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One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory.

One of the biggest challenges in forestry research is the effective and accurate measuring and monitoring of forest variables, as the exploitation potential of forest inventory products largely depends on the accuracy of estimates and on the cost of data collection. This paper presented a novel computational method of low-cost forest inventory using global navigation satellite system (GNSS) signals in a crowdsourcing approach. Statistical features of GNSS signals were extracted from widely available GNSS devices and were used for predicting forest attributes, including tree height, diameter at breast height, basal area, stem volume, and above-ground biomass, in boreal forest conditions. The basic evidence of the predictions is the physical correlations between forest variables and the responses of GNSS signals penetrating through the forest. The random forest algorithm was applied to the predictions. GNSS-derived prediction accuracies were comparable with those of the most accurate 2-D remote sensing techniques, and the predictions can be improved further by integration with other publicly available data sources without additional cost. This type of crowdsourcing technique enables the collection of up-to-date forest data at low cost, and it significantly contributes to the development of new reference data collection techniques for forest inventory. Currently, field reference can account for half of the total costs of forest inventory.

Abstract In case of a core melt-down accident in a light water nuclear reactor, hydrogen is produced during reactor core degradation and released into the reactor building. This subsequently creates a combustion hazard. A local ignition of the combustible mixture may generate standing flames or initially slow propagating flames. Depending on geometry, mixture composition and turbulence level, the flame can accelerate or be quenched after a certain distance. The loads generated by the combustion process (increase of the containment atmosphere pressure and temperature) may threaten the integrity of the containment building and of internal walls and equipment. Turbulent deflagration flames may generate high pressure pulses, temperature peaks, shock waves and large pressure gradients which could severely damage specific containment components, internal walls and/or safety equipment. The evaluation of such loads requires validated codes which can be used with a high level of confidence. Currently, turbulence and steam effect on flame acceleration, flame deceleration and flame quenching mechanisms are not well reproduced by combustion models usually implemented in safety tools and further model enhancement and validation are still needed. For this purpose, two hydrogen deflagration benchmark exercises have been organised in the framework of the SARNET network. The first benchmark was focused on turbulence effect on flame propagation. For this purpose, three tests performed in the ENACCEF facility were considered. They concern vertical flame propagation in an initially homogenous mixture with 13 vol.% hydrogen content and different geometrical configurations. Three blockage ratios of 0, 0.33 and 0.6 were considered to generate different levels of turbulence. The second benchmark objective was the investigation of the diluting effect on flame propagation. Thus, three tests performed in the ENACCEF facility using the same blockage ratio of 0.63 and three different initial gas compositions (with 10, 20 and 30 vol.% diluents) have been considered. Since ENACCEF runs at ambient temperature, a surrogate to steam was used consisting of a mixture of 0.6He + 0.4CO 2 on molar basis. This paper aims to present the benchmarks conclusions regarding the ability of LP and CFD combustion models to predict the effect of turbulence and diluent on flame propagation.

Abstract In case of a core melt-down accident in a light water nuclear reactor, hydrogen is produced during reactor core degradation and released into the reactor building. This subsequently creates a combustion hazard. A local ignition of the combustible mixture may generate standing flames or initially slow propagating flames. Depending on geometry, mixture composition and turbulence level, the flame can accelerate or be quenched after a certain distance. The loads generated by the combustion process (increase of the containment atmosphere pressure and temperature) may threaten the integrity of the containment building and of internal walls and equipment. Turbulent deflagration flames may generate high pressure pulses, temperature peaks, shock waves and large pressure gradients which could severely damage specific containment components, internal walls and/or safety equipment. The evaluation of such loads requires validated codes which can be used with a high level of confidence. Currently, turbulence and steam effect on flame acceleration, flame deceleration and flame quenching mechanisms are not well reproduced by combustion models usually implemented in safety tools and further model enhancement and validation are still needed. For this purpose, two hydrogen deflagration benchmark exercises have been organised in the framework of the SARNET network. The first benchmark was focused on turbulence effect on flame propagation. For this purpose, three tests performed in the ENACCEF facility were considered. They concern vertical flame propagation in an initially homogenous mixture with 13 vol.% hydrogen content and different geometrical configurations. Three blockage ratios of 0, 0.33 and 0.6 were considered to generate different levels of turbulence. The second benchmark objective was the investigation of the diluting effect on flame propagation. Thus, three tests performed in the ENACCEF facility using the same blockage ratio of 0.63 and three different initial gas compositions (with 10, 20 and 30 vol.% diluents) have been considered. Since ENACCEF runs at ambient temperature, a surrogate to steam was used consisting of a mixture of 0.6He + 0.4CO 2 on molar basis. This paper aims to present the benchmarks conclusions regarding the ability of LP and CFD combustion models to predict the effect of turbulence and diluent on flame propagation.

District heating is a network of pipes through which heat is delivered from a centralised source. It is expected to play an important role in the decarbonisation of the energy sector in the coming years. In district heating, heat is traditionally generated through fossil fuels, often with combined heat and power (CHP) units. However, increasingly, waste heat is being used as a low carbon alternative, either directly or, for low temperature sources, via a heat pump. The design of district heating often has competing objectives: the need for inexpensive energy and meeting low carbon targets. In addition, the planning of district heating schemes is subject to multiple sources of uncertainty such as variability in heat demand and energy prices. This paper proposes a decision support tool to analyse and compare system designs for district heating under uncertainty using stochastic ordering (dominance). Contrary to traditional uncertainty metrics that provide statistical summaries and impose total ordering, stochastic ordering is a partial ordering and operates with full probability distributions. In our analysis, we apply the orderings in the mean and dispersion to the waste heat recovery problem in Brunswick, Germany.

District heating is a network of pipes through which heat is delivered from a centralised source. It is expected to play an important role in the decarbonisation of the energy sector in the coming years. In district heating, heat is traditionally generated through fossil fuels, often with combined heat and power (CHP) units. However, increasingly, waste heat is being used as a low carbon alternative, either directly or, for low temperature sources, via a heat pump. The design of district heating often has competing objectives: the need for inexpensive energy and meeting low carbon targets. In addition, the planning of district heating schemes is subject to multiple sources of uncertainty such as variability in heat demand and energy prices. This paper proposes a decision support tool to analyse and compare system designs for district heating under uncertainty using stochastic ordering (dominance). Contrary to traditional uncertainty metrics that provide statistical summaries and impose total ordering, stochastic ordering is a partial ordering and operates with full probability distributions. In our analysis, we apply the orderings in the mean and dispersion to the waste heat recovery problem in Brunswick, Germany.

This paper presents a stochastic factor based approach to mid-term modeling of spot prices in deregulated electricity markets. The fundamentals affecting the spot price are modeled independently and a market equilibrium model combines them to form spot price. Main advantage of the model is the transparency of the generated prices because each underlying factor and the dynamics between factors can be modeled and studied in detail. Paper shows realistic numerical examples on the forerunner Scandinavian electricity market. The model is used to price an exotic electricity derivative.

This paper presents a stochastic factor based approach to mid-term modeling of spot prices in deregulated electricity markets. The fundamentals affecting the spot price are modeled independently and a market equilibrium model combines them to form spot price. Main advantage of the model is the transparency of the generated prices because each underlying factor and the dynamics between factors can be modeled and studied in detail. Paper shows realistic numerical examples on the forerunner Scandinavian electricity market. The model is used to price an exotic electricity derivative.