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Reducing aircraft fuel consumption has become a paramount research area, focusing on optimizing operational parameters like speed and altitude during the cruise phase. However, the existing methods for fuel reduction often rely on complex experimental calculations and data extraction from embedded systems, making practical implementation challenging. To address this, this study aims to devise a simple and accessible approach using available information.

In this paper, a novel analytic method to estimate and optimize fuel consumption for aircraft equipped with jet engines is proposed, with a particular emphasis on speed and altitude parameters. The dynamic variations in weight caused by fuel consumption during flight are also accounted for. The derived fuel consumption equation was rigorously validated by applying it to the Boeing 737–700 and comparing the results against the fuel consumption reference tables provided in the Boeing manual. Remarkably, the equation yielded closely aligned outcomes across various altitudes studied. In the second part of this paper, a pioneering approach is introduced by leveraging the particle swarm optimization algorithm (PSO). This novel application of PSO allows us to explore the equation’s potential in finding the optimal altitude and speed for an actual flight from Algiers to Brussels.

The results demonstrate that using the main findings of this study, including the innovative equation and the application of PSO, significantly simplifies and expedites the process of determining the ideal parameters, showcasing the practical applicability of the approach.

The suggested methodology stands out for its simplicity and practicality, particularly when compared to alternative approaches, owing to the ready availability of data for utilization. Nevertheless, its applicability is limited in scenarios where zero wind effects are a prevailing factor.

The research opens up new possibilities for fuel-efficient aviation, with a particular focus on the development of a unique fuel consumption equation and the pioneering use of the PSO algorithm for optimizing flight parameters. This study’s accessible approach can pave the way for more environmentally conscious and economical flight operations.

The purpose of this study is to provide the description of a computational methodology to model the combined propulsive systems of hydrogen propelled air-breathing scramjet vehicles and to evaluate the pollutant and climate-changing emissions.

Emissions indexes of nitrogen oxide (EINO) and water vapour released by the air turbo rocket (ATR) and dual mode ramjet (DMR) engines of the STRATOFLY air-breathing, hypersonic scramjet vehicle, propelled by hydrogen/air were evaluated. ATR engine operation was assessed for several cruise conditions in both subsonic and supersonic flight regimes in Ecosimpro software, which is an object-oriented thermodynamic design and simulation platform. ATR combustor inlet flow conditions play a key role in the computation of species mass fractions, and these conditions are highly dependent on turbomachinery performance and engine flight regime. A propulsive operational database was created by varying mass flow rates of fuel and flight conditions such as cruise speed and altitude to investigate possible engine operations. The all-inlet conditions in this map are provided to the Cantera-Python chemical/combustion chemistry solver implementing a specially designed and formulated 0D kinetic-thermodynamic methodology successfully used to model and simulate the electric spark ignition required to activate the combustion process of the reacting mixture in the ATR combustion chambers, whereas the coupled aero-thermodynamic/aero-propulsive 0D/1D code i.e. Scramjet PREliminary Aerothermodynamic Design (SPREAD), designed and developed by the Italian Aerospace Research Centre (CIRA) was used for DMR calculations. Results show low emissions of NO according to the optimized design of the ATR; on the other hand, a tuning of operational conditions is needed for DMR, with its complete re-design to be more conclusive. Analogously, the released amount of water vapour is in good agreement with the required combustion efficiency and the expected propulsive performance.

Results show low emissions of NO according to the optimized design of the ATRs; on the other hand, a tuning of operational conditions is needed for DMR, with its complete re-design to be more conclusive. Analogously, the released amount of water vapour is in good agreement with the required combustion efficiency and the expected propulsive performance.

Applications of innovative 0D/1D chemical kinetic methodology and in-house codes.

The purpose of this study is to present a comprehensive review of the fundamental concepts and terminologies pertaining to different types of aluminium metal matrix composites, their joining techniques and challenges, friction stir welding (FSW) process, post-welding characterizations and basic control theory of FSW, followed by the discussions on the research reports in these areas.

Joining of aluminium metal matrix composites (Al-MMC) poses many challenges. These materials have their demanding applications in versatile domains, and hence it is essential to understand their weldability and material characteristics. FSW is a feasible choice for joining of Al-MMC over the fusion welding because of the formation of narrow heat affected zone and minimizing the formation of intermetallic compounds at weld interface. The goal in FSW is to generate enough thermal energy by friction between the workpiece and rotating tool. Heat energy is generated by mechanical interaction because of the difference in velocity between the workpiece and rotating tool. In the present work, a detailed survey is done on the above topics and an organised conceptual context is presented. A complete discussion on significance of FSW process parameters, control schemes, parameter optimization and weld quality monitoring are presented, along with the analysis on relation between the interdependent parameters.

