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The fundamental changes in the energy sector, due to the rise of renewable energy resources and the possibilities of the digitalisation process, result in the demand for new methodologies for testing Smart Grid concepts and control strategies. Using the Power Hardware-in-the-Loop (PHIL) methodology is one of the key elements for such evaluations. PHIL and other in-the-loop concepts cannot be considered as plug’n’play and, for a wider adoption, the obstacles have to be reduced. This paper presents the comparison of two different setups for the evaluation of components and systems focused on undisturbed operational conditions. The first setup is a conventional PHIL setup and the second is a simplified setup based on a quasi-dynamic PHIL (QDPHIL) approach which involves fast and continuously steady state load flow calculations. A case study which analyses a simple superimposed voltage control algorithm gives an example for the actual usage of the quasi-dynamic setup. Furthermore, this article also provides a comparison and discussion of the achieved results with the two setups and it concludes with an outlook about further research.

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN. 150 sets of data were generated in different days by the reference heat exchanger model to training the network. Regression between desired target and prediction ANN output for training , validation, testing and all samples show reasonably values are equal to one (R=1) . 50 sets of data were generated to test the network and compare between desired and predicated exit temperature (water temp. and air temp.) show a good agreement ( ).

Abstract: Simulation software is often a fundamental component in systems biology projects and provides a key aspect of the integration of experimental and analytical techniques in the search for greater understanding and prediction of biology at the systems level. It is important that the modelling and analysis software is reliable and that techniques exist for automating the analysis of the vast amounts of data which such simulation environments generate. A rigorous approach to the development of complex modelling software is needed. Such a framework is presented here together with techniques for the automated analysis of such models and a process for the automatic discovery of biological phenomena from large simulation data sets. Illustrations are taken from a major systems biology research project involving the in vitro investigation, modelling and simulation of epithelial tissue. [Copyright &y& Elsevier]

The last-generation CMIP6 global circulation models (GCMs) are currently used to interpret past and future climatic changes and to guide policymakers, but they are very different from each other; for example, their equilibrium climate sensitivity (ECS) varies from 1.83 to 5.67 °C (IPCC AR6, 2021). Even assuming that some of them are sufficiently reliable for scenario forecasts, such a large ECS uncertainty requires a pre-selection of the most reliable models. Herein the performance of 38 CMIP6 models are tested in reproducing the surface temperature changes observed from 1980–1990 to 2011–2021 in three temperature records: ERA5-T2m, ERA5-850mb, and UAH MSU v6.0 Tlt. Alternative temperature records are briefly discussed but found to be not appropriate for the present analysis because they miss data over large regions. Significant issues emerge: (1) most GCMs overestimate the warming observed during the last 40 years; (2) there is great variability among the models in reconstructing the climatic changes observed in the Arctic; (3) the ocean temperature is usually overestimated more than the land one; (4) in the latitude bands 40° N–70° N and 50° S–70° S (which lay at the intersection between the Ferrel and the polar atmospheric cells) the CMIP6 GCMs overestimate the warming; (5) similar discrepancies are present in the east-equatorial pacific region (which regulates the ENSO) and in other regions where cooling trends are observed. Finally, the percentage of the world surface where the (positive or negative) model-data discrepancy exceeds 0.2, 0.5 and 1.0 °C is evaluated. The results indicate that the models with low ECS values (for example, 3 °C or less) perform significantly better than those with larger ECS. Therefore, the low ECS models should be preferred for climate change scenario forecasts while the other models should be dismissed and not used by policymakers. In any case, significant model-data discrepancies are still observed over extended world regions for all models: on average, the GCM predictions disagree from the data by more than 0.2 °C (on a total mean warming of about 0.5 °C from 1980–1990 to 2011–2021) over more than 50% of the global surface. This result suggests that climate change and its natural variability remain poorly modeled by the CMIP6 GCMs. Finally, the ECS uncertainty problem is discussed, and it is argued (also using semi-empirical climate models that implement natural oscillations not predicted by the GCMs) that the real ECS could be between 1 and 2 °C, which implies moderate warming for the next decades. [ABSTRACT FROM AUTHOR]

The fundamental changes in the energy sector, due to the rise of renewable energy resources and the possibilities of the digitalisation process, result in the demand for new methodologies for testing Smart Grid concepts and control strategies. Using the Power Hardware-in-the-Loop (PHIL) methodology is one of the key elements for such evaluations. PHIL and other in-the-loop concepts cannot be considered as plug'n'play and, for a wider adoption, the obstacles have to be reduced. This paper presents the comparison of two different setups for the evaluation of components and systems focused on undisturbed operational conditions. The first setup is a conventional PHIL setup and the second is a simplified setup based on a quasi-dynamic PHIL (QDPHIL) approach which involves fast and continuously steady state load flow calculations. A case study which analyses a simple superimposed voltage control algorithm gives an example for the actual usage of the quasi-dynamic setup. Furthermore, this article also provides a comparison and discussion of the achieved results with the two setups and it concludes with an outlook about further research. [ABSTRACT FROM AUTHOR]