aims to work as a kind of new method for constructing kinetic model to bridge the macro and micro descriptions 
The two branches have different goals and consequently have different rules  The current DBM is developed from the second branch of LBM and focusing more on the Thermodynamic Non-Equilibrium (TNE) behaviors that the macro modeling generally ignore  It breaks through the continuity and near-equilibrium assumptions of traditional fluid modeling, discards the lattice gas image of standard LBM, and adds various methods based on phase space for checking, exhibiting, describing and analyzing the non-equilibrium state and resulting effects 
More information extraction technologies and analysis methods for complex field are introduced with time  The numerical simulation includes three parts, as shown in Fig    (1) Physical modelling, (2) Algorithm design, (3) Numerical experiments and analysis of complex physical fields  The research of equation algorithm corresponds to the part (2) of the above three parts 
The DBM aims at parts (1) and (3) of the three mentioned above  It belongs to a physical model construction method rather than a numerical solution for the equations  The tasks of DBM are to: (i) Ensure the rationality of the physical model (theoretical model) and balance the simplicity for the problem to be studied; (ii) Try to extract more valuable physical information from massive data and complex physical fields 
Based on the coarse-grained modeling method of nonequilibrium statistical physics, the DBM aims to solve the following dilemma: (i) The traditional hydrodynamic modelings are based on the continuous hypothesis (or near-equilibrium hypothesis)  They only concern the evolution of three conserved kinetic moments of the distribution function, i e  the density, momentum and energy, so their physical functions are insufficient 
(ii) The situation that the MD can be used is restricted to too small spatial-temporal scales  The physical requirement for the modeling is that except for the Hydrodynamic Non-Equilibriums (HNE), the most related TNE are also needed to be captured  Theoretically, the Boltzmann equation is suitable for all-regime flows, including the continuum regime, slip regime, transition regime, and free molecule flow regime 
Based on the Chapman-Enskog (CE) multiscale analysis , through retaining various orders of Kn number (or considering different orders of TNE effects), the Boltzmann equation can be reduced to the various orders of hydrodynamic equations  They can be used to describe the hydrodynamic behaviors, i e , the conservations of mass, momentum and energy , in corresponding flow regimes 
Because what the traditional hydrodynamic equations describe are only the conservation laws of mass, momentum and energy  Consequently, it should be pointed out that, the information lost in the traditional hydrodynamic equations increases sharply with increasing the Kn number  With increasing the Kn number, to ensure the describing capability not to decrease significantly, the more appropriate hydrodynamic equations should be the Extended Hydrodynamic Equations (EHEs) which include not only the evolution equations of conserved kinetic moments but also the most relevant nonconserved kinetic moments of distribution function 
For convenience of description we refer the modeling method that derives EHEs from the fundamental kinetic equation to Kinetic Macroscopic Modeling (KMM) method  It is clear that, the complex process of CE expansion is necessary and the simulation is still based on the macroscopic equations in KMM  As a comparison, the DBM is a kind of Kinetic Direct Modeling (KDM) method 
In DBM modeling, the CE analysis is only used to quickly determine which kinetic moments should keep values unchanged, the final EHEs are not needed, and the simulation is not based on the complicated EHEs  As the TNE degree of the flow to be described rises gradually, the complexity of the derivation process and difficulty of numerical simulation in the KMM method increase sharply 
However, in the DBM method, to describe flows in a one-order more deeper depth of TNE, only two more related kinetic moments need to be added  Since without needing to derive and solve the EHEs, as the TNE degree deepens, the complexity of the DBM approach increases much slower than that of KMM method 
The core step in DBM modeling is to provide a feasible scheme for detecting, describing, presenting, and analyzing TNE effects and behaviors beyond traditional macroscopic modeling  Based on the non-equilibrium statistical physics, we can use the non-conservative moments of ( f − f eq ) to describe how and how much the system deviates from the thermodynamic equilibrium state and to check corresponding effects due to deviating from the thermodynamic equilibrium 
The non-conservative moments of ( f − f eq ) open a phase space, and this space and its subspaces provide an intuitive geometric correspondence for describing complex TNE system properties  The development of schemes for checking TNE state, extracting TNE information and describing corresponding TNE effects in DBM are seen in Table  
Actually, this set of TNE describing methods has been applied in many kinds of complex fluid systems such as hydrodynamic instability system , combustion and detonation systems , multiphase flow system , plasma system , etc  Besides the scheme for detecting, describing, presenting, and analyzing TNE effects and behaviors, the DBM incorporates other methods for analyzing the complex physical field 
One of them is the tracer particle method  The introduction of the tracer particle method makes the gradually blurred interface appear clearly   The rest of the paper is structured as follows  Section 2 shows Year Scheme for investigating TNE effects and behaviors Before 2012 Two classes of LBMs did not show a significant difference in physical function 
2012 Use the non-conservative moments of ( f − f eq ) to check and describe TNE   This is the starting point of current DBM approach  2015 Open TNE phase space based on non-conservative moments of ( f − f eq ) and define a TNE strength using the distance from a state point to the origin  This is the starting point of the phase space description method  
2018 Extend the distance concepts in phase space to describe the TNE difference/similarity of TNE states and kinetic processes   2021 Further extend the phase space description methodology to any set of system characteristics   the modeling method  Then, the numerical simulations and results are presented in Section 3, which includes two subsections 
Section 4 concludes the paper  Other complementary information is given in the Appendix 

