Search results for: numerical validation

Authors: Sheng-Bo Zhang, Huan-Hao Zhang, Zhi-Hua Chen, Chun Zheng

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In this paper, the Richtmyer-Meshkov instability has been studied numerically by using the high-resolution Roe scheme based on the two-dimensional unsteady Euler equation, which was caused by the interaction between shock wave and the helium circular light gas cylinder with different component distributions. The numerical results further discuss the deformation process of the gas cylinder, the wave structure of the flow field and quantitatively analyze the characteristic dimensions (length, height, and central axial width) of the gas cylinder, the volume compression ratio of the cylinder over time. In addition, the flow mechanism of shock-driven interface gas mixing is analyzed from multiple perspectives by combining it with the flow field pressure, velocity, circulation, and gas mixing rate. Then the effects of different initial component distribution conditions on interface instability are investigated. The results show when the diffusion interface transit to the sharp interface, the reflection coefficient gradually increases on both sides of the interface. When the incident shock wave interacts with the cylinder, the transmission of the shock wave will transit from conventional transmission to unconventional transmission. At the same time, the reflected shock wave is gradually strengthened, and the transmitted shock wave is gradually weakened, which leads to an increase in the Richtmyer-Meshkov instability. Moreover, the Atwood number on both sides of the interface also increases as the diffusion interface transit to the sharp interface, which leads to an increase in the Rayleigh-Taylor instability and the Kelvin-Helmholtz instability. Therefore, the increase in instability will lead to an increase the circulation, resulting in an increase in the growth rate of gas mixing rate.

Keywords: shock wave, He light cylinder, Richtmyer-Meshkov instability, Gaussian distribution

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Authors: Sara Sallam, Mohamed Taqi, Naoual Belouaggadia

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Solar thermal power plants using parabolic trough collectors (PTC) are currently a powerful technology for generating electricity. Most of these solar power plants use thermal oils as heat transfer fluid. The latter is heated in the solar field and transfers the heat absorbed in an oil-water heat exchanger for the production of steam driving the turbines of the power plant. Currently, we are seeking to develop PTCs with direct steam generation (DSG). This process consists of circulating water under pressure in the receiver tube to generate steam directly into the solar loop. This makes it possible to reduce the investment and maintenance costs of the PTCs (the oil-water exchangers are removed) and to avoid the environmental risks associated with the use of thermal oils. The pressure drops in these systems are an important parameter to ensure their proper operation. The determination of these losses is complex because of the presence of the two phases, and most often we limit ourselves to describing them by models using empirical correlations. A comparison of these models with experimental data was performed. Our calculations focused on the evolution of the pressure of the liquid-vapor mixture along the receiver tube of a PTC-DSG for pressure values and inlet flow rates ranging respectively from 3 to 10 MPa, and from 0.4 to 0.6 kg/s. The comparison of the numerical results with experience allows us to demonstrate the validity of some models according to the pressures and the flow rates of entry in the PTC-DSG receiver tube. The analysis of these two parameters’ effects on the evolution of the pressure along the receiving tub, shows that the increase of the inlet pressure and the decrease of the flow rate lead to minimal pressure losses.

Keywords: direct steam generation, parabolic trough collectors, Ppressure drop, empirical models

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Authors: Ibrahim Rassoul, E-K. Si Ahmed

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The growth, deformation, and detachment of fluid droplets adherent to solid substrates is a problem of fundamental interest with numerous practical applications. Specific interest in this proposal is the problem of a droplet on a fibrous, hydrophobic substrate subjected to body or external forces (gravity, convection). The past decade has seen tremendous advances in proton exchange membrane fuel cell (PEMFC) technology. However, there remain many challenges to bring commercially viable stationary PEMFC products to the market. PEMFCs are increasingly emerging as a viable alternative clean power source for automobile and stationary applications. Before PEMFCs can be employed to power automobiles and homes, several key technical challenges must be properly addressed. One technical challenge is elucidating the mechanisms underlying water transport in and removal from PEMFCs. On the one hand, sufficient water is needed in the polymer electrolyte membrane or PEM to maintain sufficiently high proton conductivity. On the other hand, too much liquid water present in the cathode can cause 'flooding' (that is, pore space is filled with excessive liquid water) and hinder the transport of the oxygen reactant from the gas flow channel (GFC) to the three-phase reaction sites. The aim of this work is to investigate the stability of a liquid water droplet emerging form a GDL pore, to gain fundamental insight into the instability process leading to detachment. The approach will combine analytical and numerical modeling with experimental visualization and measurements.

