Chapter 1

Preliminaries

Abstract

In this chapter we present some preliminary material which will be needed in order to study stationary models for generalized Newtonian fluids. We begin with the functional analytic framework. In particular, we define Lebesgue-, Sobolev- and Orlicz-spaces and describe their basic properties. After this we present the Lipschitz truncation method in its classical framework and present two applications. Finally, we discuss some modelling aspects concerning power law fluids and provide a historical overview on the mathematical theory of weak solutions for stationary flows.

Keywords

Lebesgue spaces; Sobolev spaces; Orlicz spaces; Lipschitz truncation; Stationary generalized Newtonian fluids; Historical comments on existence theory

1.1 Lebesgue & Sobolev spaces

In this section we define various function spaces. For proofs, further details and references we refer to [5].

Definition 1.1.1

Classical function spaces

Let GRdImage be open and kNImage. We define

C(G):={u:GR:u is continuous},Ck(G):={u:GR:iu is continuous for i=0,...,k},C(G):={u:GR:iu is continuous for all iN0}.

Image

Remark 1.1.1

In Definition 1.1.1 if we replace G by the closed set GImage we are considering functions whose derivatives are continuous up to the boundary of G.

Definition 1.1.2

Let GRdImage be open and α(0,1]Image. We define

Cα(G):={u:GR:supxy|u(x)u(y)||xy|α<},Cα(G):={u:GR:uCα(K) for all KG}

Image

as the set of (locally) α-Hölder continuous functions.

Remark 1.1.2

In the case α=1Image we obtain the set of Lipschitz continuous functions.

Definition 1.1.3

Let GRdImage be open and u:GRImage. We define the support of u by

sptu:={xG:u(x)0}.

Image

By C00(G)Image and C0(G)Image we denote subclasses of C0(G)Image and C(G)Image whose elements satisfy sptuGImage.

Definition 1.1.4

Lebesgue spaces

Let (X,Σ,μ)Image be a measure space. We define

Lp(X,Σ,μ):={u:XR:u is μ-measurable,X|u|pdμ<},1p<,L(X,Σ,μ):={u:XR:u is μ-measurable,infμ(N)=0supxXN|u(x)|<}.

Image

The elements of Lp(X,Σ,μ)Image are equivalence classes. Two functions u and v belong to the same equivalence class if u=vImage μ-almost everywhere in XImage, i.e. if

μ{xX:u(x)v(x)}=0.

Image

Remark 1.1.3

• Lp(X,Σ,μ)Image is a Banach space together with the natural norm

up:=uLp(X):=(X|u|pdμ)1pfor1p<,u:=uL(X):=infμ(N)=0supxXN|u(x)|.

Image

• We set p=pp1Image for 1<p<Image, p=Image for p=1Image and p=1Image for p=Image. Let 1p<Image and vLp(X,Σ,μ)Image. Then the mapping

Lp(X,Σ,μ)uGuvdμ

Image

belongs to the dual space Lp(X,Σ,μ)Image. On the other hand each element of Lp(X,Σ,μ)Image can be represented via a function vLp(X,Σ,μ)Image. In particular, the mapping

Lp(X,Σ,μ)v(Lp(X,Σ,μ)uXuvdμ)Lp(X,Σ,μ)

Image

is an isomorphism.

• The space Lp(X,Σ,μ)Image is reflexive if and only if 1<p<Image.

• If μ(X)<Image we have that Lq(X,Σ,μ)Lp(X,Σ,μ)Image for 1pqImage. In particular, the following holds

uLp(X)c(d,p,q,μ(X))uLq(X)

Image

for every uLq(X,Σ,μ)Image.

• We can define vector- or matrix-valued LpImage-spaces component-wise. For simplicity we do not mention this in the notation. However, when considering functions with values in an infinite dimensional spaces X we denote this by writing Lp(X,Σ,μ;X)Image.

The most important special case is (X,Σ,μ)=(G,B(Rd),Ld)Image where GRdImage, B(Rd)Image is the Borel σ-algebra on RdImage and LdImage the d-dimensional Lebesgue measure. In this case we abbreviate the notation by setting Lp(G):=Lp(G,B(Rd),Ld)Image. If 1p<Image then C0(G)Image is dense in Lp(G)Image. In particular, for every uLp(G)Image there is (um)C0(G)Image such that umuImage in Lp(G)Image.

Definition 1.1.5

Local Lebesgue spaces

Let GRdImage be open. We define

Lploc(G):={u:GR:uLp(K) for all KG}for1p.

Image

Definition 1.1.6

Weak derivative

Let GRdImage be open and uL1loc(G)Image.

• u is called weakly differentiable with respect to the i-th variable if there is a function viL1loc(G)Image such that

Guiφdz=Gviφdzfor allφC0(G).

Image

We call viImage the weak derivative of u with respect to the i-th variable. If weak derivatives with respect to all variables exist, we call u weakly differentiable and denote by u=(v1,...,vn)Image the weak gradient of u.

• Let αNd0Image and |α|:=α1+...+αnImage. For a smooth function u we denote by Dαu:=|α|uα1x1...αnxnImage its α-th classical derivative. A function uL1loc(G)Image is called k-times weakly differentiable if for all αNd0Image with |α|kImage there is a function vαL1loc(G)Image such that

GuDαφdz=(1)|α|Gvαφdzfor allφC0(G).

Image

We call Dαu=vαImage the α-th weak derivative.

Definition 1.1.7

Sobolev spaces

For kNImage and 1pImage we define

Wk,p(G):={u:GR,DαuLp(G), for all αNd0,|α|k},Wk,p0(G):=C0(G)Wk,p(G).

Image

Remark 1.1.4

• Wk,p(G)Image (1pImage) is a Banach space together with the norm

uk,p:=uWk,p(G):=(|α|kG|Dαu|pdz)1pfor1p<,uk,:=uWk,(G):=|α|kinfLd(N)=0supxGN|Dαu(x)|.

Image

• The space Wk,p(G)Image is reflexive iff 1<p<Image.

• A sequence (uk)Wk,p(G)Image (1<p<Image) converges weakly to u in Wk,p(G)Image if

GDαukvdzGDαuvdz,k,

Image

for all vLp(G,RN)Image (p:=pp1Image) and for all αNd0Image with |α|kImage.

• The dual space of Wk,p0(G)Image will be denoted by Wk,p(G)Image.

Theorem 1.1.1

Smooth approximation

Let GRdImage be open and bounded with Lipschitz boundary (i.e. ∂Ω can be locally parametrized by Lipschitz continuous functions of d1Image variables) and 1p<Image. Then C(G)Image is dense in Wk,p(G)Image. In particular, for every uWk,p(G)Image there is (um)C0(G)Image such that umuImage in Wk,p(G)Image.