Model construction

Based on the Bhatnagar-Gross-Krook (BGK) singlerelaxation model, a two-fluid DBM with a flexible specific-heat ratio is presented in this part  From the origin Boltzmann equation to a DBM, four fundamental steps are needed: (i) Simplification and modification of the Boltzmann equation according to the research requirement 
(ii) Discretization of the particle velocity space under the condition that the reserved kinetic moments keep their values unchanged  (iii) Checking the TNE state and extracting TNE information  (iv) The selection/design of the boundary conditions 

Simplification and modification of the Boltzmann equation

As we know, the collision term in the original Boltzmann contains high dimensional distribution functions  Therefore, the direct solution to it needs too much computing consumption  The most common method to simplify the collision operator is to introduce a local equilibrium distribution function ( f eq ) and write the complex collision operator in a linearized form, i e , the original BGK collision operator − 1 τ ( f − f eq ), where τ is the relaxation time  
The original BGK operator describes the situation where the system is always in the quasi-equilibrium state  Namely, it characterizes only the situation where the Kn number of the system is small enough and f ≈ f eq   The currently used BGK operator for non-equilibrium flows in the field is a modified version incorporating the meanfield theory description  
Based on the above considerations, the simplified Boltzmann equation describing the SBI process is where the two-dimensional equilibrium distribution function is ) where ρ, T , v, u, I, R, and η are the mass density, temperature, particle velocity vector, flow velocity vector, the number of the extra degrees of freedom including molecular rotation and vibration inside the molecules, gas constant, and a free parameter that describes the energy of the extra degrees of freedom, respectively 
The specific-heat ratio is flexible by adjusting parameter I, i e , γ = (D + I + 2)/(D + I), where D = 2 represents the two-dimensional space 