Results from the study present the research gaps in the FSW studies for joining of the aluminium-based metal matrix composites, and they highlight further scope of studies pertaining to this domain.

It is observed that the survey done on FSW of Al-MMCs and their control theory give an insight into the fundamental concepts pertaining to this research area to enhance interdisciplinary technology exploration.

This study aims to apply association rule mining (ARM) to uncover specific associations between operating components of a chiller system and improve its coefficient of performance (COP), hence reducing the electricity use of buildings with central air conditioning.

First, 13 operating variables were identified, comprising measures of temperatures and flow rates of system components and their switching statuses. The variables were grouped into four bins before carrying out ARM. Strong rules were produced to associate the variables and switching statuses with different COP classes.

The strong rules explain existing constraints on practising chiller sequencing and prioritise variables for optimisation. Based on strong rules for the highest COP class, the optimal operating strategy involves rescheduling chillers and their associated components in pairs during a high load operation. Resetting the chilled water supply temperature is the next best strategy, followed by resetting the condenser water entering temperature, subject to operating constraints.

This study considers the even frequency method with four bins only. Replication work can be done with other discretisation methods and different numbers of classes to compare potential differences in the bin ranges of the optimised variables.

The strong rules identified by ARM highlight associations between variables and high or low COPs. This supports the selection of critical variables and the operating status of system components to maximise the COP. Tailor-made optimisation strategies and the associated electricity savings can be further evaluated.

Previous studies applied ARM for chiller fault detection but without considering system performance under the interaction of different components. The novelty of this study is its demonstration of ARM’s intelligence at discovering associations in past operating data. This enables the identification of tailor-made energy management opportunities, which are essential for all engineering systems. ARM is free from the prediction errors of typical regression and black-box models.

The purpose of this paper is to investigate the impact of ultrasonic vibration (UV) and tool pin profile on mechanical properties and microstructural behaviour of AA7075-T651 and AA6061-T6 joints was analysed.

The joints were fabricated using three different tool pin profiles such as cylindrical, square and triangle. For each tool pin profile, two different UV powers of 1.5 kW and 2 kW were used.

On both the advancing and retreating sides of the weld, the thermo-mechanically affected zone has the lowest microhardness. In all joints, the tensile fracture locations match to the minimum hardness values. Field emission scanning electron microscope fractography of tensile tested specimens reveals heterogeneous modes of brittle, shear and ductile fracture. Three-point bending analysis was performed to determine the ductility and soundness of the weld joint. The acoustic softening effect of UV, as well as the static and dynamic ratio of tool pin profile, plays an important role in determining the material flow and mechanical behaviour of the joint.

Dissimilar aluminium joining fascinates many applications like aircraft, aerospace, automobiles, ship building and electronics, where fusion welding is a very intricate process because of the deviation in its physical and chemical properties.

From this study investigation, it is found that the square pin profiled tool with 2 kW UV power produces metallurgical defect-free and mechanically sound weld with maximum tensile strength, hardness and bending load of 297 MPa, 151HV and 3.82 kN, respectively.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances.

The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios.

In particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios.

The optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.

The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

The purpose of this paper is to identify and quantify the key components of the overall cost of software development when warranty coverage is given by a developer. Also, the authors have studied the impact of imperfect debugging on the optimal release time, warranty policy and development cost which signifies that it is important for the developers to control the parameters that cause a sharp increase in cost.

An optimization problem is formulated to minimize software development cost by considering imperfect fault removal process, faults generation at a constant rate and an environmental factor to differentiate the operational phase from the testing phase. Another optimization problem under perfect debugging conditions, i.e. without error generation is constructed for comparison. These optimization models are solved in MATLAB, and their solutions provide insights to the degree of impact of imperfect debugging on the optimal policies with respect to software release time and warranty time.

A real-life fault data set of Radar System is used to study the impact of various cost factors via sensitivity analysis on release and warranty policy. If firms tend to provide warranty for a longer period of time, then they may have to bear losses due to increased debugging cost with more number of failures occurring during the warrantied time but if the warranty is not provided for sufficient time it may not act as sufficient hedge during field failures.

Every firm is fighting to remain in the competition and expand market share by offering the latest technology-based products, using innovative marketing strategies. Warranty is one such strategic tool to promote the product among masses and develop a sense of quality in the user’s mind. In this paper, the failures encountered during development and after software release are considered to model the failure process.