Beam FEA with interactive preprocessor

3aaa6aeb-66d8-49f6-b5f7-a1463f90da18 160 Beam FEA with interactive preprocessor Martin Kraska 2D horizontal beam finite element analysis with exact representation of trapezoidal distributed loads. Features am interactive preprocessor and graphic and tabular output FHB 4 5 decimal

&[DATE] &[TIME] - &[FILENAME]

Templates

beam beam 01 - 11 - 110101 - 52 mat black 1 lightgray 14 mat 11001021 mat 14 mat :

These are the objects that can be placed in the modeller region to define the model.

s force force 0001 s * - 22 mat blue 2 blue 0.2 1 - 1 s * - 0011 s * - 1 - 1 s * - 42 mat (* blue 2 blue 24 mat 1104021 mat 14 mat :

support constraint w=w.0 1 - 01001 - 1 - 042 mat black 1 white 14 mat 1101021 mat 14 mat :

sym constraint φ=0 000122 mat black 2 white 1 - 01022 mat #00000000 00000000024 mat 1103021 mat 14 mat :

I J dist distributed load 000 I - 1 J - 1042 mat blue 0 #550000ff 14 mat 1502021 mat 14 mat :

φ.1 φ.2 arc a 02 matrix : φ φ.1 φ.2 φ.1 30 ° * + range a a φ cos φ sin 12 mat stack : for a 31 line :

circle hinge 0 ° * 360 ° * arc eval black 0 white 14 mat 1101021 mat 14 mat :

s moment moment s - 10101 - s 1 - 42 mat blue 2 #00000000 s - 1 0.8 s * - 1.2 0.8 s * - 0.8 s - 142 mat blue 2 blue 1 - (s - 1 0.8 s * - 1.2 0.8 s * - 0.8 1 s * - 142 mat * blue 2 blue 34 mat 1303021 mat 14 mat :

Vorlage beam support sym circle 11 dist 10 dist 01 dist 1 force 1 - force 1 - moment 1 moment 111 sys :

t.beam 1 :

t.fpos 8 :

Set "Vorlage" as template in the context menu of the modeler region.

t.cw 2 :

t.fneg 9 :

t.cφ 3 :

t.mpos 10 :

t.hinge 4 :

t.mneg 11 :

The items in the model are identified by template number.

t.qq 5 :

t.q0 6 :

t.0q 7 :

preprocessing

BeamPre(Mout,E,N) Beam FEA preprocessing. 
Mout: Modeller output, E,N: element  and node tables for the solver BeamSol() model E# N# BeamPre t 1 time : E# 0 : N# 0 : build EI(x) and create nodes at beam ends beams model t.beam 1 findrows : EI 0 : nodes 01 matrix : j 1 beams rows range x.1 beams j 2 el beams j 4 el 2 / - : x.2 beams j 2 el beams j 4 el 2 / + : EI EI beams j 5 el EI * x.1 x ≤ (x x.2 ≤ (& (* + : nodes nodes x.1 x.2 21 mat stack eval : 41 line for x EI EI : build q(x) and create nodes at distributed load ends q 0 : items t.qq 11 t.q0 10 t.0q 0133 mat : k 1 items rows range dist model items k 1 el 1 findrows : dist 0 ≠ j 1 dist rows range x.1 dist j 2 el dist j 4 el 2 / - : x.2 dist j 2 el dist j 4 el 2 / + : q q dist j 5 el q.0 * items k 3 el x x.1 - x.2 x.1 - / (* items k 2 el x.2 x - x.2 x.1 - / * + (* x.1 x ≤ (x x.2 ≤ (& (* + : nodes nodes x.1 x.2 21 mat stack eval : 41 line for 0 if 21 line for x q q : nodal items (make sure that there are nodes at these positions) k t.cw t.cφ t.hinge t.fpos t.fneg t.mpos t.mneg 17 mat items model k 1 findrows : items 0 ≠ j 1 items rows range nodes nodes items j 2 el 11 mat stack eval : 11 line for 0 if 21 line for merge identical nodes tol 0.05 : nsort nodes sort : k 1 : N# 11 el nsort 1 el eval : N# 17 el 0 : j 2 nsort rows range N# k 1 el nsort j el - abs tol > k k 1 + : N# k 1 el nsort j el eval : 21 line 0 if for x n.N node number for a given x value nn 0 : j 1 N# rows range x N# j 1 el - abs tol < nn j : 0 if for nn 41 line : create elements. Mitigate zero stiffness to a low value E# 05 matrix : j 1 N# rows 1 - range EI.e N# j 1 el N# j 1 + 1 el + 2 / EI 0.0000001 EI * 12 mat max eval : q.I N# j 1 el tol + q : q.J N# j 1 + 1 el tol - (q : EI.e 0 > E# E# j j 1 + EI.e q.I q.J 15 mat stack eval : 0 if 41 line for add nodal items: template nr, N column, value items t.cw 41 w constraint flag t.cw 5 item 3 el - w constraint value t.cφ 61 φ constraint flag t.fpos 2 item 5 el F value t.fneg 2 item 5 el - F value t.mpos 3 item 4 el item 5 el (* M value t.mneg 3 item 4 el item 5 el - (* M value 74 mat : k 1 items rows range tmp model items k 1 el 1 findrows : tmp 0 ≠ 1 continue if j 1 tmp rows range item tmp j row : item trace n item 2 el n.N : pos items k 2 el : value items k 3 el : N# n pos el value eval : 61 line for 31 line for add hinges hinges model t.hinge 1 findrows : hinges 0 ≠ j 1 hinges rows range n hinges j 2 el n.N : tmp 1 E# rows range E# augment : er tmp n 2 findrows 1 el eval : add new node at the same location N# N# hinges j 2 el 00000017 mat stack : n.h N# rows : add hinge element (type is recognized by zero lenght in FEA) n n.h EI 0015 mat trace E# E# n n.h EI 0015 mat stack eval : modify element to the right E# er 1 el n.h : 111 line for 0 if 1 time t - 341 line :