Theorem 1.1.2

Sobolev

Let GRdImage be open.

a) The embeddings

W1,p0(G)Ldpdp(G)ifp<d,W1,p0(G)C1dp(G)ifp>d,Ld(G)<,

Image

are continuous.

b) Let GRdImage be open and bounded with Lipschitz-boundary. The embeddings

W1,p(G)Ldpdp(G)ifp<d,W1,p(G)C1dp(G)ifp>d,

Image

are continuous.

Theorem 1.1.3

Kondrachov

Let GRdImage be open and bounded. The embedding

W1,p0(G)Lq(G),q<pddp,

Image

is compact for all p<dImage.

In Definition 1.1.7 we have interpreted the boundary values of a Sobolev function as follows: u=0Image on ∂G iff uW1,p0(G)Image, where W1,p0(G)Image for p<Image denotes the closure of C0(G)Image in W1,p(G)Image. We will develop a rigorous definition of boundary values and show that it coincides with the former one. In order to do so we need functions which are integrable over the boundary of G. For GRdImage open let Lp(G)Image be equal to the set of all Hd1Image-measurable functions with

uLp(Ω):=(G|u|pdHd1)1p<.

Image

Here Hd1Image denotes the (d1)Image-dimensional Hausdorff measure. For C1Image-functions we define the operator tr:C1(G)Lp(G)Image by tru:=u|GImage.

Lemma 1.1.1

Let uC1(G)Image with GRdImage open and bounded with Lipschitz–boundary. Then we have

truLp(G)c(d,p,G)uW1,p(G).

Image

For uW1,p(G)Image we consider the approximation sequence umC(G)Image with umu1,p0Image. Its existence follows from Theorem 1.1.1. Lemma 1.1.1 shows that (trum)Image is a Cauchy-sequence in Lp(G)Image. We define its limit (which does not depend on the special choice of the sequence) as the trace of u. The result is a linear operator tr:W1,p(G)Lp(G)Image which coincides with the classical trace operator on C1(G)Image.

Theorem 1.1.4

Let ΩRdImage be open and bounded with Lipschitz boundary. Then we have

W1,p0(G)={uW1,p(G);tru=0}

Image

for 1p<Image.

All results of this section generalize in a straightforward manner to spaces of vector-valued functions. In order to keep the notation simple we do not use target spaces in the notation for our function spaces. It will follow from the context (and the bold-symbol) when we are dealing with these. In fluid mechanics the velocity field is a function from RdGRdImage. In this setting we need function spaces of solenoidal (that is, divergence-free) functions. We will use the following notation for 1p<Image

W1,pdiv(G):={ψW1,p(G):divψ=0},C0,div(Ω):={ψC0(G):divψ=0},Lpdiv(G):=C0,div(G)L2(G),W1,p0,div(G):=C0,div(G)W1,p(G).

Image

Finally we write W1,pdiv(G)Image for the dual of W1,p0,div(G)Image.

1.2 Orlicz spaces

In this section we present some important properties of Orlicz spaces (see [125] and [5]).

A function A:[0,)[0,]Image is called a Young function if it is convex, left-continuous, vanishing at 0, and neither identically equal to 0 nor to ∞. Thus, with any such function, it is uniquely associated a (non-trivial) non-decreasing left-continuous function a:[0,)[0,]Image such that

A(s)=s0a(r)drfors0.

Image (1.2.1)

The Young conjugate ˜AImage of A is the Young function defined by

˜A(s)=sup{rsA(r):r0}fors0.

Image

For ˜AImage we have the representation formula

˜A(s)=s0a1(r)drfors0,

Image (1.2.2)

where a1Image denotes the (generalized) left-continuous inverse of a. Moreover, for every Young function A,

rA1(r)˜A1(r)2rforr0

Image (1.2.3)

as well as

˜˜A=A.

Image (1.2.4)

Let A be a Young function of the form (1.2.1). Then the convexity of A and A(0)=0Image imply

A(λs)λA(s)for allλ[0,1]

Image (1.2.5)

and all s0Image. If λ1Image, then

λA(s)A(λs)fors0.

Image (1.2.6)

As a consequence, if λ1Image, then

A1(λs)λA1(s)fors0,

Image (1.2.7)

where A1Image denotes the (generalized) right-continuous inverse of A.

A Young function A is said to satisfy the Δ2Image-condition if there exists a positive constant K such that

A(2s)KA(s)fors0.

Image (1.2.8)

If (1.2.8) just holds for ss0Image for some s0>0Image, then A is said to satisfy the Δ2Image-condition near infinity. We say that A satisfies the 2Image-condition [near infinity] if ˜AImage satisfies the Δ2Image-condition [near infinity].

A Young function A is said to dominate another Young function B near infinity if there exist positive constants c and s0Image such that

B(s)A(cs)forss0.

Image (1.2.9)

The functions A and B are called equivalent near infinity if they dominate each other near infinity.

Let G be a measurable subset of RdImage, and let u:GRImage be a measurable function. Given a Young function A, the Luxemburg norm associated with A, of the function u is defined as

uLA(G):=inf{λ:GA(|u|λ)dx1}.

Image

The collection of all measurable functions u for which this norm is finite is called the Orlicz space LA(G)Image. It turns out to be Banach function space. The subspace of LA(G)Image of those functions u such that Gu(x)dx=0Image will be denoted by LA(G)Image. A Hölder-type inequality in Orlicz spaces takes the form

vL˜A(G)supuLA(G)Gu(x)v(x)dxuLA(G)2vL˜A(G)

Image (1.2.10)

for every vL˜A(G)Image. If |G|<Image, then

LA(G)LB(G)if and only if A dominates B near infinity.

Image (1.2.11)

The decreasing rearrangement u:[0,)[0,]Image of a measurable function u:GRImage is the (unique) non-increasing, right-continuous function which is equimeasurable with u. Thus,

u(s)=sup{t0:|{xG:|u(x)|>t}|>s}fors0.

Image

The equimeasurability of u and uImage implies that

uLA(G)=uLA(0,|G|)

Image (1.2.12)

for every uLA(G)Image.

The Lebesgue spaces Lp(G)Image, corresponding to the choice Ap(t)=tpImage, if p[1,)Image, and A(t)=χ(1,)(t)Image, if p=Image, are a basic example of Orlicz spaces. Other instances of Orlicz spaces are provided by the Zygmund spaces LplogαL(G)Image, and by the exponential spaces expLβ(G)Image. If either p>1Image and αRImage, or p=1Image and α0Image, then LplogαL(G)Image is the Orlicz space associated with a Young function equivalent to tp(logt)αImage near infinity. Given β>0Image, expLβ(G)Image denotes the Orlicz space built upon a Young function equivalent to etβImage near infinity.

An important tool will be the following characterization of Hardy type inequalities in Orlicz spaces [46, Lemma 1].

Lemma 1.2.1

Let A and B be Young functions, and let L(0,]Image.

