Predictive modeling of the spatiotemporal evolution of an environmental hazard and its sensor network implementation

Predicting accurately the spatiotemporal evolution of a diffusive environmental hazard is of paramount importance for its effective containment. We approximate the front line of a hazard with a set of line segments (local front models). We model the progression characteristics of these front segments by appropriately modified 2D Gaussian functions. The modified Gaussian model parameters are adjusted based on the solution of a Kullback-Leibler (KL) divergence minimization problem. The whole scheme can be realized by a wireless sensor network by forming dynamically triplets of cooperating sensor nodes along the path of the hazard. It is shown that the algorithm can track effectively the front characteristics (in terms of direction and speed) even in the presence of faulty sensor nodes

Collaborative sensor network algorithm for predicting the spatiotemporal evolution of hazardous phenomena

We present a novel decentralized Wireless Sensor Network (WSN) algorithm which can estimate both the speed and direction of an evolving diffusive hazardous phenomenon (e.g. a wildfire, oil spill, etc.). In the proposed scheme we approximate a progressing hazard’s front as a set of line segments. The spatiotemporal evolution of each line segment is modeled by a modified 2D Gaussian function. As the phenomenon evolves, the parameters of this model are updated based on the analytical solution of a Kullback – Leibler (KL) divergence minimization problem. This leads to an efficient WSN distributed parameters estimation algorithm that can be implemented by dynamically formed clusters (triplets) of collaborating sensor nodes. Computer simulations show that our approach is able to track the evolving phenomenon with reasonable accuracy even if a percentage of sensors fails due to the hazard and/or the phenomenon has a time varying speed

Estimating the spatiotemporal evolution characteristics of diffusive hazards using wireless sensor networks

There is a fast growing interest in exploiting Wireless Sensor Networks (WSNs) for tracking the boundaries and predicting the evolution properties of diffusive hazardous phenomena (e.g. wildfires, oil slicks etc.) often modeled as “continuous objects”. We present a novel distributed algorithm for estimating and tracking the local evolution characteristics of continuous objects. The hazard’s front line is approximated as a set of line segments, and the spatiotemporal evolution of each segment is modeled by a small number of parameters (orientation, direction and speed of motion). As the hazard approaches, these parameters are re-estimated using adhoc clusters (triplets) of collaborating sensor nodes. Parameters updating is based on algebraic closed-form expressions resulting from the analytical solution of a Bayesian estimation problem. Therefore, it can be implemented by microprocessors of the WSN nodes, while respecting their limited processing capabilities and strict energy constraints. Extensive computer simulations demonstrate the ability of the proposed distributed algorithm to estimate accurately the evolution characteristics of complex hazard fronts under different conditions by using reasonably dense WSNs. The proposed in-network processing scheme does not require sensor node clocks synchronization and is shown to be robust to sensor node failures and communication link failures, which are expected in harsh environments

Experimental assessment of a helical coil heat exchanger operating at subcritical and supercritical conditions in a small-scale solar organic rankine cycle

In this study, the performance of a helical coil heat exchanger operating at subcritical and supercritical conditions is analysed. The counter-current heat exchanger was specially designed to operate at a maximal pressure and temperature of 42 bar and 200 °C, respectively. The small-scale solar organic Rankine cycle (ORC) installation has a net power output of 3 kWe. The first tests were done in a laboratory where an electrical heater was used instead of the concentrated photovoltaic/thermal (CPV/T) collectors. The inlet heating fluid temperature of the water was 95 °C. The effects of different parameters on the heat transfer rate in the heat exchanger were investigated. Particularly, the performance analysis was elaborated considering the changes of the mass flow rate of the working fluid (R-404A) in the range of 0.20–0.33 kg/s and the inlet pressure varying from 18 bar up to 41 bar. Hence, the variation of the heat flux was in the range of 5–9 kW/m2. The results show that the working fluid’s mass flow rate has significant influence on the heat transfer rate rather than the operational pressure. Furthermore, from the comparison between the experimental results with the heat transfer correlations from the literature, the experimental results fall within the uncertainty range for the supercritical analysis but there is a deviation of the investigated subcritical correlations

Review of experimental research on supercritical and transcritical thermodynamic cycles designed for heat recovery application