model qPlot t 1 time : build q(x) and create nodes at distributed load ends q 0 : items t.qq 11 t.q0 10 t.0q 0133 mat : k 1 items rows range dist model items k 1 el 1 findrows : dist 0 ≠ j 1 dist rows range x.1 dist j 2 el dist j 4 el 2 / - : x.2 dist j 2 el dist j 4 el 2 / + : q q dist j 5 el q.0 * items k 3 el x x.1 - x.2 x.1 - / (* items k 2 el x.2 x - x.2 x.1 - / * + (* x.1 x ≤ (x x.2 ≤ (& (* + : 31 line for 0 if 21 line for x q q : 61 line :

Solution

BeamSol(N,E,Nres,Eres) Beam FEA solver. N,E node and element matrices. Each line in N has the entries
(x, F, M, w-flag, w-value, φ-flag, φ-value). Flags=1 means constraint to given value. 
Each line in E has the entries (I, J, EI, qI, qJ). Zero length elements are hinges with w-stiffnes EI.
Nres are nodal results (x, w, φ, F, M, RF, RM), Eres are element results for plotting with the columns 
(x, q, Q, M, φ, w) N# E# Nres# Eres# BeamSol t 1 time : n.e E# rows : n.n N# rows : K 2 n.n * 2 n.n * matrix : F 2 n.n * 1 matrix : u 2 n.n * 1 matrix : provide functions for result computation x w 120 w.I * l 3^* EI * x x x 340 EI * 2 w.J - w.I + (* - l φ.J φ.I + (* + (* l 4^q.J * + (* l 2^7 l 2^* q.I * x x q.J * x - 5 l * + (q.I * + (* - (* + (* - 260 EI * 3 w.J - w.I + (* - l φ.J 2 φ.I * + (* + (* l 4^q.J * + (* 3 l 4^* q.I * + (l * + (* 120 φ.I * l 3^* EI * - (* + 120 l 3^* EI * / : x φ 120 φ.I * EI * l 3^* x x 5 x * x q.J * x - 4 l * + (q.I * + (* l 2^* 3340 EI * 2 w.J - w.I + (* - l φ.J φ.I + (* + (* l 4^q.J * + (* 7 l 4^* q.I * + (* - (* 2260 EI * 3 w.J - w.I + (* - l φ.J 2 φ.I * + (* + (* l 4^q.J * + (* 3 l 4^* q.I * + (* l * + (* - 120 EI * l 3^* / : x M 260 EI * 3 w.J - w.I + (* - l φ.J 2 φ.I * + (* + (* l 4^q.J * + (* 3 l 4^* q.I * + (l * x 10 x * x q.J * x - 3 l * + (q.I * + (* l 2^* 3340 EI * 2 w.J - w.I + (* - l φ.J φ.I + (* + (* l 4^q.J * + (* 7 l 4^* q.I * + (* - (* + 60 l 3^* / - : x Q 340 EI * 2 w.J - w.I + (* - l φ.J φ.I + (* + (* l 4^q.J * + (* 7 l 4^* q.I * + 10 x * x q.J * x - 2 l * + (q.I * + (* l 2^* - 20 l 3^* / : Element stiffness and element load vector EI l K.e l 0 > 12 EI * l 3^/ 6 EI * l 2^/ - 12 EI * l 3^/ - 6 EI * l 2^/ - 6 EI * l 2^/ - 4 EI * l / 6 EI * l 2^/ 2 EI * l / 12 EI * l 3^/ - 6 EI * l 2^/ 12 EI * l 3^/ 6 EI * l 2^/ 6 EI * l 2^/ - 2 EI * l / 6 EI * l 2^/ 4 EI * l / 44 mat EI 0 EI - 00000 EI - 0 EI 0000044 mat if : q.I q.J l f.e l 3 q.J * 7 q.I * + (* 20 / l 2^2 q.J * 3 q.I * + (* 60 / - l 7 q.J * 3 q.I * + (* 20 / l 2^3 q.J * 2 q.I * + (* 60 / 41 mat : Assemble global stiffness and element loads i.e 1 E# rows range I E# i.e 1 el : J E# i.e 2 el : EI E# i.e 3 el : q.I E# i.e 4 el : q.J E# i.e 5 el : l