Items tagged with model model Tagged Items Feed

loock i have 3 models and a Matrix
>m3:=importmatrix("loslobos.txt");
>model[1]:=a*x;
>model[2]:=a^2*x+b;
>model[3]:=C-x^2+a;

so i apply a command named DataFit and it gives me 
>g:=Datafit(matrix,model[1]);

and that throws
         g:=[3,[a:=5],33];
so
         >g[1];
3
         >g[2];
5
         >g[3];
33
the variable that i care about is g[1] so every 3 models have differents g[1] so i need to pick the smallest g[1] and then use his model.
Example:=
>g:=Datafit(matrix,model[1]);
g:=[3,[a:=5],33];
>gg:=Datafit(matrix,model[2]);
gg:=[10,[a:=5,b:=10],44];
>G:=Datafit(matrix,model[3]);
G:=[1.1,[C:=5,a:=2],88];

then 

>plots:-display(plot(m3, style = point, symbol = diamond, symbolsize = 9), plot(eval("MODEL THAT SHOULD BE PLOT", THE G[2] OF THE MODEL), x = 0 .. 27, color = black));

The right one here would be 

>plots:-display(plot(m3, style = point, symbol = diamond, symbolsize = 9), plot(eval(model[3], G[2]), x = 0 .. 27, color = black));

so how do i select the model[3] for my plot? i knnow how the eval works so the problem here is to pick the right model please Help i hope that i isn't that Hard thnx
   

The model of fixed-bed adsorption column

Fluid phase:

PDE:= diff(U(x, tau),tau)+ psi*Theta*diff(U(x, tau),x)-(1/Pe)*psi*Theta*diff(U(x, tau),$(x, 2))=-3*psi*xi*(U(x, tau)-Q/K);

 

IBC:={U(x, 0) = 0,U(0, tau) = 1+(1/Pe)*(D[1](U))(0, tau),(D[1](U))(1, tau)=0};

Particle:

PDE:= diff(Q(r, tau), tau) = diff(Q(r, tau), $(r, 2))+(2/r)*diff(Q(r, tau),r);

IBC:={Q(r, 0) = 0,(D[1](Q))(0, tau) = 0,(1/K)*(D[1](Q))(1, tau)=xi*(U-Q(1, tau)/K)};

Pe:=0.01:

psi:=6780:

Theta:=3.0:

xi:=10000:

I will really appreciate your help. Thanks in anticipation.

The following error occurred when I simulate a build-in model, anyone could help me to solve this problem? Thanks first

I want to make the model which moves along the specific direction (translational), So I used the "prismatic joint" to give the direction that I want to enforce. However, "Prismatic joint" only offers the direction along the "X", "Y", and "Z" axes though I want to give other direction.

 

Is there any way to give the specific direction (vector) to make my model move in that way ?

Hi there,

I would like to compute and display the nullclines of a set of ordinary differential equations.

AFAIK, I can compute the nullclines in Maple by defining the equations and solving the system

e.g.:

# Define the equations
eq1 := u(t)*(1-u(t)/kappa)-u(t)*v(t) = 0;
eq2 := g*(u(t)-1)*v(t) = 0;

# Solve the system (i.e. compute the nullclines)
sol := solve({eq1, eq2}, {u(t), v(t)});

However, I am not quite able to imagine how to display them over a dfieldplot or a phaseportrait.

Attached is an example with some differential equations, and their vector field and trajectories: MaplePrimes_Predator_prey_model_nullclines.mw.

It can be use to illustrate how to (compute and) display the nullclines.

 

Thank you,

jon

This application calculates the number of photons reaching a camera sensor for a given exposure. A blackbody model of the sun is generated. The "Sunny 16" rule for exposure is demonstrated. Calculations are done using units.Photon_Exposure_Array.mw

Photon ExposureNULLNULL

Blackbody Model of the Sun

    h := Units:-Standard:-`*`(Units:-Standard:-`*`(0.6626069e-33, Units:-Standard:-`^`(Unit('m'), 2)), Units:-Standard:-`*`(Unit('kg'), Units:-Standard:-`/`(Unit('s')))): 

