Most multiphase flow models are developed and validated using a limited set of experimental data. Even when extensive experimental data are available, they are collected at laboratory conditions and/or test facilities, whose operating ranges and scales do not usually coincide with the field conditions. Therefore, models are routinely used for extrapolations to conditions where experimental data is not available. For example, this is often the case for very remote operations such as deep water applications. In this paper, we present three approaches to quantify experimental data and model prediction uncertainty for multiphase flow applications. We demonstrate these methods using three applications, estimation of experimental data uncertainty of erosion extent measurements, quantification of prediction uncertainty of critical velocity for sand transport, and of liquid entrainment in two-phase flow.


Multiphase flow occurs in many systems encountered in chemical, petroleum, and nuclear industries. Because efficient design and operation of these systems depend on the ability to predict the flow characteristics, several multiphase flow models have been developed. These models are based on mechanistic approaches, which utilize the conservation equations of mass, momentum, and energy along with empirical or semi-mechanistic closure relationships and adjustable parameters. The resulting equations are solved using various numerical methods and algorithms. The inputs to these models include fluid properties such as density, viscosity and surface tension, flow geometry such as diameter and inclination of the pipeline, and operating conditions such as superficial gas and liquid velocities. The outputs may be flow patterns, pressure gradients, liquid hold-up, etc.

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