(i) There exists a constant C such that

1ss0f(r)drLB(0,L)CfLA(0,L)

Image (1.2.13)

for every fLA(0,L)Image if and only if either L<Image and there exist constants c>0Image and t00Image such that

ttt0B(s)s2dsA(ct)for tt0,

Image (1.2.14)

or L=Image and (1.2.14) holds with t0=0Image. In particular, in the latter case, the constant C in (1.2.13) depends only on the constant c appearing in (7.0.1).

(ii) There exists a constant C such that

Lsf(r)drrLB(0,L)CfLA(0,L)

Image (1.2.15)

for every fLA(0,L)Image if and only if either L<Image and there exist constants c>0Image and t00Image such that

ttt0˜A(s)s2ds˜B(ct)for tt0,

Image (1.2.16)

or L=Image and (1.2.16) holds with t0=0Image. In particular, in the latter case, the constant C in (1.2.15) depends only on the constant c appearing in (1.2.16).

Assume now that G is an open set. The Orlicz–Sobolev space W1,A(G)Image is the set of all functions in LA(G)Image whose distributional gradient also belongs to LA(G)Image. It is a Banach space endowed with the norm

uW1,A(G):=uLA(G)+uLA(G).

Image

We also define the subspace of W1,A(G)Image of those functions which vanish on ∂G as

W1,A0(G)={uW1,A(G):the continuation of u by 0 is weakly differentiable}.

Image

In the case where A(t)=tpImage for some p1Image, and ∂G is regular enough, such a definition of W1,A0(G)Image can be shown to reproduce the usual space W1,p0(G)Image defined as the closure in W1,p(G)Image of the space C0(G)Image of smooth compactly supported functions in G. In general, the set of smooth bounded functions is dense in LA(G)Image only if A satisfies the Δ2Image-condition (just near infinity when |G|<Image), and hence, for arbitrary A, our definition of W1,A0(G)Image yields a space which can be larger than the closure of C0(G)Image in W1,A0(G)Image even for smooth domains. On the other hand, if G is a Lipschitz domain, namely a bounded open set in RdImage which is locally the graph of a Lipschitz function of d1Image variables, then

W1,A0(G)=W1,A(G)W1,10(G),

Image

where W1,10(G)Image is defined as usual.

Lemma 1.2.2

Let A be a Young-function satisfying the Δ2Image-condition and GRdImage open.

• The following holds

W1,A0(G)=C0(G)W1,A(G)=W1,A(G)W1,10(G).

Image

• For every uW1,AImage there is a sequence (uk)C(G)W1,A(G)Image such that ukuImage in W1,A(G)Image.

Lemma 1.2.3

Let A be a Young function satisfying the Δ2Image- and the 2Image-condition. Then LA(G)Image is reflexive with

LA(G)L˜A(G).

Image

1.3 Basics on Lipschitz truncation

The purpose of the Lipschitz truncation technique is to approximate a Sobolev function uW1,pImage by λ-Lipschitz functions uλImage that coincide with u up to a set of small measure. The functions uλImage are constructed nonlinearly by modifying u on the level set of the Hardy–Littlewood maximal function of the gradient ∇u. This idea goes back to Acerbi and Fusco [13]. Lipschitz truncations are used in various areas of analysis: calculus of variations, in the existence theory of partial differential equations, and in regularity theory. We refer to [62] for a longer list of references.

The basic idea is to take a function uW1,p(Rd)Image, where p1Image, and cut values where the maximal function of its gradient is large. The Hardy–Littlewood maximal operator is defined by

M(v)(x)=supB:xBB|v|dy

Image

for vL1loc(Rd)Image which can be extended to vector- (or matrix-)valued functions by setting M(v)=M(|v|)Image. Basic properties of the maximal operator are summarized in the following lemma (see e.g. [134] and [141, Lemma 3.2] for d)).

Lemma 1.3.1

a) Let vL1loc(Rd)Image and λ>0Image. The level-set {xRd:|M(v)(x)|>λ}Image is open.

b) The strong-type estimate

M(v)Lp(Rd)cpvLp(Rd)vLp(Rd)

Image

holds for all p(1,]Image.

c) The weak-type estimate

Ld({xRd:|M(v)(x)|>λ})cpvppλpvLp(Rd)

Image

holds for all p[1,)Image and all λ>0Image.

d) We have the estimate

{M(v)>λ}|v|pdxcp{|v|>λ/2}|v|pdxp[1,)

Image

for all λ>0Image.

The Lipschitz truncation will be defined via the maximal function of the gradient. For uW1,p(Rd)Image the “bad set” is defined by

Oλ:={xRd:M(u)(x)>λ},

Image (1.3.17)

where λ0Image. If we have uW1,p(G)Image for GRdImage it has to be extended to uW1,p(Rd)Image. This can be done in an obvious way if uW1,p0(G)Image where the extension is zero outside G. In the general case we may apply [5, Thm. 4.26]. Now, for x,yRdOλImage we have a.e.

|u(x)u(y)|c|xy|(M(u)(x)+M(u)(y))2cλ|xy|,

Image

see, e.g., [112]. Hence u is Lipschitz-continuous in RdOλImage with Lipschitz constant proportional to λ. By a standard extension theorem (see e.g. [71, p. 201]) we can extend u (defined in RdOλImage) to uλImage (defined in RdImage) such that the Lipschitz constant is preserved. (When dealing with this simple extension it is necessary to cut large values of M(u)Image as well. We neglect this for brevity.) This means we have

|uλ|cλinRd.

Image (1.3.18)

Moreover, by construction we have

{xRd:uuλ}Oλ.

Image

This and Lemma 1.3.1 c) imply

Ld({xRd:uuλ})Ld(Oλ)cuppλp.

Image (1.3.19)

Combining (1.3.18) and (1.3.19) shows

Rd|uλ|pdx=RdOλ|uλ|pdx+Oλ|uλ|pdxRdOλ|u|pdx+cλpLd(Oλ)cRd|u|pdx.

Image

We obtain the following stability result

uλpcup.

Image (1.3.20)

The basic properties (1.3.18)(1.3.20) are already enough to make the Lipschitz truncation a powerful tool for numerous applications. We present two rather classical ones.

Lower semi-continuity in W1,pImage.

Let GRdImage be an open and bounded with Lipschitz boundary. Suppose further that F:G×Rd×D[0,)Image is a continuous function with p-growth (p>1Image), i.e.,

F(x,Q)c|Q|p+g(x)QRd×D

Image (1.3.21)

with a constant c0Image and a non-negative function gL1(G)Image. We are interested in minimizing the functional

GF[w]=GF(x,w)dx

Image

defined for functions w:GRDImage. An important concept in showing the existence of minimizers is the lower semi-continuity of GFImage with respect to an appropriate topology. The functional GFImage is called W1,pImage-weakly lower semi-continuous if

GF[v]liminfnGF[vn]

Image (1.3.22)

provided vnvImage in W1,p(G)Image for mImage. The functional GFImage is called W1,Image-weakly lower semi-continuous if

GF[v]liminfnGF[vn]

Image

provided vnvImage in W1,(G)Image for nImage.

