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Many a times, the information about the boundary heat flux is obtained only through inverse approach by locating the thermocouple or temperature sensor in accessible boundary. Most of the work reported in literature for the estimation of unknown parameters is based on heat conduction model. Inverse approach using conjugate heat transfer is found inadequate in literature. Therefore, the purpose of the paper is to develop a 3D conjugate heat transfer model without model reduction for the estimation of heat flux and heat transfer coefficient from the measured temperatures.

A 3 D conjugate fin heat transfer model is solved using commercial software for the known boundary conditions. Navier–Stokes equation is solved to obtain the necessary temperature distribution of the fin. Later, the complete model is replaced with neural network to expedite the computations of the forward problem. For the inverse approach, genetic algorithm (GA) and particle swarm optimization (PSO) are applied to estimate the unknown parameters. Eventually, a hybrid algorithm is proposed by combining PSO with Broyden–Fletcher–Goldfarb–Shanno (BFGS) method that outperforms GA and PSO.

The authors demonstrate that the evolutionary algorithms can be used to obtain accurate results from simulated measurements. Efficacy of the hybrid algorithm is established using real time measurements. The hybrid algorithm (PSO-BFGS) is more efficient in the estimation of unknown parameters for experimentally measured temperature data compared to GA and PSO algorithms.

Surrogate model using ANN based on computational fluid dynamics simulations and in-house steady state fin experiments to estimate the heat flux and heat transfer coefficient separately using GA, PSO and PSO-BFGS.

This study aims to collect real-time, in situ data from polymer melt extrusion (ME) 3D printing and use only the collected data to non-destructively identify printed parts that contain defects.

A set of sensors was created to collect real-time, in situ data from polymer ME 3D printing. A variance analysis was completed to identify an “acceptable” range for filament diameter on a popular desktop 3D printer. These data were used as the basis of a quality evaluation process to non-destructively identify spatial regions of printed parts in multi-part builds that contain defects.

Anomalous parts were correctly identified non-destructively using only in situ collected data.

This methodology was developed by varying the filament diameter, one of the most common reasons for print failure in ME. Numerous other printing parameters are known to create faults in melt extruded parts, and this methodology can be extended to analyze other parameters.

To the best of the authors’ knowledge, this is the first report of a non-destructive evaluation of 3D-printed part quality using only in situ data in ME. The value is in improving part quality and reliability in ME, thereby reducing 3D printing part errors, plastic waste and the associated cost of time and material.

This study aims to evaluate the influence of oleic acid (OA)-capped Al₂O₃ nanoparticles on the tribological performance of conventional lube oil. The goal is to determine the optimal nanoparticle concentration that enhances lubricant efficiency by reducing friction and wear.

The research involved preparing nanolubricants with four different concentrations of Al₂O₃ nanoparticles: 0.05, 0.1, 0.25 and 0.5 wt.%. Tribological performance was assessed using a four-ball tribotester, which measured the coefficient of friction (COF) and wear scar diameter (WSD) under standardized testing conditions.

The experimental results revealed that the nanolubricant containing 0.1 wt.% OA-Al₂O₃ nanoparticles exhibited the most significant improvement in tribological performance. This formulation achieved a 38.84% reduction in COF and a 23.87% reduction in WSD compared to the base lubricant. These findings demonstrate the effectiveness of incorporating OA-capped Al₂O₃ nanoparticles in reducing friction and wear, thereby enhancing the overall performance of conventional lubricants.

This study demonstrates the benefits of OA-capped Al₂O₃ nanoparticles in lubricants, including a 38.84% reduction in COF and a 23.87% reduction in WSD. By systematically analyzing different nanoparticle concentrations, it identified that 0.1% by weight of nanoparticles is the most effective formulation for reducing friction and wear.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0236/

The RZ5 mg alloy is used in automotive and aerospace applications including helicopter gearboxes and aircraft components. These components are prone to the wear as per the demands. The present work is the study of the significance of hard particle/ceramic, i.e. titanium carbide (TiC) in RZ5 mg alloy to protect the machine components from wear.

The abrasive wear analysis of in-situ RZ5-TiC magnesium matrix composite is considered for the study. The primary focus of the present work is to analyze the effects of varying control parameters, i.e. Wt.% of TiC, sliding distance and applied load on the responses, i.e. weight loss and coefficient of friction. Full factorial design of the experiment based on statistical analysis is used.

It is observed that the individually Wt.% of TiC and sliding distance show the comparatively significant effect on both responses. Similarly, the interaction between sliding distance and Wt.% of TiC indicated the considerable impact on weight loss. The regression equations are developed and validated for estimating responses. It is observed that the percentage errors are not appearing more than 10 per cent of responses. Therefore, the close agreement between measured and predicted values shows the adequacy of the model. The control factor is optimized using multi-response optimization. The variations of the order of 2.47 and 2.35 per cent in target value of the coefficient of friction and weight loss are achieved.

The current manuscript provides a detailed abrasive wear statistical analysis of RZ5-TiC composite. The influence of control parameters on the responses using the full factorial design, the main effect plots and interaction effects are presented.

Marine exploration is becoming an important element of pervasive computing underwater target tracking. Many pervasive techniques are found in current literature, but only scant research has been conducted on their effectiveness in target tracking.

