parent
b4db8b612e
commit
2750267b94
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@ -126,6 +126,21 @@ def evaluate_d_delta_l_i_sigma_i(val_delta_l_li, val_sigma_i):
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(beta_i, math.atan(h_array[i] / l_array[i])),
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]
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)
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manual_val = exp.manual_diff_delta_li_sigma_i(
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h_array[i],
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l_array[i],
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lambda_m_data,
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lambda_i_array[i],
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val_sigma_i[i],
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sigma_m_data,
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alpha_data,
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t_data,
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t_e_data,
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t_m_data,
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elastic_data,
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)
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if math.fabs(val - manual_val) > 1e-5:
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raise Exception("d_delta_l_i_sigma_i 自动和手动微分不匹配")
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val_list.append(val)
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return val_list
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@ -201,6 +216,21 @@ def evaluate_d_sigma_i1_d_delta_l_i(val_delta_l_li, val_sigma_i):
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for index, li in enumerate(delta_Li):
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_val = _val.subs(li, _val_delta_l_li[index])
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pass
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manual_val = exp.manual_diff_sigma_i1_d_l_i(
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h_array[i],
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l_array[i],
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h_array[i + 1],
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l_array[i + 1],
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string_g / conductor_n,
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area_data,
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lambda_i_array[i],
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lambda_i_array[i + 1],
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val_sigma_i[i],
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string_length,
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math.fsum(val_delta_l_li[0 : i + 1]),
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)
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if math.fabs(manual_val - _val) > 1e-5:
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raise Exception("d_sigma_i1_delta_L_i 自动和手动微分不匹配")
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col.append(_val)
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row.append(col)
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return sympy.Matrix(row)
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@ -237,11 +267,11 @@ def evaluate_d_sigma_i1_d_delta_sigma_i(val_delta_li):
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def solve():
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# 初始化
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val_delta_li = [0.1 for i in range(span_count)]
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# val_delta_li = [0.15864687475316822, -0.1935189734784845, 0.03478489898855073]
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val_delta_li = [0.1 for _ in range(span_count)]
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# val_delta_li = [0.15931589580330385, -0.19294219226439696, 0.035236744603670586, -0.03124795962518869, 0.034422847061933395, 0.1639551737321582, -0.18876605469917845, 0.03862307099222713, -0.01858088432390902]
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val_sigma_i = [sigma_m_data for _ in range(span_count)]
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# val_sigma_i = [175.38451579479482, 176.01015153076614, 175.88355419459572]
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val_sigma_i = [175.44277576372207, 176.07105062365383, 175.94897602628689, 175.95574563415994, 175.83116207025824, 175.84778616876434, 176.5132398968081, 176.4405408454816, 176.51871673188586]
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# val_sigma_i = [sigma_m_data for _ in range(span_count)]
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loop = 0
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while True:
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@ -333,7 +363,7 @@ def solve():
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x_list = list(x)[0]
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abs_min = [math.fabs(_x) for _x in x_list]
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abs_min.sort()
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if abs_min[-1] < 1e-5:
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if abs_min[-1] < 1e-6:
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break
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print("最大偏差{max_dx}".format(max_dx=abs_min[-1]))
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# 更新变量
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@ -346,7 +376,7 @@ def solve():
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print(loop)
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print(val_delta_li)
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print(val_sigma_i)
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verify(val_delta_li,val_sigma_i)
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verify(val_delta_li, val_sigma_i)
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def verify(val_delta_li, val_sigma_i):
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32
data.py
32
data.py
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@ -1,4 +1,4 @@
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#loop_end = 10000000 # 最大循环次数
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# loop_end = 10000000 # 最大循环次数
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loop_end = 1000000 # 最大循环次数
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# 架线时的状态
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# 取外过无风
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@ -10,13 +10,35 @@ alpha = 0.0000155 # 导线膨胀系数 1/°C
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elastic = 95900 # 弹性系数 N/mm2
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area = 154.48 # 导线面积 mm2
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lambda_m = 14.8129 / area # 导线比载 N/(m.mm)
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lambda_i_array = [lambda_m*0.9,lambda_m*1.3,lambda_m,lambda_m,lambda_m]
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lambda_i_array = [
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lambda_m * 0.9,