Discretization of the particle velocity space and determination of f σ ,eq i

The continuous Boltzmann equation should be discretized for simulating  Specifically, the continuous velocity space can be replaced by a limited number of particle velocities  So that the values of continuous kinetic moments can be obtained from the summation form of kinetic moments  In this process, it requires the reserved kinetic moments, which are used to characterize the system behaviors, keep their values unchanged after discretizing the velocity space 
Namely, f the reserved kinetic moments  According to the CE analysis, f can be expressed by f eq   Therefore, in the process of discretization, the reserved kinetic moments of f eq should keep their values unchanged, i e , where i represents the kind of discrete velocities and α (α = x or y) is the direction in cartesian coordinate 
To simulate the interaction between two different fluids, a two-fluid DBM should be constructed  Based on the singlerelaxation model, the discrete two-fluid Boltzmann equation can be written as : where σ represents the types of material particle and f σ ,eq i = f σ ,eq i (ρ σ , u, T)  In two-fluid DBM, the macroscopic quantities of the mixture and each component are
where ρ σ and u σ are the mass density and flow velocity of the component σ , respectively  ρ and u represent the mass density and flow velocity of the mixture, respectively  There exist two kinds of temperature (internal energy) definitions in two-fluid DBM because the definition of temperature (internal energy) depends on the flow velocity to be chosen as a reference 
The first definition is by choosing the velocity of the mixture to be a reference, i e , So that the expressions of temperature of component σ and mixture are where We can also choose the flow velocity of component as a reference, i e , , where u σ is the flow velocity of component σ   The corresponding definitions of temperature for component σ and the mixture are
where ∆E * I is It is clear to see that these two definitions of temperature for mixture are the same, but for temperature of component σ are different  We choose the first definition in this manuscript  To solve the Eq  ( ), it is necessary to determine the values of f σ ,eq i   Its values depend on the reserved kinetic moments which characterize the main system behaviors 
In DBM modeling, the CE multiscale analysis is used to determine quickly the reserved kinetic moments  Specifically, when constructing a DBM which only the first order term of Kn number is retained (i e , only the first order TNE effects are retained), seven kinetic moments should be reserved, i e , the M 0 , M 1 , M 2,0 , M 2 , M 3,1 , M 3 , M 4,2  
Two more kinetic moments ( M 4 and M 5,3 ) are needed when the second order TNE is considered   However, it should be noted that the function of CE analysis in DBM modeling is only to determine the kinetic moments that need to be preserved  Whether or not to derive the hydrodynamic equations does not affect the DBM simulation 
The kinetic moments used in our physical modeling are shown in the Appendix B  Their expressions can be obtained by integrating v and η with continuous-form f eq   For better understanding, the Appendix C gives the two-fluid hydrodynamic equations recovered from the Boltzmann equation  The kinetic moments in Appendix B can be written in matrix form, i e , C • f σ ,eq = fσ,eq , (
where C is the matrix of discrete velocity and feq represents the kinetic moments  A proper discrete velocity model is needed to confirm the values of f σ ,eq i   The f σ ,eq can be obtained by solving the inverse matrix, i e , f σ ,eq = C −1 • fσ,eq , where C −1 is the inverse matrix of C  It is very convenient to obtain the inverse matrix of C through some mathematical softwares such as Mathematica, etc 
The D2V16 model is chosen in this paper, its sketches can be seen in Fig    The specific values of D2V16 are given in the following equations: where "cyc" indicates cyclic permutation and c is an adjustable parameter of the discrete velocity model  The sketch of η in D2V16 is η i = η 0 for i = 1 − 4, and η i = 0 for i = 5 − 16 

Checking the TNE state and extracting TNE information

Many physical quantities can characterize the degree of TNE in a fluid system, such as relaxation time, Kn number, viscosity, heat conduction, the gradients of macroscopic quantity, etc  They are all helpful to characterize the TNE strength and describe the TNE behaviors of a fluid system from their perspectives 
But it is not enough only relying on these quantities  Besides the above physical quantities describing the TNE behaviors, in DBM modeling, we can also use the non-conservative moments of ( f − f eq ) to characterize the TNE state and extract TNE information from the fluid system  Fundamentally, four TNE quantities can be defined in a firstorder DBM, i e , ∆ σ * 2 , ∆ σ * 3,1 , ∆ σ * 3 , and ∆ σ * 4,2  
Their definitions can be seen in Table , where v * i = v i − u represents the central velocity and u is the macro flow velocity of the mixture  Physically, ∆ σ * 2 = ∆ σ * 2,αβ e α e β and ∆ σ * 3,1 = ∆ σ * 3,1 e α represent the viscous stress tensor (or non-organized momentum flux, NOMF) and heat flux tensor (or non-organized energy flux, NOEF), respectively 
The e α (e β ) is the unit vector in the α (β ) direction  The later two higher-order TNE quantities contain more condensed information  Specifically, and it indicates the flux information of ∆ σ * 2   To describe the TNE strength of the whole fluid system, some TNE quantities contained more condensed information are also defined, i e ,
Other TNE quantities can be defined based on specific requirements  All the independent components of TNE characteristic quantities open a highdimensional phase space, and this space and its subspaces provide an intuitive image for characterizing the TNE state and understanding TNE behaviors   It should be emphasized that: (i) The TNE strength/intensity/degree is the most basic parameter of non-equilibrium flow description; And any definition of non-equilibrium strength/intensity/degree depends on the research perspective 
(ii) The physical meaning of D * m,n is the TNE strength of this perspective  (iii) From a certain perspective, the TNE strength is increasing; While from a different perspective, the TNE strength, on the other hand, may be decreasing  It is normal, one of the concrete manifestations of the complexity of non-equilibrium flow behavior 
Strictly speaking, those TNE intensity and effect descriptions that do not account for the research perspective are not correct  Do not explain the research perspective, the corresponding is not dependent on the research perspective 