Due to (1.3.21) the right concept for the functional GFImage is W1,pImage-weak lower semi-continuity. It can be deduced from W1,Image-weak lower semi-continuity by the Lipschitz truncation, see Lemma 1.3.3 below. Using this idea Acerbi and Fusco [1] showed the W1,pImage lower semi-continuity of GFImage in the case where F is only quasi-convex. Note that W1,Image-weak lower semi-continuity is a consequence of the definition of quasi-convexity, see [1, Thm. 2.1]. For brevity we do not discuss the concept of quasi-convexity and refer instead to the fundamental papers [16] and [115].

Lower integrability for the p-Laplace system.

Consider the system

div(|v|p2v)=divFinG,

Image (1.3.23)

v=0onG.

Image (1.3.24)

Here, G is an open set in RdImage, with d2Image, the exponent p(1,)Image and the function F:ΩRd×DImage is given. A weak solution to (1.3.23) is a function vW1,p0(G)Image such that

G|v|p2v:φdx=GF:φdx

Image

for all φW1,p0(G)Image. Its existence can be shown via standard methods provided FLp(G)Image. We are concerned here with the question of how the regularity of F transfers to v (particularly to |v|p2vImage). In the linear case p=2Image this is answered by the classical theory of Calderón and Zygmund [43]. It says that FLq(G)Image implies vLq(G)Image for all q(1,)Image. Note that the case q<2Image, where q is below the duality exponent pImage, is included. In that situation existence of weak solutions is not clear a priori. There has been a great deal of effort in obtaining a corresponding result for the nonlinear case p2Image such that

FLq(G)|v|p2vLq(G)q(1,)

Image (1.3.25)

together with a corresponding estimate. This has been positively answered in the fundamental paper by Iwaniec [97] provided qpImage. An improvement to q>pδImage for some small δ>0Image has been carried out in [98] by different methods (for an overview and further references see [113]). We remark that the case q(1,pδ)Image is still open. Based on the Lipschitz truncation we can give a relatively easy proof for the estimate in the case q(pδ,p)Image using the approach in [141] (see also [40] for a more general setting and [101] for the parabolic problem), see Lemma 1.3.4 below.

Before we give proofs of these applications we present an important improvement of the Lipschitz truncation which firstly appeared in [62]. It concerns the smallness of the level-sets. Similar ideas have been used earlier for the LImage-truncation in [78].

Lemma 1.3.2

Let vLp(Rd)Image with p(1,)Image. Then there exist j0NImage and a sequence λjRImage with 22jλj22j+11Image such that

λpjLd({xRd:M(v)>λj})cp2jvpp

Image

for all jj0Image where c=cpImage is the constant in Lemma 1.3.1 b).

Proof

We have

M(v)pp=Rd0ϑp1χ{|v|>ϑ}dϑdx=RdmZ2m+12mϑp1χ{|M(v)|>ϑ}dϑdxRdmZ(2m)pχ{|M(v)|>2m+1}dxjN2j+11k=2jRd(2k)pχ{|M(v)|>22k}dx.

Image (1.3.26)

The continuity of M on Lp(Rd)Image, see Lemma 1.3.1 b), implies

jN2j+11k=2jRd(2k)pχ{|M(v)|>22k}dxcpvpp.

Image

In particular, for all jNImage

2j+11k=2jRd(2k)pχ{|M(v)|>22k}dxcpvpp.

Image

Since the sum contains 2jImage summands, there is at least one index kjImage such that

Rd(2kj)pχ{|M(v)|>22kj}dxcpvpp2j.

Image (1.3.27)

Define λj:=2kjImage and we conclude from (1.3.27) that

Rd(λj)pχ{|M(v)|>2λj}dxcpvpp2j.

Image

This proves the claim.  □

Lemma 1.3.2 shows that there is a particular sequence of levels (λj)Image such that

λpjLd(Oλj)κjvpp

Image (1.3.28)

with κj0Image for jImage. This improves the estimate (1.3.19) and does not follow from the original results by Acerbi and Fusco. It allows us to simplify the original proof of W1,pImage-lower semi-continuity from [1].

Lemma 1.3.3

Assume that the functionals GFImage defined in (1.3.22) are W1,Image-weakly lower semi-continuous for any choice of F satisfying (1.3.21). Then they are W1,pImage-weakly lower semi-continuous.

Proof

Let (vn)W1,p(G)Image be a sequence with weak limit v such that

un:=vnv0inW1,p(G).

Image

We take the sequence (λj)Image in accordance with Lemma 1.3.2 for the level-sets of uImage. We apply the Lipschitz truncation to the sequence (un)Image with level λ=λjImage, see the construction after (1.3.17), and obtain for the double sequence (un,j:=un,λj)Image

un,jcλj

Image (1.3.29)

un,j0inW1,(G)

Image (1.3.30)

λpjLd({xRd:unun,j})κj

Image (1.3.31)

due to (1.3.18)(1.3.20), where κj0Image for jImage. We obtain

liminfmG[vn]=liminfnGF(,v+un)dxliminfnGOλjF(,v+un)dx=liminfnGOλjF(v+un,j)dxliminfnGF(,v+un,j)dxlimsupnOλjF(,v+un,j)dx.

Image (1.3.32)

We can use the functional G˜FImage with

˜F(x,Q)=F(x,v+Q),QRd×D.

Image

The function ˜FImage has p-growth as required in (1.3.21) (by vLp(Ω)Image) such that

liminfnGF(,v+un,j)dx=liminfnG˜F[un,λj]G˜F[0]=GF[v].

Image (1.3.33)

This is a consequence of the W1,Image-weak lower semi-continuity of G˜FImage. Finally we have

OλjF(,v+un,j)dxcOλj(|v|p+g)dx+cOλj|un,j|pdxcOλj(|v|p+g)dx+cλdLd(Oλj)

Image

such that

limsupλlimsupnOλjF(,v+un,j)dx=0

Image (1.3.34)

by (1.3.31) and |v|p+gL1(G)Image. Combining (1.3.32)(1.3.34) shows that

GF[v]liminfmGF[vn],

Image

i.e. GFImage is W1,pImage-weakly lower semi-continuous.  □

We now turn to the proof of the lower integrability for the p-Laplace system. In addition to the Lipschitz truncation crucial ingredients are the following integral identities. Let 0<ϱ<Image, 0δ_<ϱ<δImage and (X,Σ,μ)Image be a measure space. There holds for every μ-measurable function f with |f|ϱL1(X,Σ,μ)Image

0ϑϱ1δ_({|f|>ϑ}|f|δ_dμ)dϑ=1ϱδ_X|f|ϱdμ,

Image (1.3.35)

0ϑϱ1δ({|f|ϑ}|f|δdμ)dϑ=1δϱX|f|ϱdμ.

