=PDASOLVE(eqns, vars, bcs, xdom, tdom, [options])
Note. PDASOLVE is available in ExceLab 7 only. Will be enabled in ExceLab 365 and Google Sheets in a future release.
PDASOLVE
is a versatile solver for partial differential equations that supports advanced modeling capabilities including:
Use
PDASOLVE
to solve a system of partial differential equations the following forms:
(the system can have as many equations as needed)
where $\mathit{u}=[{u}_{1},{u}_{2}]$, ${\mathit{u}}_{x}=[{u}_{1,x},{u}_{2,x}]$, ${\mathit{u}}_{xx}=[{u}_{1,xx},{u}_{2,xx}]$
Initial value for each variable u_{i} is required.
PDASOLVE
supports general boundary conditions that can be specified at arbitrary locations within the system's spatial domain.
The boundary condition types are described below:
eqns
a reference to the system formulas (f_{1}, f_{2},.., g_{1},..).
For a DAE system, algebraic equations must be listed after differential equations.
vars
a reference to the system variables in the following order (
t, x, u_{1}, u_{2},.. ,
u_{1,x}, u_{2,x},..,
u_{1,xx}, u_{2,xx},..
). The state variables (u_{1}, u_{2},..)
must be assigned initial conditions. An initial condition can be a constant or function of the spatial variable x.
All the variables and derivatives must be defined in the order listed above in
vars
regardless if they are used in the equations or not.
For a DAE system, algebraic variables should be orderd last in vars
.
bcs
A reference to a 3column matrix defining the system's boundary conditions.
The first column specifies the x locations of the boundary conditions,
the 2nd column specifies the types which can by any of the letters 'D', 'N', 'R' or 'C',
and the 3rd column specifies the boundary condition formulas.
A boundary condition formula is arranged with respect to zero on one side.
Example of a boundary conditions matrix
E  F  G  
10  0  D  =U11 
11  1  R  =UX10.5*U12 
12  0.5  C  =K1*UX1 
xdom
a vector defining the start and end values for the spatial domain as well as the desired solution output spatial values.
If an equation is defined only over a sub region of the spatial domain, also use argument 7 to define custom regions.
Format  Remarks 

{x_{s}, x_{e}}  The domain [x_{s}, x_{e}] is divided uniformly according to the allocated output array size. 
{x_{s}, x_{e}, ndiv}  The domain [x_{s}, x_{e}] is divided uniformly into ndiv subintervals. 
{x_{s}, x_{1}, x_{2}, .., x_{e}}  Solution results are reported for the supplied values only. 
tdom
a vector defining the start and end values for the time interval as well as the desired solution output time values.
Format  Remarks 

{t_{i}, t_{f}}  The interval [t_{i}, t_{f}] is divided uniformly according to the allocated output array size. 
{t_{i}, t_{f}, ndiv}  The interval [t_{i}, t_{f}] is divided uniformly intondiv subintervals. 
{t_{i}, t_{1}, t_{2}, .., t_{f}}  Solution results are reported for the supplied values only. 
m
the number of algebraic equations for an explicit DAE system, or the full mass Matrix for an implicit system.
rgns
A 2column matrix defining the subdomain for each equation. Each row specifies the left and right region endpoints for the corresponding equation in eqns
.
. By default, all equations in the system are assumed to share the entire spatial domain defined in argument 4.
tol
controls the absolute and relative error tolerances for temporal integration algorithm.
Default values are 1.0e4 for relative tolerance and 1.0e6 for absolute tolerance. Various formats are permitted as described below.
Format  Remarks 

rtol  Relative tolerance. Absolute tolerance value is set to rtol/100. Fixed for all variables. 
{rtol, atol}  Relative and absolute tolerance values. Fixed for all variables 
vector of rtol_{i}  custom relative tolerance for each system variable. Absolute tolerance is set to rtol_{i}/100. 
twocolumn array of {rtol_{i}, atol_{i}}  custom relative and absolute tolerances for each system variable. 
ctrl
a set of key/value pairs for algorithmic control as detailed below.
Description of key/value pairs for algorithmic control
Key  NDRVOUT 
Admissible Values (Integer) 

