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The purpose of this paper is to identify the energy losses factors during the hydro-mechanical conversion process at high pressure via a novel reduced order dynamic model.

A novel reduced order dynamic model of the axial piston motor was proposed, which provides an explicit insight to the compression flow losses and the Coulomb friction losses. A fully coupled dynamic model of the piston motor was obtained based on the array bond graph method. And then, a reduced order model was obtained by the composition analysis of flow and torque of the axial piston motor. After that, the energy losses estimation model was presented to predict the energy loss of the piston motor under a wide range of working conditions. The model was verified by comparing the experimental and simulation results.

The simulation result indicates that the flow loss caused by oil compression accounts for 59 per cent of the total flow loss, and the Coulomb friction torque accounts for 40 per cent of the total torque loss under a specific working condition. The compression flow loss and Coulomb friction torque are the major factors that lead to the aggravation of energy loss under extreme working conditions of the piston motor.

At high-pressure condition, the compression flow losses due to fluid compressibility cannot be neglected, and the hydro-mechanical losses in varies friction pairs should involve Coulomb friction losses. Flow and torque loss analytical expression in the model involve the design and control parameters of the piston equipment, which can realize the parameter optimization of the piston equipment for the purpose of energy-saving.

The purpose of this study is to determine the cavitation flow characteristics of the high-pressure differential control valve. The relationship between cavitation, flow coefficient and spool angle is obtained. By analyzing the relationship between different spool angles and energy loss, the energy loss at different spool angles is predicted.

A series of numerical simulations were performed to study the cavitation problem of a high-pressure differential control valve using the RNG k–e turbulence model and the Zwart cavitation model. The flow states and energy distribution at different spool angles were analyzed under specific working conditions.

The cavitation was the weakest when the spool angle was 120° or the outlet pressure was 8 MPa. The pressure and speed fluctuations of the valve in the throttle section were greater than those at other locations. By calculating the entropy production rate, the reason and location of valve energy loss are analyzed. The energy loss near the throttling section accounts for about 92.7% of the total energy loss. According to the calculated energy loss relationship between different regions of the spool angle, the relationship between any spool angle and energy loss in the [80,120] interval is proposed.

This study analyzes the cavitation flow characteristics of the high-pressure differential control valve and provides the law of energy loss in the valve through the analysis method of entropy. The relationship between spool angle and energy loss under cavitation is finally proposed. The research results are expected to provide a theoretical basis for the optimal design of valves.

Particle scale simulation of industrial particle flows using discrete element method (DEM) offers the opportunity for better understanding the flow dynamics leading to improvements in equipment design and operation that can potentially lead to large increases in equipment and process efficiency, throughput and/or product quality. Industrial applications can be characterized as large, involving complex particulate behaviour in typically complex geometries. In this paper, with a series of examples, we will explore the breadth of large scale modelling of industrial processes that is currently possible. Few of these applications will be examined in more detail to show how insights into the fundamentals of these processes can be gained through DEM modelling. Some examples of our collaborative validation efforts will also be described.

In a boundary layer ingestion (BLI) propulsion system, the fan operates continuously under distorted inflow conditions, leading to an increment of aerodynamic loss and in turn impacting the potential fuel burn reduction of the aircraft. Usually, in the preliminary design stage of a BLI propulsion system, it is essential to assess the impact of fuselage boundary layer fluids on fan aerodynamic performances under various flight conditions. However, the hub region flow loss is one of the major loss sources in a fan and would greatly influence the fan performances. Moreover, the inflow distortion also results in a complex and highly nonlinear mapping relation between loss and local physical parameters. It will diminish the prediction accuracy of the commonly used low-fidelity computational approaches which often incorporate traditional physics-based loss models, reducing the reliability of these approaches in evaluating fan performances. Meanwhile, the high-fidelity full-annulus unsteady Reynolds-averaged Navier–Stokes (URANS) approach, even though it can give rather accurate loss predictions, is extremely time-consuming. This study aims to develop a fast and accurate hub loss prediction method for a BLI fan under distorted inflow conditions.

This paper develops a data-driven hub loss prediction method for a BLI fan under distorted inflows. To improve the prediction accuracy and applicability, physical understandings of hub flow features are integrated into the modeling process. Then, the key physical parameters related to flow loss are screened by conducting a sensitivity analysis of influencing parameters. Next, a quasi-steady assumption of flow is made to generate a training sample database, reducing the computational time by acquiring one single sample from the highly time-consuming full-annulus URANS approach to a cost-efficient single-blade-passage approach. Finally, a radial basis function neural network is used to establish a surrogate model that correlates the input parameters and the output loss.