Image (1.3.36)

Both equalities are easy consequences of Fubini's Theorem. As we will apply (1.3.35) and (1.3.36) several times it is important that all estimates hold for any λ>0Image. So Lemma 1.3.2 is no use.

Lemma 1.3.4

There is a number δ>0Image such that for all q(pδ)Image the following holds. Let vW1,q(p1)0(G)Image be a weak solution to (1.3.23) with FLq(G)Image. Then we have

G||v|p2v|qdxcG|F|qdx.

Image

Proof

Take the solution v to (1.3.23) and use its Lipschitz truncation vλImage as a test-function, see the construction after (1.3.17). Note that the Lipschitz truncation can preserve zero boundary values at least in the case of a Lipschitz boundary, cf. [62, Thm. 3.2]. We obtain

RdOλ|v|p2v:vλdx=Oλ|v|p2v:vλdx+RdF:vλdx

Image

which implies by (1.3.18)

RdOλ|v|pdxcλOλ|v|p1dx+Rd|Fvλ|dx.

Image (1.3.37)

Furthermore, the following holds

{|v|λ}|v|pdx={|v|λ}Oλ|v|pdx+{|v|λ}Oλ|v|pdxλOλ|v|p1dx+RdOλ|v|pdx.

Image

Inserting this into (1.3.37) yields

{|v|λ}|v|pdxcλOλ|v|p1dx+Rd|Fvλ|dx.

Image

As a consequence of Lemma 1.3.1 d) we deduce

{|v|λ}|v|pdxcλ{|v|>λ/2}|v|p1dx+Rd|Fvλ|dx.

Image

After multiplying with λq1pImage and integrating we have

0λq1p({|v|λ}|v|pdx)dλc0λqp{|v|>λ}|v|p1dxdλ+0λq1pRd|Fvλ|dxdλ.

Image

Setting χλ:=Rd|Fvλ|dxImage we obtain on account of (1.3.35) and (1.3.36)

1pq|v|qdxcqp+1|v|qdx+0λq1pχ(λ)dλ.

Image

If q is close enough to p, say pq<˜δImage, we have

cqp+1<1pq

Image

and hence

|v|qdxc0λq1pχ(λ)dλ.

Image

We split χ(λ)=χ1(λ)+χ2(λ)Image where

χ1(λ)={M(v)λ}|Fvλ|dx,χ1(λ)={M(v)>λ}|Fvλ|dx.

Image

Note that we have |vλ|=|v|M(v)Image on {M(v)λ}Image. Setting μ=|F|LdImage and using (1.3.36) (with f=M(v)Image, δ=1Image and ϱ=qp+1Image) as well as Hölder's inequality we obtain

0λq1pχ1(λ)dλ0λq1p{M(v)λ}M(v)dμdλ=1pqRdM(v)qp+1dμ=1pqRdM(v)qp+1|F|dx1pqFq/(p1)M(v)qp+1qcFq/(p1)vqp+1q

Image

and similarly by (1.3.35) (with f=1Image, δ_=0Image and ϱ=qp+1Image)

0λq1pχ2(λ)dλc0λqp{M(v)>λ}dμdλ=cqp+1RdM(v)qp+1dμcFq/(p1)vqp+1q.

Image

Combining the estimates above implies

G|v|qdxcG|F|qp1dxq(p˜δ,p)

Image

or equivalently on setting δ=˜δp1Image

G||v|p2v|qdxcG|F|qdxq(pδ,p).

Image

 □

We now turn to an alternative approach for the extension of u|RdOλImage into the “bad set” which has been used in [33] and [60]. Instead of using classical extension theorems as in the definition after (1.3.17) one can work with a Whitney covering of the “bad set” and local approximations. This is much more flexible and allows for instants to cut only parts of the gradient (in particular the symmetric gradient) or to work with higher derivatives, cf. Chapter 3. In fact, this is so far the only successful method for parabolic problems, cf. Section 5.2. The following lemma shows how to decompose an open set. It has been proved in [33] and [65] by slightly modifying the family of closed dyadic cubes given in [93].

Lemma 1.3.5

Let ORdImage be open. There is a Whitney covering {Qi}Image of OImage with the following properties.

(W1) jQj=OImage and QjQk=Image for jkImage.

(W2) 8d(Qj)dist(Qj,O)32d(Qj)Image. In particular, if cd:=2+32dImage, then (cdQj)(RdO)Image.

(W3) If the boundaries of the two cubes QjImage and QkImage touch, then

12(Qj)(Qk)2.

Image

(W4) For a given QjImage there exists at most (3d1)2dImage cubes QkImage that touch QjImage.

On setting Qj:=98QjImage and rj:=(Qj)Image we have the following properties.

Corollary 1.3.1

Under the assumptions of Lemma 1.3.5 the following holds

(W5) jQj=OImage.

(W6) If QjImage and QkImage intersect, then the boundaries of QjImage and QkImage touch and Qj5QkImage, moreover rjrkImage and |QjQk||Qj||Qk|Image.

(W7) The family QjImage is locally 6dImage finite.

(W8) jLd(Qj)c(d)Ld(O)Image.

Lemma 1.3.6

Let ORdImage be open, {Qj}Image its Whitney covering from Lemma 1.3.5 and Qj=98QjImage. Then there is a partition of unity {φj}Image having the following properties.

(U1) φjC0(Rd)Image and suppφj=QjImage.

(U2) χ79Qj=χ78Qjφjχ98Qj=χQjImage.

(U3) |φj|cχQjrjImage and |2φj|cχQjr2jImage.

Proof

Let ˜φjC0(Rd)Image be such that supp˜φj=QjImage and

χ79Qj=χ78Qj˜φjχ98Qj=χQj.

Image

Moreover, we assume that all ˜φjImage the same function are up to translation and dyadic scaling. We define γ:=j˜φjImage and φj:=˜φjγImage such that 1γ6dImage as well as

|γ|χQjc1rjjN.

Image

Thus φjImage defines a partition of unity with the required properties.  □

For uW1,p0(G)Image (extended by zero to RdImage) we define as in (1.3.17) “the bad” set by

Oλ:={xRd:M(u)>λ}.

