In a context where digital giants are increasingly influencing the actions decided by public policies, smart data platforms are a tool for collecting a great deal of information on the territory and a means of producing effective public policies to meet contemporary challenges, improve the quality of the city, and create new services. Within the framework of the Smarter Together project, the cities of Lyon (France), Munich (Germany), and Vienna (Austria) have integrated this tool into their city’s metabolism and use it at different scales. Nevertheless, the principle remains the same: the collection (or even dissemination) of internal and external data to the administration will enable the communities, companies, not-for-profit organizations, and civic administrations to “measure” the city and identify areas for improvement in the territory. Furthermore, through open data logics, public authorities can encourage external partners to become actors in territorial action by using findings... [more]

Farnsworth Field Unit (FWU), a mature oilfield currently undergoing CO2-enhanced oil recovery (EOR) in the northeastern Texas panhandle, is the study area for an extensive project undertaken by the Southwest Regional Partnership on Carbon Sequestration (SWP). SWP is characterizing the field and monitoring and modeling injection and fluid flow processes with the intent of verifying storage of CO2 in a timeframe of 100−1000 years. Collection of a large set of data including logs, core, and 3D geophysical data has allowed us to build a detailed reservoir model that is well-grounded in observations from the field. This paper presents a geological description of the rocks comprising the reservoir that is a target for both oil production and CO2 storage, as well as the overlying units that make up the primary and secondary seals. Core descriptions and petrographic analyses were used to determine depositional setting, general lithofacies, and a diagenetic sequence for reservoir and caprock at... [more]

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low comput... [more]

Urban building energy modeling (UBEM) is arousing interest in building energy modeling, which requires a large building dataset as an input. Building use is a critical parameter to infer archetype buildings for UBEM. This paper presented a case study to determine building use for city-scale buildings by integrating the Geographic Information System (GIS) based point-of-interest (POI) and community boundary datasets. A total of 68,966 building footprints, 281,767 POI data, and 3367 community boundaries were collected for Changsha, China. The primary building use was determined when a building was inside a community boundary (i.e., hospital or residential boundary) or the building contained POI data with main attributes (i.e., hotel or office building). Clustering analysis was used to divide buildings into sub-types for better energy performance evaluation. The method successfully identified building uses for 47,428 buildings among 68,966 building footprints, including 34,401 residential... [more]

The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss... [more]

The paper presents the opportunities to apply computer graphics in an object floodlighting design process and in an analysis of object illumination. The course of object floodlighting design has been defined based on a virtual three-dimensional geometric model. The problems related to carrying out the analysis of lighting, calculating the average illuminance, luminance levels and determining the illuminated object surface area are also described. These parameters are directly tied with the calculations of the Floodlighting Utilisation Factor, and therefore, with the energy efficiency of the design as well as the aspects of light pollution of the natural environment. The paper shows how high an impact of the geometric model of the object has on the accuracy of photometric calculations. Very often the model contains the components that should not be taken into account in the photometric calculations. The research on what influence the purity of the geometric mesh of the illuminated objec... [more]

A prefabricated pumping station is a new type of pumping station that plays an important role in the construction of sponge cities in developing countries. It solves the problem of urban water-logging and makes great contributions to the sustainable development of water resources. In order to research the influence of different installation positions of pumps on the internal hydraulic performance of a prefabricated pumping station, based on ANSYS software, the computational fluid dynamics (CFD) numerical simulation method was used to analyze the internal flow state of the prefabricated pump station. In this research, the optimal geometric parameters of pump installation in a prefabricated pumping station are given. The results show that when the distance between the connecting line of two pumps and the center of the sump is L = 0.2 R, the distance between the two pumps is S = 0.6 R, and the suspension height of the two pumps is H = 0.6 D, the internal flow pattern of the prefabricated... [more]

The paper describes a case study of the safety hydraulic system damage in the working of a longwall in a Polish coal mine. The safety elements are a component of the powered roof supports which secure the shield against damage during rock burst incidents. The damage event, which occurred in the hydraulic system during the mining process, caused the uncontrolled lowering of the powered roof support height during the mining process. The uncontrolled lowering of a shield may cause the danger of the loss of the stability along the longwall working in the form of a rock burst and collapses and may represent a serious and immediate danger to the safety and health of employees. Based on the results of the computational fluid dynamics methods (CFD) analysis of the safety elements in the hydraulic system of longwall 2-leg shield, the causes of damage were diagnosed and presented. The CFD and the strength analysis by the finite element method (FEM) were used for numerical modeling. The diagrams... [more]

Heat demand of buildings and related CO2 emissions caused by energy supply contribute to global climate change. Spatial data-based heat planning enables municipalities to reorganize local heating sectors towards efficient use of regional renewable energy resources. Here, annual heat demand of residential buildings is modeled and mapped for a German federal state to provide regional basic data. Using a 3D building stock model and standard values of building-type-specific heat demand from a regional building typology in a Geographic Information Systems (GIS)-based bottom-up approach, a first base reference is modeled. Two spatial data sets with information on the construction period of residential buildings, aggregated on municipality sections and hectare grid cells, are used to show how census-based spatial data sets can enhance the approach. Partial results from all three models are validated against reported regional data on heat demand as well as against gas consumption of a municipa... [more]

