a constitutive model to predict the pseudo elastic stress strain behaviour of sma

Abstract Shape memory alloys (SMAs) are a wide class of materials characterized by the property to recover the initial shape also after high values of deformations. This is due to the ability of SMAs to change, in a reversible manner, their microstructure from an initial structure, often named austenite, to a final structure, named martensite. The transformations of microstructure can take place with or without one or more intermediate phases, but always without re-crystallization, implying a microstructure changing inside the crystals, without any new boundary creation. The stress-strain behaviour depends on the crystal structures. In this work, a simple model to predict the stress-strain behaviour of a PE SMA has been proposed. The results have been compared to an experimental tensile test carried out on a NiTi SMA alloy

analysis of the al and ti additions influences on phases generation and damage in a hot dip galvanizing process

Abstract Cheap iron-based alloys, such as Ductile Cast Irons (DCIs) and low carbon steels, are more and more used in the mechanical field because they are characterized by good strength and good workability. However, the low value of electrochemical potential of low carbon steel leads to quick environmental corrosion that can compromise the operative life of mechanical components. Therefore, it is important to protect them against corrosion even for safety and reliability reasons. The use of a traditional protection technique, like Hot Dip Galvanizing (HDG), allows low costs too. In this work, the phase formation during HDG process is presented and discussed. In particular, the influence of Al and Ti additions on the pure Zn bath is shown in the metallographic analysis, presenting also the results of pure Zn bath

Stressing the passive behavior of a Passivhaus: an evidence-based scenario analysis for a Mediterranean case study

This paper first reports the outcomes of a one-year measurement campaign of a passive house built in the Mediterranean climate of Cesena (Italy) in terms of thermal comfort parameters temperature and relative humidity and Indoor Environmental Quality (IEQ) parameter CO2 concentrations. The design carried out with the help of the steady state Passive House Planning Package (PHPP) was able to guarantee good comfort conditions during the heating period, but on the other hand, overheating occurrences during the cooling season have been recorded for almost 50% time according to EN 15251 Standard. Further analyses conducted with the help of dynamic simulations in EnergyPlus allowed identifying the insulation levels and ventilation mode as the key design factors to change in order to reduce overheating to less than 20% of time while keeping a comfortable indoor environment in winter. The simplifications that can be made by reducing the insulation material thickness (up to a third of the original value) on the roof and on the walls, replacing triple-glazed windows with double-glazed windows and implementing a hybrid ventilation strategy instead of using Mechanical Ventilation with Heat Recovery (MVHR) alone could also lead to economic savings. These savings, due to both lower construction costs and operational energy savings, amount to 8755 euros in terms of Net Present Value (NPV) over 30 years' time. The Passivhaus Standard can still be regarded as a good reference for designing low-energy and comfortable houses in a Mediterranean climate if some simplifications are made according to detailed building performance simulations

An Evolutionary Multi-Objective Optimization Framework for Bi-level Problems

Genetic algorithms (GA) are stochastic optimization methods inspired by the evolutionist theory on the origin of species and natural selection. They are able to achieve good exploration of the solution space and accurate convergence toward the global optimal solution. GAs are highly modular and easily adaptable to specific real-world problems which makes them one of the most efficient available numerical optimization methods. This work presents an optimization framework based on the Multi-Objective Genetic Algorithm for Structured Inputs (MOGASI) which combines modules and operators with specialized routines aimed at achieving enhanced performance on specific types of problems. MOGASI has dedicated methods for handling various types of data structures present in an optimization problem as well as a pre-processing phase aimed at restricting the problem domain and reducing problem complexity. It has been extensively tested against a set of benchmarks well-known in literature and compared to a selection of state-of-the-art GAs. Furthermore, the algorithm framework was extended and adapted to be applied to Bi-level Programming Problems (BPP). These are hierarchical optimization problems where the optimal solution of the bottom-level constitutes part of the top-level constraints. One of the most promising methods for handling BPPs with metaheuristics is the so-called "nested" approach. A framework extension is performed to support this kind of approach. This strategy and its effectiveness are shown on two real-world BPPs, both falling in the category of pricing problems. The first application is the Network Pricing Problem (NPP) that concerns the setting of road network tolls by an authority that tries to maximize its profit whereas users traveling on the network try to minimize their costs. A set of instances is generated to compare the optimization results of an exact solver with the MOGASI bi-level nested approach and identify the problem sizes where the latter performs best. The second application is the Peak-load Pricing (PLP) Problem. The PLP problem is aimed at investigating the possibilities for mitigating European air traffic congestion. The PLP problem is reformulated as a multi-objective BPP and solved with the MOGASI nested approach. The target is to modulate charges imposed on airspace users so as to redistribute air traffic at the European level. A large scale instance based on real air traffic data on the entire European airspace is solved. Results show that significant improvements in traffic distribution in terms of both schedule displacement and air space sector load can be achieved through this simple, en-route charge modulation scheme

Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64x64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one.Comment: Submitted to Journal of Visual Communication and Image Representation (first submission: March 20, 2017; second submission: August 2, 2017

Designing cover crop mixtures to enhance potential weed suppression in organic no-till vegetable systems.

