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This study aims to propose the use of time series autoregressive integrated moving average (ARIMA) models to predict gas path performance in aero engines. The gas path is a critical component of an aero engine and its performance is essential for safe and efficient operation of the engine.

The study analyzes a data set of gas path performance parameters obtained from a fleet of aero engines. The data is preprocessed and then fitted to ARIMA models to predict the future values of the gas path performance parameters. The performance of the ARIMA models is evaluated using various statistical metrics such as mean absolute error, mean squared error and root mean squared error. The results show that the ARIMA models can accurately predict the gas path performance parameters in aero engines.

The proposed methodology can be used for real-time monitoring and controlling the gas path performance parameters in aero engines, which can improve the safety and efficiency of the engines. Both the Box-Ljung test and the residual analysis were used to demonstrate that the models for both time series were adequate.

To determine whether or not the two series were stationary, the Augmented Dickey–Fuller unit root test was used in this study. The first-order ARIMA models were selected based on the observed autocorrelation function and partial autocorrelation function.

Further, the authors find that the trend of predicted values and original values are similar and the error between them is small.

Diesel has traditionally been considered the best-suited and most widely used fuel in various sectors, including manufacturing industries, power production, automobiles and transportation. However, with the ongoing crisis of fossil fuel inadequacy, the search for alternative fuels and their application in these sectors has become increasingly important. One particularly interesting and beneficial alternative fuel is biodiesel derived from bio sources.

In this research, an attempt was made to use biodiesel in an unconventional micro gas turbine engine. It will remove the concentric use of diesel engines for power production by improving fuel efficiency as well as increasing the power production rate. Before the fuel is used enormously, it has to be checked in many ways such as performance, emission and combustion analysis experimentally.

In this paper, a detailed experimental study was made for the use of Spirulina microalgae biodiesel in a micro gas turbine. A small-scale setup with the primary micro gas turbine and secondary instruments such as a data acquisition system and AVL gas analyser. The reason for selecting the third-generation microalgae is due to its high lipid and biodiesel production rate. For the conduction of experimental tests, certain conditions were followed in addition that the engine rotating rpm was varied from 4,000, 5,000 and 6,000 rpm. The favourable and predicted results were obtained with the use of microalgae biodiesel.

The performance and combustion results were not exactly equal or greater for biodiesel blends but close to the values of pure diesel; however, the reduction in the emission of CO was at the appreciable level for the used spirulina microalgae biodiesel. The emission of nitrogen oxides and carbon dioxide was a little higher than the use of pure diesel. This experimental analysis results proved that the use of spirulina microalgae biodiesel is both economical and effective replacement for fossil fuel.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

The purpose of this study is analysis of deformation and vibrations of turbine blades produced by high electrolyte pressure during electrochemical machining.

An experimental setup was designed, experiments were conducted and the obtained results were compared with the finite element results. The deformations were measured according to various flow rates of electrolyte. In finite element calculations, the pressure distribution created by the electrolyte on the blade surface was obtained in the ANSYS® (A finite element analysis software) Fluent software and transferred to the static structural where the deformation analysis was carried out. Three different parameters were examined, namely blade thickness, blade material and electrolyte pressure on blade disk caused by mass flow rate. The deformation results were compared with the gap distances between cathode and anode.

Large deformations were obtained at the free end of the blade and the most curved part of it. The appropriate pressure values for the electrolyte to be used in the production of blisk blades were proposed numerically. It has been determined that high pressure applications are not suitable for gap distance lower than 0.5 mm.

When the literature is examined, it is required that the high speed flow of the electrolyte is desired in order to remove the parts that are separated from the anode from the machining area during electrochemical machining. However, the electrolyte flowing at high speeds causes high pressure in the blisk blades, excessive deformation and vibration of the machined part, and as a result, contact of the anode with the cathode. This study provides important findings for smooth electro chemical machining at high electrolyte flows.

Temperature anomalies in the upper troposphere have become a reality as a result of global warming, which has a noticeable impact on aircraft performance. The purpose of this study is to investigate the total air temperature (TAT) anomaly observed during the cruise level and its impact on engine parameter variations.