Numerical simulations and results

In this section, we first validate the DBM code by comparing the DBM results with experimental results  Then, the effects of specific-heat ratio on the dynamic process and TNE behaviors on SBI are investigated 

Comparison with experimental results

In the following part, we use a first-order two-fluid DBM to simulate the interaction between a planar shock wave with a 2-D heavy-cylindrical bubbles, and compare the DBM results with the experimental results from Ref    The computational configuration can be seen in Fig    In a flow field which is filled with Air, there is a static bubble composed of 26% Air and 74% SF 6  
A shock with Ma = 1 2 would pass through the bubble from left to right  The initial conditions of ambient gas are ρ 0 = 1 29kg/m 3 , T 0 = 293K, p 0 = 101 3kPa  Ignoring the pressure difference between interior gas and ambient gas, the initial parameters of the bubble are ρ bubble = 4 859kg/m 3 , p bubble = 101 3kPa,
and T 0 = 293K  For simulating, these actual physical quantities should be transferred to dimensionless parameters  This process can refer to the Appendix A  The dimensionless conditions of macroscopic quantities of the fluid field in initial time are (ρ, T, u x , u y ) bubble = (4 0347, 1 0, 0 0, 0 0), (ρ, T, u x , u y ) 1 = (1 3416,
1 128, 0 3616, 0 0), (ρ, T, u x , u y ) 0 = (1 0, 1 0, 0 0, 0 0), where the subscript "0" ("1") represents downstream (upstream) region  In two-fluid DBM code, the distribution function f Air is used to describe the ambient gas, i e , Air  The f bubble characters the bubble which is a mixture that composed of Air and SF 6  
The grid number is N x × N y = 800 × 400, where the N x and N y are grid number in x and y direction, respectively  This grid size has passed the mesh convergence test  The below results also show that it is sufficient to meet the requirements of the following research problem  Other parameters used for the simulation are: c = 1 0, η Air = η bubble = 10 0,
I Air = 3, I bubble = 15, ∆x = ∆y = 1 2 × 10 −4 and ∆t = 1 × 10 −6   The viscosity effect is feeble compared to the shock compression effect, so it does not significantly affect the deformation of the bubble  Therefore, in this part, the relaxation time τ is set sufficiently small  The inflow (outflow) boundary condition is used in the left (right) boundary, and the periodic boundary is adopted in the y direction 
The first-order forward difference scheme is used to calculate the temporal derivative, and the second-order nonoscillatory nonfree dissipative scheme is adopted to solve the spatial derivative in Eq  ( )   Two quantitative comparisons between experimental results and DBM simulations are shown in the following part, including snapshots of schlieren images and evolutions of characteristic scales for the bubble 
The first is shown in Fig  non-organized momentum flux (NOMF) uously to form a diffracted shock (DS)  As TS propagates, it will split into three branches due to the considerable pressure perturbations caused by the gradual decay of the DS strength   Afterward, as shown in the subfigure at about t = 128µs, two high pressure regions (ROH) generate because of the interaction of these branches 
Subsequently, at about t = 148µs, the two ROHs meet, causing the shock focusing  On the one hand, at about t = 168µs, the shock focusing causes the generation of downstream-propagating second transmitted shock (STS) and upward-moving rarefaction wave  On the other hand, it will produce high pressure region inside the bubble, which later leads to a jet structure, as shown at about t = 288µs 
At about t = 428µs, due to the deposited vorticity, there will produce a pair of counter-rotating vortexes at the pole region of the bubble  The further development of the vortex pair and the effect of viscosity decrease the amplitude of the jet  Finally, the jet structure disappears  The second quantitative comparison is the interface structure described by the length and width of the bubble, as shown in Fig   
The experimental data are extracted from Fig  , in Ref    Quantitative agreements between DBM simulation and experimental results are seen  For the profile of bubble width, there are mainly two stages  At an early time (t < 150µs), it decreases to a minimum value because of the shock compression effect 
After the shock wave passes through the bubble (t > 150µs), the developed vortex pair caused by the deposited vorticity gradually dominates the growth of bubble width  Different from width evolution, the temporal variation of length experiences three stages  In the early stages (t < 150µs), it decreases quickly due to the shock compression effect 
Then, the jet structure emerges, which results in a growth in length (150µs < t < 250µs)  Because the upstream interface moves faster than the downstream interface, the bubble length would decrease at 250µs < t < 500µs  In the third stage (t > 500µs), the vortex pair forms and then leads to a continuous development of bubble length 
Both the length and width experience oscillations in the later stages due to complex wave patterns  The quantitative agreements between DBM simulation and experimental results indicate the following two facts: (i) the order of TNE considered in the current DBM is sufficient, (ii) the choosing of discrete velocities and spatial-temporal steps and simulation parameters like the relaxation times is suitable for characterizing the deformation of bubble, wave patterns, main characteristics of flow morphology 