Image

We apply Corollary 1.3.1 and Lemma 1.3.6 to OλImage to obtain a covering {Qj}Image and functions {φj}Image. Now we define

uλ:=uiIφi(uui),

Image (1.3.38)

where ui:=uQi:=QiudxdtImage. (In order to obtain a truncation with zero boundary values one has to involve cut-off function, see Chapter 3, or set ui=0Image close to the boundary, see [60].) We show first that the sum in (1.3.38) converges absolutely in L1(Rd)Image:

Rd|uuλ|dxciQi|uui|dxciQi|u|dxcRd|u|dx,

Image

where we used (U2) and the finite intersection property of QiImage, cf. (W7). We proceed by showing the estimate for the gradient

Rd|(uuλ)|dxciQi|(φi(uui))|dxciQi|u|+|uuiri|dxdtciQi|u|dxcRd|u|dx,

Image

where we used Poincaré's inequality. This shows that the definition in (1.3.38) makes sense. In particular we have

uλ={uinRdOλ,iφiuiinOλ.

Image (1.3.39)

In the following we show that uλImage is indeed Lipschitz continuous with Lipschitz constant bounded by λ.

Lemma 1.3.7

The following holds

uλL(Rd)cλ.

Image

Proof

Let xQiImage and Ai:={j:QjQi}Image, then

|uλ(x)|=|jAi(φjuj)(x)|jAi|(φj(ujui))(x)|cjAi|ujuiri|c5Qi|uuiri|dx

Image

because {φj}Image is a partition of unity, rirjImage and uiImage is constant. We also used (W6), (U3) as well as #AjcImage. By Poincaré's inequality, (W2) and the definition of OλImage we have

|uλ(x)|c5Qi|u|dxccdQi|u|dxcλ.

Image

As the {Qi}Image cover OλImage and |uλ|=|u|λImage outside OλImage the claim follows.  □

1.4 Existence results for power law fluids

The stationary flow of a homogeneous incompressible fluid in a bounded body GRdImage (d=2,3Image) is described by the equations

{divS(ε(v))=ρ(v)v+πρfinGdivv=0inG,v=0onG.

Image (1.4.40)

See for instance [23]. In physical terms this means that the fluid reached a steady state – a situation of balance. The unknown quantities are the velocity field v:GRdImage and the pressure π:GRImage. The function f:GRdImage represents a system of volume forces, while S:GRd×dsymImage is the viscous stress tensor and ρ>0Image is the density of the fluid. In order to describe a specific fluid one needs a constitutive law relating the viscous stress tensor S to the symmetric gradient ε(v):=12(v+vT)Image of the velocity v. In the simplest case this relation is linear, i.e.,

S=S(ε(v))=2νε(v),

Image (1.4.41)

where ν>0Image is the viscosity of the fluid. In this case we have divS=νΔvImage and (1.4.40) are the stationary Navier–Stokes equations (for a recent approach see [85,86]). The existence of a weak solution (where derivatives are to be understood in a distributional sense) can be established by arguments which are nowadays standard. In the case of the constitutive relation (1.4.41) the system (1.4.40) can be analysed like a linear system – the arguments used to handle the perturbation caused by (v)vImage are of a technical nature (note that this is quite different from the parabolic situation), and standard techniques lead to smooth solutions (see for instance [86]).

Only fluids with simple molecular structure e.g. water, oil and certain gases satisfy a linear relation such as (1.4.41). Those which do not are called non-Newtonian fluids (see [13]). A special class among these are generalized Newtonian fluids. Here, the viscosity is assumed to be a function of the shear rate |ε(v)|Image and the constitutive relation is

S(ε(v))=ν(|ε(v)|)ε(v).

Image (1.4.42)

An external force can produce two different reactions:

• The fluid becomes thicker (for example batter): the viscosity of a shear thickening fluid is an increasing function of the shear rate.

• The fluid becomes thinner (for example ketchup): the viscosity of a shear thinning fluid is a decreasing function of the shear rate.

The power law model for non-Newtonian/generalized Newtonian fluids

S(ε(v))=ν0(1+|ε(v)|)p2ε(v)

Image (1.4.43)

is very popular among rheologists. Here ν0>0Image and p(1,)Image is specified by physical experiments. An extensive list of specific p-values for different fluids can be found in [23]. It becomes clear that many interesting p-values lie in the interval [32,2]Image. In the following we give a historical overview concerning the theory of weak solutions to (1.4.40) and sketch the proofs, cf. [29].

Monotone operator theory (1969).

The mathematical discussion of power law models started in the late sixties with the work of Lions and Ladyshenskaya (see [106108] and [109]). Due to the appearance of the convective term div(vv)Image the equations for power law fluids (the constitutive law is given by (1.4.43)) depend significantly on the value of p. In the stationary case, the existence of a weak solution to (1.4.44), (1.4.43) can be shown by monotone operator theory for p3dd+2Image. To be precise, there is a function vW1,p0,div(G)Image such that

GS(ε(v)):ε(φ)dx=ρG(v)vφdx+ρGfφdx

Image (1.4.44)

for all φC0,div(G)Image. Note that this formulation has the advantage that the pressure does not appear but can easily be recovered later by De Rahm theory (this was first used in [109]). For the recovery of the pressure see Theorem 2.2.10. Also note that the divergence-free constraint and homogeneous boundary conditions are incorporated in the definition of the space W1,p0,div(G)Image. The condition

p>3dd+2

Image (1.4.45)

ensures that the solution itself is a test-function and the convective term is a compact perturbation. We begin with the approach based on monotone operator theory (see [109]). It does not yet contain truncations, but it is the basis of the existence theory and everything is build upon it. Let us assume that (1.4.45) holds and that we have a sequence of approximate solutions, i.e. (vn)W1,p0,div(G)Image solving (1.4.44). We want to pass to the limit. By (1.4.45), Sobolev's embedding Theorem and smooth approximation, (1.4.44) holds also for all φW1,p0,div(G)Image. So vnImage is an admissible test-function. Since G(vn)vnvndx=0Image we obtain a uniform a priori estimate in W1,p(G)Image and (after choosing an appropriate subsequence)

vnvinW1,p0,div(G).

Image (1.4.46)

Note that we also used the coercivity from (1.4.43) and Korn's inequality. Using (1.4.43) again yields

S(ε(vn))˜SinLp(G).

Image (1.4.47)

The nonlinearity in the convective term (vn)vnImage can be overcome by compactness arguments. Kondrachov's Theorem and (1.4.45) imply

vnvinL2p(G)

Image (1.4.48)

and so

(vn)vn(v)vinL2pp+1(G).

Image (1.4.49)

Using (1.4.46)(1.4.49) we can pass to the limit in the equation and obtain

G˜S:ε(φ)dx=G(v)vφdx+Gfφdx

Image (1.4.50)

for all φW1,p0,div(G)Image. It remains to be shown

˜S=S(ε(v)).

Image (1.4.51)

As S is nonlinear the weak convergence in (1.4.46) is not enough for this limit procedure. We have to apply methods from monotone operator theory. Let us consider the integral

G(S(ε(vn))S(ε(v))):(ε(vn)ε(v))dx=GS(ε(vn)):(ε(vn)ε(v))dxGS(ε(v)):(ε(vn)ε(v))dx.