Default Value  0 
Remarks  The allocated results array must have sufficient columns to hold output. 
Key  FORMAT 
Admissible Values (String) 
XCOL1 TCOL1 
Default Value  XCOL1 
Remarks  XCOL1 format is convenient to plot snapshots of solution variables at a desired time values. TCOL1 format is convenient to plot transient views of solution variables at a desired spatial locations. See Output Format section for more detail. 
Key  MAXSTEPS 
Default Value  100000 (Integer) 
Remarks  Sets an upper bound on the maximum number of integration steps that can be carried out. 
Key  INITSTEP 
Default Value  1.0e5 (Real) 
Remarks  The step size is quickly adapted. A small value is recommended if the system has fast initial transients. 
Key  STEPLIMIT 
Default Value  Unbounded (Real) 
Remarks  By setting a limit, accuracy may be improved at the expense of speed. 
Key  MAX_GRID_SPACING 
Default Value  0.01 (Real) 
Remarks  Sets a a limit on the maximum distance between adjacent grid nodes. All distances between adjacent grid nodes will be less than or equal toMAX_GRID_SPACING. This value has direct impact on computational cost. 
Key  MIN_GRID_SPACING 
Default Value  0.000001 (Real) 
Remarks  Sets a limit on the minimum distance between adjacent grid nodes. All distances between adjacent grid nodes will greater than or equal toMIN_GRID_SPACING. 
Key  MAX_GRID_NODES 
Default Value  2000 (Integer) 
Remarks  Sets a limit on the maximum allowed number of generated nodes in the system spatial domain. 
Key  MIN_GRID_NODES 
Default Value  10 (Integer) 
Remarks  Sets a limit on the minimum number of generated nodes in any subregion of the system spatial domain. 
Key  JACSTEP 
Admissible Values (Real)  0 < JACSTEP < 0.1 
Default Value  The default step is computed dynamically based on machine accuracy, preset limits and function metric. For an order(1) function it is approximately 1.0e8. 
Remarks  The default value generally produces accurate approximations; however relaxing the default value may aid convergence on some problems with unknown Jacobian such as parameterized problems. 
key  JACSCHEME 
Admissible Values (Integer)  1 for First order Euler forward scheme 2 for Second order Euler forward scheme 
Default Value  1 
Remarks  First order scheme is generally sufficient. 
To illustrate PDASOLVE
output layout, we consider a 2equation system with the following variables
(t, x, u_{1}, u_{2}, u_{1,x}, u_{2,x}, u_{1,xx}, u_{2,xx})
The snapshot view is the default format. It allows you to easily plot snapshot views for the variables at desired time points. In this format, the solution is laid out as shown in Table 1. Output spatial points are listed in the first column starting at row 3, whereas output time points are listed in blocks in the first row starting at column 2 with variables names listed in the second row.
A  B  C  D  E  F  G  H  ..  
1  t →  t_{0}  t_{0}  t_{1}  t_{1}  t_{2}  t_{2}  ..  t_{f} 
2  x ↓  u_{1}  u_{2}  u_{1}  u_{2}  u_{1}  u_{2}  ..  u_{2} 
3  x_{s}  
4  x_{1}  u_{2}(x_{1},t_{0})  
5  x_{2}  
..  ..  
N  x_{e} 
The spatial and time domain are divided uniformly according to the number of allocated rows and columns for the output array.
You can control x and t values by specifying your own step or custom values in parameters 4 and 5
for PDASOLVE
.
If you allocate a larger array than needed, unused rows and columns will be filled with zeros.
You can find out the minimum array size needed by evaluating PDASOLVE
formula initially in one cell.
We can request to report 1st and 2nd derivative variables using the key NDRVOUT with value 1 or 2. Table 2 shows the modified layout with NDRVOUT value set to 1, in which values for u_{1,x}, and u_{2,x} are also included.
A  B  C  D  E  F  G  H  I  J  ..  
1  t →  t_{0}  t_{0}  t_{0}  t_{0}  t_{1}  t_{1}  t_{1}  t_{1}  ..  t_{f} 
2  x ↓  u_{1}  u_{2}  u_{1,x}  u_{2,x}  u_{1}  u_{2}  u_{1,x}  u_{2,x}  ..  u_{2,x} 
3  x_{s}  
4  x_{1}  
5  x_{2}  u_{1}(x_{2},t_{1})  
..  ..  
N  x_{e} 
In this format, the roles of x and t are interchanged. Output time points are displayed in the first column, whereas spatial points are displayed in the first row. This format allows you to easily plot transient views of the variables at desired spatial points.
This example is discussed in detail in the following publication:
Ghaddar C.K., Rapid Modeling and Parameter Estimation of Partial Differential Algebraic Equations by a Functional Spreadsheet Paradigm.Math. Comput. Appl. 2018, 23, 39.
Where
${\Phi}_{1}(0,x)=$ ${\Phi}_{2}(0,x)=$ ${\Phi}_{3}(0,x)=0$
x = 0  x = l_{c}  x = l_{s}  x = l_{a} 

