Modeling strategies for multiple scenarios and fast simulations in large systems: applications to fire safety and energy engineering

The use of computational modeling has become very popular and important in many engineering and physical fields, as it is considered a fast and inexpensive technique to support and often substitute experimental analysis. In fact system design and analysis can be carried out through computational studies instead of experiments, that are typically demanding in terms of cost and technical resources; sometimes the systems characteristics and the technical problems make the experiments impossible to perform and the use of computational tools is the only feasible option. Demand of resources for realistic simulation is increasing due to the interest in studying complex and large systems. In these framework smart modeling approaches and model reduction techniques play a crucial role for making complex and large system suitable for simulations. Moreover, it should be considered that often more than one simulation is requested in order to perform an analysis. For instance, if a heuristic method is applied to the optimization of a component, the model has to be run a certain number of times. The same problem arises when a certain level of uncertainty affect the system parameters; in this case also many simulation are required for obtaining the desired information. This is the reason why the use of technique that allows to obtain compact model is an interesting topic nowadays. In this PhD thesis different reduction approaches and strategies have been used in order to analyze three energetic systems involving large domain and long time, one for each reduction approach categories. In all the topic considered, a smart model has been adopted and, when data were available, tested using experimental data. All the model are characterized by large domain and the time involved in the analysis are high in all the cases, therefore a method for compact model achievement is used in all the cases. The considered topics are: • Groundwater temperature perturbations due to geothermal heat pump installations, analyzed trough a multi-level model. • District heating networks (DHN), studied from both the fluid-dynamic and thermal point of view and applied to one of the larger network in Europe, the Turin district heating system (DHS), trough a Proper Orthogonal Decomposition - Radial Basis Function model. • Forest fire propagation simulation carried out using a Proper Orthogonal Decomposition projection model

Compact physical model for simulation of thermal networks

Optimal design and management of DH networks require numerical models for simulating the physical behavior of the network in various operating conditions. DH network models usually rely on the physical description of the fluid-dynamic and thermal behaviours. The use of physical models can represent a limitation in various cases: a) when extended networks are considered (several thousands of nodes); b) when multiple simulations are required in real-time; c) when multi-energy networks are optimized. In these cases, compact models are preferable

Optimization of the Thermal Load Profile in District Heating Networks through "Virtual Storage" at Building Level

Abstract Thermal storage is of extreme importance in modern district heating networks in order to increase the share of waste heat and heat produced through renewable sources and cogeneration. Nevertheless, installation of large storage volumes is not always feasible, especially in dense urban areas. A possible option consists in virtual storage, which is obtained through variation of the thermal request profiles of some of the connected buildings with the goal of producing an effect similar to that obtained using storage. To perform such approach there are three crucial elements: 1) an advanced ICT solution able provide real time information about the thermal request of the buildings and the thermodynamic conditions at the thermal substations; 2) a detailed thermo fluid-dynamic model of the district heating network able to simulate the temperature evolution along the network as the function of time; 3) a compact model of the buildings in the district able to check the acceptability of the internal temperatures following the modified strategies. The model produces changes in the start-up time of the buildings connected with the network as well as possible pauses during the day. These changes in the request profiles usually involve a slightly larger heat load. Nevertheless, peak shaving is accompanied by a reduction in heat generation of boilers and an increase in the thermal production of efficient systems, such as cogeneration units. This results in a significant reduction in the primary energy consumption. An application to the Turin district heating network, which is the largest network in Italy, is presented. In particular, a subnetwork connecting the main transport network to about 100 buildings located in the central area of the town is considered. The analysis if performed in selected days where the optimization was conducted the day before on the basis of weather forecasts and then applied to the network. Despite the changes in the request profiles could be applied only to a limited number of buildings, the analysis show that the peak request can be reduced. Simulations performed considering the application of changes to a larger number of buildings show that reduction in the primary energy consumptions of the order of 5% can be obtained

Entropy generation analysis of wildfire propagation

Entropy generation is commonly applied to describe the evolution of irreversible processes, such as heat transfer and turbulence. These are both dominating phenomena in fire propagation. In this paper, entropy generation analysis is applied to a grassland fire event, with the aim of finding possible links between entropy generation and propagation directions. The ultimate goal of such analysis consists in helping one to overcome possible limitations of the models usually applied to the prediction of wildfire propagation. These models are based on the application of the superimposition of the effects due to wind and slope, which has proven to fail in various cases. The analysis presented here shows that entropy generation allows a detailed analysis of the landscape propagation of a fire and can be thus applied to its quantitative descriptio

Automatic fouling detection in district heating substations: Methodology and tests