Introduction No-tillage in vegetable systems can provide several benefits, but it can only be implemented if there is a powerful strategy to control weeds (Morse, 1999). Cover crops are an essential part of an integrated weed management strategy in no-till organic and low input vegetable systems (Altieri et al., 2011). However, few studies focus on no-tillage practices in organic vegetable systems in European environments, particularly in Mediterranean contexts (Isik et al., 2009). Cover crop effectiveness in weed control, depend on crop traits linked with weed suppressive ability. Mixtures have been used to improve effectiveness of cover crops (Wortman et al., 2012; Smith et al., 2014, Finney et al., 2016). Nonetheless, to our knowledge, this is the first study adopting a functional approach to the design of cover crop mixtures. Our objective was to investigate the effect of functional diversity and composition of cover crops in controlling weeds before transplanting aubergine (Solanum melongena L.), highlighting the relationship between functional traits and weed suppression. Materials and Methods A field trial was performed in an organic field located at CIRAA, University of Pisa, Italy, using a randomized complete block design with 3 replicates and 18 treatments. We selected 8 cover crop species clustered into 4 functional groups as follows: i) large seeded legumes (Pisum sativum L., Vicia sativa L.) characterized by a major development in height; ii) small seeded legumes (Trifolium incarnatum L., T. squarrosum L.) that tend to rapidly cover the soil; iii) grasses (Hordeum vulgare L., Avena sativa L.) characterized by a strong competitive ability and iv) crucifers (Raphanus sativus L., Brassica nigra L.) with allelopathic potential. We designed the mixtures to create a gradient of functional diversity. We included 8 monocrop treatments, 4 two-species mixtures; 4 four-species mixtures including co-presence of 2, 3 and 4 functional groups; an eight-species mixture characterized by the highest level of species and functional diversity, and a no cover crop control. Cover crop plots (3 × 12 m) were broadcast on 27th October 2014 and devitalized on 6th May 2015 with a roller crimper followed by flame weeding. Throughout the experiment, density and height of component cover crop species was regularly recorded. Organically certified aubergine plants (cv “Dalia F1”) were transplanted 5 days after cover crop devitalization. Before devitalization, three above-ground biomass samples of 0.5 m2 per plot were collected. We separated cover crop from weed biomass, and cover crop biomass in the mixtures was further partitioned into component species. Results and Discussion We found no strong correlation between cover crop height and biomass at the time of devitalization. Instead, we found a significant negative relationship between cover crop biomass and weed biomass. This relationship was significantly influenced by treatments. The highest weed biomass was recorded for vetch, although it was significantly lower than in the control. The effect of cover crop biomass on weeds was significant for the small seeded legumes and for pea within the large seeded legumes functional group. We found no significant effect for vetch, as its development was particularly low due to poor establishment in 2014. As for grasses, the effect of cover crop biomass over weeds was not significant. In this functional group, alternative mechanisms, such as allelopathy, might have overcome the biomass effect on weeds. A clear functional differentiation between cover crop species emerges. Conclusions Cover crop mixtures showed a strong potential for weed infestation reduction, given the high amount of biomass produced (Teasdale & Abdul-Baki, 1998). As pointed out by previous research (Mirsky et al., 2013; Mohler & Teasdale 1993), a high quantity of cover crop biomass will ensure good weed suppression during subsequent cash crop cultivation. However, choice and combination of different cover crop functional groups can provide a stronger effect on weeds suppression, through mechanisms not necessarily related to higher biomass production. Our results show that functional characterization of cover crop species and the use of mixtures can be powerful tools in an integrated weed management strategy in organic or low input no-till vegetable systems

Numerical Analysis of Real Fluid Behavior Effects on a Sliding-Vane Compressor Comprehensive Model

This work presents a simulation model on a sliding vane compressor based on a lumped parameter model. The model is capable of predicting the performance of sliding-vane compressors. The model is divided into different sub-sections to evaluate the compressor's geometry, kinetics, thermodynamics, and rotor dynamics. The output of the tool includes the compressor unit's performance, such as volumetric flow rate, mechanical power, and process efficiency. The study examines the tool's ability to perform quick and efficient analyses using using either ideal or real fluid characterization, based on the REFPROP code. The code is validated against one experimental point. Simulations were conducted on a mid-size sliding-vane rotary compressor operating with three different types of working fluids from 20 °C and 1 bar (absolute) to 11 bar at 1500 rpm. In the ideal fluid case, simulations took 10–27 s, while real fluid assumptions took 1038–4329 s. The volumetric flow rate was influenced by the gas used, but changes among fluid models were not substantial, with a mean absolute percent difference of 0.5%. Mechanical power consumption was affected by the fluid choice and gas model, leading to a mechanical power difference between 0.4 and 1.1% in the ideal gas case. The specific mechanical work showed greater deviations among the fluids, with methane molar mass coherently increasing its value. Results show that the model developed is able to assess the major phenomena of sliding-vane compressors, and the ideal fluid model should be preferred when possible since computational times are significantly reduced with comparable results