Plank Constant       

  kb := Units:-Standard:-`*`(Units:-Standard:-`*`(0.1380650e-22, Units:-Standard:-`*`(Units:-Standard:-`^`(Unit('m'), 2), Units:-Standard:-`/`(Units:-Standard:-`^`(Unit('s'), 2)))), Units:-Standard:-`*`(Unit('kg'), Units:-Standard:-`/`(Unit('K')))): 

Boltzman Constant  

c := Units:-Standard:-`*`(0.2997925e9, Units:-Standard:-`*`(Unit('m'), Units:-Standard:-`/`(Unit('s')))):  ``

Light Speed

Rsun := Units:-Standard:-`*`(Units:-Standard:-`*`(6.955, Units:-Standard:-`^`(10, 8)), Unit('m')): ``

Sun Radius  

Re_orb := Units:-Standard:-`*`(Units:-Standard:-`*`(1.496, Units:-Standard:-`^`(10, 11)), Unit('m')): ``

Earth Orbit

Tsun := Units:-Standard:-`*`(5800, Unit('K')): ``

Sun Color Temperature     

 tf_atm := .718: 

Transmission Factor  

 

Sun: Spectral Radiant Exitance to Earth: Spectral Irradiance                   

  "M(lambda):=(2*Pi*h*c^(2))/((lambda)^(5))*1/((e)^((h*c)/(lambda*kb*Tsun))-1)*(Rsun/(Re_orb))^(2)*tf_atm:" NULL

evalf(M(Units:-Standard:-`*`(555, Unit('nm')))) = 1277414308.*Units:-Unit(('kg')/(('m')*('s')^3))"(->)"1.277414308*Units:-Unit(('W')/(('nm')*('m')^2))NULL

Photopic Relative Response VP vs λ

 

csvFile := FileTools[Filename]("/VPhotopic.csv")NULL = "VPhotopic.csv"NULL

VPdata := ImportMatrix(csvFile) = Vector(4, {(1) = ` 471 x 2 `*Matrix, (2) = `Data Type: `*float[8], (3) = `Storage: `*rectangular, (4) = `Order: `*Fortran_order})NULLNULL

 

`λP` := [seq(1 .. 4000)]:

VP := ArrayInterpolation(VPdata, `λP`):             (ArrayInterpolation for x,y data VPdata returns y' for new x data lambdaP)

NULLVParray := [`$`([`λP`[n], VP[n]], n = 1 .. 4000)]:                     

Mearth := [`$`([n, Units:-Standard:-`*`(Units:-Standard:-`*`(M(Units:-Standard:-`*`(n, Unit('nm'))), Unit('nm')), Units:-Standard:-`*`(Units:-Standard:-`^`(Unit('s'), 3), Units:-Standard:-`/`(Unit('kg'))))], n = 1 .. 4000)]:````

``

dualaxisplot(plot([Mearth], lambda = 300 .. 900, style = line, color = [blue], labels = ["λ (nm)", "M (W/nm m^2)"], title = "Spectral Radiant Exitance of the Sun", titlefont = ["ARIAL", 15], legend = [Exitance], size = [800, 300]), plot([VParray], style = line, color = [green], labels = ["λ (nm)", "Relative Response"], legend = [Units:-Standard:-`*`(Units:-Standard:-`*`(Photopic, Relative), Response)]))

 

``

 

 

 

Illuminance in Radiometric and Photometric Units:

E__r := sum(Units:-Standard:-`*`(M(Units:-Standard:-`*`(lambda, Unit('nm'))), Unit('nm')), lambda = 200 .. 4000) = 984.7275549*Units:-Unit(('kg')/('s')^3)"(->)"984.7275549*Units:-Unit(('W')/('m')^2)NULL

NULL

E__po := Units:-Standard:-`*`(Units:-Standard:-`*`(683.002, Units:-Standard:-`*`(Unit('lm'), Units:-Standard:-`/`(Unit('W')))), sum(Units:-Standard:-`*`(Units:-Standard:-`*`(VP[lambda], M(Units:-Standard:-`*`(lambda, Unit('nm')))), Unit('nm')), lambda = 200 .. 4000)) = HFloat(91873.47376063903)*Units:-Unit('lx')NULL

Translation from Illuminance to Luminance for Reflected Light;