Water droplets released from the sea surface represent one of the major causes of ice accretion on marine vessels. A one-dimensional model of the freezing of a spherical water droplet moving in cold air was developed. The crystallization model allows one to obtain an analytical solution if a uniform temperature distribution over the liquid’s core is assumed. The model was validated using STAR CCM+ Computational fluid dynamics (CFD) code. A collision of a partially frozen droplet with a solid wall assuming the plastic deformation of an ice crust was also considered. The ratio of the crust deformation to the crust thickness was evaluated. It was assumed that if this ratio were to exceed unity, the droplet would stick to the wall’s surface due to ice bridge formation caused by the water released from the droplet’s core.

Understanding to what extent the emergence of prosumers and prosumagers organized in energy communities can impact the organization and operation of power grids has been one of the major recent research avenues at the European level. In renewable-based communities aiming to reach some level of energy self-sufficiency, a key issue to be addressed is assessing how the presence of end-users playing different roles in the system (self-consuming, producing and trading, performing demand management, etc.) can influence the overall system performance. In this setting, this paper combines Distributed Artificial Intelligence and optimization approaches to assess how prosumagers and consumers pursuing different goals can influence the energy self-sufficiency of a local energy community. The residential demand is accurately modeled, and the agents’ preferences are considered in the modeling to represent a smart community. The results show that although energy community members may have conflictin... [more]

In the context of the European Green Deal, the manufacturing industry faces environmental challenges due to its high demand for electrical energy. Thus, measures for improving the energy efficiency or flexibility are applied to address this problem in the manufacturing industry. In order to quantify energy efficiency or flexibility potentials, it is often necessary to predict or forecast the energy consumption. This paper presents a systematic review of state-of-the-art of existing approaches to predict or forecast the energy consumption in the manufacturing industry. Seventy-two articles are classified according to the defined categories System Boundary, Modelling Technique, Modelling Focus, Modelling Horizon, Modelling Perspective, Modelling Purpose and Model Output. Based on the reviewed articles future research activities are derived.

This study investigates geometric parameters of commercially available or recently published models of catalyst substrates for passenger vehicles and provides a numerical evaluation of their influence on heat-up behavior. Parameters considered to have a significant impact on the thermal economy of a monolith are: internal surface area, heat transfer coefficient, and mass of the converter, as well as its heat capacity. During simulation experiments, it could be determined that the primary role is played by the mass of the monolith and its internal surface area, while the heat transfer coefficient only has a secondary role. Furthermore, an optimization loop was implemented, whereby the internal surface area of a commonly used substrate was chosen as a reference. The lengths of the thin wall and high cell density monoliths investigated were adapted consecutively to obtain the reference internal surface area. The results obtained by this optimization process contribute to improving the hea... [more]

Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities’ energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries, NTLs represent around 15% of total energy generated in 2018, varying between 5 and 30% depending on the country because of the strong correlation with social, economic, political, and technical variables. According to this, electric utilities have a strong interest in finding new techniques and methods to mitigate this problem as much as possible. This research presents the results of determining... [more]

Preformed coils are used in electrical machines to improve the copper slot fill factor. A higher utilization of the machine can be realized. The improvement is a result of both, low copper losses due to the increased slot fill factor and an improved heat transition out of the slot. In this study, the influence of these two aspects on the operational improvement of the machine is studied. Detailed simulation models allow a separation of the two effects. A preform wound winding in comparison to a round wire winding is studied. Full machine prototypes as well as motorettes of the two designs are built up. Thermal finite element models of the stator slot are developed and parameterized with the help of motorette microsections. The resulting thermal lumped parameter model is enlarged to represent the entire electric machine. Electromagnetic finite element models for loss calculation and the thermal lumped parameter models are parameterized using test bench measurements. The developed models... [more]

The point kinetic model is a system of differential equations that enables analysis of reactor dynamics without the need to solve coupled space-time system of partial differential equations (PDEs). The random variations, especially during the startup and shutdown, may become severe and hence should be accounted for in the reactor model. There are two well-known stochastic models for the point reactor that can be used to estimate the mean and variance of the neutron and precursor populations. In this paper, we reintroduce a new stochastic model for the point reactor, which we named the Langevin point kinetic model (LPK). The new LPK model combines the advantages, accuracy, and efficiency of the available models. The derivation of the LPK model is outlined in detail, and many test cases are analyzed to investigate the new model compared with the results in the literature.

