Flood Estimation and Prediction Using Particle Filters

Data assimilation methods have received increased attention to accomplish uncertainty assessment and enhancement of forecasting capability in various areas. Despite their potential, applicable software frameworks for probabilistic approaches and data assimilation are still limited because most hydrologic modeling software are based on a deterministic approach. In this study, we developed a hydrologic modeling framework for data assimilation, namely MPI-OHyMoS. While adapting object-oriented features of the original OHyMoS, MPI-OHyMoS allows user to build a probabilistic hydrologic model with data assimilation. In this software framework, sequential data assimilation based on particle filtering is available for any hydrologic models considering various sources of uncertainty originating from input forcing, parameters, and observations. Ensemble simulations are parallelized by a message passing interface (MPI), which can take advantage of high-performance computing (HPC) systems. Structure and implementation processes of data assimilation via MPI-OHyMoS are illustrated using a simple lumped model. We apply this software framework for uncertainty assessment of a distributed hydrologic model in synthetic and real experiment cases. In the synthetic experiment, dual state-parameter updating results in a reasonable estimation of parameters to cover synthetic true within their posterior distributions. In the real experiment, dual updating with identifiable parameters results in a reasonable agreement to the observed hydrograph with reduced uncertainty of parameters

Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters

Demands on the disaster response capacity of the European Union are likely to increase, as the impacts of disasters continue to grow both in size and frequency. This has resulted in intensive research on issues concerning spatially-explicit information and modelling and their multiple sources of uncertainty. Geospatial support is one of the forms of assistance frequently required by emergency response centres along with hazard forecast and event management assessment. Robust modelling of natural hazards requires dynamic simulations under an array of multiple inputs from different sources. Uncertainty is associated with meteorological forecast and calibration of the model parameters. Software uncertainty also derives from the data transformation models (D-TM) needed for predicting hazard behaviour and its consequences. On the other hand, social contributions have recently been recognized as valuable in raw-data collection and mapping efforts traditionally dominated by professional organizations. Here an architecture overview is proposed for adaptive and robust modelling of natural hazards, following the Semantic Array Programming paradigm to also include the distributed array of social contributors called Citizen Sensor in a semantically-enhanced strategy for D-TM modelling. The modelling architecture proposes a multicriteria approach for assessing the array of potential impacts with qualitative rapid assessment methods based on a Partial Open Loop Feedback Control (POLFC) schema and complementing more traditional and accurate a-posteriori assessment. We discuss the computational aspect of environmental risk modelling using array-based parallel paradigms on High Performance Computing (HPC) platforms, in order for the implications of urgency to be introduced into the systems (Urgent-HPC).Comment: 12 pages, 1 figure, 1 text box, presented at the 3rd Conference of Computational Interdisciplinary Sciences (CCIS 2014), Asuncion, Paragua

A model for an economic evaluation of energy systems using TRNSYS

This paper presents a technical-economic model for the evaluation of energy systems called Energy Assessment Tool of Energy Projects (EATEP). It was created with the TRaNsient System Simulation Tool (TRNSYS) and works in parallel to the technical simulations in this software. The EATEP links, in hourly time steps, technical and economic variables that can determine the functioning of energy systems and the profitability of the investment required for their implementation. The economic calculation procedure, as described in the European standard EN 15459:2007, of the Energy Performance of Buildings Directive (EPBD) of the European Commission, has been adapted to the characteristics of TRNSYS to develop the calculation methodology of the EATEP. The final use of this resulting tool is the evaluation of the energy self-consumption of communities from the technical-economic point of view, analyzing the investment in distributed generation systems by consumers, prosumers and energy producers. The operation of the EATEP has been validated through two cases that demonstrate the wide range of its applicability and versatility. In the first case, the calculation of indicators identifies the best alternative among various investment options in the evaluation of self-consumption energy systems. The second case, evaluates systems in which producers, consumers and prosumers exchange energy and economic flows; the tool calculates indicators of costs, revenue and income (the margin between revenue and costs).Postprint (author's final draft

Implementing Air Pollution and Health Damage Costs in Urban Multi-Energy Systems Modelling

The growing global urbanization rate implies that the sustainability challenges are increasingly concentrated in cities. At today, around 75% of global energy is consumed in urban areas, so efforts must be addressed to transform existing urban energy systems into more sustainable systems. In this perspective, a key aspect to evolve toward a cleaner and affordable energy system is the development of Multi-Energy Systems (MES) modelling, whereby heat, electricity, fuels, transport, and other energy carriers closely interact with each other at various scales. MES can optimize technical, economic and environmental performance with respect to “traditional” independent energy systems, at both the operational and the planning stage. This paper presents a development of the existing MESsi modelling platform, consisting in the implementation of a model estimating the impacts on air quality and human health. MESsi is a novel distributed infrastructure for modelling and co-simulating Multi-Energy-Systems. It exploits modern software design patterns (i.e. microservices) to guarantee scalability, extendibility and easy maintenance of the system. Thus, MESsi is flexible in modelling and co-simulating different energy flows in a single solution made of different interoperable modules that can be deployed in a plug-and-play fashion. The module to be implemented in MESsi infrastructure is the DIATI integrated dispersion and externalities model (DIDEM). The DIDEM model is based on the impact pathway approach, linking the simulation of pollutants dispersion to the concentration-exposure-response functions provided by latest WHO recommendations. An overview of the potential integration steps in the modelling infrastructure is described in this paper. A discussion on possible application scenarios that have different spatio-temporal resolutions is also reported. The integration of DIDEM model in MESsi platform allows the inter-connection of a detailed impact assessment to a high-level energy system simulation