This research paper, introduces a Shifted Rayleigh Filter (SHRF) for three-dimensional (3 D) underwater target tracking. A comparison is drawn between the SHRF and previously proven method Unscented Kalman Filter (UKF).

SHRF is especially suitable for long-range scenarios to track a target with less solution convergence compared to UKF. In this analysis, the problem of determining the target location and speed from noise corrupted measurements of bearing, elevation by a single moving target is considered. SHRF is generated and its performance is evaluated for the target motion analysis approach.

The proposed filter performs better than UKF, especially for long-range scenarios. Experimental results from Monte Carlo are provided using MATLAB and the enhancements achieved by the SHRF techniques are evident.

Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.

This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.

The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

Homogeneity in the main business of renting office spaces among commercial facilities has led to fierce competition. To retain tenant attractiveness, many are now concerned about the quality of facilities management (FM) services in addition to the rent, office space and location. The quality of FM service can be attained with successful service encounters. Thus, this paper aims to establish an initial platform on which tenant satisfaction in FM service encounters can be achieved.

The preliminary survey focused on gaining insights into FM encounters in commercial sector and applicability of service attributes under SERVQUAL model. The detailed survey concentrated on determining tenant perceptions on satisfactory levels of service attributes developed in the preliminary study and relationships between FM encounters and different service attributes. The collection of descriptive and inferential statistics was used to analyze the results.

This study’s findings reveal assurance and empathy to be highly correlated to tenant satisfaction, while other attributes are less correlated. However, perceptions of satisfaction levels of tenants on tangibility and reliability provides contradictory results to its correlation values. Satisfaction level in remote service encounters is lower compared to phone and face-to-face encounters. Complexity and management concerns toward physical facilities are imperative to uplift satisfaction in remote encounters.

The scope of study was limited to FM encounters in Sri Lankan Grade “A” commercial office properties with high quality standard finishes, state-of-the-art systems, exceptional accessibility and a definite market presence in Colombo.

The study developed a preliminary framework that guides users to identify the best combinations of service attributes with respective FM encounters, where tenant satisfaction needs to be achieved.

Omnichannel retail are catching increasing attention from multiple research area, due to its widespread and essential application in retailing industry. The principal object of this paper is to systematically review current studies on omnichannel retail in information systems, operations and marketing research area. Further, the second purpose of this study is to provide insights and guides on omnichannel research to facilitate advance research in the area.

A systematic review on omnichannel retail in retailing industry has been conducted. 33 research articles from 2014 to 2020 from Financial Times 50 journals (FT 50 journals) were identified to be reviewed. The articles were reviewed on the basis of study area namely: information systems, operations and marketing. These research areas were further divided into subcategories to provide in-depth and crystal clear review of literature.

The review outcome showed that omnichannel topics are active in management studies. There are three main research areas: information systems, operations management and marketing. This study found that IT enhances channel capabilities and expands demand in omnichannel retail; operations can be better optimized based on understanding of cross-channel interactions, product category and retailer types; consumer heterogeneity, product category, channel capabilities and retailer type are all found to contribute to complexities of omnichannel marketing.

This study provides a pioneering review to gauge the updated literature concerned with omnichannel research in terms of IT, operations and marketing perspective. Further, a systematic literature review provides insights and guides for future significant studies on potential research subjects.

Doppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and challenging problem in an underwater environment.

The system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken, the speeds of target and observer, environmental conditions, number of sensors considered for measurements and so on. Degrees of nonlinearity (DoNL) for these problems are analyzed using a proposed measure of nonlinearity (MoNL) for state estimation.

In this research, the authors analyzed MoNL for state estimation and computed the conditional MoNL (normalized) using different filtering algorithms where measurements are obtained from a single sensor array (i.e. HMS). MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is, that is, to measure nonlinearity of a problem.

Algorithms are evaluated for various scenarios with different angles on the target bow (ATB) in Monte-Carlo simulation. Computation of root mean squared (RMS) errors in position and velocity is carried out to assess the state estimation accuracy using MATLAB.

The present work aims to formulate nanolubricants and improve antiwear, antifriction and extreme pressure (EP) performances of castor oil (CO) with surface-modified CuO nanoparticles as an additive in the boundary lubrication regime.

In this study, CuO nanoparticles are modified with a surfactant sodium dodecyl sulfate (SDS) by means of a chemical method. These modified nanoparticles with varying concentrations of 0.1, 0.25, 0.5 and 1.0%w/v were used to formulate the nanolubricants. The tribological properties of non-formulated and formulated CO were examined using a four-ball tester. The tribological test results were compared with paraffin oil (PO) for similar compositions.

The nanoparticle concentrations in base oils were optimized by wear scar diameter (WSD) and load carrying capacity during antiwear and EP tests, respectively. In the antiwear test, the maximum reductions in WSD were 28.3 and 22.2 per cent; however, the coefficient of friction was reduced by 34.6 and 17.3 per cent at optimum nanoparticle concentrations in CO and PO, respectively. A significant improvement in the weld load was observed for both nanolubricants.

This work indicates that nanoparticle-based CO in industrial applications provides on par or better results than mineral oil. Also, it has a negligible hazardous impact on our eco-system.