 

Object Reflectance          R__o:      

Object Luminance           L__po := proc (R__o) options operator, arrow; R__o*E__po/(Pi*Unit('sr')) end proc:                evalf(L__po(1)) = HFloat(29244.234968360346)*Units:-Unit(('cd')/('m')^2) 

 

Illuminance of a Camera Sensor  Eps applied for time texp determines Luminous Exposure Hp;

Ideal Illuminance is determined by the exposure time texp, effective f-number N and to a less extent the angle to the optical axis θ;

 

• 

H       Luminous Exposure

• 

Eps     Illuminance to the Camera

• 

N                                               Effective F-Number

• 

texp             Exposure Time

• 

θ        Angle to the Optical Axis    

 

E__ps_ideal = Units:-Standard:-`*`(Units:-Standard:-`*`(Units:-Standard:-`*`(Pi, Units:-Standard:-`/`(4)), L__po), Units:-Standard:-`*`(Units:-Standard:-`^`(cos(theta), 4), Units:-Standard:-`/`(Units:-Standard:-`^`(N, 2)))):

H__p_ideal = Units:-Standard:-`*`(E__ps_ideal, t__exp):

 

The camera meter determines the exposure time texp to balance the object luminance, reflectance and effective f-number. It does this based on an internal constant k and the camera ISO s.

• 

s        ISO Gain (Based on saturation at 3 stops above the average scene luminance)

• 

k       Reflected Light Meter Calibration Constant      k__m := Units:-Standard:-`*`(Units:-Standard:-`*`(12.5, Unit('lx')), Unit('s')):  

                                                                                                  for Nikon, Canon and Sekonic

• 

c        Incident Light Meter Calibration Constant       c__m := Units:-Standard:-`*`(Units:-Standard:-`*`(250, Unit('lx')), Unit('s')):        

                                                                                                  for Sekonic with flat domeNULL

N^2/t__exp = `#mrow(mi("\`E__po\`"),mo("⋅"),mi("s"))`/c__m                        (Incident Light Meter)  NULL 

Units:-Standard:-`*`(Units:-Standard:-`^`(N, 2), Units:-Standard:-`/`(t__exp)) = Units:-Standard:-`*`(`#mrow(mi("\`L__po\`"),mo("⋅"),mi("s"))`, Units:-Standard:-`/`(k__m)):                        (Reflected Light Meter)

NULL

Solve for H in terms of the Camera Meter Constant k and s

 

Es = Units:-Standard:-`*`(Units:-Standard:-`*`(Units:-Standard:-`*`(Pi, Units:-Standard:-`/`(4)), Lo), Units:-Standard:-`*`(Units:-Standard:-`^`(cos(theta), 4), Units:-Standard:-`/`(Units:-Standard:-`^`(N, 2)))): NULL

t = Units:-Standard:-`*`(Units:-Standard:-`*`(km, Units:-Standard:-`^`(N, 2)), Units:-Standard:-`/`(Units:-Standard:-`*`(Lo, s))):NULL

NULL

NULL

H = Es*t

H = Units:-Standard:-`*`(Units:-Standard:-`*`(Units:-Standard:-`*`(Units:-Standard:-`*`(Pi, Units:-Standard:-`/`(4)), Lo), Units:-Standard:-`*`(Units:-Standard:-`^`(cos(theta), 4), Units:-Standard:-`/`(Units:-Standard:-`^`(N, 2)))), Units:-Standard:-`*`(Units:-Standard:-`*`(km, Units:-Standard:-`^`(N, 2)), Units:-Standard:-`/`(Units:-Standard:-`*`(Lo, s))))"(=)"H = (1/4)*Pi*cos(theta)^4*km/sNULLNULL

 t = H/Es

t = Units:-Standard:-`*`(Units:-Standard:-`*`(Units:-Standard:-`*`(Pi, Units:-Standard:-`/`(4)), Units:-Standard:-`*`(Units:-Standard:-`*`(Units:-Standard:-`^`(cos(theta), 4), km), Units:-Standard:-`/`(s))), Units:-Standard:-`/`(Units:-Standard:-`*`(Units:-Standard:-`*`(Units:-Standard:-`*`(Pi, Units:-Standard:-`/`(4)), Lo), Units:-Standard:-`*`(Units:-Standard:-`^`(cos(theta), 4), Units:-Standard:-`/`(Units:-Standard:-`^`(N, 2))))))"(=)"t = km*N^2/(Lo*s)NULLNULL

