Design of tensegrity structures using parametric analysis and stochastic search

Tensegrity structures are lightweight structures composed of cables in tension and struts in compression. Since tensegrity systems exhibit geometrically nonlinear behavior, finding optimal structural designs is difficult. This paper focuses on the use of stochastic search for the design of tensegrity systems. A pedestrian bridge made of square hollow-rope tensegrity ring modules is studied. Two design methods are compared in this paper. Both methods aim to find the minimal cost solution. The first method approximates current practice in design offices. More specifically, parametric analysis that is similar to a gradient-based optimization is used to identify good designs. Parametric studies are executed for each system parameter in order to identify its influence on response. The second method uses a stochastic search strategy called probabilistic global search Lausanne. Both methods provide feasible configurations that meet civil engineering criteria of safety and serviceability. Parametric studies also help in defining search parameters such as appropriate penalty costs to enforce constraints while optimizing using stochastic search. Traditional design methods are useful to gain an understanding of structural behavior. However, due to the many local minima in the solution space, stochastic search strategies find better solutions than parametric studies.     

Increased hot-plate ignition probability for nanoparticle-laden diesel fuel

The present study attempts to improve the ignition properties of diesel fuel by investigating the influence of adding aluminum and aluminum oxide nanoparticles to diesel. As part of this study, droplet ignition experiments were carried out atop a heated hot plate. Different types of fuel mixtures were used; both particle size (15 and 50 nm) as well as the volume fraction (0%, 0.1%, and 0.5%) of nanoparticles added to diesel were varied. For each type of fuel mixture, several droplets were dropped on the hot plate from a fixed height and under identical conditions, and the probability of ignition of that fuel was recorded based on the number of droplets that ignited. These experiments were repeated at several temperatures over the range of 688-768 degrees C. It was observed that the ignition probability for the fuel mixtures that contained nanoparticles was significantly higher than that of pure diesel.     

Optimized clustering-based discovery framework on Internet of Things

The Journal of Supercomputing - With the proliferation of technology, a system of connected and interconnected devices, henceforth referred to as Internet of Things, is emerging as a viable method...     

Unsteady squeezing flow of magnetic hybrid nanofluids within parallel plates and entropy generation

Nowadays, due to the novel thermal effectiveness, a new class of fluid, named “hybrid nanofluid,” is used. It has significant applications in domestic and industrial fields. In this study, we investigated the entropy generation and heat transfer of unsteady squeezing magnetic hybrid nanofluid flow between parallel plates by considering heat source/sink and thermal radiation. In this analysis, carbon nanotubes (CNTs) (single‐walled carbon nanotube and multiwalled carbon nanotube) are considered as nanoparticles that are dispersed in water‐ethylene glycol (EG) mixtures (ie, 70%W + 30%EG and 50%W + 50%EG). For the analysis of the physical behavior of hybrid nanofluids, new models related to hybrid nanofluids are incorporated. From this study, it has been observed that as the hybrid nanofluids moved away from the surface, the entropy generation outlines accelerated with an increase in magnetic field values. Moreover, an increase in the volume fraction of CNTs, the thermal conductivity of 50%W + 50%EG + CNTs hybrid nanofluid is greater than 70%W + 30%EG + CNTs hybrid nanofluid.     

Financial sustainability of rural water supply: An analysis of cost recovery,revenue collection,and efficiency

Maintaining a financial sustainable pipe water supply (PWS) is one of the serious challenges in sustainable provision of water supply in rural areas. Poor revenue collection and willingness to pay for the service is adding severity to the problem. This study analyses the factors affecting revenue collection in rural water utilities and measures its efficiency in maximizing revenue and average hour of water supply per day. The findings convey that cost recovery is a failure in rural water supply even they are not able to generate revenue. Inactive community participation, the notion of free water supply, unaccountable payment is main reason for poor revenue collection. Therefore, active community participations, educating the household on water supply, adoption of accountable payment mode will be a step towards revenue generation. To attain efficiency, the utilities can expand their output revenue and hours of water supply by 22% without altering their input. This study is an empirical contribution in the field of sustainable provision of water supplies, which address the problem of poor revenue generation in rural water supply and find out the reason for it.     

Processing of Mica for Extraction of Alumina and Potash Values

Mica sample containing 34% alumina and 9% potash has been investigated as a potential dual source of alumina and potash. It was found that planetary milling had a strong effect on structure breakdown as well as on the release of alumina and potash values. Both alumina and potash were extracted from mica using mechanical milling followed by hydrochloric acid leaching and precipitation process. The optimal conditions derived from statistical design yielded 72% alumina and 75% potash values in the form of γ-alumina and sylvinite. The analysis of the final precipitate was carried out by different characterization techniques such as XRD, SEM–EDS, XRF, BET surface area analysis.