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lambda_m * 1.3,
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lambda_m,
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lambda_m,
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lambda_m,
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lambda_m * 0.9,
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lambda_m * 1.3,
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lambda_m,
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lambda_m,
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lambda_m,
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]
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# 取400m代表档距下
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sigma_m = 28517 / area # 架线时，初伸长未释放前的最低点水平应力。单位N/mm2
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span_count = 3 # 几个档距
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span_count = 9 # 几个档距
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# n个档距,n-1个直线塔
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h_array = [0, 0, 0, 0, 0]
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l_array = [400, 300, 300, 500, 300]
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h_array = [
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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]
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l_array = [400, 300, 300, 500, 300, 400, 300, 300, 500, 300]
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t = 15
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epsilon = 1e-4 # 收敛判据
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conductor_n = 6 # 导线分裂数
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56
exp.py
56
exp.py
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@ -27,7 +27,7 @@ def delta_li():
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t_m,
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sigma_m,
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sigma_i,
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beta_i
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beta_i,
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) = sympy.symbols(
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"""
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delta_l_i,
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@ -106,6 +106,7 @@ def fun_sigma_i1(delta_Li):
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for f in delta_Li:
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_t += f
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return _t
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_sigma_i1 = sigma_i1 - (
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(
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G_i / 2 / A # G_i传入时已考虑导线分裂数
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@ -116,3 +117,56 @@ def fun_sigma_i1(delta_Li):
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+ sigma_i / b_i() * sympy.sqrt(stringlen_i ** 2 - b_i() ** 2)
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) / (sympy.sqrt(stringlen_i ** 2 - b_i() ** 2) / b_i() + h_i1 / l_i1)
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return _sigma_i1
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def manual_diff_delta_li_sigma_i(
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h_i, l_i, lambda_m, lambda_i, sigma_i, sigma_m, alpha, t_i, t_e, t_m, E
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):
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beta_i = math.atan(h_i / l_i)
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A = (
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(l_i * math.cos(beta_i)) ** 2
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/ 24
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* ((lambda_m / sigma_m) ** 2 - (lambda_i / sigma_i) ** 2)
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+ (sigma_i - sigma_m) / E / math.cos(beta_i)
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+ alpha * (t_i + t_e - t_m)
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)
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B = 1 + (lambda_i * l_i / sigma_i) ** 2 / 8
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dA_dsigma_i = ((l_i * math.cos(beta_i)) ** 2) / 24 * 2 * lambda_i ** 2 * (
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sigma_i ** (-3)
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) + 1 / E / math.cos(beta_i)
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dB_dsigma_i = -2 * (lambda_i * l_i) ** 2 / 8 * (sigma_i ** (-3))
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_t = -l_i / (math.cos(beta_i) ** 2) * (dA_dsigma_i * B - A * dB_dsigma_i) / (B ** 2)
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return _t
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def manual_diff_sigma_i1_d_l_i(
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h_i, l_i, h_i1, l_i1, Gi, A, lambda_i, lambda_i1, sigma_i, stringlen, b_i
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):
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beta_i = math.atan(h_i / l_i)
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beta_i1 = math.atan(h_i1 / l_i1)
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A = (
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Gi / 2 / A
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+ lambda_i * l_i / 2 / math.cos(beta_i)
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+ lambda_i1 * l_i1 / 2 / math.cos(beta_i1)
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+ sigma_i * h_i / l_i
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+ sigma_i / b_i * ((stringlen ** 2 - b_i ** 2) ** 0.5)
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)
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B = ((stringlen ** 2 - b_i ** 2) ** 0.5)/b_i + h_i1 / l_i1
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dA_d_delta_L1 = (
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sigma_i
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* (
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-((stringlen ** 2 - b_i ** 2) ** -0.5) * (b_i ** 2)
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- (stringlen ** 2 - b_i ** 2) ** 0.5
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)
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/ (b_i ** 2)
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)
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dB_d_delta_L1 = (
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1
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/ (b_i ** 2)
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* (
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-((stringlen ** 2 - b_i ** 2) ** -0.5) * (b_i ** 2)
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- (stringlen ** 2 - b_i ** 2) ** 0.5
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)
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)
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_t = -(dA_d_delta_L1 * B - A * dB_d_delta_L1) / (B ** 2)
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return _t
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Reference in New Issue