N# J 1 el N# I 1 el - : j i.g j 2 ≤ (0 ≠ (j 21 - I + (* + (* j 2 ≤ (0 ≡ (j 22 - J + (* + (* + (: i 14 range j 14 range K i i.g j i.g el K i i.g j i.g el EI l K.e i j el + eval : for F i i.g el F i i.g el q.I q.J l f.e i el + eval : 21 line for 81 line for add concentrated loads and constraints in 1 N# rows range F 2 in * 1 - el F 2 in * 1 - el N# in 2 el + : F 2 in * el F 2 in * el N# in 3 el + : u 2 in * 1 - el N# in 4 el N# in 5 el * : u 2 in * el N# in 6 el N# in 7 el * : c 2 in * 1 - el N# in 4 el : c 2 in * el N# in 6 el : 61 line for F.e F eval : reduce the system using constraints cc 12 n.n * range c augment : constr cc 12 findrows 1 col eval : free cc 02 findrows 1 col eval : F F K u * - eval : i 1 free rows range F.R i el F free i el el : j 1 free rows range K.R i j el K free i el free j el el : 11 line for 21 line for solve the system u.R K.R 1 -^F.R * eval : free rows 1 ≡ u.R u.R 11 mat : 0 if expand solution to full system i 1 free rows range u free i el el u.R i el : for Nodal (DOF) results: x, w, φ, F, M, FR, MR Nres# 07 matrix : R K u * F.e - eval : j 1 n.n range Nres# Nres# N# j 1 el u 2 j * 1 - el u 2 j * el F.e 2 j * 1 - el F.e 2 j * el R 2 j * 1 - el R 2 j * el 17 mat stack eval : for Element results: x, q, Q, M, φ, w Eres# 06 matrix : n 8 : i.e 1 n.e range I E# i.e 1 el : J E# i.e 2 el : 12 mat x.I N# I 1 el : x.J N# J 1 el : q.I E# i.e 4 el : q.J E# i.e 5 el : w.I u 2 I * 1 - el : w.J u 2 J * 1 - el : φ.I u 2 I * el : φ.J u 2 J * el : 42 mat l x.J x.I - : EI E# i.e 3 el eval : l 0 > j 0 n range x l j n / * : q q.J x x.I - l / * q.I x.J x - l / * + : Eres# Eres# x x.I + q x Q x M x φ x w 16 mat stack eval : 31 line for 0 if 51 line for 1 time t - 391 line :

Postprocessing

BeamPost[Res,T,P) Beam FEA postprocessing. Res: element results from BeamSol(), 
T: Modeller template, P: Modeller predefined layoutE Res# T# P# BeamPost t 1 time : M Res# 4 col - : Q Res# 3 col - : w Res# 6 col (: x Res# 1 col (: M.max M max M min abs 0.001 13 mat max : Q.max Q max Q min abs 0.001 13 mat max : w.max w max w min abs 0.001 13 mat max : P# 10055020015 mat : T# R x M M.max / augment x x rows el 0 x 1 el 022 mat stack red 1 #55ff0000 x Q Q.max / augment x x rows el 0 x 1 el 022 mat stack forestgreen 1 #5500ff00 x w w.max / augment black 2 #00000000 x 1 el 0 x x rows el 022 mat black 1 black x 1 el 1 - x 1 el 122 mat black 1 black 54 mat eval 110010021 mat 14 mat 11 sys : 1 time t - 111 line :

M# c# Absmax D M# c# col : D min abs D max 12 mat max 21 line :

Nhead x w φ F M S:F.R S:M.R 17 mat :

Instructions

Beam segment. Thickness scales EI. Segments can overlap, then EI adds up.