Image

The second term on the right-hand-side vanishes for nImage as a consequence of (1.4.46) and S(ε(v))Lp(G)Image. For the first term one we use the equation for vnImage and obtain

GS(ε(vn)):(ε(vn)ε(v))dx=G(vn)vn(vnv)dx+Gf(vnv)dx0,n.

Image

This is a consequence of (1.4.46) and (1.4.49). Plugging all together we have shown

G(S(ε(vn))S(ε(v))):(ε(vn)ε(v))dx0,n.

Image

The strict monotonicity of S implies ε(vn)ε(v)Image a.e. and hence (1.4.51).

L Image -truncation (1997).

Examining the three-dimensional situation we see that the bound p>95Image is very restrictive since many interesting liquids lie beyond it. For example polyethylene oxide (polyethylene is the most common plastic) has lower flow behaviour indices: the experiments presented in [23] (table 4.1-2, p. 175) suggest values between 1.53 and 1.6 depending on the temperature. The first attempt to lower the bound for p was an approach via LImage-truncation by Frehse, Málek and Steinhauer (see [78], see also [129]). The term

G(v)vφdx

Image

is defined for all φL(G)Image if

p>2dd+1.

Image (1.4.52)

Instead of testing the equation by v (which is not permitted) they used the function vLL(G)Image, L1Image, whose LImage-norm is bounded by L and which equals v on a large set.

In order to give an overview of this method we assume that (1.4.52) holds and that we have a sequence of approximated solutions to (1.4.44) with uniform a priori estimates in W1,p0,div(G)Image. Note that test-functions have to be bounded as (v)vImage is only an integrable function. We will demonstrate how to obtain a weak solution combining ideas of [78] and [140].

Again we have (1.4.46) and (1.4.47) but instead of (1.4.48) and (1.4.49) only the following hold

vnvinLp(G),

Image (1.4.53)

(vn)vn(v)vinLσ(G),

Image (1.4.54)

where σ:=pdp(d+1)2d(1,)Image, cf. (1.4.52). We still obtain (1.4.50) for all φW1,p0,divL(G)Image and the goal is to show (1.4.51). We are faced with the problem that the solution is not an admissible test-function any more. So an approach via monotone operator theory as described before will fail. Instead of testing with un:=vnvImage we use a truncated function. As functions from the class W1,p0,divL(G)Image are admissible we cut values of unImage which are too large and obtain a bounded function. For LNImage we define

ΨL:=L=1ψ2,ψδ(s):=ψ(δs),

Image

where ψC0([0,2])Image, 0ψ1Image, ψ1Image on [0,1]Image and 0ψ2Image. Now we use the test-function un,L:=ΨL(|un|)unImage and neglect for a moment the fact that it is not divergence-free. For fixed L the function un,LImage is essentially bounded (in terms of L) and we obtain for nImage

un,L0inLq(G)for allq<.

Image (1.4.55)

Now we test with un,LImage which implies (using (1.4.54) and (1.4.55) for the integral G(vn)vnun,LdxImage)

limsupnGΨL(|un|)(S(ε(vn))S(ε(v))):ε(un)dxlimsupnGΨL(|un|)(S(ε(vn))S(ε(v))):ΨL(|un|)undx.

Image (1.4.56)

Now one needs that

ΨL(|un|)unLp(G)

Image

uniformly in L and n which follows from the definition of ΨLImage. This allows us to show that the left-hand-side of (1.4.56) is bounded in L and hence there is a subsequence (in fact one has to take a diagonal sequence) such that for nImage

σ,n:=G(S(ε(vn))˜S)):ψ2(|un|)ε(un)dxσ,N0.

Image

One can show easily that σImage is increasing in and so σ0=0Image, i.e.,

G(S(ε(vn))S(ε(v))):ψ1(|un|)ε(un)dx0,n0.

Image (1.4.57)

As ψ1(t)=1Image for t1Image and un0Image in L2(G)Image this yields

G((S(ε(vn))S(ε(v))):ε(un))Θdx0,n0,

Image (1.4.58)

for all Θ<1Image. Due to the monotonicity of S we deduce (1.4.51).

As divun,L0Image we have to correct the divergence by means of the Bogovskiĭ-operator. It is solution operator to the divergence equation with respect to zero boundary conditions. See Section 2.1. Additional terms appear which can be handled similarly.

Remark 1.4.5

In [78] the limit case p=2dd+1Image is also included based on the fact that (v)vImage has divcurlImage structure and hence belongs to the Hardy space H1(Rd)Image.

Lipschitz truncation (2003).

Although we can now cover a wide range of power law fluids there remain several with lower values of p. The experiments presented in [23] (table 4.1-2, p. 175) suggest values for 2% hydroxyethylcellulose (hydroxyethylcellulose is a gelling and thickening agent derived from cellulose, used in cosmetics, cleaning solutions, and other household products) between 1.19 and 1.25 depending on the temperature.

Since divv=0Image we can rewrite

G(v)vφdx=Gvv:ε(φ)dx,

Image

so that appropriate test-functions have to be Lipschitz continuous provided vvL1(G)Image. This condition is satisfied for p2dd+2Image by Sobolev's embedding. Otherwise one cannot define the convective term (at least in the stationary case). This bound therefore seems to be optimal.

In the case

p>2dd+2

Image (1.4.59)

the existence of a weak solution to (1.4.44), (1.4.43) was first established in [79]. This is the first paper where the Lipschitz truncation was used in the context of fluid mechanics. Here one approximates the function v by a Lipschitz continuous function vλImage with vλcλImage instead of a bounded function as in the approach via LImage-truncation.

Assume that (1.4.59) holds and that we have a sequence of solutions (vn)W1,p0,div(G)Image to

GS(ε(vn)):ε(φ)dx=Gvnvn:φdx+Gfφdx

Image (1.4.60)

for all φW1,0,div(G)Image which is uniformly bounded. Again we have (1.4.46) and (1.4.47) and by Kondrachov's Theorem and (1.4.59)

vnvinL2σ(G),vnvnvvinLσ(G),

Image (1.4.61)

where σ(1,12pddp)Image, cf. (1.4.59). So we can pass to the limit in (1.4.60) and obtain

G˜S:ε(φ)dx=Gvv:φdx+Gfφdx.

Image (1.4.62)

In order to show ˜S=S(ε(v))Image it is enough to have (1.4.58). Introduce the Lipschitz truncation un,λImage of un:=vnvImage, cf. Section 1.3. Then (1.4.58) follows from

G(S(ε(vn))S(ε(v))):ε(un,λ)dx0,n0,

Image (1.4.63)

and (1.3.31). As a consequence of un,λcλImage the Lipschitz truncation features much better convergence properties than the original function. In particular, we have

un,λ0inL(G),un,λ0inL(G),

Image

recall (1.3.29) and (1.3.30). Taking this into account, (1.4.63) follows from (1.4.60) and (1.4.61).

