Esempio n. 1
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def INCKE(KS, KEl):
#STIFF下位程序, 将单元刚度矩阵迭代插入总体刚度矩阵
    mn = ref.arrIndexSort(KEl[0])[:,1].T
    ij = ref.arrIndexSort(KEl[0])[:,0].T
    KE = KEl[1]
    map(lambda i: map(lambda j: AINCKE([[mn[i], mn[j]],KS], [[i,j],KE]), ij), ij)
    return KS
Esempio n. 2
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def insKE(KS, lKE):
#GKS() sub reduce() function, insert KE of a single element
#lKE = [[[LableInElement, NodeLable],[0,91],[1,92], ...], KE]
    mn = ref.arrIndexSort(lKE[0])[:,1].T
    ij = ref.arrIndexSort(lKE[0])[:,0].T
    KE = lKE[1]
    map(lambda i: map(lambda j: insKEIJ([[mn[i], mn[j]],KS], [[i,j],KE]), ij), ij)
    return KS
Esempio n. 3
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def INCKE(KS, KEl):
    #STIFF下位程序, 将单元刚度矩阵迭代插入总体刚度矩阵
    mn = ref.arrIndexSort(KEl[0])[:, 1].T
    ij = ref.arrIndexSort(KEl[0])[:, 0].T
    KE = KEl[1]
    map(
        lambda i: map(lambda j: AINCKE([[mn[i], mn[j]], KS], [[i, j], KE]), ij
                      ), ij)
    return KS
Esempio n. 4
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def insKE(KS, lKE):
    #KS() sub reduce() function, insert KE of a single element
    #lKE = [[[LableInElement, NodeLable],[0,91],[1,92], ...], KE]
    mn = ref.arrIndexSort(lKE[0])[:, 1].T
    ij = ref.arrIndexSort(lKE[0])[:, 0].T
    KE = lKE[1]
    map(
        lambda i: map(lambda j: insKEIJ([[mn[i], mn[j]], KS], [[i, j], KE]), ij
                      ), ij)
    return KS
Esempio n. 5
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def FEM(inputDict):
#有限元
    HIP = ref.arrIndexSort(inputDict['HIP'])
    HIP = np.matrix(HIP[:,1:3].reshape(-1,1))
    KS = STIFF(inputDict)
    KS = INSCD(KS, inputDict['BIU'])
    Solution = np.linalg.solve(KS, HIP)
    Solution = np.array(Solution.reshape((-1,2)))
    return Solution
Esempio n. 6
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def FEM(inputDict):
    #有限元
    HIP = ref.arrIndexSort(inputDict['HIP'])
    HIP = np.matrix(HIP[:, 1:3].reshape(-1, 1))
    KS = STIFF(inputDict)
    KS = INSCD(KS, inputDict['BIU'])
    Solution = np.linalg.solve(KS, HIP)
    Solution = np.array(Solution.reshape((-1, 2)))
    return Solution
Esempio n. 7
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def STIFF(FEMinput):
#生成总体刚度矩阵
    IJMH = FEMinput['IJMH']
    E = FEMinput['TEVW']['E']
    PR = FEMinput['TEVW']['pr']
    t = FEMinput['TEVW']['t']
    XY = FEMinput['XY']
    KS = np.zeros((FEMinput['XY'].shape[0]*2, FEMinput['XY'].shape[0]*2))
    XY = ref.arrIndexSort(XY)
    IJMH = ref.lstIndexSort(IJMH)
    KEs = map(lambda EN: STE(XY[EN[1][:,1],:], E, PR, t, EN[0], IJMH), IJMH)#单元刚度矩阵List
    KS = np.matrix(reduce(INCKE, KEs, KS))
    return  KS
Esempio n. 8
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def STIFF(FEMinput):
    #生成总体刚度矩阵
    IJMH = FEMinput['IJMH']
    E = FEMinput['TEVW']['E']
    PR = FEMinput['TEVW']['pr']
    t = FEMinput['TEVW']['t']
    XY = FEMinput['XY']
    KS = np.zeros((FEMinput['XY'].shape[0] * 2, FEMinput['XY'].shape[0] * 2))
    XY = ref.arrIndexSort(XY)
    IJMH = ref.lstIndexSort(IJMH)
    KEs = map(lambda EN: STE(XY[EN[1][:, 1], :], E, PR, t, EN[0], IJMH),
              IJMH)  #单元刚度矩阵List
    KS = np.matrix(reduce(INCKE, KEs, KS))
    return KS
Esempio n. 9
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def GKS(FEMinput):
    #Calculate global stiffness matrix
    IJ = FEMinput['IJ']
    E = FEMinput['TEV']['E']
    V = FEMinput['TEV']['V']
    T = FEMinput['TEV']['T']
    XY = FEMinput['XY']

    KS = np.zeros((XY.shape[0]*2, XY.shape[0]*2))
    XY = ref.arrIndexSort(XY)
    IJ = ref.lstIndexSort(IJ)
    KE = KEsquare(E, V, T)

    KEsl = [KEdcr(KE, EN[0], IJ) for EN in IJ]#List of Element stiffness matrix
    KS = np.matrix(reduce(insKE, KEsl, KS))
    return KS
Esempio n. 10
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def GKS(FEMinput):
    #Calculate global stiffness matrix
    IJ = FEMinput['IJ']
    E = FEMinput['TEV']['E']
    V = FEMinput['TEV']['V']
    T = FEMinput['TEV']['T']
    XY = FEMinput['XY']

    KS = np.zeros((XY.shape[0] * 2, XY.shape[0] * 2))
    XY = ref.arrIndexSort(XY)
    IJ = ref.lstIndexSort(IJ)
    KE = KEsquare(E, V, T)

    KEsl = [KEdcr(KE, EN[0], IJ)
            for EN in IJ]  #List of Element stiffness matrix
    KS = np.matrix(reduce(insKE, KEsl, KS))
    return KS
Esempio n. 11
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def INSCD(KS, BIU):
#插入支承条件, 修改总体刚度矩阵
    BIU = ref.arrIndexSort(BIU)
    KS = np.matrix(reduce(INSCDS, BIU, KS))
    return KS
Esempio n. 12
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def INSCD(KS, BIU):
    #插入支承条件, 修改总体刚度矩阵
    BIU = ref.arrIndexSort(BIU)
    KS = np.matrix(reduce(INSCDS, BIU, KS))
    return KS