Effects of specific-heat ratio on SBI

The major of current works on SBI research have not focused on specific-heat ratio effects  In this part, the simulation parameters are fine-adjusted based on the parameters in Section 3 1 to highlight the influence of specific-heat ratio  Through adjusting the extra degree of freedom I, five cases with various specific-heat ratios of the bubble are simulated, i e , γ = 1 4,
1 28, 1 18, 1 12, and 1 09  Two kinds of analysis methods, including tracer particle method and two-fluid model, are used to characterize qualitatively the macroscopic behaviors such as the shape, deformation process, mixing degree, etc  The related TNE behaviors are also studied 

Effects of specific-heat ratio on jet shape, deformation process, and average motion

We first observe the specific-heat ratio effect on the bubble shape from the view of density contour and images of particle tracer visually  As shown in Fig  , pictures with three typical moments are plotted, i e , t = 0 07,t = 0 11, and t = 0 16  The odd rows represent density contours and the even rows are tracer particle images 
It can be seen that the specific-heat ratio significantly affects the length and shape of the jet structure  The smaller the specific-heat ratio is, the stouter the jet structure can be seen  The reason is that the specific-heat ratio significantly changes the propagation speed of shock waves and wave patterns inside the bubble 
The specific-heat ratio also influences the vortex structure in early stage but contributes little effects to it in later stage  In the later stage, for cases with different specific-heat ratios, the differences in vortex pairs are almost invisible  Then, the effects of specific-heat ratio on deformation process are analyzed 
Shown in Fig  are the evolutions of characteristic scales which used to describe the bubble size, i e , width and length  It can be seen that the smaller the specific-heat ratio of bubble, the smaller the bubble width and length  For the fluid with smaller specific-heat ratio, it is easier to be compressed 
Therefore, the characteristic scales of bubbles with smaller specific-heat ratio tend to be compressed smaller  It can also be seen that the case with the largest specific-heat ratio reaches the minimum characteristic scales firstly  The reason is that the shock wave propagates faster in case with larger specific-heat ratio 
Through the method of tracer, information on the average motion of the bubble is easy to obtain  Shown in Fig  are the average position and average velocity of the bubble, with different specific-heat ratios  It is found that, in the shock compression stage (t < 0 03), the effect of specific-heat ratio contributes little to the average motion 
However, when the shock wave passes through the bubble (t > 0 03), a larger specific-heat ratio speeds up the average motion of the bubbles  The reason is that the bubbles with smaller specific-heat ratio need more energy to compress their size, so their translational energy is smaller 

Effects of specific-heat ratio on vortex motion

Vorticity is one of the most important physical quantities in describing the vortex motion  In the 2-D case, the vorticity can be calculated by the following equation: The positive (negative) value of ω represents the positive (negative) direction along the z axis  Vorticity contours at t = 0 134, with various specific-heat ratios, are shown in Fig   
The discernable difference between cases with various specific-heat ratios can be observed  The arrows in the vorticity images point out the obvious differences around the interface between case γ = 1 4 and case γ = 1 09  That is to say, there exists influences of specific-heat ratio on the rotational motion of the bubble 
The strength of vorticity is described by circulation Γ, where Γ = ∑ ω∆x∆y  Γ + = ∑ ω| ω>0 ∆x∆y is the positive circulation and Γ − = ∑ ω| ω<0 ∆x∆y represents the negative circulation  Figure shows the temporal evolution of circulations on SBI process  It can be seen that the values of Γ are equal to zero all the time because the values of Γ + and Γ − are the same 
But they point in the opposite direction  In the shock compression stage (t < 0 03), the specific-heat ratio effect contributes little to the circulation of the bubble  When the shock wave sweeps through the bubble (t > 0 03), the specific-heat ratio affects the value of circulation obviously  The cases with a smaller specific-heat ratio experiences a larger range of amplitude of change, which is caused by its good compressibility 

