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In this work, the design problem of hydrodynamic plain journal bearings is formulated as a multi-objective optimization problem to improve bearing performance under different operating conditions.

The problem is solved using a hybrid approach combining genetic algorithm and sequential quadratic programming. The selected state variables are oil leakage flow rate, power loss and minimum oil film thickness. The selected design variables are the radial clearance, length-to-diameter ratio, oil viscosity, oil supply pressure and oil supply groove angular position. A validated empirical model is adopted to provide relatively accurate estimation of the bearing state variables with reduced computations. Pareto optimal solution sets are obtained for different operating conditions, and secondary selection criteria are proposed to choose a final optimum design.

The adopted hybrid optimization approach is a random search algorithm that generates a different solution set for each run, thus a different bearing design. For a number of runs, it is found that the key design variables that significantly affect the optimum state variables are the bearing radial clearance, oil viscosity and oil supply pressure. Additionally, oil viscosity is found to represent the significant factor that distinguishes the optimum designs obtained using the implemented secondary selection criteria. Finally, the results of the proposed optimum design framework at different operating conditions are presented and compared.

The proposed multi-objective formulation of the bearing design problem can provide engineers with a systematic approach and an important degree of flexibility to choose the optimum design that best fits the application requirements.

Notes that the real test of maintenance stratagem success (or failure in financial terms) can only be resolved when a comparison of machine maintenance costs can be made to some benchmark standard. Presents a comparative study between two models developed to predict the average hourly maintenance cost of tracked hydraulic excavators operating in the UK opencast mining industry. The models use the conventional statistical technique multiple regression, and artificial neural networks. Performance analysis using mean percentage error, mean absolute percentage error and percentage cost accuracy intervals was conducted. Results reveal that both models performed well, having low mean absolute percentage error values (less than 5 percent) indicating that predictor variables were reliable inputs for modelling average hourly maintenance cost. Overall, the neural network model performed slightly better as it was able to predict up to 95 percent of cost observations to within ≤q £5. Moreover, summary statistical analysis of residual values highlighted that predicted values using the neural network model are less subject to variance than the multiple regression model.

The application of robots to industrial problems often requires grasping and manipulation of the work piece. The robot is able to perform a task adequately only when it is assigned proper tooling and adequate methods of grasping and handling work pieces. The design of such a task requires an in‐depth knowledge of several interrelated subjects including: gripper design, force, position, stiffness and compliance control and grasp configurations. In this paper, we review the research finding on these subjects in order to present in a concise manner, which can be easily accessed by the designers of robot task, the information reported by the researchers, and identify based on the review, future research directions in these areas.

This paper aims to study knowledge integration mechanisms in an interdisciplinary research project. It develops the theoretical literature on the concept of knowledge integration, particularly the adoption of different organizational mechanisms. The research helps to explain how to adopt different integration mechanisms in the various steps of a typical university industry research project.

This research relies on a case study. The authors used three data collection techniques: internal document analysis, observation/site visits and semi-structured interviews. Documentary analysis was used to understand the organizational structure and to identify knowledge integration issues. Observation and site visits at university research laboratories were used to increase understanding on particular issues. Staff interviewed included managers and academic researchers.

Findings are primarily related to a better understanding of choices of knowledge integration mechanisms in a university industry research project. A crucial aspect was the level of mutual understanding of specialist knowledge. When project members were derived from different sides of the university-industry border, there was a major need for recurring to more structured knowledge integration mechanisms, even if the scientific background of participants was homogeneous.

Previous studies on the relationships between university and industry deals with the issue of governing the knowledge exchange on the border at a macro level; conversely, an interesting research gap is represented by knowledge integration mechanisms to be deployed on the university-industry border, especially at micro level. In this paper, the authors do not cope explicitly with university-industry coordination mechanisms but use the University-Industry border to study knowledge integration in interdisciplinary setting. The study is exploratory, which may be useful in generating future research hypotheses, connecting the features of research projects with the need to achieve knowledge integration.

