This study investigates using tailor-developed combustion and air-handling system concepts to achieve high-efficiency, clean gasoline compression ignition (GCI) combustion, aimed at addressing a future heavy-duty ultralow NOx standard of 0.027 g/kWh at the vehicle tailpipe and the tightening CO2 limits around the world by combining GCI with a cost-effective engine aftertreatment system. The development activities were conducted based on a 15 L heavy-duty diesel engine. By taking an analysis-led design approach, a first-generation (Gen1) GCI engine concept was developed and tested, encompassing tailor-designed piston bowl geometry, fuel spray pattern, and swirl motion paired with a customized, fixed-geometry, two-stage turbocharging system and a high-pressure EGR loop with two-stage cooling. Across four key steady-state operating points, the Gen1 GCI concept demonstrated 85−95% lower smoke and 2−3% better diesel-equivalent gross indicated fuel consumption compared to the diesel baseline... [more]

The objective of this article is to review the methodologies used in the last 15 years to estimate the power loss in wind turbines due to their exposure to adverse meteorological conditions. Among the methods, the use of computational fluid dynamics (CFD) for the three-dimensional numerical simulation of wind turbines is highlighted, as well as the use of two-dimensional CFD simulation in conjunction with the blade element momentum theory (BEM). In addition, a brief review of other methodologies such as image analysis, deep learning, and forecasting models is also presented. This review constitutes a baseline for new investigations of the icing effects on wind turbines’ power outputs. Furthermore, it contributes to a continuous improvement in power-loss prediction and the better response of icing protection systems.

The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize the latest updates in machine learning technology to accurately and timely disclose different system anomalies. This paper addresses this issue by evaluating the performance of different machine learning schemes and applying them to detect anomalies on photovoltaic components. The following schemes are evaluated: AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest. These models can identify the PV system’s healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such a complex solution space.

Large databases of legacy hydrocarbon reservoir and well data provide an opportunity to use modern data mining techniques to improve our understanding of the subsurface in the presence of uncertainty and improve predictability of reservoir properties. A data mining approach provides a way to screen dependencies in reservoir and fluid data and enable subsurface specialists to estimate absent properties in partial or incomplete datasets. This allows for uncertainty to be managed and reduced. An improvement in reservoir characterisation using machine learning results from the capacity of machine learning methods to detect and model hidden dependencies in large multivariate datasets with noisy and missing data. This study presents a workflow applied to a large basin-scale reservoir characterization database. The study aims to understand the dependencies between reservoir attributes in order to allow for predictions to be made to improve the data coverage. The machine learning workflow comp... [more]

The installed capacity of photovoltaic power generation occupies an increasing proportion in the power system, and its stability is greatly affected by the fluctuation of solar radiation. Accurate prediction of solar radiation is an important prerequisite for ensuring power grid security and electricity market transactions. The current mainstream solar radiation prediction method is the deep learning method, and the structure design and data selection of the deep learning method determine the prediction accuracy and speed of the network. In this paper, we propose a novel long short-term memory (LSTM) model based on the attention mechanism and genetic algorithm (AGA-LSTM). The attention mechanism is used to assign different weights to each feature, so that the model can focus more attention on the key features. Meanwhile, the structure and data selection parameters of the model are optimized through genetic algorithms, and the time series memory and processing capabilities of LSTM are u... [more]

We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well... [more]

Due to the low boiling point of hydrogen, the boiling phenomenon is usually encountered in the applications of liquid hydrogen. The underlying mechanism as well as the physical performance of film boiling over horizontal cylinders are not yet fully understood, especially for the various diameters. In this paper, pool film boiling of hydrogen over horizontal cylinders with diameters ranging from 0.2 to 30 mm is investigated based on the volume-of-fluid method. By the analysis of the gas−liquid interface evolution and the heat transfer mechanism, the cylinders are divided into wire heaters, transition heaters, and tube heaters. The results show that the heat transfer of the wire is affected by the evolution of a single bubble, while the heat transfer of the tube is mainly affected by the movement of multiple crescent-shaped gas structures along the surface. Besides, with the increase in cylinder diameter, the bubble detachment diameter increases, the heat flux decreases correspondingly,... [more]

