Steady-state simulation of the seawater greenhouse condenser

This paper presents an integrated steady-state model simulating the condenser of the seawater greenhouse in Oman. The developed model is capable of predicting the outlet air temperature and humidity, the outlet seawater temperature and the condensation rate. Validation experiments showed a good conformity between the predicted and measured values within the calibration ranges at high and low air flowrates. The mean predictive error (PE) for the predicted condensation rate was 15.25 and 22.67 ml/min at high and low flowrates, respectively and the index of agreement (IA) was 0.96 and 0.98, respectively. The model also accurately predicted the outlet humidity ratio with PE values of -0.00006 and -0.00018 kg/kg for high and low air flowrates, respectively and IA values of 1.00 and 0.99, respectively. The model showed a small discrepancy between the measured and predicted outlet air temperature but yet with an PE value of 0.35 and 2.44oC at high and low air flowrates, respectively and IA values of 0.92 and 0.86, respectively. This discrepancy was not due to an inaccuracy related to the simulation but rather due to an inaccuracy related to measurements caused by the non-horizontal airflow pattern. The accuracy of the model to predict the outlet seawater temperature was excellent with an PE of -0.33 and -0.10oC for high and low air flowrates, respectively and IA values of 0.98 and 0.99, respectively. Model’s accuracy was also evaluated using three additional statistical prediction indicators; coefficient of determination, mean absolute predictive error and root mean square error. It was found that all prediction indicators for high and low air flowrates were very good

The application of probabilistic climate change projections: a comparison of methods of handling uncertainty applied to UK irrigation reservoir design

Climate projections are increasingly being presented in terms of uncertainties and probability distributions rather than median or ‘most-likely’ values. The current national UK climate change projections, UKCP09, provide 10,000 probabilistic projections (PP) and 11 spatially coherent projections (11SCP) for three future emission scenarios. In contrast, previous iterations such as UKCIP02 provided only a single ‘most-likely’ (deterministic) projection for each. This move from deterministic to probabilistic methods of communicating climate change information, whilst increasing the wealth of the data, complicates the process of adaptation planning by communicating extra uncertainty to the public and decision-makers. This paper examines the application of probabilistic climate change projections and explores the impact of uncertainty on decision-making, using a case study of irrigation reservoir design at three sites in the UK. The implications of sub-sampling the PP using both simple random and Latin-hypercube sampling are also explored. The study found that the choice of dataset has a much larger impact on irrigation reservoir design than emission uncertainty. The study confirmed the dangers of inadequate sample size, particularly when applying decision criteria based on extreme events, and found that more advanced stratified sampling techniques did not noticeably improve the reproducibility of decision outcomes