Effects of specific-heat ratio on mixing degree

The mixing process is a fundamental research content on SBI  In two-fluid DBM, the mixing degree at each fluid unit can be defined as M = 4 • M A M B , where M σ represents the mass fraction of component σ   The higher the value of M, the higher the mixing amplitude  Images of density (first cow) and mixing degree M (second row) at several typical moments are shown in Fig   
As can be seen, the mass mixing occurs in the region where two media contact  In addition, the mixing degree M g described the whole fluid field can be defined where the symbol "−" indicates integrating the M σ over the whole fluid field and then dividing the grid size N x • N y   Shown in Fig  is the temporal evolution of the global mixing degree M g  
As can be seen, temporal profiles of the global mixing degree show two stages: t < 0 03 and t > 0 03  When t < 0 03, there is almost no difference between cases with various specific-heat ratios  But for t > 0 03, the stronger the specific-heat ratio effect, the larger the mixing degree  Actually, there are mainly two indicators that measure the global mixing degree: the amplitude of mixing and the area of the mixing zone between two fluids 
At the stage t > 0 03, the shock compression dominates the mix by enhancing the mixing amplitude and increasing the area of the mixing zone simultaneously  In this stage, the specific-heat ratio effect contributes little to the mix  However, when the shock passes through the bubble, the deformation of interface and the evolution of vortex core both significantly raise the area of the mixing zone 
As can be seen in Fig  , the smaller specific-heat ratio of bubble, the stronger global mixing degree of fluid field  Intuitively, for the fluid with smaller specific-heat ratio, it is easier to deform and compress, which is beneficial for the fluid mixing  It can also be explained by the diffusion formula, i e ,  
The specific-heat ratio affects both the temperature T and the gradient of density simultaneously  Therefore, these two aspects comprehensively influence the material diffusion between the two fluids  Due to the complex reflected shock wave, the global mixing degree shows a tendency for oscillating growth 

Effects of specific-heat ratio on TNE behaviors

The investigation of TNE behaviors is of great importance for understanding the kinetics process on SBI  These TNE quantities describe the fluid system deviating from the thermodynamic state from their own perspectives  The effects of specific-heat ratio on global TNE strength, i e , D * 2 , D * 3 , D * 3,1 , and D * 4,2 , are shown in Fig   
It can be seen that the effects of specific-heat ratios on various TNE quantities are different  Theoretically, the influence of specific-heat ratio on the non-equilibrium effect is reflected in two aspects: transport coefficient and macroscopic quantity gradient  For example, on the one hand, the specific-heat ratio reduces heat conductivity, while on the other hand, it enhances the temperature gradient 
Therefore, the effect of specific heat ratio on NOEF is the comprehensive result of the competition between the two aspects  As shown in Fig  , the smaller the specific-heat ratio, the stronger strength of D * 3,1   It indicates that the specific-heat ratio increase the strength of D * 3,1 by raising the heat conductivity  
For the strength of D * 3 , as shown in Fig  (b), it is seen that it decreases as the specific-heat ratio becomes small  The reason is that a smaller specific-heat ratio decreases the temperature gradient  Effects of specific-heat ratio on D * 4,2 show two-stage  In the shock compression stage (t < 0 03), the smaller specificheat ratio, the larger the strength of D * 4,2  
But the situation is reversed at the stage t > 0 03  For strength of D * 2 , the specificheat effects are more significant in later stage 

