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Quasilinear parabolic problems with nonlinear boundary conditions

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Then it follows that gw is of the form<br />

gw = da ∗ ψ1 + db ∗ ψ2, <strong>with</strong> ψ1, ψ2 ∈ Y. (5.18)<br />

In case that p > 1 + 2/δa, we discover the additional compatibility <strong>conditions</strong><br />

ψ1|t=0 = 2<br />

3γ∇x · v0 − 4<br />

3γ∂yw0, ψ2|t=0 = −γ∇x · v0 − γ∂yw0, if p > 1 + 2 . (5.19)<br />

δa<br />

All in all we have established necessity of<br />

(N1) (5.13), (5.14), (5.15), (5.16), (5.17), (5.18), (5.19).<br />

5.3.2 The case δa ≤ δb: sufficiency of (N1)<br />

We now want to prove the converse. So assume that the data satisfy all <strong>conditions</strong> in<br />

(N1). At first glance, it seems to be a hard task to solve (5.12) for it is a coupled system<br />

in the unknown functions v, w and since, in contrast to the previous <strong>problems</strong>, we have<br />

to cope <strong>with</strong> two kernels. To overcome these difficulties, the basic idea is to introduce an<br />

appropriate auxiliary function p by the aid of which (5.12) can be decoupled and made<br />

amenable to the results from Chapter 3.<br />

a and introduce the inverse convolution operators<br />

To start <strong>with</strong>, we set k = b + 4<br />

3<br />

A = (a∗) −1 and K = (k∗) −1 in Lp(J; X), where X is Lp(R n+1<br />

+ ) or Lp(Rn ). This<br />

makes sense since a and b are of type (E) and in view of Lemma 2.6.2(ii). Further let<br />

F = (A + Dn) 1<br />

2 and G = (K + Dn) 1<br />

2 <strong>with</strong> natural domains, Dn denoting the negative<br />

Laplacian on R n . We proceed now in three steps.<br />

Step 1. Assume for the moment that (v, w) is a solution of (5.12) and define the pressure<br />

p by means of<br />

p = −∇x · v − ∂yw. (5.20)<br />

Then it follows from (5.12) that<br />

and<br />

⎧<br />

⎪⎨<br />

⎪⎩<br />

∂tv − da ∗ (∆xv + ∂2 yv) + (db + 1<br />

∂tw − da ∗ (∆xw + ∂2 yw) + (db + 1<br />

3da) ∗ ∇xp = fv (J × R n+1<br />

+ )<br />

3da) ∗ ∂yp = fw (J × R n+1<br />

+ )<br />

−da ∗ γ∂yv − da ∗ γ∇xw = gv (J × R n )<br />

−γ∂yw − γ∇x · v = γp (J × R n )<br />

v|t=0 = v0 (R n+1<br />

+ )<br />

w|t=0 = w0 (R n+1<br />

+ )<br />

(5.21)<br />

−da ∗ γ∂yw + 1 2<br />

1<br />

2 (db − 3da) ∗ γp = 2gw. (5.22)<br />

Applying −∇x· to the first, −∂y to the second equation of (5.21) and adding them yields<br />

further<br />

�<br />

∂tp − (db + 4<br />

3da) ∗ (∆xp + ∂2 yp) = −∇x · fv − ∂yfw (J × R n+1<br />

+ )<br />

p|t=0 = −∇x · v0 − ∂yw0 (R n+1<br />

+ ),<br />

(5.23)<br />

where here ∇x· and ∂y are meant in the distributional sense. This shows one direction<br />

of<br />

(5.12), (5.20) ⇔ (5.21), (5.22), (5.23). (5.24)<br />

84

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