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David Parra-Guevara et al Int. Journal of Engineering Research and Applications ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.763-776 emission rate Q(t ) . The condition (67) means that there is no pollutant flow at x  0 (the closed boundary), and condition (68) means that at x  1 (the open boundary) the flow of pollutant is proportional to  with   0 . As a result, the mass of pollutant in D increases due to the emission rate Q and decreases because of the outflow at the boundary point x  1 according to the following mass balance equation:

  ( x, t )dx  Q(t )   (1, t ) t 0 1

 2 A    2 x

2

 ( x, t )     dk Q( )e   t   d  cos(k x) k 1

2 k

0

identity

 A , g    , A g 

diffusion model (66)-(69) leads to the following adjoint model

g 2 g   2  p( x, t ), 0  x  1, 0  t  T t x g  (0, t )  0, t  0 x g  (1, t )   g (1, t ), t  0 x g ( x,T )  0, 0  x  1 the

forcing

 2 sin  k ]1 , k  1, 2,K , 

is an orthogonal system of functions

(eigenfunctions), and the corresponding frequencies k (eigenvalues) are the roots of equation

 cos( )-sin( ) = 0 . Such frequencies can be efficiently calculated by Newton’s method [35]. Note that for the numerical experiments of this section we used the Fejér series [36,37], calculated from the Fourier series (70), in order to have the pointwise convergence to  ( x, t ) . That is, in practice we consider in (70) the Fejér coefficients: www.ijera.com

is and

(71) (72) (73) (74)

defined R is

as the

tj

 (R, t j )   Q(t ) g j ( x0 , t )dt 0

that corresponds to equation (11). Here, the adjoint function g j can be expressed as 

g j ( x0 , t )   ck e

 k2  t j t

k 1

cos(k x)k 1

the

(70)

where

d k  2cos(k x0 )[1 

to

monitoring point in domain D . As it was shown in Section II, the use of solution of adjoint problem (71)-(74) leads to the formula

By solving the Sturm-Liouville problem for the diffusion model (66)-(69), its non-negative solution can be expressed by Fourier series [34] as:

Lagrange

p( x, t )   ( x  R ) (t  t j ) ,

  A ,       dx   2 (1, t )  x  0

instead of d k , where K is the truncation number of the series. On the other hand, the application of

where

is nonnegative:

t

)  (k  1)  d k  1   d k , k  1, 2,K K , K  

Similarly to the general dispersion model (19)-(27), the diffusion model (66)-(69) is well posed, since its solution exists, is unique and continuously depends on the initial condition and forcing. This follows from the fact that the problem operator A defined as

1

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cos(k x0 ), t  t j

(75)

where

ck  2cos(k R)[1 

 2 sin  k ]1 , k  1, 2,K 

are Fourier coefficients of series (75). In the numerical experiments, as it was mentioned before, we consider in the series (75) the Fejér coefficients

)  (k  1)  ck  1   ck , k  1, 2,K K , K   instead of c k , where K is the truncation number of the series. In this example, due to the steady dispersion conditions, the basic kernel 773 | P a g e

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