Vertical support. Place it at y=0 for zero displacement. Shift vertically for displaced supports (non-homogeneous constraints)

Constraint φ=0 (symmetry line)

Hinge, place anywhere. To a certain extent the solver can handle mechanisms (gives huge displacement values)

Distributed load, individual segments can be stacked or overlap to create arbitrary piecewise linear q(x). Scale the values with and with the height of the segments.

q.0

Point loads. Scale with the height.

Moment load. Scale with the distance and the length of the arrows.

Drag and drop objects from the palette to the canvas. x and y values are taken from the center of the object. Resize them with the mouse. Double-click them to set numerical values (does not work in SMath cloud).

Re-calculation is triggered by any changes in the model.

Robustness features:

- Nodes with less than tol=0,01 distance are merged. - All nodes are connected with elements. Elements with zero stiffness (resulting from loads or constraints outside beams)are set to a very low stiffness in order to prevent solver crash - zero max result values are set to a small value in order to keep the diagram scaling alive.

out

EI 100000 :

q.0 0.1 :

Function calls

out E N BeamPre 0.029 sec *

N E Res.N Res.E BeamSol 0.579 sec *

Res.E T P BeamPost 0.053 sec *

M.max Res.E 4 Absmax :

Q.max Res.E 3 Absmax :

w.max Res.E 6 Absmax :

x.out Res.E 1 col :

M.out Res.E 4 col :

Q.out Res.E 3 col :

w.out Res.E 6 col 1 - (* :

M.func x.out M.out augment :

Q.func x.out Q.out augment :

w.func x.out w.out augment :

post

M.max 9558

Q.max 138

w.max 36.211

post w.max Q.max M.max P T Res.N N 'q.0 'EI Nhead Absmax BeamPost BeamSol qPlot BeamPre t.0q t.q0 t.qq t.mneg t.hinge t.mpos t.cφ t.fneg t.cw t.fpos t.beam moment circle arc dist sym support force beam lastError post w.out Q.out M.out x.out w.max Q.max M.max T Res.N N 'q.0 'EI Nhead Absmax BeamPost BeamSol qPlot BeamPre t.0q t.q0 t.qq t.mneg t.hinge t.mpos t.cφ t.fneg t.cw t.fpos t.beam Vorlage arc lastError post w.max Q.max M.max 'q.0 'EI t.0q t.q0 t.qq t.mneg t.hinge t.mpos t.cφ t.fneg t.cw t.fpos t.beam

Res.N 1 csort eval
-100.000	0.000	-0.575	0.000	0.000	-137.579	0.000
-67.000	18.137	-0.500	50.000	-416.667	0.000	0.000
-17.000	35.101	-0.142	50.000	416.667	0.000	0.000
0.000	36.202	0.015	100.000	0.000	0.000	0.000
17.000	34.622	0.168	34.980	-249.917	0.000	0.000
67.000	17.310	0.484	15.020	166.750	0.000	0.000
100.000	0.000	0.545	0.000	0.000	-112.421	0.000
#
x	w	φ	F	M	S:F.R	S:M.R
#
#
#

Martin, I really like the approach you took in the 1st iteration of your BeamFEA, when you displyed Shear, Moment and Displacement curves separately - it is quite useful to tell demand at any given point just by looking at plot. Unfortunately after trying a few times I failed to shade the area under the curve - could you tell me what I am missing? Also Is there away to set X-axis and Y-axis "ticks" to a reasonable minimum value automatically? It will make chart easier to read.

I have also attempted to visialise demands for distributed load (not in table), this is as far as I got:

out qPlot 5067 - (x ≤ (x 17 - (≤ (& (* 67 x - (17 x ≤ (x 67 ≤ (& (* + 25 /

How can I plot this using Draw2D()? - something did not seem to work for me.

If I can make it work I will attempt to summarize input loads on a single plot.

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Disable 10 10 10 60 60 -2 -4 Interactive Disable MouseX1 MouseY1 MouseW1 Disable Azimuth1 Zenith1 Disable MinX1 MaxX1 Disable MinY1 MaxY1 Disable MinZ1 MaxZ1 point_type dot ≡ points_joined true ≡ filled_func 0 ≡ Q.func points xrange x.out Min x.out Max 2 1 sys ≡ yrange Q.out Min 1.05 * Q.out Max 1.05 * 2 1 sys ≡ x.out Min 0 x.out Max 0 2 2 mat points 7 1 sys 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