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This paper presents an experimental study of indoor thermal environment near a full-scale glass facade with different types of shading devices under varying climatic conditions in winter. Interior glazing and shading temperature, operative temperature and radiant temperature asymmetry were measured for facade sections with roller shades and venetian blinds at different positions. Interior glass surface temperatures can be high during sunny days with low outdoor temperature. Shading systems significantly improved operative temperature and radiant temperature asymmetry during cold sunny days, depending on their properties and tilt angle. During cloudy days the impact was smaller, however the shading layers could still decrease the amount of heat loss through the facade. A transient building thermal model, which also calculates indoor environmental indices under the presence of solar radiation, was developed and compared with the experimental measurements. Part II of this paper uses this validated model with a transient, two-node thermal comfort model (including transmitted solar radiation) for assessment of indoor environmental conditions with different building envelope and shading properties, facade location and orientation.

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

In this paper, fault prognosis of aircraft jet engines are considered using computationally intelligent-based methodologies to ensure flight safety and performance. Two different dynamic neural networks namely, the nonlinear autoregressive neural networks with exogenous input (NARX) and the Elman neural networks are developed and designed for this purpose. The proposed dynamic neural networks are designed to capture the dynamics of two main degradations in the jet engine, namely the compressor fouling and the turbine erosion. The health status and condition of the engine is then predicted subject to occurrence of these deteriorations. In both proposed approaches, two scenarios are considered. For each scenario, several neural networks are trained and their performance in predicting multi-flights ahead turbine output temperature are evaluated. Finally, the most suitable neural network for prediction is selected by using the normalized Bayesian information criterion model selection. Simulation results presented demonstrate and illustrate the effective performance of our proposed neural network-based prediction and prognosis strategies.

Abstract Recharging infrastructure (RI) deployment plays a vital role in improving the public recharging availability for transport electrification. Decarbonizing transportation using low-emission electricity requires massive RI network. Even though the consumers are reluctant to purchase electric vehicles (EVs) until RIs are sufficiently placed, the investors are not willing to invest in RIs due to recharging demand uncertainties. Therefore, a scientific planning framework is needed to ensure the sustainable deployment of EV-RIs in complex networks. In this study, a lifecycle thinking-based multi-period infrastructure-planning framework is proposed to develop sustainable public EV-RIs in an urban context. This framework consists of a temporal model to find the dynamic EV-RI demands, a stochastic model to obtain travel distances, and a multi-objective optimization model to select the best desirable capacities and locations for potential EV-RIs. A case study of a typical medium-scale municipality in Canada was assessed using the proposed framework and validated using conventional infrastructure planning scenarios. The geo-processing data, regional travel behaviors, and recharging characteristics were used as model inputs. The results of the case study showed that the proposed framework can be used to estimate multi-period public recharging demands, minimize lifecycle costs, maximize service coverage and infrastructure utilization, and ensure reasonable paybacks compared to conventional planning approaches. Moreover, this framework can be used to compare different investment assistances, which are required in the early stages of the RI deployment process to encourage investors. Furthermore, government and private institutions can use this framework to identify recharging demands, permitting, and developing RIs in the long-run.

Abstract The residential sector accounts for most of energy-consumption in developing countries in the form of traditional energy. The use of commercial energy is nominal and confined mostly to urban areas where fuelwood is already monetized. A model, based on an end-use/process analysis approach, is developed on a spreadsheet, which is capable of simulating scenarios to address issues of increasing traditional energy-demand caused by population growth, sustainable supply capacity of the existing energy resources, potential for development of new and renewable energy resources, technology. This paper is divided into two parts: general energy issues and the modelling approach, and the application of this approach to Nepal in the context of fuelwood-supply sustainability.

In this paper, we present the conjugate heat transfer analysis in a 167-kVA dry-type transformer using the parallel version of the computational fluid dynamics code Fluent 6.0. The renormalization group kappa-epsiv model is proposed to compute the turbulent aspect of the convective airflow inside the transformer metal tank for Air Natural/Air Natural cooling conditions. An experimental approach was used to assess Joule losses in the low-/high-voltage windings and eddy-current losses in the magnetic core. The resulting mathematical model was solved using 14 compute nodes on a distributed machine.

AbstractConcrete is a valuable construction material with high mechanical strength and durability, used extensively in the construction industry. It is produced by mixing sand, stones, cement, and water in different proportions depending on the desired quality of the final product. Water reducers are additional chemical ingredients used in concrete to reduce the quantity of water required in the concrete mixture. When added to concrete, water reducers increase the workability and flowability of concrete in the freshly mixed state and improve the mechanical strength and durability of the final hardened product. This review paper describes the different types and applications of concrete water reducers used in the construction industry including their working mechanisms and fluidity effects on concrete properties. It discusses the production of synthetic and bio‐based concrete water reducers and reviews the present challenges involved in the preparation of bio‐based concrete water reducers from renewable resources. © 2023 Society of Industrial Chemistry and John Wiley & Sons Ltd.

A ring main unit (RMU) is used in the power distribution system to provide three-phase supply. This RMU needs proper grounding for reliable and smooth power supply to residential areas. In this paper, ground resistance of an RMU is measured using fall-of-potential method. The ground resistance is measured by Fluke 1625 Advanced earth tester equipment and the value is found to be 1.58 Ω. Soil resistivity is also measured near the RMU and calculated by CYME GRD software. The COMSOL Multiphysics software is used to simulate ground resistance, and the simulation results are found to be in good agreement while compared to the experimental results.

Fe-based catalysts for O 2 reduction have been prepared on four carbon supports (Vulcan, Black Pearl, Norit, and a developmental carbon, RC2, from Sid Richardson Carbon Corp.). Four preparation procedures have been used with each carbon support and the catalytic activities for the reduction of oxygen in H 2 SO 4 , pH 1, and in polymer electrolyte membrane fuel cell tests are compared for all the catalysts, which are nominally loaded with 0.2 wt % Fe, using iron II acetate as Fe precursor. The catalytic activity of these Fe-based catalysts greatly depends upon the chosen carbon support and also upon the preparation procedure used. The results are rationalized in terms of N content at the surface of the catalysts; the larger the N content, the better the catalytic activity. The best catalysts are obtained after refluxing either RC2 or Norit in HNO 3 before adsorbing iron acetate on the oxidized carbon supports and heat-treating the resulting materials at 900°C in an atmosphere containing NH 3 . The surface nitrogen content of these catalysts, measured by XPS, is 2.5 and 4.1 atom %, respectively. For the Fe-based catalyst prepared on Norit and tested in fuel cell, the mass activity at low current regime, expressed in A/mg Fe, is only slightly lower than the A/mg Pt recorded for a state-of-the-art, Pt-based membrane electrode assembly.