In an effort to improve the stability and secure operation of the grid, regulatory bodies are opening Ancillary Services Markets participation to Distributed Energy Resources (DERs), energy storage systems, and demand response. Within this framework, this study proposes a model that simulates the coordinated operation of an aggregate of power plants, including non-dispatchable DERs and, as regulating units, Combined Heat and Power (CHP) generation and electrochemical energy storage systems. A Monte Carlo procedure is adopted to realistically create a population of aggregation scenarios. The real-time operation of the DER portfolio is managed through a Heuristic Greedy-Indexing logic, which allows the Aggregator to select the optimal control action to implement according to the technical and economic quantities characterizing the market and the grid. The techno-economic performance of the proposed algorithm is evaluated by simulating its interaction with the electricity markets. Finally... [more]

The EN ISO 52016-1:2017 standard introduced a new methodology for the hourly calculation of energy needs that allows the study of the dynamic energy performance of buildings. In this study, a comparative analysis was carried out between two heat transfer models for opaque building elements: the one described in the new standard EN ISO 52016-1:2017 (Annex B) and that proposed by the Italian national annex (Annex A). The analysis, carried out on 1854 cases, showed better results for the heating period than for the cooling period, with a lower Root-Mean-Square Error and Coefficient of Variation of the Root-Mean-Square Error for the model proposed by the Italian National Annex. Increasing the performance of the building by decreasing the solar transmission coefficient of the glazed surfaces leads to a worse Root-Mean-Square Error of about 11%. In addition, a sensitivity analysis of the thermo-physical parameters of the opaque building components was carried out and an alternative method fo... [more]

This paper discusses the Reliability, Availability, Maintainability, and Safety (RAMS) of an electrical power supply system in a large European hospital. The primary approach is based on fuzzy logic and Petri nets, using the CPNTools software to simulate and determine the most important modules of the system according to the Automatic Transfer Switch. Fuzzy Inference System is used to analyze and assess the reliability value. The stochastic versus fuzzy approach is also used to evaluate the reliability contribution of each system module. This case study aims to identify and analyze possible system failures and propose new solutions to improve the system reliability of the power supply system. The dynamic modeling is based on block diagrams and Petri nets and is evaluated via Markov chains, including a stochastic approach linked to the previous analysis. This holistic approach adds value to this type of research question. A new electrical power supply system design is proposed to increa... [more]

VANETs have gained much attention from both industry and academia because of their characteristics, such as dynamic topology. There are various applications of VANETs that are classified on the basis of safety, efficiency, commercial usage, and productive areas. This paper presents an IoT-based formal model for vehicle-life integration enabling RSUs with the help of different approaches. We have developed a model that uses vehicle scenarios in smart transportation systems so that quick data transmission is provided between the source and destination vehicles. Further, fog-based RSUs provide a wide range to communicate with hospitals and emergency vehicles to deal with emergency situations. All the appropriate entities are connected to ensure a consistent traffic flow for the arrival of an emergency vehicle in emergency places. The UML, graph theory, and VDM-SL formal technique are used to represent this system. To model the network and discover appropriate paths for V2V communication,... [more]

The purpose of this paper is to analyze the magnetic field distribution over a disc with magnets. This disk is part of an electromagnetic microgenerator that allows the generation of electricity as a result of changes in the magnetic field. The other part of the microgenerator is the structure of the coils. In the previous work of the authors, a complete microgenerator system was presented where the coils were made using thick-film and low-temperature co-fired ceramic (LTCC) technology. Several studies related to the influence of the shape and number of coils on the generated power were carried out, as well as the realization of complete electromagnetic microgenerators with voltage rectifying circuits. Until now, a disc with 28 neodymium magnets of size 10 × 3 × 1.5 mm3 was used for testing. In order to optimize the structure of the microgenerator with respect to the disc with magnets and thus increase the generated power, it was decided to perform appropriate tests to analyze the magn... [more]

The superheater and re-heater piping components in supercritical thermal power units are prone to creep and fatigue failure fracture after extensive use due to the high pressure and temperature environment. Therefore, safety assessment for superheaters and re-heaters in such an environment is critical. However, the actual service operation data is frequently insufficient, resulting in low accuracy of the safety assessment. Based on such problems, in order to address the issues of susceptibility of superheater and re-heater piping components to creep, inaccurate fatigue failure fracture, and creep−fatigue coupling rupture in a safety assessment, their remaining life prediction and reliability, as well as the lack of actual service operation data, multisource heterogeneous data generated from actual service of power plants combined with deep learning technology was used in this paper. As such, three real-time operating conditions’ data (temperature, pressure, and stress amplitude) during... [more]