$\sigma \left(0\right){\Phi}_{1,x}=1$  ${\Phi}_{1,x}=0$  ${\Phi}_{3,x}=0$  ${\Phi}_{3}=0$ 
${\Phi}_{2,x}=0$  $\kappa \left({l}_{c}\right){{\Phi}_{2,x}}^{}=\kappa \left({l}_{c}\right){{\Phi}_{2,x}}^{+}$  $\kappa \left({l}_{s}\right){{\Phi}_{2,x}}^{}=\kappa \left({l}_{s}\right){{\Phi}_{2,x}}^{+}$  ${\Phi}_{2,x}=0$ 
Working with named system variables, parameters, and property functions, we define the complete Excel model including equations, regions, and boundary conditions formulas as shown in Table 1.
A  B  C  D  E  F  G  H  
1  System Variables with initial values  System Parameters  

2  t  lc  2.00E04  
3  x  ls  2.254E04  
4  phi_1  0  la  4.254E04  
5  phi_2  0  beta  1  
6  phi_3  0  gamma  0.5  
7  phi1x  mc  1E+06  
8  phi2x  
9  phi3x  Mass Matrix  
10  phi1xx  1E+06  =mc  0  
11  phi2xx  =mc  1E+06  =mc  
12  phi3xx  0  =mc  1E+06  
13  
14  Property Functions  x Domain  0  =la  
15  sigma  =IF(x<=lc,1,IF(x<ls,0,1))  t Interval  0  1  Boundary conditions matrix  
16  kappa  =beta*IF(x<=lc,1,IF(x<ls,2,1))  0  R  =sigma*phi1x1  
17  a  =gamma*IF(x<=lc,1E6,IF(x<ls,0,1E6))  0  N  =phi2x  
18  f  =IF(x<=lc,3*EXP(2*t),0)  =lc  N  =phi1x  
19  =lc  C  =kappa*phi2x  
20  System RHS Equations  Regions  =ls  C  =kappa*phi2x  
21  eq1  =sigma*phi1xxa*(phi_1phi_2f)  0  =lc  =ls  N  =phi3x  
22  eq2  =kappa*phi2xx+a*(phi_1phi_2f)  0  =la  =la  N  =phi2x  
23  eq3  =sigma*phi3xxa*(phi_3phi_2f)  =ls  =la  =la  D  =phi_3 
To aid readability, we assign assign names for selected Excel ranges in Table 1 which represent input arguments to PDASOLVE
as shown in Table 2.
Excel Range  Assigned Name 