Capital theory has taken a new turn with the theoretical discovery that wage curves tend to get linear in random systems, the larger they are, and with the confirmation that empirical wage curves do not deviate a great deal from linearity. The present chapter adds to these results by arguing that reswitching becomes less likely for larger systems, while Wicksell effects are almost surely present. But it can also be shown that the elasticity of substitution is likely to be small in random systems so that a policy to lower real wages will not easily generate much additional employment in a closed economy. A new perspective on employment policies is therefore called for.

The aim of the paper is to establish the COst-Specific Air Range (COSAR) as a new figure-of-merit based on the cost of energy to optimise the flight profile of a hybrid energy aircraft.

After reviewing the expression and the application of the specific air range (SAR) and of the energy-specific air range (ESAR), the need of a new figure-of-merit for flight technique optimisation of hybrid energy aircraft is motivated. Based on the specific cost of the energies consumed, the mathematical expression of COSAR is derived. To enable optimum economics operations, a cost index (CI) derivation is introduced for a variety of hybrid-electric concepts to consider the additional time-related cost. The application of COSAR and of the CI is demonstrated for cruise optimisation of a hybrid-electric retrofit aircraft concept.

As a consequence of the consumption of multiple energy sources in a hybrid aircraft, optimisation according to the objective functions SAR and ESAR leads to minimum in-flight CO2 emissions and minimum energy consumption for a given stage length. While the optimisation of a single energy source aircraft according to these figures-of-merit directly results in minimum energy cost for a given unit range, this statement is no longer true for hybrid-energy aircraft. Consequently, introducing a new figure-of-merit established on the specific cost of the energies consumed enables flight technique optimisation for minimum energy cost of hybrid-energy aircraft. Additionally, the related time-cost is taken into account by means of a CI definition for minimum operating cost.

COSAR may serve as an alternative to SAR used today as the standard figure-of-merit for fuel optimised flight profile. Using COSAR and the CI allow airlines to adapt the flight profiles of hybrid-energy aircraft fleets according to the energy market price and their related cost of time to determine optimum economical flight profile.

Using COSAR as a figure-of-merit, the flight profile of hybrid energy aircraft can be optimised for minimum energy cost. Time-related costs are considered for optimum operating economics by utilisation of the CI definition for hybrid energy aircraft.

Introduction Operations research, i.e. the application of scientific methodology to operational problems in the search for improved understanding and control, can be said to have started with the application of mathematical tools to military problems of supply bombing and strategy, during the Second World War. Post‐war these tools were applied to business problems, particularly production scheduling, inventory control and physical distribution because of the acute shortages of goods and the numerical aspects of these problems.

In this paper, we focus on the quality of a 2D elastic finite element analysis.

Our objective is to control the discretization parameters in order to achieve a prescribed local quality level over a dimensioning zone. The method is based on the concept of constitutive relation error.

The method is illustrated through 2D test examples and shows clearly that in terms of cost, this technique provides an additional benefit compared to previous methods.

The saving would be even more significant if this mesh adaptation technique were applied in three dimensions. Indeed, in 3D problems, the computing cost is vital and, in general, it is this cost that sets the limits.

This tool is directly usable in the design stage.

The new tool developed guarantees a local quality level prescribed by the user.

Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

The paper presents a technique for predicting the energy consumption of unregulated energy loads (UELs) in office buildings. It also presents an approach for determining a set of optimum values required by the technique.

The proposed technique uses the optimum power drawn and optimum usage period in three modes of device operation, for the prediction. The usage modes are active mode, idle (low active) mode and off mode. The optimum powers and usage times are inserted into a linear mathematical equation to predict the energy consumption. Regarding the approach for determining the optimum values, the non-dominated sorting genetic algorithm II (NSGA-II) is applied to a range of values obtained from field measurements. The proposed prediction method and approach for determining optimum values were tested using data of energy consumption of UELs in a case study facility.

Test results show that the method for predicting the energy consumption of UELs in offices is highly accurate and suitable for adoption by energy modelers, building designers and building regulatory agencies. The approach for determining the optimum values is also effective and can aid the establishment of workable benchmark values.

A new and simple model has been developed for the prediction of unregulated energy. A method for determining a set of optimum values of power and usage periods required by the model has also been developed. Furthermore, optimum values have been suggested that can be fine-tuned for use as benchmark values. The proposed approaches are the first of their kind.