The data-driven hub loss model shows higher prediction accuracy than the traditional physics-based loss models. It can accurately capture the circumferentially and radially nonuniform variation trends of the losses and the associated absolute magnitudes in a BLI fan under different blade load, inlet distortion intensity and rotating speed conditions. Compared with the high-fidelity full-annulus URANS results, the averaged relative prediction errors of the data-driven hub loss model are kept less than 10%.

The originality of this paper lies in developing a new method for predicting flow loss in a BLI fan rotor blade hub region. This method offers higher prediction accuracy than the traditional loss models and lower computational time cost than the full-annulus URANS approach, which could realize fast evaluations of fan aerodynamic performances and provide technical support for designing high-performance BLI fans.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.

The purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.

Due to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.

China's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.

The paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

The purpose of this paper is to deal with the modeling of a claw pole alternator (CPA) by a 3D magnetic equivalent circuit (MEC) taking into account the saturation and magnetic armature reaction effects then its utilization for the prediction of the machine losses.

Following the derivation of the proposed model, it is validated experimentally at no-load and load operations. Proposed MEC is applied to the investigation of a conventional CPA losses.

The CPA efficiency is affected by different loss mechanisms. Indeed, the copper losses are dominant at lower speeds, while the iron and ventilation ones are more significant at high speeds.

An experimental validation of the losses computed by the MEC shall be treated in the future.

The CPA is equipping most if not all embedded generating systems of road vehicles. The improvement of its efficiency is of great importance.

The MEC-based prediction of the CPA losses represents the major contribution of the present work.

The purpose of this paper is to an analytical approach-based prediction of the eddy current loss in the PMs of a concentrated winding machine equipped with 12 slots in the stator and ten poles in the rotor.

The investigation of the PM eddy current loss has been carried out using an analytical model and a 2D time-stepped transient finite element analysis (FEA).

It has been found, in the case of the treated machine, that just the subharmonic of rank 1 and the harmonic of rank 7 have significant contributions to the eddy current loss in the PMs.

A shift between the results yielded by the developed analytical model and those computed by FEA has been noticed. This limitation is mainly due to the slotting effect which has been omitted in the analytical model.

Fractional slot PM machines are currently given an increasing attention in automotive applications. The prediction of their iron loss in an attempt to rethink their design represents a crucial efficiency benefit.

The analytical prediction of the eddy current loss in each PM then in all PMs and their validation by FEA represent the major contribution of this work.

To avoid braking accidents caused by excessive wear of brake pad, this study aims to achieve online prediction of brake pad wear life (BPWL).

A simulated braking test bench for automobile disc brake was used. The correlation and mechanism between the three braking condition parameters of initial braking speed, braking pressure and initial braking temperature and the tribological performance were analyzed. The different artificial neural network (ANN) models of wear loss were discussed. Genetic algorithm was used to optimize the ANN model. The structure scheme of the online prediction system of BPWL was discussed and completed.

The results showed that the braking conditions were positively correlated with the wear loss, but negatively correlated with the friction coefficient. The prediction accuracy of back propagation (BP) ANN model was higher. The model was optimized by genetic algorithm, and the average deviation of prediction results was 4.67%. By constructing the online monitoring system of automobile braking conditions, the online prediction of BPWL based on the ANN model could be realized.

The research results not only have important theoretical significance for the study of BPWL but also have practical value for guiding the maintenance and replacement of automobile brake pads and avoiding the occurrence of braking accidents.

This paper aims to collect the energy consumption data and carry out energy consumption analysis of chemical enterprises, which is helpful to grasp the working conditions of each equipment accurately and to perfect the demand side management (DSM) for the user in the terminal.

The paper proposes a load monitoring system of chemical enterprises to collect the energy consumption data and carry out energy consumption analysis. An Elman neural network based on sparrow search algorithm is proposed to predict the power consumption change and distribution trend of enterprises in the future production cycle. The calculation efficiency and prediction accuracy have been significantly improved.

The paper analyzes the energy saving effect of energy efficiency management as well as “avoiding peak and filling valley” measures, and reasonable control requirements and assumed conditions are put forward to study the operability of enterprise energy saving measures from the DSM.

Because of the chosen enterprise data, the prediction accuracy needs to be further improved. Therefore, researchers are encouraged to test the proposed methodology further.

The paper includes implications for the development of energy consumption analysis and load forecasting of chemical enterprises and perfects the DSM for the user.

This paper fulfills an identified need to study how to forecast the power load and improve the management efficiency of energy consumption.