Empirical methodology is used in this study, and it is based on measurements and observations of anomalous phenomena on the tropopause. The primary data were taken from the Boeing 747-8F's enhanced flight data recorder, which refers to the quantitative method, while the qualitative method is based on a literature review and interviews. The GEnx Integrated Vehicle Health Management system was used for the study's evaluation of engine performance to support the complete range of operational priorities throughout the entire engine lifecycle.

The study's findings indicate that TAT and SAT anomalies, which occur between 270- and 320-feet flight level, have a substantial impact on aircraft performance at cruise altitude and, as a result, on engine parameters, specifically an increase in fuel consumption and engine exhaust gas temperature values. The TAT and Ram Rise anomalies were the focus of the atmospheric deviations, which were assessed as major departures from the International Civil Aviation Organizations–defined International Standard Atmosphere, which is obvious on a positive tendency and so goes against the norms.

Necessary fixed flight parameters gathered from the aircraft's enhanced airborne flight recorder (EAFR) via Aeronautical Radio Incorporated (ARINC) 664 Part 7 at a certain velocity and altitude interfacing with the diagnostic program direct parameter display (DPD), allow for analysis of aircraft performance in a real-time frame. Thus, processed data transmits to the ground maintenance infrastructure for future evaluation and for proper maintenance solutions.

A real-time analysis of aircraft performance is possible using the diagnostic program DPD in conjunction with necessary fixed flight parameters obtained from the aircraft's EAFR via ARINC 664 Part 7 at a specific speed and altitude. Thus, processed data is transmitted to the ground infrastructure for maintenance to be evaluated in the future and to find the best maintenance fixes.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.

This study aims to investigate the feasibility of proposed microgrid (MG) that comprises photovoltaic, wind turbines, battery energy storage and diesel generator to supply a residential building in Grindelwald which is chosen as the test location.

Three operational configurations were used to run the proposed MG. In the first configuration, the electric energy can be vended and procured utterly between the main-grid and MG. In the second configuration, the energy trade was performed within 15 kWh as the maximum allowable limit of energy to purchase and sell. In the third configuration, the system performance in the stand-alone operation mode was investigated. A whale optimization technique is used to determine the optimal size of MG in all proposed configurations. The cost of energy (COE) and other measures are used to evaluate the system performance.

The obtained results revealed that the first configuration is the most beneficial with COE of 0.253$/KWh and reliable 100%. Furthermore, the whale optimization algorithm is sufficiently feasible as compared to other techniques to apply in the applications of MG.

The value of the proposed research is to investigate to what extend the integration between MG and main-grid is beneficial economically and technically. As opposed to previous research studies that have focused predominantly only on the optimal size of MG.

The purpose of this study is to prepare a state-of-the-art review on advanced ceramic materials including their fabrication techniques, characteristics, applications and wettability.

This review paper presents the various types of advanced ceramic materials according to their compounding elements, fabrication techniques of advanced ceramic powders as well as their consolidation, their characteristics, applications and wetting properties. Hydrophobic/hydrophilic properties of advanced ceramic materials are described in the paper with their state-of-the-art application areas. Optical properties of fine ceramics with their intrinsic characteristics are also presented within. Special focus is given to the brief description of application-based manipulation of wetting properties of advanced ceramics in the paper.

The study of wetting/hydrophobicity/hydrophilicity of ceramic materials is important by which it can be further modified to achieve the required applications. It also makes some sense that the material should be tested for its wetting properties when it is going to be used in some important applications like biomedical and dental. Also, these advanced ceramics are now often used in the fabrication of filters and membranes to purify liquid/water so the study of wetting characteristics of these materials becomes essential. The optical properties of advanced ceramics are equally making them suitable for many state-of-the-art applications. Dental, medical, imaging and electronics are the few sectors that use advanced ceramics for their optical properties.

This review paper includes various advanced ceramic materials according to their compounding elements, different fabrication techniques of powders and their consolidation, their characteristics, various application area and hydrophobic/hydrophilic properties.