圆柱储罐侧壁小孔泄漏特性实验及泄漏参数快速评估方法

Experimental study on the characteristics of small-hole leakage on the sidewall of cylindrical tanks and a method for rapid evaluation of leakage parameters

  • 摘要:
    目的 圆柱储罐侧壁小孔泄漏的传统快速评估多采用恒定流量系数假设,在小孔径、低雷诺数工况下易产生显著偏差。为提高泄漏流量、排液时间及液位状态的快速评估精度,构建面向事故初期快速研判的泄漏参数评估方法。
    方法 搭建常压圆柱储罐侧壁小孔泄漏实验装置,以自来水为介质,系统开展8种不同孔径条件下的变液位泄漏实验,获取泄漏流量、排液时间及射流射程等关键数据,实验雷诺数范围为4×103~2.5×104。在此基础上,分析流量系数随孔径、雷诺数变化的动态特征,建立流量系数修正模型并引入排液过程计算;再以孔径、液位及有效水头为输入变量,构建多层感知机(Multilayer Perceptron, MLP)射程预测模型与液位反演模型,并通过交叉验证检验其泛化能力,实现基于外部泄漏特征对储罐内部状态的快速反演。
    结果 在3~10 mm孔径范围内,流量系数并非常数,其取值受孔口几何特征与雷诺数共同影响;小孔径(3~4 mm)工况下流量系数均值约0.885,较经典锐边孔口理论的流量系数均值(0.61~0.65)高36%~45%。引入修正后的流量系数可将小孔径(3~4 mm)有效反演区间(液位500~200 mm)的排液时间预测误差由约33%~37%降至2%以内;在全程排液尺度上,排液时间随孔径近似呈幂律递减(拟合指数为-1.954,R2为0.999 8)。射程预测模型的决定系数R2达0.983,在小孔径敏感区其平均绝对百分比误差(Mean Absolute Percentage Error, MAPE)由传统理论的11%~18%降至1.5%~3.5%;液位反演模型的整体MAPE为1.91%。
    结论 基于流量系数修正、射程预测、液位反演3个模块,构建了圆柱储罐侧壁小孔泄漏的快速评估框架,可协同输出泄漏流量、剩余液位、排液时间及影响距离等关键参数。该方法适用于常压、单相、低黏度液体的侧壁小孔泄漏场景,可为事故初期快速研判及数字化安全评估提供技术参考。

     

    Abstract:
    Objective Traditional rapid evaluation methods for small-hole leakage on the sidewall of cylindrical tanks often assume a constant discharge coefficient. This leads to significant deviations under small-aperture and low-Reynolds-number conditions. To improve the evaluation accuracy of leakage flow rate, drainage time and liquid level state, this study develops an evaluation method for leakage parameters to support quick judgment during the initial stage of accidents.
    Methods An experimental apparatus was set up to simulate small-hole leakage on the sidewall of atmospheric cylindrical tanks. Using tap water as the test medium, variable liquid-level leakage experiments were systematically conducted across eight different aperture sizes. Key data—including leakage flow rate, drainage time, and jet range—were acquired, with the experimental Reynolds number ranging from 4×103 to 2.5×104. On this basis, the dynamic variations of the discharge coefficient with respect to aperture size and Reynolds number were analyzed, and a discharge coefficient correction model was established and integrated into the drainage process calculations. Finally, a Multilayer Perceptron (MLP)-based jet range prediction model and a liquid level inversion model were constructed using aperture size, liquid level, and effective head as input variables. Cross-validation was adopted to verify the generalization ability of the models, enabling rapid inversion of the internal tank state based on external leakage characteristics.
    Results Within the 3–10 mm aperture range, the discharge coefficient was not constant but was jointly influenced by orifice geometric features and the Reynolds number. For small apertures (3–4 mm), the mean discharge coefficient was approximately 0.885, which was 36%–45% higher than the mean value (0.61–0.65) of the classic sharp-edged orifice theory. After adopting the corrected discharge coefficient, the prediction error for drainage time within the effective inversion interval (liquid level: 200–500 mm) for small apertures (3–4 mm) decreased from roughly 33%–37% to less than 2%. Throughout the entire drainage process, drainage time decreased following a power-law trend as aperture size increased, yielding a fitting exponent of −1.954 and a coefficient of determination R2 of 0.999 8. The R2 of the jet range prediction model reached 0.983. In the small-aperture sensitive region, the Mean Absolute Percentage Error (MAPE) was reduced from the traditional theoretical range of 11%–18% to 1.5%–3.5%. The overall MAPE of the liquid level inversion model was 1.91%.
    Conclusion This study develops a rapid evaluation framework for small-hole leakage on the sidewall of cylindrical tanks, comprising three modules: discharge coefficient correction, jet range prediction, and liquid level inversion. The framework collaboratively outputs key parameters including leakage flow rate, residual liquid level, drainage time, and impact distance. Applicable to low-viscosity, single-phase liquid leakage under atmospheric pressure, this method provides a technical reference for rapid emergency decision-making and digital safety assessments during the initial stage of leakage accidents.

     

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