An enhanced geothermal system (EGS) proposed on the basis of hot dry rock mining technology has become a focus of geothermal research. A novel procedure for coupled simulation of thermal and fluid flow models (NPCTF) is derived to model heat flow and thermal energy absorption characteristics in rough-walled rock fractures. The perturbation method is used to calculate the pressure and flow rate in connected wedge-shaped cells at pore-scale, and an approximate analytical solution of temperature distribution in wedge-shaped cells is obtained, which assumes an identical temperature between the fluid and fracture wall. The proposed method is verified in Barton and Choubey (1985) fracture profiles. The maximum deviation of temperature distribution between the proposed method and heat flow simulation is 13.2% and flow transmissivity is 1.2%, which indicates the results from the proposed method are in close agreement with those obtained from simulations. By applying the proposed NPCTF to real... [more]

The Double Decker Turbine (DDT) is a recent design introduced for oscillating water column (OWC) devices. Its major contribution is the combination of two typical solutions in just one prototype: a self-rectifying performance, to deal with the bidirectional flow, and the twin-turbine concept, allowing the use of unidirectional turbines. This is achieved by a set of two concentric turbines, called external and internal turbines (ExT—InT). In this work, Computational Fluid Dynamics (CFD) numerical model is developed to study in detail the performance of a DDT, where geometrical components for both turbines have been taken from previous works of the authors. The ANSYS-Fluent code was first executed by means of a URANS simulation with a realizable k-ε turbulence model to obtain the performance curve of the turbine under steady conditions. Results obtained reveal its potential with respect to other solutions in the current state-of-the-art of OWC solutions for Wave Energy Conversion. Follow... [more]

In order to analyze the safety of nuclear power plants (NPP), interactions between thermomechanical and automation processes, the on-site electrical grid, and the off-site transmission system should be studied in detail. However, an initial survey of simulation tools used for the modelling and simulation of NPP shows that existing simulation tools have some drawbacks in properly simulating the aforementioned interactions. In fact, they simulate detailed electrical power systems and thermomechanical systems but neglect the detailed interactions of the electrical system with thermomechanical and automation processes. To address this challenge, this paper develops an open-source co-simulation platform which connects Apros, a proprietary simulator of the thermomechanical and automation processes in NPP, to power system simulators. The proposed platform provides an opportunity to simulate both the electrical and thermomechanical systems of an NPP simultaneously, and study the interactions b... [more]

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure−volume−temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating pa... [more]

Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification techniques for adaptive frequency hopping (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on the accuracy of BlueEar’s AFH map prediction. To this end, in this paper, we revisit the spectrum sensing-based classifier, one of the subchannel classification techniques in BlueEar. At first, we build an independent implementation of the spectrum sensing-based classifier with one Ubertooth sniffing radio. Using the implementation, we conduct a subchannel classification experiment with several machine learning classifiers where spectrum features are utilized. Our results show that higher accuracy can be achieved... [more]

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation... [more]

The single axis solar tracker based on flat panels is used in large solar plants and in distribution-level photovoltaic systems. In order to achieve this, the solar tracking systems generally need to work by tracking the sun’s position with dozens, maybe hundreds of movements along the day with a maximal known tracking error within the specifications. A novel model is proposed along this work based on the control of the angle deviation within a (polar) single axis configuration. This way an optimization of the harnessing of solar energy can be achieved with as few panel displacements as possible in order to decrease the wear in the mechanical parts of the equipment and the energy consumed by it. This tracking approach was implemented with as few as seven positions along the day and got an estimated theoretical value of 99.27% of the total collected energy in a continuous tracking system. Regarding an annual average basis, it would be about 96.5% of a dual axis system according to the p... [more]

One of the primary factors for generating heating and cooling loads in apartment houses is infiltration. However, the evaluation method for infiltration rates has not been well established for the apartment houses in Korea. The existing method measures air change per hour of a house at 50 Pa (ACH50) and divides it by the leakage−infiltration ratio, N = 20, as suggested by the Lawrence Berkeley National Laboratory (LBL). In this study, a method to evaluate the average infiltration rate of an apartment house using blower door tests and simulations is suggested. Six sets of blower door tests were conducted, and the measurement data were used to estimate the flow coefficients and pressure exponents of all infiltration routes. The values were used as the input data for EnergyPlus to calculate the natural air change per hour values (ACHn) of two households. The calculated ACHn values were compared to the ACHn values calculated using the LBL method, which is commonly used in Korea. Through th... [more]

This paper proposes a critical review of the different aspects concerning magnetic refrigeration systems, and performs a detailed analysis of thermodynamic cycles, using mathematical models found in the literature. Langevin’s statistical mechanical theory faithfully describes the physical operation of a refrigeration machine working according to a magnetic Ericsson cycle. Results of mathematical and real experimental models are compared to deduce which best describes the Ericsson cycle. The theoretical data are not perfectly consistent with the experimental data; there is a maximum deviation of about 30%. Numerical and experimental data confirm that very high Coefficient of Performance (COP) values of more than 20 can be achieved. The analysis of the Brayton cycle consisted of finding the mathematical model that considers the irreversibility of these machines. Starting from the thermodynamic properties of magnetocaloric materials based on statistical mechanics, the efficiency of an irr... [more]