Drinking Water Temperature Modelling in Domestic Systems

Moerman, Andreas; Blokker, Mirjam; Vreeburg, J.H.G.; Hoek, Jan Peter van der

doi:10.1016/j.proeng.2014.11.170

Cited by 23 publications

(20 citation statements)

References 2 publications

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“…For these locations the stochastic variation in residence time (in a street, over the day, between days) means that the temperature of the tap samples may vary a lot and the temperature model developed by KWR would be difficult to validate in this case (Blokker et al 2014a). In contrast, the temperature in the drinking water installation (DWI) is influenced very much by the demand pattern, and not mainly by the ambient temperature (Moerman et al 2014). In this study, SIMDEUM enabled to put specific demand patterns for toilet cisterns, washing machines and hot and cold water for showering etc.…”

Section: Water Quality Modellingmentioning

confidence: 98%

Review of applications of SIMDEUM, a stochastic drinking water demand model with small temporal and spatial scale

Blokker¹,

Agudelo-vera²,

Moerman³

et al. 2017

Preprint

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<p><strong>Abstract.</strong> Many researchers have developed drinking water demand models with various temporal and spatial scales. A limited number of models are available at a temporal scale of one second and a spatial scale of a single home. Reasons for building these models were described in the papers in which the models were introduced, along with a discussion on potential applications. However, the predicted applications are seldom re-examined. As SIMDEUM, a stochastic end-use model for drinking water demand, has often been applied in research and practice since it was developed, we are re-examining its applications in this paper. SIMDEUM&#8217;s original purpose was to calculate maximum demands in order to be able to design self-cleaning networks. Yet, the model has been useful in many more applications. This paper gives an overview of the many fields of application of SIMDEUM and shows where this type of demand model is indispensable and where it has limited practical value. This overview also leads to an understanding of requirements on demand models in various applications.</p>

show abstract

Section: Water Quality Modellingmentioning

confidence: 98%

Review of applications of SIMDEUM, a stochastic drinking water demand model with small temporal and spatial scale

Blokker¹,

Agudelo-vera²,

Moerman³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…For these locations the stochastic variation in residence time (in a street, over the day, between days) means that the temperature of the tap samples may vary a lot and the temperature model developed by KWR would be difficult to validate in this case (Blokker et al 2014a). In contrast, the temperature in the 30 drinking water installation (DWI) is influenced very much by the demand pattern, and not mainly by the ambient temperature (Moerman et al 2014). In this study, SIMDEUM enabled to put specific demand patterns for toilet cisterns, washing machines and hot and cold water for showering etc.…”

Section: Water Quality Modelling 10mentioning

confidence: 98%

reply to RC2

Blokker¹

2017

Preprint

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“…For these locations, the stochastic variation in residence time (in a street, over the day, between days) means that the temperature of the tap samples may vary a lot, and the temperature model developed by KWR would be difficult to validate in this case . In contrast, the temperature in the drinking water installation (DWI) is heavily influenced by the demand pattern, and not mainly by the ambient temperature (Moerman et al, 2014). In this study, SIMDEUM enabled the setting of specific demand patterns for toilet cisterns, washing machines and hot and cold water for showering on the respective faucets in an EPANET model of the drinking water installation.…”

Section: Water Quality Modellingmentioning

confidence: 99%

“…A study showed that future demands are uncertain, but a range of demand scenarios can be taken into account and the uncertainty of the future can thus be incorporated into transitions in the (design of the) DWDS . (Moerman et al, 2014). (b) A detailed view of one tap in the DWI experimental set-up.…”

Section: Prediction Of Future Water Demandmentioning

confidence: 99%

“…In Italy, a student developed her own version of a SIMDEUM model (Spina et al, 2014). Other students have used the SIMDEUM results in the hydraulic networks of drinking water installations (Moerman, 2013, when he did his internship with KWR; Malinovskis, 2013;Zlatanovic, 2017). Most students have struggled to collect country-specific or case study SIMDEUM input data and have used generic Dutch data for some of the parameters that they could not find.…”

Section: Application Of Simdeum Outside the Developers' Research Groupmentioning

confidence: 99%

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Review of applications for SIMDEUM, a stochastic drinking water demand model with a small temporal and spatial scale

Blokker

Agudelo-Vera

Moerman

et al. 2017

Drink. Water Eng. Sci.

View full text Add to dashboard Cite

Abstract. Many researchers have developed drinking water demand models with various temporal and spatial scales. A limited number of models is available at a temporal scale of 1 s and a spatial scale of a single home. The reasons for building these models were described in the papers in which the models were introduced, along with a discussion on their potential applications. However, the predicted applications are seldom re-examined. SIMDEUM, a stochastic end-use model for drinking water demand, has often been applied in research and practice since it was developed. We are therefore re-examining its applications in this paper. SIMDEUM's original purpose was to calculate maximum demands in order to design self-cleaning networks. Yet, the model has been useful in many more applications. This paper gives an overview of the many fields of application for SIMDEUM and shows where this type of demand model is indispensable and where it has limited practical value. This overview also leads to an understanding of the requirements for demand models in various applications.

show abstract

Drinking Water Temperature Modelling in Domestic Systems

Cited by 23 publications

References 2 publications

Review of applications of SIMDEUM, a stochastic drinking water demand model with small temporal and spatial scale

Review of applications of SIMDEUM, a stochastic drinking water demand model with small temporal and spatial scale

reply to RC2

Review of applications for SIMDEUM, a stochastic drinking water demand model with a small temporal and spatial scale

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