Abstract
Digital twins of urban drainage systems require simulation models that can adequately replicate the physical system. All models have their limitations, and it is important to investigate when and where simulation results are acceptable and to communicate the level of performance transparently to end users. This paper first defines a classification of four possible 'locations of uncertainty' in integrated urban drainage models. It then develops a structured framework for identifying and diagnosing various types of errors. This framework compares model outputs with in-sewer water level observations based on hydrologic and hydraulic signatures. The approach is applied on a real case study in Odense, Denmark, with examples from three different system sites: a typical manhole, a small flushing chamber, and an internal overflow structure. This allows diagnosing different model errors ranging from issues in the underlying asset database and missing hydrologic processes to limitations in the model software implementation. Structured use of signatures is promising for continuous, iterative improvements of integrated urban drainage models. It also provides a transparent way to communicate the level of model adequacy to end users.
Highlights
Stimulated by the emergence of increasing amounts of monitoring data the expectations to digital twins (DTs) in the water sector are high, as they are anticipated to provide improved insights and overview of the infrastructure systems for water distribution, drainage, and treatment (Fuertes et al 2020; Therrien et al 2020; Pedersen et al 2021a; Valverde-Pérez et al 2021)
This study argues for tailoring its use towards the detailed hydrodynamic part of integrated urban drainage system models that is based on detailed representation of physical system attributes
This paper presents a structured framework for diagnosing errors in integrated urban drainage models intended for continuous, iterative improvements of living digital twins
Summary
Stimulated by the emergence of increasing amounts of monitoring data the expectations to digital twins (DTs) in the water sector are high, as they are anticipated to provide improved insights and overview of the infrastructure systems for water distribution, drainage, and treatment (Fuertes et al 2020; Therrien et al 2020; Pedersen et al 2021a; Valverde-Pérez et al 2021). Coupled to the physical system, simulation models are among the most important features, and (at least) four different model categories are distinguished Two of these are prototyping models used for planning or design purposes, and two are living models used for control or operation purposes, living here referring to the coupling of close-to real-time observations from an ever-changing physical twin (which may change over time) with a.
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