H__p := proc (s, theta) options operator, arrow; (1/4)*Pi*k__m*cos(theta)^4/s end proc:                                              

  evalf(H__p(100, 0)) = 0.9817477044e-1*Units:-Unit(('cd')*('s')/('m')('radius')^2)"(->)"0.9817477044e-1*Units:-Unit(('lx')*('s'))NULL

 

Note:  Meters are typically set for a scene reflectance 3 stops below 100% or 12.5%.

           

  E__ps := proc (N, R__o, theta) options operator, arrow; (1/4)*Pi*Unit('sr')*R__o*E__po*cos(theta)^4/(Pi*Unit('sr')*N^2) end proc:               

 evalf(E__ps(16, Units:-Standard:-`/`(Units:-Standard:-`^`(2, 3)), 0)) = HFloat(11.215023652421756)*Units:-Unit('lx')                                                                                                   

t__exp_ideal := proc (N, s, R__o) options operator, arrow; H__p(s, theta)/E__ps(N, R__o, theta) end proc:                                     

  evalf(t__exp_ideal(16, 100, Units:-Standard:-`/`(Units:-Standard:-`^`(2, 3)))) = HFloat(0.008753862094289947)*Units:-Unit('s') NULL NULL

 

 

Actual exposure time includes typical lens losses;

 m := Units:-Standard:-`/`(80):``

Magnification  

  T := .9:``

Lens Transmittance

 F := 1.03:``

Lens Flare

V := 1: ``

Vignetting

 

                                                  ``

Total Lens Efficiency

q := Units:-Standard:-`*`(Units:-Standard:-`*`(Units:-Standard:-`*`(T, F), V), Units:-Standard:-`^`(Units:-Standard:-`+`(1, Units:-Standard:-`-`(m)), 2)):                                      evalf(q) = .9039698438NULL

 

Replacing Eps with q*Eps we get the "Sunny 16" relation between exposure time and ISO;  NULL

t__exp := proc (N, s, R__o) options operator, arrow; H__p(s, theta)/(q*E__ps(N, R__o, theta)) end proc:NULL               evalf(t__exp(16, 100, Units:-Standard:-`/`(Units:-Standard:-`^`(2, 3)))) = HFloat(0.009683798806264942)*Units:-Unit('s')NULL

t__exp_alt := proc (N, s, R__o) options operator, arrow; k__m*N^2*Pi/(s*q*R__o*E__po) end proc:                  evalf(t__exp_alt(16, 100, Units:-Standard:-`/`(Units:-Standard:-`^`(2, 3)))) = HFloat(0.00968379880412244)*Units:-Unit('s') 

• 

The Number of Photons NP Reaching the Sensor Area A;

• 

Circle of confusion for 24x36mm "Full Frame" for 1 arcminute view at twice the diagonal:

                          A__cc := Units:-Standard:-`*`(Units:-Standard:-`*`(Pi, Units:-Standard:-`^`(Units:-Standard:-`*`(12.6, Unit('`μm`')), 2)), Units:-Standard:-`/`(4)):    

     

• 

  Sensor Bandwidth                                          Photopic Response VP

• 

  Exposure Time for Zone 5: Rscene=12.5% , Saturation in Zone 8 Rscene=100%

• 

  Camera ISO differs from Saturation ISO. Typical Saturation ISO is 2300 when the camera is set to 3200. See DxoMark.