In the course of the development of automated driving, there has been increasing interest in obtaining ground truth information from sensor recordings and transferring road traffic scenarios to simulations. The quality of the “ground truth” annotation is dictated by its accuracy. This paper presents a method for calibrating the accuracy of ground truth in practical applications in the automotive context. With an exemplary measurement device, we show that the proclaimed accuracy of the device is not always reached. However, test repetitions show deviations, resulting in non-uniform reliability and limited trustworthiness of the reference measurement. A similar result can be observed when reproducing the trajectory in the simulation environment: the exact reproduction of the driven trajectory does not always succeed in the simulation environment shown as an example because deviations occur. This is particularly relevant for making sensor-specific features such as material reflectivities... [more]

During the operation of HVDC extruded cables, voltage polarity reversal (VPR) is considered one of the most severe conditions for cable insulation. In this paper, a bipolar charge transport model developed for cylindrical geometry is improved by introducing ionic carriers from impurity dissociation for the simulation of space charge and electric field in an HVDC extruded cable with thick polymeric insulation under VPR, and the construction of the geometric model is based on a practical 160 kV DC polymeric cable. The influence of polarity reversal period (PRP) and temperature gradient (TG), formed by the load current flowing through the conductor, on the space charge evolution and the electric field distribution are investigated. The mechanisms of the charge dynamics and the field distortion affected by the PRP and the TG are also discussed. The results show that under TG, the maximum transient field appears near the interface between the conductor shield and insulation in the early sta... [more]

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells—increasing or decreasing the fluid flow rates across the wells—and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. In this study, we describe a new approach combining reservoir modeling and machine learning to produce models that enable such a strategy. Our computational approach allows us, first, to translate sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy, and second, to find optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) al... [more]

Virtual power plants (VPP) emerge as a new participant that, in order to maximise their visibility and income, represents a group of distributed energy resources (DER) in the electricity market. However, this DER aggregation brings challenges, such as fluctuating renewable sources dependent on weather variables and guaranteeing power set points. One way to deal with these intermittencies is to incorporate the energy storage system (ESS) into the VPPs. Therefore, this paper presents a novel bidding strategy of VPP that includes modelling the uncertainty associated with solar generation using information gap decision theory (IGDT) and the optimal sizing of ESS systems so as to deal with solar generation fluctuations. Additionally, a study is carried out to determine the economic viability of this methodology in the short, medium and long terms using the return on investment (ROI).

This study examines the kinetics of pyrolysis and oxidation of hydrochars through thermal analysis. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) techniques were used to investigate the decomposition profiles and develop two distributed activation energy models (DAEM) of hydrochars derived from the hydrothermal carbonization of grape seeds produced at different temperatures (180, 220, and 250 °C). Data were collected at 1, 3, and 10 °C/min between 30 and 700 °C. TGA data highlighted a decomposition profile similar to that of the raw biomass for hydrochars obtained at 180 and 220 °C (with a clear distinction between oil, cellulosic, hemicellulosic, and lignin-like compounds), while presenting a more stable profile for the 250 °C hydrochar. DSC showed a certain exothermic behavior during pyrolysis of hydrochars, an aspect also investigated through thermodynamic simulations in Aspen Plus. Regarding the DAEM, according to a Gaussian model, the severity of the... [more]

A laboratory-scale solid oxide fuel cell (SOFC) system using liquefied natural gas (LNG) as a fuel is designed to be used as an energy converter on seagoing vessels (MultiSchIBZ project). The presented system design phase is supported by thermodynamic system simulation. As heat integration plays a crucial role with regard to fuel recirculation and endothermic pre-reforming, the heat exchanger and pre-reforming component models need to exhibit a high degree of accuracy throughout the entire operating range. Compact additively manufactured tube-bundle and plate-fin heat exchangers are designed to achieve high heat exchange efficiencies at low pressure losses. Their heat transfer correlations are derived from experimental component tests under operating conditions. A simulation study utilizing these heat exchanger characteristics is carried out for four configuration variants of pre-reforming and heat integration. Their system behaviour is analyzed with regard to the degree of pre-reformi... [more]