Abstract Diagnosis of anomalies in heat exchangers of district heating substations is an essential point to assure high comfort level in buildings, as well as to exploit energy sources efficiently. The aim of this paper is to propose a methodology for automatically detecting fouling in the heat exchangers located in the substations of a district heating system. The methodology is tailored for large district heating networks, where a large number of buildings should be examined with reasonable availability of data. Fouling is analysed using only the data collected by the meters installed in the substations: the mass flow rate on the primary side and the temperatures on both sides of the heat exchanger. Evaluation is difficult due to the rawness of the data gathered and the variable operating conditions, which are adjusted on the basis of the external temperatures and set-points. The software created to implement the proposed methodology receives rough data as the input and it is able to manage data gap and lack of data. Furthermore, it provides a graphical output, which can be used for assisting the operators who manage the network and plan the cleaning schedules. The software has been tested considering space heating substations in six distribution networks of the Turin district heating system, for a total amount of 325 heat exchangers. A regular application of the approach and the cleaning of the heat exchangers presenting fouling is expected to lead to an average annual decrease of about 1.6% of the primary energy consumption in the entire network

Trade-off between optimal design and operation in district cooling networks

Especially in densely populated areas, district cooling represents an opportunity to reduce energy consumption and emissions. Nevertheless, this technology is characterised by large capital costs which impede its diffusion. As a consequence, optimization tools can significantly help to unleash their potential. In this paper, a methodology is proposed to combinedly optimize the design and operation of a district cooling system based on a Mixed Integer Quadratic Programming. The model is compared to the design only optimization, based on a properly tailored heuristic approach. The models, when applied to a case study characterized by seasonal demand, provide similar solutions, which differ by 0.5 % in terms of objective value for a standard scenario. The simultaneous design and operation optimization does not provide sensible savings with respect to optimizing solely the design. A sensitivity analysis is performed to prove the robustness of the results. The results showed that the simulta- neous operation and design optimization would be limited to 1 % of total costs in the case of seasonal cooling demand. On the other hand, if the cooling demand persists throughout the year, as in tropical climates, the combined optimization provides significant benefits, since these savings reach 4.7 % of total costs

Two-stage stochastic programming for the design optimization of district cooling networks under demand and cost uncertainty

The major limitations of district cooling systems are the high capital costs, which make design optimization tools necessary to maximize the potential benefits. Decision makers when designing district cooling have to handle cost and demand uncertainties that further increase the investment risks. On the other hand, the possible evolution of cooling demand during the years, shall be taken into account in the first design stages, in order to allow network expansion in the future. In this paper, a novel two-stage stochastic programming model is therefore proposed for the optimal design of district cooling networks under demand and cost uncertainty. The model was also applied to a case study and the results showed that it is more convenient to build smaller district cooling networks (and eventually enhance them in the future if the cooling demand and electricity costs will increase) rather than building larger systems from the beginning. In addition, it was found that the uncertainties in electricity cost and cooling demand are the ones that most influence the optimal solution. The impact of the stochastic model was evaluated with respect to deterministic approaches, resulting up to 5% less expensive in terms of expected cost and with a three years lower payback time. A second model formulation was also implemented, with more rigid constraints, which limit the amount of pipes that can be installed in a single branch. With this formulation, the model tends to connect more buildings and to install larger pipes from the beginning, but the solution in terms of expected cost is only 0.4% more expensive than the more flexible one. Lastly, it was analysed the impact of asset residual value at the end of project life, revealing that neglecting it would lead to connecting more buildings initially, but in most scenarios the network would not be expanded in the future

Integration of a 1D model with FDS for multiscale analysis of tunnels

Numerical simulations are used to reduce the number of tests required in a lot of scientific fields. It works in that way in the field of Fire science with the usage of CFD (Computational Fire Dynamics). Fire simulations take less time to complete as computer sciences advance. But tunnel simulations with long domains still take long times limiting the opportunities to develop applications in fields that require live time results, like risk assessment, emergency systems, etc. A Multiscale algorithm is presented. This algorithm integrates Whitesmoke, a 1D algorithm developed to simulate fluid flow in networks, into FDS (Fire Dynamics Simulator), a 3D LES program used to simulate fire dynamics. The aim of this integration is optimizing both the calculation time and accuracy, using the fast solutions of the 1D in uniform zones and the detailed solutions of the FDS in complex areas. The accuracy of the Multiscale is evaluated by comparing it to full 3D simulations. In this case, a tunnel of 4.8m x 4.8m and 600m of length is simulated. The flow velocities and temperature of Multiscale and FDS simulations are compared. The Multiscale model achieves a time saving that is closely proportional to the portion of the domain calculated with the 1D sub-model. And, even when the simulation time is shorter the difference with the outputs obtained by the FDS is small in temperature, velocities and backlayering extension. The presented model is capable of reducing the time necessary to make a tunnel fire simulation without jeopardizing its results. Still, the Multiscale has some areas to improve and develop, as its boundary conditions, which should be improved further in the future

A feasibility study on the potential expansion of the district heating network of Turin

Reduction in energy consumptions and CO2 emissions and increase in the use of renewable energy sources can be reached through large scale implementation of energy efficiency measures. In urban contexts, district heating (DH) systems are expected to allow integration of waste heat and thermal renewable sources. In this work we propose a GIS-based model for the technical feasibility analysis of possible expansions of existing DH networks. The application to the City of Turin is presented as a case study