B21:B23  Eqns 
B2:B12  Vars 
F16:H23  BCs 
D14:E14  xDom 
D15:E15  tDom 
C10:E12  M 
C21:D23  Rgns 
Next, we evaluate the formula
=PDASOLVE(Eqns, Vars, BCs, xDom, tDom, M, Rgns, , {"FORMAT","TCOL1"})
in array G15:S27 and obtain the solution as shown in Table 3.
G  H  I  J  K  L  M  N  O  P  Q  R  S  
15  x  0  0  0  0.0002  0.0002  0.0002  0  0.0002254  0.0002254  0.0004254  0.0004254  0.00043 
16  t  phi_1  phi_2  phi_3  phi_1  phi_2  phi_3  phi_1  phi_2  phi_3  phi_1  phi_2  phi_3 
17  0  0  0  0  0  0  0  0  0  0  0  0  0 
18  0.1  0.0490406  0.0031269  0  0.0491381  0.0026646  0  0  0.0026093  0.0003937  0  0.002235  0 
19  0.2  0.0888748  0.004782  0  0.0889728  0.0043945  0  0  0.0043481  0.0003311  0  0.004033  0 
20  0.3  0.1204078  0.0061393  0  0.1205061  0.0058129  0  0  0.0057737  0.0002801  0  0.005507  0 
21  0.4  0.1451792  0.0072539  0  0.1452778  0.0069774  0  0  0.0069442  0.0002385  0  0.006716  0 
22  0.5  0.1644121  0.0081697  0  0.164511  0.0079339  0  0  0.0079055  0.0002046  0  0.00771  0 
23  0.6  0.1791579  0.0089244  0  0.179257  0.0087217  0  0  0.0086973  0.0001769  0  0.008528  0 
24  0.7  0.1902228  0.0095468  0  0.190322  0.0093711  0  0  0.0093499  0.0001544  0  0.009201  0 
25  0.8  0.1983065  0.0100621  0  0.1984059  0.0099083  0  0  0.0098896  0.0001361  0  0.009758  0 
26  0.9  0.2039649  0.01049  0  0.2040644  0.010354  0  0  0.0103374  0.0001213  0  0.01022  0 
27  1  0.2076535  0.0108468  0  0.2077531  0.0107253  0  0  0.0107105  0.0001093  0  0.010605  0 
In Figure 1, we plot phi2(x,t) at the interface x = l_{c} together with imperical values for Φ(l_{c},t) Clearly the default model does not agree well with experimental data. In Example 2, we will show you how to optimize the model parameters so it agrees with experimental data accurately.
The result shown in Table 1, is insufficient to verify the flux continuity conditions $\kappa \left({l}_{c}\right){{\Phi}_{2,x}}^{}=\kappa \left({l}_{c}\right){{\Phi}_{2,x}}^{+}$ and $\kappa \left({l}_{s}\right){{\Phi}_{2,x}}^{}=\kappa \left({l}_{s}\right){{\Phi}_{2,x}}^{+}$ are staisfied at the regions interfaces x = l_{c} and x = l_{s}. Here, we illustrate use of additional optional parameters to report the 1st derivatives at custom spatial points just before and after x = l_{c} and x = l_{s}, as well as, enhance the solution accuracy.
Table 4 shows the definition of new optional parameters consisting of: custom output points in G20:N20 which is named as xOut; relative tolerances in D21:D23 which is named as rTols; and a set of key/value pairs in A21:B23 which is named Cntrls. The keys include NDRVOUT with a value of 1, to report the first derivative in the output, and MAX_GRID_SPACING which ensures that the maximum distance between computational spatial grid nodes does not exceed the specified value of 1.0e5.
A  B  C  D  E  F  G  H  I  J  K  L  M  N  
20  Solver Options  Rel. Tolerance  xout  0  =lc0.001*lc  =lc  =lc+0.001*lc  =ls0.001*ls  =ls  =ls+0.001*ls  =la  

21  FORMAT  TCOL1  0.0001  
22  NDRVOUT  1  0.000001  
23  MAX_GRID_SPACING  0.00001  0.000001 
We evaluate the enhanced formula
=PDASOLVE(Eqns, Vars, BCs, xOut, tDom, M, Rgns, rTols, Cntrls)
in array A50:AW62 to obtain a new solution. We show in Table 5 values for phi2x just before and after
the interfaces at x = l_{c} and x = l_{s}. A quick inspection of the ratios
${{\Phi}_{2,x}}^{}/{{\Phi}_{2,x}}^{+}=\kappa \left({{l}_{c}}^{+}\right)/\kappa \left({{l}_{c}}^{}\right)=2$, and
${{\Phi}_{2,x}}^{}/{{\Phi}_{2,x}}^{+}=\kappa \left({{l}_{s}}^{+}\right)/\kappa \left({{l}_{s}}^{}\right)=0.5$
in Table 5 shows that the flux continuity conditions are satisfied at all times
L  R  X  AD  AJ  AP  
50  x  0.0001998  0.0002  0.0002002  ..  0.00022517  0.000225  0.000225625 
51  t  phi2X  phi2x  phi2x  phi2x  phi2x  phi2x  
52  0  0  Undef  0  0  Undef  0  
53  0.1  0.999180576  Undef  0.49957693  0.44674817  Undef  0.891514551  
54  0.2  0.999180576  Undef  0.49959529  0.44905949  Undef  0.89617388  
55  0.3  0.999180577  Undef  0.49961286  0.45127134  Undef  0.900632825  
56  0.4  0.999180568  Undef  0.49962967  0.45338715  Undef  0.904898145  
57  0.5  0.999180579  Undef  0.49964575  0.45541108  Undef  0.908978265  
58  0.6  0.999180562  Undef  0.49966113  0.45734714  Undef  0.912881228  
59  0.7  0.999180583  Undef  0.49967585  0.45919913  Undef  0.916614717  
60  0.8  0.999180566  Undef  0.49968992  0.46097071  Undef  0.920186109  
61  0.9  0.999180582  Undef  0.49970339  0.46266537  Undef  0.92360242  
62  1  0.999180573  Undef  0.49971627  0.46428644  Undef  0.926870399 
This example is a continuation of Example 1.
The computed values for phi2 at the interface x = l_{c} based on the default system parameters, are clearly in disagreement with the observed data as shown in Figure 1 of Example 1. The observed values for Φ(l_{c},t) are give in Table 6.
M  N  
1  time  Emperical phi2 