With an increase in the aging population in many countries worldwide, much attention is being paid to the study of thermal comfort for the elderly. Because the elderly spend most of their time indoors, the demand for air conditioning is expected to increase, and it is important to study the thermal comfort of the elderly and appropriate operation plans for air conditioning. In this study, we conducted a field survey of thermal comfort and building energy simulation for an air-conditioned nursing home in Nagano, Japan. The field survey was conducted between June 2020 and June 2021. Over 80% of the subjects were satisfied with the indoor thermal environment. The thermal neutral temperature of the elderly was 25.9 °C in summer and 23.8 °C in winter. Future weather data was used to predict the future heating and cooling loads of the nursing home. The results showed that the total heat load may not change significantly, as the decrease in heating load compensates for the increase in cooling... [more]

Natural smoke control systems in staircases are one of the available systems used for the protection of escape routes from smoke and are one of the most utilised for medium to high rise buildings. However, their effectiveness (as well as other ventilation systems) is strongly dependent on the weather conditions, especially the outdoor temperature. This paper describes the results of real-scale experiments of the airflow in a staircase’s natural smoke exhaust system. The experimental staircase was localised in a medium-high building, “LabFactor” at the Lodz University of Technology in Poland. The experiments were performed over a period of six months, from February to July 2019, and included measurements of external and internal air temperature as well as the airflow through the staircase. The results obtained enabled an evaluation of the effectiveness of the ventilation and natural smoke exhaust system in the staircase, in relation to external temperatures. It was found that natural sm... [more]

Environment-friendly and renewable energy resources are the need of each developed and undeveloped country. Solar energy is one of them, thus accurate forecasting of it can be useful for electricity supply companies. This research focuses on analyzing the daily global solar radiation (GSR) data of Najran province located in Saudi Arabia and proposed a model for the prediction of global horizontal irradiance (GHI). The weather data is collected from Najran University. After inspecting the data, I we found the dependent and independent variables for calculating the GHI. A dataset model has been trained by creating tensor of variables belonging to air, wind, peak wind, relative humidity, and barometric pressure. Furthermore, six machine learning algorithms convolutional neural networks (CNN), K-nearest neighbors (KNN), support vector machines (SVM), logistic regression (LR), random forest classifier (RFC), and support vector classifier (SVC) techniques are used on dataset model to predict... [more]

Understanding subsurface hydrocarbon migration is a crucial task for petroleum geoscientists. Hydrocarbons are released from deeply buried and heated source rocks, such as shales with a high organic content. They then migrate upwards through the overlying lithologies. Some hydrocarbon becomes trapped in suitable geological structures that, over a geological timescale, produce viable hydrocarbon reservoirs. This work investigates how intelligent agent models can mimic these complex natural subsurface processes and account for geological uncertainty. Physics-based approaches are commonly used in petroleum system modelling and flow simulation software to identify migration pathways from source rocks to traps. However, the problem with these simulations is that they are computationally demanding, making them infeasible for extensive uncertainty quantification. In this work, we present a novel dynamic screening tool for secondary hydrocarbon migration that relies on agent-based modelling. I... [more]

Sludge is one of the main pollutants from sewage treatment and contains a high content of water and organic matter. The co-combustion of sludge and coal can bring about the energy conversion of sludge. However, the high moisture content in sludge and the inorganic pollutants generated by co-combustion have adverse effects on combustion and the environment. In this work, through experimentation, it was demonstrated that co-combustion does not release obvious toxic elements or create an environmental hazard. On the basis of the TG/DTG curves, the ignition points of sludge and coal and the temperature of each group were obtained, which provided boundary conditions for a numerical simulation. Co-combustion with various mixing ratios and moisture contents was studied via the numerical simulation of a 330 MW boiler. The numerical results show that a high mixing ratio reduced the boiler temperature and created more moisture and fuel NOx. When the mixing ratio reached 40%, the boiler temperatu... [more]

An inter-turn fault (ITF) is one of the most frequent induction motor faults; thus, many previous works have studied its model and diagnosis. However, previous works, simplifying the specific distorted flux distribution by the ITF, presented induction motor fault models and focused on the fault signal analysis for diagnoses. Consequently, these results are only adequate for the pretested motor and sensitive to fault signal distortion. This paper presents an induction motor ITF model in the stationary DQ frame, for a model-based diagnosis. Furthermore, to describe the distorted flux distribution along the air gap by the ITF, the rotor flux linkages are described in the independent DQ frame of every pole, and the mutual flux linkages among the rotor, stator, and ITF windings are specifically modeled. Hence, the proposed full model has many current states and mutual inductances to describe the high pole number motor. A simplified model is also proposed for easier usage in the diagnosis, w... [more]