We review different mathematical models proposed in literature to describe fluid-dynamic aspects in membrane-based water filtration systems. Firstly, we discuss the societal impact of water filtration, especially in the context of developing countries under emergency situations, and then review the basic concepts of membrane science that are necessary for a mathematical description of a filtration system. Secondly, we categorize the mathematical models available in the literature as (a) microscopic, if the pore-scale geometry of the membrane is accounted for; (b) reduced, if the membrane is treated as a geometrically lower-dimensional entity due to its small thickness compared to the free flow domain; (c) mesoscopic, if the characteristic geometrical dimension of the free flow domain and the porous domain is the same, and a multi-physics problem involving both incompressible fluid flow and porous media flow is considered. Implementation aspects of mesoscopic models in CFD software are also discussed with the help of relevant examples.

No abstract

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of climate crisis are being increasingly felt, the need of accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on climatic variables using statistical techniques as well as Artificial Intelligence (AI) for a specific area of interest utilising data from in situ meteorological station, and produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location. As a result, an accurate representation of the microclimate in a specific are is achieved. To achieve this high accuracy in situ measurements and prediction techniques are used. As prediction techniques, Seasonal Auto Regressive Integrated Moving Average (SARIMA), AI techniques like the Long-Short-Term-Memory (LSTM) Neural Network and hybrid combinations of the two are used. Variables of interest are divided in the easier to predict temperature and humidity that are more periodic and less chaotic, and the wind speed as an example of a more stochastic variable with no known seasonality and patterns. Our results show that the examined Hybrid methodology performs the best at temperature and wind speed forecasts, closely followed by the SARIMA whereas LSTM perform better overall at the humidity forecast, even after the correction of the Hybrid to the SARIMA model.

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of the climate crisis are being increasingly felt, the need for accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on meteorological variables using statistical techniques as well as artificial intelligence (AI) for a specific area of interest utilising data from an in situ meteorological station, and to produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location; in our case, in a hotel in the northern area of Crete, Greece. The temporal resolution of the measurements used was 3 h and the forecast horizon considered here was up to 2 days. As prediction techniques, seasonal autoregressive integrated moving average (SARIMA), AI techniques like the long short-term memory (LSTM) neural network and hybrid combinations of the two are used. Multiple meteorological variables are considered as input for the LSTM and hybrid methodologies, like temperature, relative humidity, atmospheric pressure and wind speed, unlike the SARIMA that has a single variable. Variables of interest are divided into those that present seasonality and patterns, such as temperature and humidity, and those that are more stochastic with no known seasonality and patterns, such as wind speed and direction. Two benchmark techniques are used for comparison and quantification of the added predictive ability, namely the climatological forecast and the persistence model, which shows a considerable amount of improvement over the naive prediction methods, especially in the 1-day forecasts. The results indicate that the examined hybrid methodology performs best at temperature and wind speed forecasts, closely followed by the SARIMA, whereas LSTM performs better overall at the humidity forecast, even after the correction of the hybrid to the SARIMA model. Lastly, different hybrid methodologies are discussed and introduced for further improvement of meteorological predictions.

<p>The world’s oceans have been studied and monitored for many decades to enhance our understanding. In today’s world, with the explosion of new data provided by many different Earth observation sources and the availability of advanced computing infrastructures (cloud computing, HPC, IoT, Big Data), creating a digital representation of the ocean is becoming a reality. The EC recently funded the H2020 ILIAD project, which aims at establishing an interoperable, data-intensive Digital Twin of the Ocean (DTO). The ILIAD DTO will integrate real-time sensing of ocean variables, state-of-the-art high-resolution models, modern data analytics and digital infrastructures to create virtual representations of physical processes and understand their behaviour, anticipating and predicting their response to simulated events and future changes. ILIAD will enable an ecosystem of interoperable DTOs, integrating the plethora of existing EU Earth Observing and Modelling Digital Infrastructures. It will fuse a large volume of diverse data and will enhance ocean data infrastructures with additional observation technologies and citizen science.  ILIAD will provide a virtual environment representing the ocean, capable of running predictive management scenarios and will utilize Big Data analytics for forecasting of spatiotemporal events and pattern recognition. Several DT pilots will be undertaken in several key thematic areas such as offshore wind energy, wave and tidal energy, biodiversity assessments, marine pollution and more. </p> <p>The current work presents ongoing activities for a coastal, high-resolution Digital Twin pilot for Cretan Sea, to be demonstrated in the frame of ILIAD project. The pilot focuses on oil spill pollution monitoring and forecasting. The DT environment combines high-resolution forecasting services based on numerical weather (WRF), hydrodynamic (NEMO), sea state (WAVEWATCH III) and particle tracking models (MEDSLIK-II), enhanced by the integration of  Sentinel data and real-time observations from novel, low cost current and waves meters, drifting trackers, as well as citizen science. WRF model is applied for forecasting of meteorological variables at  ̴ 3 km resolution by dynamic downscaling of coarser resolution climatic modelling forecast data (NOAA’s GFS). This way, higher computational accuracy is achieved over Cretan Sea, thus revealing finer wind scales phenomena. The downscaled weather forecasting data are used to force NEMO and WAVEWATCH III, to obtain high-resolution forecasts of important marine parameters, such as sea currents, temperature, salinity and waves over a fine grid of   ̴ 1 km for the coastal area of Crete. For oil spills, the DT of Cretan Sea will integrate operational analysis of Sentine-1 images, triggering MEDSLIK-II oil spill model once an oil spill event or anomaly is identified. Adjusting forecasts to observations by reinitialising the model with updated observational patterns will contribute to the forecast error growth being implicitly accounted for and minimized. The pilot DT virtual environment will allow on-demand simulations of predictive scenarios of oil spill events and response strategies.  </p> <p>The aim of the Digital Twin is to aid the immediate response in case of accidental oil releases, minimize the damage and reduce the time for environmental recovery.</p> <p><strong>Acknowledgement:</strong> This research has received funding from the European Union’s H2020 RIA programme under GA No 101037643.</p>