We again neglected the fact that divun,λ0Image. There are two options for overcoming this. In [79] the authors introduce the pressure πnImage and decompose it with respect to the terms appearing in the equation. This requires some technical effort but all terms can be handled. An easier way is presented in [62] where the divergence is corrected using the Bogovskiĭ operator as indicated in the approach via LImage-truncation.

References

[1] E. Acerbi, N. Fusco, Semicontinuity problems in the calculus of variations, Arch. Ration. Mech. Anal. 1984;86(2):125–145.

[2] E. Acerbi, N. Fusco, A regularity theorem for minimizers of quasiconvex integrals, Arch. Ration. Mech. Anal. 1987;99(3):261–281.

[3] E. Acerbi, N. Fusco, An approximation lemma for W1,pImage functions, In: Material Instabilities in Continuum Mechanics. Edinburgh, 1985–1986. Oxford Sci. Publ.. New York: Oxford Univ. Press; 1988:1–5.

[5] R.A. Adams, Sobolev Spaces. Pure Appl. Math.. New York: Academic Press, Inc.; 1975;vol. 65.

[13] G. Astarita, G. Marrucci, Principles of Non-Newtonian Fluid Mechanics. London–New York: McGraw–Hill; 1974.

[16] J.M. Ball, Convexity conditions and existence theorems in nonlinear elasticity, Arch. Ration. Mech. Anal. 1977;63:337–403.

[23] R. Bird, R. Armstrong, O. Hassager, Dynamics of Polymeric Liquids, vol. 1: Fluid Mechanics. second edition John Wiley; 1987.

[29] D. Breit, Existence theory for generalized Newtonian fluids, In: Recent Advances in Partial Differential Equations and Applications. Contemp. Math.. Providence, RI: Amer. Math. Soc.; 2016;vol. 666:99–110.

[33] D. Breit, L. Diening, M. Fuchs, Solenoidal Lipschitz truncation and applications in fluid mechanics, J. Differ. Equ. 2012;253:1910–1942.

[40] M. Bulíček, S. Schwarzacher, Existence of very weak solutions to elliptic systems of p-Laplacian type, Calc. Var. Partial Differ. Equ. 2016;55:52.

[43] A.P. Calderón, A. Zygmund, Singular integrals and periodic functions, Stud. Math. 1954;14:249–271.

[46] A. Cianchi, A sharp embedding theorem for Orlicz–Sobolev spaces, Indiana Univ. Math. J. 1996;45:39–65.

[60] L. Diening, Ch. Kreuzer, E. Süli, Finite element approximation of steady flows of incompressible fluids with implicit power-law-like rheology, SIAM J. Numer. Anal. 2013;51(2):984–1015.

[62] L. Diening, J. Málek, M. Steinhauer, On Lipschitz truncations of Sobolev functions (with variable exponent) and their selected applications, ESAIM Control Optim. Calc. Var. 2008;14:211–232.

[65] L. Diening, M. Růžička, J. Wolf, Existence of weak solutions for unsteady motions of generalized Newtonian fluids, Ann. Sc. Norm. Super. Pisa, Cl. Sci. (5) 2010;IX:1–46.

[71] H. Federer, Geometric Measure Theory. Grundlehren Math. Wiss.. Berlin: Springer Verlag; 1969;vol. 153.

[78] J. Frehse, J. Málek, M. Steinhauer, An existence result for fluids with shear dependent viscosity—steady flows, Nonlinear Anal. 1997;30:3041–3049.

[79] J. Frehse, J. Málek, M. Steinhauer, On analysis of steady flows of fluids with shear-dependent viscosity based on the Lipschitz truncation method, SIAM J. Math. Anal. 2003;34(5):1064–1083.

[85] G.P. Galdi, An introduction to the mathematical theory of the Navier–Stokes equations, vol. I. Springer Tracts Nat. Philos.. Berlin–New York: Springer; 1994;vol. 38.

[86] G.P. Galdi, An Introduction to the Mathematical Theory of the Navier–Stokes Equations, vol. II. Springer Tracts Nat. Philos.. Berlin–New York: Springer; 1994;vol. 39.

[93] L. Grafakos, Classical and Modern Fourier Analysis. Upper Saddle River, NJ: Pearson Education, Inc.; 2004.

[97] T. Iwaniec, Projections onto gradient fields and LpImage-estimates for degenerated elliptic operators, Stud. Math. 1983;75(3):293–312.

[98] T. Iwaniec, p-harmonic tensors and quasiregular mappings, Ann. Math. (2) 1992;136(3):589–624.

[101] J. Kinnunen, J.L. Lewis, Very weak solutions of parabolic systems of p-Laplacian type, Ark. Mat. 2002;40(1):105–132.

[106] O.A. Ladyzhenskaya, The Mathematical Theory of Viscous Incompressible Flow. New York: Gorden and Breach; 1969.

[107] O.A. Ladyzhenskaya, On some new equations describing dynamics of incompressible fluids and on global solvability of boundary value problems to these equations, Trudy Steklov's Math. Inst. 1967;102:85–104.

[108] O.A. Ladyzhenskaya, On some modifications of the Navier–Stokes equations for large gradients of velocity, Zap. Nauč. Semin. Leningrad. Otdel. Mat. Inst. Steklov (LOMI) 1968;7:126–154.

[109] J.L. Lions, Quelques méthodes de résolution des problèmes aux limites non linéaires. Paris: Dunod, Gauthier-Villars; 1969.

[112] J. Malý, W.P. Ziemer, Fine Regularity of Solutions of Elliptic Partial Differential Equations. American Mathematical Society; 1997.

[113] G. Mingione, Towards a nonlinear Calderon–Zygmund theory, Quad. Mat. 2009;23:371–458.

[115] C.B. Morrey, Quasi-convexity and the semicontinuity of multiple integrals, Pac. J. Math. 1952;2:25–53.

[125] M.M. Rao, Z.D. Ren, Theory of Orlicz Spaces. Pure Appl. Math.. New York–Basel–Hong Kong: Marcel Dekker, Inc.; 1991;vol. 146.

[129] M. Růžička, Electrorheological Fluids: Modeling and Mathematical Theory. Lect. Notes Math.. Berlin: Springer; 2000;vol. 1748.

[134] E.M. Stein, Harmonic analysis: real-variable methods, orthogonality, and oscillatory integrals, In: Monographs in Harmonic Analysis, III. Princeton, NJ: Princeton University Press; 1993 with the assistance of Timothy S. Murphy.

[140] J. Wolf, Existence of weak solutions to the equations of nonstationary motion of non-Newtonian fluids with shear-dependent viscosity, J. Math. Fluid Mech. 2007;9:104–138.

[141] B. Yan, On a reverse estimate for Hodge decompositions of p-Laplacian type operators, J. Differ. Equ. 2001;173:160–177.

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