Effects of specific-heat ratio on entropy production rate

and entropy production The concepts of entropy are commonly used in complex flows   In DBM, there are two kinds of en- tropy production rates, i e , ṠNOEF and ṠNOMF   They are key factors in compression science field  The former is induced by temperature gradient and the NOEF (∆ * 3,1 )  The latter is affected by velocity gradient and the NOMF (∆ * 2 ) 
The entropy production rates are defined by the following formulas : Integrating the ṠNOEF and ṠNOMF over time t, the entropy generations over this period of time are obtained, i e , S NOEF = t 0 ṠNOEF dt and S NOMF = t 0 ṠNOMF dt  Plotted in Fig  ) and 14(b) are the temporal evolution of ṠNOMF and ṠNOEF , respectively 
The evolution of entropy generation rate is related to two aspects: (i) the propagation of the shock wave, and (ii) the deformation of the bubble  The former generates a macroscopic quantity gradient, and the latter makes the contact interface wider, longer, and deformed  Depending on the location of the shock wavefront, there exist two critical moments in this SBI process: (i) at around t = 0 03, the shock wave just sweeps through the bubble, and (ii) at t = 0 06, the shock wave exits the flow field 
Therefore, the temporal evolution of the entropy production rate shows three stages, i e , t < 0 03, 0 03 < t < 0 06, and t > 0 06  At the stage t < 0 03, the shock compression stage, the shock effects compress the bubble  It generates the large macroscopic quantity gradients, resulting in a quick increase of ṠNOMF  
At around t = 0 03, the shock wave passed through the bubble  So the values of ṠNOMF decreases  The values of ṠNOMF would continue to decrease due to the gradually wider contact interface caused by the diffusion effect  At around t = 0 06, the shock wave comes out of the flow field so that the values of ṠNOMF drops rapidly 
In the third stage, i e , t > 0 06, because of the diffusive effect, the general trend of ṠNOMF is downward  However, it shows an oscillatory trend due to the influence of various reflected shock waves  The specific heat ratio indirectly changes the value of ṠNOMF by changing the velocity gradient  The smaller the specific-heat ratio, the larger ṠNOMF  
Different understanding can be seen in Fig  , where the temporal evolution of ṠNOEF is plotted  In the first stage (t < 0 03), cases with different specific-heat ratios show various trends  At the stage where the bubble deformation is not very large, i e , 0 03 < t < 0 06, values of ṠNOEF fluctuate near the average value 
In the third stage (t > 0 06), evolutions of ṠNOEF in cases with larger specific-heat ratios show an apparent growing tendency  Differently, the values of ṠNOEF in cases with smaller specific-heat ratios remain almost unchanged  The influence of specific heat ratio on the ṠNOEF , similar with the effect on NOEF, is also affected by the heat conductivity and the temperature gradient 
It can be seen that, except for the case of γ = 1 09, the larger the specific-heat ratio, the higher entropy production rate ṠNOEF   The temporal evolutions of ṠNOEF of case γ = 1 09 and case γ = 1 12 are very similar  Consequently, the specific-heat ratio increases the ṠNOEF by raising the temperature gradient 
Further understanding can be seen in Fig  , where the entropy productions over this period are plotted  For convenience, the sum and difference between S NOMF and S NOEF are also plotted in the figure  The variation range of S NOEF is larger than that of S NOMF   It indicates that the influence of specific-heat ratio on S NOEF is more significant than that on S NOMF  
Effects of specific-heat ratio on entropy production caused by NOMF and NOEF are contrary  Specifically, it can be seen that the entropy production contributed by NOMF increases with re- duced specific-heat ratio  But the entropy production caused by NOEF first reduces with decreasing specific-heat ratio and then approaches to a saturation value 
The S NOEF in case γ = 1 09 is almost the same with it in case γ = 1 12  When the specificheat ratio γ is smaller than a threshold value γ c (γ c ≈ 1 315), the entropy production induced by NOEF is more significant than that caused by NOMF  However, in the case of γ > γ c , the situation reverses  The temporal evolution of the total entropy production (S NOMF +S NOEF ) is similar to the S NOEF profile 
The difference between S NOMF and S NOEF increases with decreasing specific-heat ratio 