 

NULL

The average number of photons for exposure time based on Reflectance of the scene  relative to the metered value:    

Zone 5;   R__meter := R__scene: 

NP := proc (s, R__o, theta) options operator, arrow; (1/4)*t__exp(N, s, R__meter)*A__cc*q*R__scene*cos(theta)^4*(sum(VP[lambda]*M(lambda*Unit('nm'))*Unit('nm')*lambda*Unit('nm')/(h*c), lambda = 200 .. 4000))/N^2 end proc: 

                                                                               evalf(NP(2300, 1, Units:-Standard:-`*`(0, Unit('deg')))) = HFloat(2191.5645712603696)  NULL

Zone 8;       R__meter := Units:-Standard:-`*`(R__scene, Units:-Standard:-`/`(Units:-Standard:-`^`(2, 3))):   NULL

NP__sat := proc (s, theta) options operator, arrow; (1/4)*t__exp(N, s, R__meter)*A__cc*q*R__scene*cos(theta)^4*(sum(VP[lambda]*M(lambda*Unit('nm'))*Unit('nm')*lambda*Unit('nm')/(h*c), lambda = 200 .. 4000))/N^2 end proc:  NULL

                                                                              evalf(NP__sat(2300, Units:-Standard:-`*`(0, Unit('deg')))) = HFloat(17532.516570082957)NULL

NULL

 

Approximate Formula

 

H__sat := proc (s__sat) options operator, arrow; H__p(s__sat, 0)*E__ps(N, 1, 0)/E__ps(N, 1/8, 0) end proc:      

                                                                                       evalf(H__sat(s__sat)) = HFloat(78.53981635)*Units:-Unit(('cd')*('s')/('m')('radius')^2)/s__satNULLNULL

Average Visible Photon Energy

P__e_ave := Units:-Standard:-`*`(Units:-Standard:-`/`(Units:-Standard:-`+`(850, -350)), sum(Units:-Standard:-`*`(Units:-Standard:-`*`(h, c), Units:-Standard:-`/`(Units:-Standard:-`*`(lambda, Unit('nm')))), lambda = 350 .. 850)):                    evalf(P__e_ave) = 0.3533174192e-18*Units:-Unit('J') 

NPtyp := proc (s__sat) options operator, arrow; H__sat(s__sat)*A__cc/(683.002*(Unit('lm')/Unit('W'))*P__e_ave) end proc: 

                               evalf(NPtyp(2300)) = HFloat(17644.363333654386)"(->)"HFloat(17644.363333654386)NULL

NULL

 

Download Photon_Exposure_Array.mw

Hello,

I study the model of crankshaft in MapleSim.

For the moment, i manage to plot different parameters function of the time. For exemple, the angle of the crank function of the time or the displacement of the slider function of the time.

But, i like to plot the displacement of the slider funtion of the angle of the crank.

Is it possible and how can i do this?

Thank you for your help.

Hello,

It's a very easy question but i don't find for the moment.

I want to measure an angle in a revolute joint. For that purpose, i use a probe to measure the angle. The angle is measured in rad. How can i do to change in deg ?

Thank you for your help.

Hello,

It's a very easy question but i don't find for the moment.

I want to measure an angle in a revolute joint. For that purpose, i use a probe to measure the angle. The angle is measured in rad. How can i do to change in deg ?

Thank you for your help.

The general formula of GHR (Gazis-Herman-Rothery), the most well known car following model, is given by

an - acceleration of vehicle n implemented at time t
v - speed of follower vehicle
deltax and deltav - relative spacing and speeds respectively between follower and leader vehicle at a
     ...

Hi,

I'm using the DirectSearch package in a 10 periods model and in the first period i get this values:

"
> DirectSearch[SolveEquations](sys, assume = positive);
Warning, complex or non-numeric value encountered; trying to find a feasible point
[HFloat(1.1842542076623546e-32),

Vector[column](%id = 18446744078126621390), [

x1a = HFloat(4204.651582462925),

x1c = HFloat(4204.651582462925),

I am new to maplesim. I want to model the human hand and constrain the fingers to a certain range of angle. Then apply a controller to analyse its controllability and also see the dynamic equation formulation

Hi

I am trying to model the change in lifetime versus mass and have the formula;

> T := 6.58*10^38*10^(-25)/x^3;

> plot(T, x = 10^10, 10^9, 10^8, 10^7, 10^6, 10^5, 10^4, 10^3, 10^2, 10, 10^0);
Error, (in plot) unexpected options: [x = 10000000000, 1000000000, 100000000, 10000000, 1000000, 100000, 10000, 1000, 100, 10, 1]
I want to plot for values between x=10^-11 and 0, i have this error and i am not sure why.
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