Supercritical operation is considered a main technique to achieve higher cycle efficiency in various thermodynamic systems. The present paper is a review of experimental investigations on supercritical operation considering both heat-to-upgraded heat and heat-to-power systems. Experimental works are reported and subsequently analyzed. Main findings can be summarized as: steam Rankine cycles does not show much studies in the literature, transcritical organic Rankine cycles are intensely investigated and few plants are already online, carbon dioxide is considered as a promising fluid for closed Brayton and Rankine cycles but its unique properties call for a new thinking in designing cycle components. Transcritical heat pumps are extensively used in domestic and industrial applications, but supercritical heat pumps with a working fluid other than CO2 are scarce. To increase the adoption rate of supercritical thermodynamic systems further research is needed on the heat transfer behavior and the optimal design of compressors and expanders with special attention to the mechanical integrity

Simulation-driven emulation of collaborative algorithms to assess their requirements for a large-scale WSN implementation

Assessing how the performance of a decentralized wireless sensor network (WSN) algorithm's implementation scales, in terms of communication and energy costs, as the network size increases is an essential requirement before its field deployment. Simulations are commonly used for this purpose, especially for large-scale environmental monitoring applications. However, it is difficult to evaluate energy consumption, processing and memory requirements before the algorithm is really ported to a real WSN platform. We propose a method for emulating the operation of collaborative algorithms in large-scale WSNs by re-using a small number of available real sensor nodes. We demonstrate the potential of the proposed simulation-driven WSN emulation approach by using it to estimate how communication and energy costs scale with the network’s size when implementing a collaborative algorithm we developed in for tracking the spatiotemporal evolution of a progressing environmental hazard

Optimal Financial Insulation Thickness of a Broiler House

A common method for environmental control in livestock facilities is to use thermal insulation for exterior walls and roof. In regions with cool winters increasing the insulation thickness decreases the heating requirements, however in regions with hot summers may lead to unwanted increase of inside temperature, thus intensifying cooling loads. It is worth noting that financial thickness optimization of a broiler house external walls and gable roofs insulation for different orientations has not been sufficiently addressed, thus a detailed transient simulation was used to model existing energy audited broiler house and to calculate its annual heating and cooling loads. For that, hourly climatic data and all the heat and moisture gains and losses resulting from birds, heat flow through the building envelope and ventilation were taken into account. An economic analysis based on the Life Cycle Savings (LCS) method was performed for the walls and gable roofs for various insulation thicknesses and orientations. The comparison of the annual heating load per unit area with that of similar energy audited broiler houses was satisfactory. The optimum insulation thickness of external walls and gable roofs was found to be between 4.0 cm and 4.5 cm depending on their orientation, while the wall facing north offered the greatest economic benefit compared to other orientations. According to the results, the annual cooling load was 3.3 times higher than that of heating

Investigation of the Use of Low Temperature Geothermal Organic Rankine Cycle Engine in an Autonomous Polygeneration Microgrid

Low-enthalpy geothermal resources (<150 °C) can be used for electricity generation and are widespread around the world, occurring at shallow depths. At the same time, in many parts of the world, there are existing low-enthalpy geothermal wells that are used for a multitude of applications such as for buildings’ heating and agriculture-related applications. The dominant technology to convert low-grade heat (<150 °C) to electricity is the Organic Rankine Cycle (ORC). The autonomous polygeneration microgrid (APM) concept aims to holistically meet in a sustainable way the needs of an off-grid community in terms of electrical loads, space heating and cooling, potable water production through desalination, and the use of hydrogen as fuel for transportation, in the most cost-effective manner possible. Photovoltaics (PVs) and wind turbines have been investigated extensively, since PVs can be installed practically anywhere in the world and wind turbines in areas with sufficient wind potential. The aim of this paper is to investigate techno-economically the potential of utilizing low-enthalpy geothermal resources in small-scale APMs through an ORC engine to fully satisfy the needs of small settlements. In order to accomplish this task with confidence, a case study for the Greek island of Milos has been developed and a typical settlement has been considered. It is worth mentioning that experimental results from a realized low-power (<10 kWe) ORC engine manufactured to operate at temperatures up to 140 °C are used to add reliability in the calculations. In order to meet the needs of the people, four different APMs based on PVs, wind turbines, and geothermal ORC of different but appropriate configurations were designed and sized through optimization. The optimization process was based on particle swarm optimization (PSO). The comparative examination of the results shows that the use of a low-power, low-temperature ORC engine in an APM is technically feasible; more cost effective than the configurations based on PVs, wind turbines, or combination of both; and has increased environmental sustainability