Conclusions

Specific-heat ratio effects on the interaction between a planar shock wave and a 2-D heavy-cylindrical bubble are studied by a two-fluid DBM which has a flexible specific-heat ratio and includes several schemes for analyzing the complex physical fields  Besides the HNE that NS easily describes, the DBM pays more attention to the related TNE that NS is not convenient to describe 
First, both the snapshots of schlieren images and evolutions of characteristic scales from DBM simulation are compared with those from experiment  The quantitative agreements between them indicate the following two facts: (i) the order of TNE considered in the current DBM is sufficient, (ii) the choosing of discrete velocities, spatial-temporal steps, and simulation parameters like the relaxation times are suitable for the following physical researches 
Then, five cases with various specific-heat ratios are simulated  Several analysis methods for complex physical fields, including the description scheme of TNE behaviors, tracer particle method, and two-fluid model, are used to characterize the effects of specific-heat ratio on the bubble shape, deformation process, average motion, vortex motion, mixing degree of the fluid system, TNE strength, and entropy production 
Specifically, for bubble shape, bubbles with different specific-heat ratios display various jet structures  The smaller the specific-heat ratio is, the stouter the jet structure can be seen  For the case with smaller specific-heat ratio, the fluid is easier to be compressed  So, the characteristic scales of bubbles with smaller specific-heat ratio tend to be compressed smaller 
For the bubble, the smaller the specific-heat ratio, the slower average motion  In the shock compression stage, the specific-heat ratio contributes little effects to the vortex motion  Differently, after the shock passes through the bubble, it significantly influences the vorticity around the interface and the corresponding amplitude of circulation due to the development of KHI 
The larger the difference in specific-heat ratio between the bubble and ambient gas, the higher the degree of material mixing  Effects of specific-heat ratio on various TNE quantities are different  These differences consistently show the complexity of TNE flows which is still far from a clear understanding 
In addition, it is found that the temporal evolution of the entropy production rates ṠNOMF and ṠNOEF both show three stages because of the influence of the shock wave location  The smaller the specific-heat ratio, the larger the velocity gradient, which indirectly enhances the strength of ṠNOMF   The specific-heat ratio increases the ṠNOEF by raising the temperature gradient 
The influence of specific-heat ratio on S NOEF is more significant than that on S NOMF   Effects of specific-heat ratio on entropy production caused by NOMF and NOEF are contrary  Specifically, the entropy production contributed by NOMF increases with reduced specific-heat ratio  But the entropy production caused by NOEF first reduces with decreasing specific-heat ratio and then approaches to a saturation value 
When the specific-heat ratio γ is smaller than a threshold value γ c (γ c ≈ 1 315), the entropy production induced by NOEF is more significant than that caused by NOMF  However, in the case of γ > γ c , the situation reverses  The fundamental research in this paper helps to understand the interaction mechanism between shock waves and bubbles in ICF, supersonic combustors, underwater explosions, etc 
The effects of viscosity and heat conduction on the interaction between shock waves and bubbles will be studied in the following work  where the subscript "m, n" means that the m-order tensor is contracted to n-order tensor  According to the CE multiscale analysis, the Boltzmann-BGK equation can be reduced to the hydrodynamic equations 
In the following part, the derivation process from Boltzmann-BGK equation to a two-fluid hydrodynamic equation are shown  More details can see the reference presented by Zhang et al    The discrete Boltzmann equation for component σ is (C 1) In Eq  C 1 there are two equilibrium distribution functions, i e , f σ ,seq = f σ ,seq (ρ σ , u σ , T σ ) and f σ ,eq = f σ ,eq (ρ σ , u, T ) 
For convenience, S σ i is defined as We perform the CE expansion around the f σ ,seq   That is, the distribution function f σ i can be expanded as where ε is a coefficient referring to Knudsen number  The partial derivatives of time and space can also be expanded to Substituting the above four equations into Eq 
(C 1), we can obtain (C 6) When retaining to ε terms, the following equation is obtained When retaining to ε 2 terms, we can obtain where M 2,αβ ( f σ ,(1) ) = ∑ i v iα v iβ f σ ,(1) i and M 3,1,α ( f σ ,(1) ) =   Substituting Eq  (C 14) into the above three equations, and replacing the time derivatives with the space derivatives, we obtain It should be noted that the ability to recover the corresponding level of macroscopic fluid mechanics equations is only part of the physical function of DBM 
The corresponding to the physical functions of DBM is the EHEs, which, in addition to the conserved moments evolution equations corresponding to the three conservation laws of mass, momentum and energy, also includes some of the most closely related nonconserved moments evolution equations  We refer the EHEs derivation based on kinetic equation to as KMM 
The necessity of the expanded part, the evolution equations of the relevant non-conserved moments, increases rapidly as increasing the degree of non-continuity/non-equilibrium  As the degree of non-continuity/non-equilibrium increases, the complexity will rapidly make KMM simulation studies, deriving and solving EHE, impossible 



ground_truth: The specific-heat ratio affects the average motion of the bubble. The bubbles with smaller specific-heat ratios have slower average motion.