GAS TURBINE PERFORMANCE DIGITAL TWIN FOR REAL-TIME EMBEDDED SYSTEMS

This contribution reports on the development of Performance Digital Twin for industrial Small Gas Turbines. The objective of this study was the development of automation systems with control and monitoring functionalities, capable of addressing the requirements of future gas turbine plants for increased availability and reliability by use of Digital Twin technology. The project explored development of Performance Digital Twin based on Real-Time Embedded computing, which can be leveraged with Internet-of-Things (IOT) Cloud Platforms. The proposed solution was provided in a form of modular software for a range of hardware platforms, with corresponding functionalities to support advanced control, monitoring, tracking and diagnostics strategies. The developed Digital Twin was designed to be used in off-line mode to assist the software commissioning process and in on-line mode to enable early detection of degradation and fault modes typical for gas path components. The Performance Digital Twin is based on a dynamic gas turbine model which was augmented with a Kalman tuner to enable performance tracking of physical assets. To support heterogeneity of gas turbine Distributed Control Systems (DCS), this project explored deployment of Digital Twin on multiple platforms. In the paper, we discuss model-based design techniques and tools specific for continuous, discrete and hybrid systems. The hybrid solution was deployed on PC-based platform and integrated with engine Distributed Control System in the field. Monitoring of gas turbine Performance Digital Twin functionalities has been established via Remote Monitoring System (STA-RMS). Assessment of deployed solution has been carried out and we present results from the field trial in this paper. The discrete solution was deployed on a range of Programable Logical Controller (PLC) platforms and has been tested by integrating Digital Twin in virtual engine Distributed Control System network. The Performance Digital Twin was embedded in Single Master PLC and Master-Slave PLC configurations, and we present results from the system testing using virtual gas turbine assets. The IoT Platform MindSphere was integrated within virtual engine network, and in this contribution, we explore expansion of the developed system with Cloud based applications and services

A NUMERICAL APPROACH TO OHMIC LOSSES ASSESSMENT IN CONCENTRATING PHOTOVOLTAIC SYSTEMS

It is well known that concentrator solar cells operating under concentration experience a number of physical effects which affect their performances. In particular, ohmic losses can determine a noticeable performance worsening of concentrator solar cells. The goal of this dissertation is to develop a distributed electrical model of solar cell in order to simulate the operation of concentrator solar cells in a number of working conditions characteristic of Concentrating Photovoltaic (CPV) systems, such as uneven illumination profiles with arbitrary spectral distributions. To this end a mixed optical-electrical simulation tool has been developed in order to assess the performances of a typical concentrator solar cell in the case of illumination provided by different kinds of concentrators; in particular a Fresnel lens, a parabolic mirror and a freeform mirror have been considered and compared. At high concentration factors front contact grid pattern has a key role in extracting photogenerated charges, and hence it is another factor that can strongly affect the cell performances; for this reason the above mentioned distributed electrical model has also been applied to the assessment of ohmic losses impact on concentrator silicon solar cells performances in the presence of different kinds of front contact grid patterns. In particular, a comb-like geometry, a square-like geometry and a novel fractal autosimilar geometry have been simulated and compared. Another aspect investigated in this dissertation is the formation of voids in the solder joint region, during soldering process of concentrator solar cells to Metal Core Printed Circuit Boards (MC-PCB). Some commercially available silicon solar cells have been soldered in such a way that a great number of voids have arisen and their distribution has been revealed by X-ray inspection. Electrical and thermal behaviour of one of the cells has been assessed by a joint thermal-electrical simulation tool. In this thesis electrical, optical and thermal simulations have been performed by means of ORCAD PSPICE software, ZEMAX software and ADINA 8.7 software, respectively

DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments

With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST

Uncertainty explicit assessment of off-the-shelf software: A Bayesian approach

Assessment of software COTS components is an essential part of component-based software development. Poorly chosen components may lead to solutions of low quality and that are difficult to maintain. The assessment may be based on incomplete knowledge about the COTS component itself and other aspects (e.g. vendor’s credentials, etc.), which may affect the decision of selecting COTS component(s). We argue in favor of assessment methods in which uncertainty is explicitly represented (‘uncertainty explicit’ methods) using probability distributions. We provide details of a Bayesian model, which can be used to capture the uncertainties in the simultaneous assessment of two attributes, thus, also capturing the dependencies that might exist between them. We also provide empirical data from the use of this method for the assessment of off-the-shelf database servers which illustrate the advantages of ‘uncertainty explicit’ methods over conventional methods of COTS component assessment which assume that at the end of the assessment the values of the attributes become known with certainty