From af6d2bbc93226f17593645a935729334dc740cd6 Mon Sep 17 00:00:00 2001 From: SOR <3272194354@qq.com> Date: Sun, 28 Jun 2026 11:36:59 +0800 Subject: [PATCH] =?UTF-8?q?=E6=95=B0=E9=87=8F=E7=BA=A7=E9=97=AE=E9=A2=98?= =?UTF-8?q?=E4=BF=AE=E6=AD=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Thermal conductivity.py | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/Thermal conductivity.py b/Thermal conductivity.py index 344fc57..1023f9b 100644 --- a/Thermal conductivity.py +++ b/Thermal conductivity.py @@ -21,18 +21,18 @@ Thermal Conductivity & Specific Heat Calculator (Steady-State Plate Method) 其中 R = 样品厚度, ΔT = 加热面温度 - 中心面温度 3. 温度转换 (热电偶): - ΔT = ΔU / S - 其中 S = 热电偶灵敏度系数 (mV/K), ΔU = U_加热面 - U_中心面 + ΔT = ΔU / (1000 · S) + 其中 S = 热电偶灵敏度系数 (mV/K), ΔU = U_加热面 - U_中心面 (单位: uV) 4. 比热容 (冷却法): - c = q_c · F / (m · |κ|) + c = 2 · q_c · F / (m · |κ|) 其中 m = 样品质量, κ = dT/dτ|_冷却 为冷却曲线在稳态温度处的斜率 5. 不确定度传递 — 导热系数 λ = V²·R / (4·F·r·ΔT): u_r(λ) = √[ (2·u_V/V)² + (u_R/R)² + (u_F/F)² + (u_r/r)² + (u_ΔT/ΔT)² ] u(ΔU) 取稳态区间多点的 A 类 + 仪器 B 类合成; u_r(ΔT) ≈ u(ΔU)/|ΔU| - 6. 不确定度传递 — 比热容 c = V² / (2·r·m·|κ|): + 6. 不确定度传递 — 比热容 c = V² / (r·m·|κ|): u_r(c) = √[ (2·u_V/V)² + (u_r/r)² + (u_m/m)² + (u_κ/|κ|)² ] u_κ 由冷却曲线线性拟合的斜率标准差给出 """ @@ -40,7 +40,6 @@ Thermal Conductivity & Specific Heat Calculator (Steady-State Plate Method) from utils import ( scientific_round, calculate_stats, - input_data_group, linear_regression ) @@ -108,8 +107,9 @@ def compute_thermal_conductivity( time_min = list(range(1, n + 1)) # 1 ~ n 分钟 # --- 1. 转换为温度差 ΔT(t) --- + S_uV = S_sensitivity * Decimal("1000") # 将 mV/K 转换为 uV/K 以匹配 uV 数据 delta_uV = [heating_uV[i] - center_uV[i] for i in range(n)] - delta_T = [dv / S_sensitivity for dv in delta_uV] + delta_T = [dv / S_uV for dv in delta_uV] # --- 2. 判断稳态 --- ss_idx_center = find_steady_state(center_uV) @@ -118,7 +118,7 @@ def compute_thermal_conductivity( # 稳态区间 ΔU 列表（A 类不确定度来源） steady_delta_uV = delta_uV[ss_idx:] - delta_T_steady = sum(steady_delta_uV) / Decimal(len(steady_delta_uV)) / S_sensitivity + delta_T_steady = sum(steady_delta_uV) / Decimal(len(steady_delta_uV)) / S_uV center_steady_avg = sum(center_uV[ss_idx:]) / Decimal(len(center_uV[ss_idx:])) heating_steady_avg = sum(heating_uV[ss_idx:]) / Decimal(len(heating_uV[ss_idx:])) @@ -133,7 +133,7 @@ def compute_thermal_conductivity( if n >= 3: last_n = min(5, n) x_fit = [Decimal(str(t * 60)) for t in time_min[-last_n:]] # 秒 - y_fit = [center_uV[i] / S_sensitivity for i in range(-last_n, 0)] # K (相对) + y_fit = [center_uV[i] / S_uV for i in range(-last_n, 0)] # K (相对) dT_dtau, _, _, _ = linear_regression(x_fit, y_fit) # K/s else: dT_dtau = Decimal("0") @@ -172,14 +172,15 @@ def compute_specific_heat(cooling_uV, S_sensitivity, q_c, F, m_sample): u_dTdtau — |κ| 的不确定度 (K/s), 由线性拟合给出 """ n = len(cooling_uV) + S_uV = S_sensitivity * Decimal("1000") # 将 mV/K 转换为 uV/K 以匹配 uV 数据 x_cool = [Decimal(str(i * 60)) for i in range(n)] # 秒 - y_cool = [uv / S_sensitivity for uv in cooling_uV] # 相对温度 (K) + y_cool = [uv / S_uV for uv in cooling_uV] # 相对温度 (K) k, _, _, u_k = linear_regression(x_cool, y_cool) dT_dtau_cool = abs(k) # 冷却时斜率为负，取绝对值 u_dTdtau = abs(u_k) - c = (q_c * F / (m_sample * dT_dtau_cool)) if dT_dtau_cool != 0 else Decimal("0") + c = (Decimal("2") * q_c * F / (m_sample * dT_dtau_cool)) if dT_dtau_cool != 0 else Decimal("0") return c, dT_dtau_cool, u_dTdtau @@ -236,7 +237,7 @@ def compute_uncertainties( ur_c_sq = (Decimal("2") * ur_V)**2 + ur_r**2 + ur_m**2 + ur_cool**2 ur_c = Decimal(str(math.sqrt(float(ur_c_sq)))) - c_val = (q_c * F / (m_sample * dT_dtau_cool)) if dT_dtau_cool != 0 else Decimal("0") + c_val = (Decimal("2") * q_c * F / (m_sample * dT_dtau_cool)) if dT_dtau_cool != 0 else Decimal("0") u_c = c_val * ur_c return {