Keywords: polymer electrolyte fuel cell, water droplet, gas diffusion layer, contact angle, surface tension

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Pavan K. Emani, Shashank Kothari, V. S. Phanikanth

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The ultimate load analysis of RC pile groups has assumed a lot of significance under liquefying soil conditions, especially due to post-earthquake studies of 1964 Niigata, 1995 Kobe and 2001 Bhuj earthquakes. The present study reports the results of numerical simulations on pile groups subjected to monotonically increasing lateral loads under design amounts of pile axial loading. The soil liquefaction has been considered through the non-linear p-y relationship of the soil springs, which can vary along the depth/length of the pile. This variation again is related to the liquefaction potential of the site and the magnitude of the seismic shaking. As the piles in the group can reach their extreme deflections and rotations during increased amounts of lateral loading, a precise modeling of the inelastic behavior of the pile cross-section is done, considering the complete stress-strain behavior of concrete, with and without confinement, and reinforcing steel, including the strain-hardening portion. The possibility of the inelastic buckling of the individual piles is considered in the overall collapse modes. The model is analysed using Riks analysis in finite element software to check the post buckling behavior and plastic collapse of piles. The results confirm the kinds of failure modes predicted by centrifuge test results reported by researchers on pile group, although the pile material used is significantly different from that of the simulation model. The extension of the present work promises an important contribution to the design codes for pile groups in liquefying soils.

Keywords: collapse load analysis, inelastic buckling, liquefaction, pile group

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Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: Vangmayee V. Subban, Suzan Deelan. Pinto, Somashekara Haralakatta Shivananjappa, Shwetha Prabhu, Jayashree S. Bhat

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Introduction and Need: During early years of schooling, the instruction in the schools mainly focus on children’s word decoding abilities. However, the skilled readers should master all the components of reading such as word decoding, reading fluency and comprehension. Nevertheless, the relationship between each component during the process of learning to read is less clear. The studies conducted in alphabetical languages have mixed opinion on relative contribution of word decoding and reading fluency on reading comprehension. However, the scenarios in alphasyllabary languages are unexplored. Aim and Objectives: The aim of the study was to explore the role of word decoding, reading fluency on reading comprehension abilities in children learning to read Kannada between the age ranges of 5.6 to 8.6 years. Method: In this cross sectional study, a total of 60 typically developing children, 20 each from Grade I, Grade II, Grade III maintaining equal gender ratio between the age range of 5.6 to 6.6 years, 6.7 to 7.6 years and 7.7 to 8.6 years respectively were selected from Kannada medium schools. The reading fluency and reading comprehension abilities of the children were assessed using Grade level passages selected from the Kannada text book of children core curriculum. All the passages consist of five questions to assess reading comprehension. The pseudoword decoding skills were assessed using 40 pseudowords with varying syllable length and their Akshara composition. Pseudowords are formed by interchanging the syllables within the meaningful word while maintaining the phonotactic constraints of Kannada language. The assessment material was subjected to content validation and reliability measures before collecting the data on the study samples. The data were collected individually, and reading fluency was assessed for words correctly read per minute. Pseudoword decoding was scored for the accuracy of reading. Results: The descriptive statistics indicated that the mean pseudoword reading, reading comprehension, words accurately read per minute increased with the Grades. The performance of Grade III children found to be higher, Grade I lower and Grade II remained intermediate of Grade III and Grade I. The trend indicated that reading skills gradually improve with the Grades. Pearson’s correlation co-efficient showed moderate and highly significant (p=0.00) positive co-relation between the variables, indicating the interdependency of all the three components required for reading. The hierarchical regression analysis revealed 37% variance in reading comprehension was explained by pseudoword decoding and was highly significant. Subsequent entry of reading fluency measure, there was no significant change in R-square and was only change 3%. Therefore, pseudoword-decoding evolved as a single most significant predictor of reading comprehension during early Grades of reading acquisition. Conclusion: The present study concludes that the pseudoword decoding skills contribute significantly to reading comprehension than reading fluency during initial years of schooling in children learning to read Kannada language.

