id summary reporter owner description type status priority milestone component version resolution keywords cc 2839 JAcobi matrix not calculated correctly when I give Linearization time in simulate-command hage02@… willi braun " I have OpenModelica 1.9.0 for Windows, and I want to get the Jacobian (linearization). When I simulate a model and I do Simulation flags: Jacobian Matrix it is generated for a lot of timesteps (=ok), but when I add a Linearization Time(optional): I get a generated linear model with A (from A-B-C-D-matrix) which should be the Jacobian , but the elements are false. I get values of 3e-314 etc., clearly nok. Happens with also the smallest models (copy/paste the following model): model probeer Modelica.Mechanics.Translational.Components.Mass mass1(m = 1, s(start = 0.1)) annotation(Placement(visible = true, transformation(origin = {-18.1034,26.4368}, extent = {{-10,-10},{10,10}}, rotation = 0))); Modelica.Mechanics.Translational.Components.Spring spring1(c = 1000) annotation(Placement(visible = true, transformation(origin = {14.9425,25.8621}, extent = {{-10,-10},{10,10}}, rotation = 0))); Modelica.Mechanics.Translational.Components.Fixed fixed1 annotation(Placement(visible = true, transformation(origin = {52.5862,24.1379}, extent = {{-10,-10},{10,10}}, rotation = 0))); equation connect(spring1.flange_b,fixed1.flange) annotation(Line(points = {{24.9425,25.8621},{52.5862,25.8621},{52.5862,24.1379},{52.5862,24.1379}})); connect(mass1.flange_b,spring1.flange_a) annotation(Line(points = {{-8.10345,26.4368},{4.31034,26.4368},{4.31034,26.4368},{4.31034,26.4368}})); annotation(Icon(coordinateSystem(extent = {{-100,-100},{100,100}}, preserveAspectRatio = true, initialScale = 0.1, grid = {2,2})), Diagram(coordinateSystem(extent = {{-100,-100},{100,100}}, preserveAspectRatio = true, initialScale = 0.1, grid = {2,2})), experiment(StartTime = 0, StopTime = 1, Tolerance = 0.000001)); end probeer; The jacobian should be: 0 1 -1000 0 Output (linearization at t=0): stdout | info | Linearization will performed at point of time: 0.000000 LOG_INIT | info | ### START INITIALIZATION ### LOG_INIT | info | updating start-values LOG_INIT | info | initialization method: symbolic [solves the initialization problem symbolically - default] LOG_SOTI | info | ### SOLUTION OF THE INITIALIZATION ### | | | | | states variables | | | | | | [1] Real mass1.v(start=0, nominal=1) = 0 (pre: 0) | | | | | | [2] Real mass1.s(start=0.1, nominal=1) = 0.1 (pre: 0.1) | | | | | derivatives variables | | | | | | [3] Real der(mass1.v) = -100 (pre: 0) | | | | | | [4] Real der(mass1.s) = 0 (pre: 0) | | | | | other real variables | | | | | | [5] Real spring1.f(start=0, nominal=1) = -100 (pre: 0) | | | | | | [6] Real spring1.s_rel(start=0, nominal=1) = -0.1 (pre: 0) | | | | | | [7] Real mass1.a(start=0, nominal=1) = -100 (pre: 0) | | | | | | [8] Real mass1.flange_b.s(start=0, nominal=1) = 0.1 (pre: 0) | | | | | | [9] Real mass1.flange_a.s(start=0, nominal=1) = 0.1 (pre: 0) | | | | | | [10] Real mass1.flange_a.f(start=0, nominal=1) = 0 (pre: 0) | | | | | integer variables | | | | | boolean variables | | | | | string variables LOG_INIT | info | ### END INITIALIZATION ### LOG_STATS | info | ### STATISTICS ### LOG_STATS | info | timer | | | | | 4.61831e-005s [ 6.6%] pre-initialization | | | | | 0.000128286s [ 18.3%] initialization | | | | | 0s [ 0.0%] steps | | | | | 0s [ 0.0%] creating output-file | | | | | 0s [ 0.0%] event-handling | | | | | 0s [ 0.0%] overhead | | | | | 0.000525608s [ 75.1%] simulation | | | | | 0.000700077s [100.0%] total LOG_STATS | info | events | | | | | 0 state events | | | | | 0 time events LOG_STATS | info | solver | | | | | 0 steps taken | | | | | 0 calls of functionODE | | | | | 0 evaluations of jacobian | | | | | 0 error test failures | | | | | 0 convergence test failures LOG_STATS | info | ### END STATISTICS ### Caluculate one col: LOG_JAC | info | seed: data->simulationInfo.analyticJacobians[index].seedVars[0]= 1.000000 LOG_JAC | info | seed: data->simulationInfo.analyticJacobians[index].seedVars[1]= 0.000000 LOG_JAC | info | write in jac[0]-[0,0]=6.36599e-314 from row[0]=6.36599e-314 LOG_JAC | info | write in jac[1]-[0,1]=9.00067e-308 from row[0]=9.00067e-308 Caluculate one col: LOG_JAC | info | seed: data->simulationInfo.analyticJacobians[index].seedVars[0]= 0.000000 LOG_JAC | info | seed: data->simulationInfo.analyticJacobians[index].seedVars[1]= 1.000000 LOG_JAC | info | write in jac[2]-[1,0]=6.36599e-314 from row[1]=6.36599e-314 LOG_JAC | info | write in jac[3]-[1,1]=9.00067e-308 from row[1]=9.00067e-308 LOG_JAC | info | Print jac: 6.36599e-314 6.36599e-314 9.00067e-308 9.00067e-308 LOG_STATS | info | model linear_probeer | | | | parameter Integer n = 2; // states | | | | parameter Integer k = 0; // top-level inputs | | | | parameter Integer l = 0; // top-level outputs | | | | parameter Real x0[2] = {0,0.1}; | | | | parameter Real u0[0] = {i for i in 1:0}; | | | | parameter Real A[2,2] = [6.365987385741524e-314,6.365987385741524e-314;9.000674322924625e-308,9.000674322924625e-308]; | | | | parameter Real B[2,0] = zeros(2,0); | | | | parameter Real C[0,2] = zeros(0,2); | | | | parameter Real D[0,0] = zeros(0,0); | | | | Real x[2](start=x0); | | | | input Real u[0]; | | | | output Real y[0]; | | | | | | | | Real x_Pmass1Pv = x[1]; | | | | Real x_Pmass1Ps = x[2]; | | | | | | | | equation | | | | der(x) = A * x + B * u; | | | | y = C * x + D * u; | | | | end linear_probeer; | | | | stdout | info | Linear model is created! " defect new high Future *unknown* trunk linearization