Greater New Orleans is surrounded by wetlands, the Mississippi River and two lakes. Excess rain can only be drained off with pumping systems or by evaporation due to the bowl-like shape of a large part of the city. As part of the solution to make New Orleans climate adaptive, green infrastructure has been implemented that enable rainfall infiltration and evapotranspiration of stored water after Hurricane Katrina in 2005. The long-term efficiency of infiltrating water under sea level with low permeable soils and high groundwater tables is often questioned. Therefore, research was conducted with the full-scale testing method measuring the infiltration capacity of 15 raingardens and 6 permeable pavements installed in the period 2011–2022. The results show a high variation of empty times for raingardens and swales: 0.7 to 54 m/d. The infiltration capacity decreased after saturation (ca 30% decrease in empty time after refilling storage volume) but all the tested green infrastructure met the guideline to be drained within 48 h. This is in contrast with the permeable pavement: only two of the six tested locations had an infiltration capacity higher than the guideline 10 inch/h (254 mm/h). The results are discussed with multiple stakeholders that participated in ClimateCafe New Orleans. Whether the results are considered unacceptable depends on a number of factors, including its intended purpose, site specific characteristics and most of all stakeholder expectations and perceptions. The designing, planning and scheduling of maintenance requirements for green infrastructure by stormwater managers can be carried out with more confidence so that green infrastructure will continue to perform satisfactorily over the intended design life and can mitigate the effects of heavy rainfall and droughts in the future.
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Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
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Evaluation of the hydrological performance of grassed swales usually needs long-term monitoring data. At present, suitable techniques for simulating the hydrological performance using limited monitoring data are not available. Therefore, current study aims to investigate the relationship between saturated hydraulic conductivity (Ks) fitting results and rainfall characteristics of various events series length. Data from a full-scale grassed swale (Enschede, the Netherlands) were utilized as long-term rainfall event series length (95 rainfall events) on the fitting outcomes. Short-term rainfall event series were extracted from these long-term series and used as input in fitting into a multivariate nonlinear model between Ks and its influencing rainfall indicators (antecedent dry days, temperature, rainfall, rainfall duration, total rainfall, and seasonal factor (spring, summer, autumn, and winter, herein refer as 1, 2, 3, and 4). Comparison of short-term and long-term rainfall event series fitting results allowed to obtain a representative short-term series that leads to similar results with those using long-term series. A cluster analysis was conducted based on the fitting results of the representative rainfall event series with their rainfall event characteristics using average values of influencing rainfall indicators. The seasonal index (average value of seasonal factors) was found to be the most representative short rainfall event series indicator. Furthermore, a Bayesian network was proposed in the current study to predict if a given short-term rainfall event series is representative. It was validated by a data series (58 rainfall events) from another full-scale grassed swale located in Utrecht, the Netherlands. Results revealed that it is quite promising and useful to evaluate the representativeness of short-term rainfall event series used for long-term hydrological performance evaluation of grassed swales.
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