2  0  0 
3  0.1  0.002052705 
4  0.2  0.00380538 
5  0.3  0.005944579 
6  0.4  0.006668727 
7  0.5  0.008436437 
8  0.6  0.009136398 
9  0.7  0.009734491 
10  0.8  0.010031262 
11  0.9  0.010050529 
12  1  0.010387955 
Our task is to compute optimal values for the design parameters γ, and m_{c} which minimize the sum of square residuals between our
model predictions and the observed data in Table 6. We have two options to solve this dynamical minimization problem: Excel builtin NLP Solver;
and NLSOLVE
.
We demonstrate both procedures below.
We define an objective formula to be minimized. The objective formula calculates the summation of the square residuals between the observed values and their corresponding computed values from the solution result in Table 3 of Example 1. The definition of the objective formula is shown in S2 of Table 7 with initial value of 0.00033.
P  S  
1  =(L18N2)^2  Objective 

2  =(L19N3)^2  =SUM(P1:P10) 
3  =(L20N4)^2  
4  =(L21N5)^2  
5  =(L22N6)^2  
6  =(L23N7)^2  
7  =(L24N8)^2  
8  =(L25N9)^2  
9  =(L26N10)^2  
10  =(L27N11)^2  
10  =(L28N12)^2 
The remaining task is to configure and run Excel Solver command. We configure the solver exactly as shown in Figure 2. We select to minimize the objective formula S2, by varying gamma and mc, subject to the upper bound constraint mc <= 10e6 which is added for numerical stability since larger values for the offdiagonal mass matrix element m_{c} leads to a divergent system.
We run the solver which reports a feasible solution in less than a minute of computing time as shown in the Answer Report generated by the Excel Solver in Figure 3. Upon accepting the solution, Excel automatically updates the spreadsheet to reflect the optimal computed values. The optimized solution for phi2 is shown in Figure 4 exhibiting excellent agreement with observed values.
NLSOLVE
Unlike Excel Solver which requires an objective formula, NLSOLVE
requires the list of individual residual constraint formulas which we define
as shown in Table 8.
R  
1  =ARRAYVAL(L18)N2 
2  =ARRAYVAL(L19)N3 
3  =ARRAYVAL(L20)N4 
4  =ARRAYVAL(L21)N5 
5  =ARRAYVAL(L22)N6 
6  =ARRAYVAL(L23)N7 
7  =ARRAYVAL(L24)N8 
8  =ARRAYVAL(L25)N9 
9  =ARRAYVAL(L26)N10 
10  =ARRAYVAL(L27)N11 
10  =ARRAYVAL(L27)N12 
What is ARRAYVAL
? ARRAYVAL is one of
the criterion functions that allow us to define dynamic constraints on the solution array for input to NLSOLVE
.
In this case we are simply picking a value from the solution but in general, a criterion function can be used to specify more complex constraint on the solution.
Next, we evaluate the formula =NLSOLVE(R1:R10,(gamma,mc))
in array S4:T8 and obtain virtually the same solution found by Excel Solver
as shown in Table 9. However, NLSOLVE
, converges in fewer iterations in less than one third of the time.
Since NLSOLVE is a pure spreadsheet function, it does not modify its arguments or update the spreadsheet calculation.
Therefore, to update the spreadsheet result, the computed values for gamma and mc must copied manually to
their respective variable cells D6 and D7 of Table 3 of Example 1.
S  T  
4  gamma  0.195638747 
5  mc  958050.8287 
6  SSERROR  1.61147E06 
7  ITRN  10 
8  TIME (s)  18.729 
PDASOLVE
implements the method of lines. Spatial discretization is carried out using a secondorder variablestep finite difference scheme,
and the resulting implicit DAE system is integrated by RADAU5 with adaptive step control.