     

Impact of a Multi-step Heat Treatment on Different Manufacturing Routes of 18CrNiMo7-6 Steel

Metallurgical and Materials Transactions A - Effect of an optimized multi-step heat treatment routine on conventional (machining from wrought bar stock) and alternate manufacturing routes (hot...     

Compact UWB Antenna with High Rejection Triple Band-Notch Characteristics for Wireless Applications

In this paper, small printed flower-shape triple notch ultra-wideband (UWB) monopole antenna with high band rejection is presented. Notch bands include WiMAX (IEEE802.16 3.30–3.80 GHz), WLAN IEEE802.11a/h/j/n (5.15–5.35, 5.25–5.35, 5.47–5.725, 5.725–5.825 GHz), and X-band downlink satellite system (7.1–7.9 GHz). By including inverted T-shape stub and etching two C-shaped slots on the radiating patch, triple band-notch function is obtained with measured high band rejection (VSWR = 14.52 at 3.58 GHz, VSWR = 15.88 at 5.69 GHz and VSWR = 6.95 at 7.61 GHz) and covers a UWB useable fractional bandwidth of 114.30% (2.74–10.57 GHz = 7.83 GHz). In short the antenna offers triple band-notch UWB systems as a compact multifunctional antenna to reduce the number of antennas installed in wireless devices for accessing multiple wireless networks with wide radiation pattern. The proposed antenna has a small size of about 0.25λ × 0.30λ at 4.2 GHz (first resonance frequency), which has a size reduction of 30% with respect to the earlier published antenna. Both the experimental and simulated results of the proposed antenna are presented, indicating that the antenna is a good candidate for various UWB applications.

     

Ultra-Sparse Classifiers Through Minimizing the VC Dimension in the Empirical Feature Space

Sparse representations have gained much interest due to the rapid growth of intelligent embedded systems, the need to reduce the time to mine large datasets, and for reducing the footprint of recognition based applications on portable devices. Computational learning theory tells us that the Vapnik–Chervonenkis (VC) dimension of a learning model directly impacts the structural risk and the generalization ability of a learning model. The minimal complexity machine (MCM) was recently proposed as a way to learn a hyperplane classifier by minimizing a tight bound on the VC dimension; results show that it learns very sparse representations that yield test set accuracies that are comparable to the state-of-the-art. The MCM formulation works in the primal itself, both when the classifier is learnt in the input space and when it is learnt implicitly in a higher dimensional feature space. In the latter case, the hyperplane is constructed in the empirical feature space (EFS). In this paper, we examine the hyperplane restricted to the EFS. The EFS is a finite dimensional vector space spanned by the image vectors in a higher dimensional feature space. Since the VC dimension of a linear hyperplane classifier is exactly the number of features used by the classifier, the dimension of the EFS is a direct measure of both the sparsity of the model and the VC dimension. This allows us to formulate optimization problems that focus on learning sparse representations, and yet generalize well. We derive an EFS version of the MCM, that allows us to minimize the model complexity and improve sparsity. We also propose a novel least squares version of the MCM in the EFS. Experimental results demonstrate that the EFS variants yield sparse models with generalization comparable to the state-of-the-art.     

Development of hard/soft ferrite nanocomposite for enhanced microwave absorption

Nickel and zinc substituted strontium hexaferrite, SrFe₁₁Zn_0.5Ni_0.5O₁₉ (SrFe₁₂O₁₉/NiFe₂O₄/ZnFe₂O₄) nanoparticles having super paramagnetic nature are synthesized by co-precipitation of chloride salts using 7.5 M sodium hydroxide solution. The resulting precursors are heat treated (HT) at 900 and 1200 °C for 4 h in nitrogen atmosphere. During heat treatment, transformation proceeds as a constant rate of nucleation and three dimensional growth with an activation energy of 176.79 kJ/mol. The hysteresis loops show an increase in saturation magnetization from 1.042 to 59.789 emu/g with increasing HT temperatures. The ‘as-synthesized’ particles with spherical and needle shapes have size in the range of 20–25 nm. Further, these spherical and needle shaped nanoparticles tend to change their morphology to hexagonal plate and pyramidal shapes with increase in HT temperatures. The effect of such a systematic morphological transformation of nanoparticles on dielectric (complex permittivity and permeability) and microwave absorption properties are estimated in X band (8.2–12.2 GHz). The maximum reflection loss of the composite reaches −29.62 dB (99% power attenuation) at 10.21 GHz which suits its application in RADAR absorbing materials.