Application of Artificial Neural Network (ANN) for modeling oxide-based nanofluids dynamic viscosity

This paper presents an Artificial Neural Network (ANN) model for predicting the dynamic viscosity of oxide nanoparticles suspension in water and ethylene glycol. The model accounts for the effect of temperature, nanoparticle volume fraction, nanoparticle diameter, cluster of nanoparticles average size, and base fluid properties. The model was trained on a set of data obtained by the present authors and tested on data coming from other authors. The model shows a fair agreement in predicting experimental data: the mean absolute percentage error (MAPE) is 4.15%. The characteristic parameters of the ANN model are reported in details in the paper.     

Long-time simulation of water distribution systems for the design of small hydropower systems

The implementation of small hydropower systems (SHPS) in existing Alpine Water distribution systems (WDS) is a meaningful strategy for the reduction of CO₂ emissions. Typically, the water consumption patterns of one or several representative days are used for the design procedure of these systems. For the design of the SHPS in WDS, the most important issue is to preserve the water supply without constraints. However, regarding water quality issues (especially related to water age), SHPS can exert a positive influence on the WDS performance. In this study, a long-time simulation model (10 years) is established for a WDS model and is subsequently used for the design of the SHPS. This study is performed to establish a more efficient design of these systems and to evaluate the long-term effects on the pressure and water quality issues, i.e., water age. With a simulation time over one decade on an hourly basis, full load hours and water flows for different turbine sizes are determined in detail. Based on that information, the detailed design and optimisation of the SHPS were performed, and the most cost-efficient device size was determined.     

On the impact of FIT policies on renewable energy investment: Based on the solar power support policies in China's power market

In 2013, the feed-in tariff (FIT) policy was issued in China to promote the investment in renewable technology, but then it was revised because this policy brought a heavy financial burden to the government. By considering the intermittence of renewable resources, we model the implemented Chinese FIT policies and analyze their impact on renewable energy investment in the power market. The open-loop model is employed to simulate the China's power market organized with Power Purchase Agreement, and the closed-loop game is used to characterize the spot power market. Meanwhile, the strategic capacity choices of power generators in two games are compared under four different policy schemes: (i) free competition, (ii) FIT via fixed subsidy, (iii) FIT via price premium and (iv) Chinese FIT by cross control (CFCC). The results show that the CFCC policy is a good alternative to well control the investment in renewable technology, as it can be seen as a comprise between free competition and FIT via fixed subsidy policy. Furthermore, compared with the other three policy schemes, the CFCC policy is capable of keeping renewable power generators from deviating the equilibrium, which implies higher robustness in regulating the electricity spot market.     

Effects of carbon fiber length on the tribological properties of paper-based friction materials

Four kinds of paper-based friction materials reinforced with carbon fibers of 100, 400, 600 and 800 μm were prepared by paper-making processes. Experimental results showed that the friction materials became porous with fiber length increasing. The friction torque curves were flat except the sample with 100 μm fibers. The wear rate of the sample with 100 μm fibers was only 1.40×10⁻⁵ mm³/J. Tiny debris and fine scratches formed in the worn surface were the reason for excellent wear resistance of friction pairs with 100 μm fibers. The friction pairs with 400, 600 and 800 μm fibers showed typically abrasive wear and fatigue wear.     

A noun-based approach to feature location using time-aware term-weighting

ContextFeature location aims to identify the source code location corresponding to the implementation of a software feature. Many existing feature location methods apply text retrieval to determine the relevancy of the features to the text data extracted from the software repositories. One of the preprocessing activities in text retrieval is term-weighting, which is used to adjust the importance of a term within a document or corpus. Common term-weighting techniques may not be optimal to deal with text data from software repositories due to the origin of term-weighting techniques from a natural language context.ObjectiveThis paper describes how the consideration of when the terms were used in the repositories, under the condition of weighting only the noun terms, can improve a feature location approach.MethodWe propose a feature location approach using a new term-weighting technique that takes into account how recently a term has been used in the repositories. In this approach, only the noun terms are weighted to reduce the dataset volume and avoid dealing with dimensionality reduction.ResultsAn empirical evaluation of the approach on four open-source projects reveals improvements to the accuracy, effectiveness and performance up to 50%, 17%, and 13%, respectively, when compared to the commonly-used Vector Space Model approach. The comparison of the proposed term-weighting technique with the Term Frequency-Inverse Document Frequency technique shows accuracy, effectiveness, and performance improvements as much as 15%, 10%, and 40%, respectively. The investigation of using only noun terms, instead of using all terms, in the proposed approach also indicates improvements up to 28%, 21%, and 58% on accuracy, effectiveness, and performance, respectively.ConclusionIn general, the use of time in the weighting of terms, along with the use of only the noun terms, makes significant improvements to a feature location approach that relies on textual information.     

Arm motion analysis using genetic algorithm for rehabilitation and healthcare

The worlds population is quickly aging. With an aging society, an increase in patients with brain damage is predicted. In rehabilitation, the analysis of arm motion is vital as various day to day activities relate to arm movements. The therapeutic approach and evaluation method are generally selected by therapists based on his/her experience, which can be an issue for quantitative evaluation in any specific movement task. In this paper, we develop a measurement system for arm motion analysis using a 3D image sensor. The method of upper body posture estimation based on a steady-state genetic algorithm (SSGA) is proposed. A continuous model of generation for an adaptive search in dynamical environment using an adaptive penalty function and island model is applied. Experimental results indicate promising results as compared with the literature.     

Tool condition monitoring in interrupted cutting with acceleration sensors

Tool breakage is a serious issue in conditions with highly variable stress such as interrupted turning. The tool may fail suddenly though commonly tool failure is preceded by other symptoms such as chipping or fracture of tool edges and tool wear before the complete failure. These symptoms can be used to predict reliably complete tool failure. In the case of a complete failure, the surface integrity of the workpiece is commonly ruined causing waste, making the individual events one of the most expensive failures in small series and flexible manufacturing in addition to collisions. In earlier studies, tool wear has been monitored by force sensors. There are also methods for estimating cutting force with acceleration sensors. In this study, it is demonstrated that it is possible to estimate tool deflection, connected to main cutting force, with acceleration sensor and use this information for detecting the chipping and small fracture of the tool edge. The method presented in this study can be used as a predictor for complete tool failure and thus prevent waste.     

Optimum mechanical behavior of calcium phosphate cement/hydroxyl group functionalized multi-walled carbon nanotubes/bovine serum albumin composite using metaheuristic algorithms

Injectable calcium phosphate cements have been introduced as adjuncts to internal fixation for treating selected fractures. These cements harden without producing much heat, develop compressive strength, and are remodeled slowly in vivo. The main purpose of the cement is to fill voids in metaphyseal bone, thereby reducing the need for bone graft. However, such cements may also improve the holding strength around metal devices in osteoporotic bone. This paper presents the optimum mechanical behavior of calcium phosphate cement/hydroxyl group functionalized multi-walled carbon nanotubes/bovine serum albumin (CPC/MWCNT-OH/BSA) composites in terms of compressive strength using well-known metaheuristic optimizers. The process parameters studied were wt% of MWCNT-OH (0.2–0.5 wt%) and wt% of BSA (5–15 wt%). The obtained results from metaheuristic algorithms were compared with the results from the response surface methodology (RSM) in the literature. The results obtained from metaheuristic algorithms outperformed the results given by the RSM in terms of less error percentage and high compressive strength.