Keywords: alphasyllabary, pseudo-word decoding, reading comprehension, reading fluency

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Authors: Vladimir A. Vinnikov

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The paper proposes a model of an inhomogeneous rock mass with initially random distribution of microcracks on mineral grain boundaries. It describes the behavior of cracks in a medium under the effect of thermal field, the medium heated instantaneously to a predetermined temperature. Crack growth occurs according to the concept of fracture mechanics provided that the stress intensity factor K exceeds the critical value of Kc. The modeling of thermally induced acoustic emission memory effects is based on the assumption that every event of crack nucleation or crack growth caused by heating is accompanied by a single acoustic emission event. Parameters of the thermally induced acoustic emission memory effect produced by cyclic heating and cooling (with the temperature amplitude increasing from cycle to cycle) were calculated for several rock texture types (massive, banded, and disseminated). The study substantiates the adaptation of the proposed model to humidity interference with the thermally induced acoustic emission memory effect. The influence of humidity on the thermally induced acoustic emission memory effect in quasi-homogeneous and banded rocks is estimated. It is shown that such modeling allows the structure and texture of rocks to be taken into account and the influence of interference factors on the distinctness of the thermally induced acoustic emission memory effect to be estimated. The numerical modeling can be used to obtain information about the thermal impacts on rocks in the past and determine the degree of rock disturbance by means of non-destructive testing.

Keywords: degree of rock disturbance, non-destructive testing, thermally induced acoustic emission memory effects, structure and texture of rocks

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Authors: M. Almutairi, S. Hadjiloucas

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The harmonic distortion of voltage is important in relation to power quality due to the interaction between the large diffusion of non-linear and time-varying single-phase and three-phase loads with power supply systems. However, harmonic distortion levels can be reduced by improving the design of polluting loads or by applying arrangements and adding filters. The application of passive filters is an effective solution that can be used to achieve harmonic mitigation mainly because filters offer high efficiency, simplicity, and are economical. Additionally, possible different frequency response characteristics can work to achieve certain required harmonic filtering targets. With these ideas in mind, the objective of this paper is to determine what size single tuned passive filters work in distribution networks best, in order to economically limit violations caused at a given point of common coupling (PCC). This article suggests that a single tuned passive filter could be employed in typical industrial power systems. Furthermore, constrained optimization can be used to find the optimal sizing of the passive filter in order to reduce both harmonic voltage and harmonic currents in the power system to an acceptable level, and, thus, improve the load power factor. The optimization technique works to minimize voltage total harmonic distortions (VTHD) and current total harmonic distortions (ITHD), where maintaining a given power factor at a specified range is desired. According to the IEEE Standard 519, both indices are viewed as constraints for the optimal passive filter design problem. The performance of this technique will be discussed using numerical examples taken from previous publications.

Keywords: harmonics, passive filter, power factor, power quality

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Authors: Yepeng Cheng, Yasuhiko Morimoto

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Customer relationship analysis is vital for retail stores, especially for supermarkets. The point of sale (POS) systems make it possible to record the daily purchasing behaviors of customers as an identification point of sale (ID-POS) database, which can be used to analyze customer behaviors of a supermarket. The customer value is an indicator based on ID-POS database for detecting the customer loyalty of a store. In general, there are many supermarkets in a city, and other nearby competitor supermarkets significantly affect the customer value of customers of a supermarket. However, it is impossible to get detailed ID-POS databases of competitor supermarkets. This study firstly focused on the customer value and distance between a customer's home and supermarkets in a city, and then constructed the models based on logistic regression analysis to analyze correlations between distance and purchasing behaviors only from a POS database of a supermarket chain. During the modeling process, there are three primary problems existed, including the incomparable problem of customer values, the multicollinearity problem among customer value and distance data, and the number of valid partial regression coefficients. The improved customer value, Huff’s gravity model, and inverse attractiveness frequency are considered to solve these problems. This paper presents three types of models based on these three methods for loyal customer classification and competitors’ influence analysis. In numerical experiments, all types of models are useful for loyal customer classification. The type of model, including all three methods, is the most superior one for evaluating the influence of the other nearby supermarkets on customers' purchasing of a supermarket chain from the viewpoint of valid partial regression coefficients and accuracy.

Keywords: customer value, Huff's Gravity Model, POS, Retailer

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Authors: Gopika Rajagopal, Sudheesh Thiyyakkandi

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Back-to-back reinforced earth (RE) walls are extensively used in these days as bridge abutments and highway ramps, owing to their cost efficiency and ease of construction. High quality select fill is the most suitable backfill material due to its excellent engineering properties and constructability. However, industries are compelled to use low quality, locally available soil because of its ample availability on site. However, several failure cases of such walls are reported, especially subsequent to rainfall events. The stiffness of reinforcement is one of the major factors affecting the performance of RE walls. The present study focused on analyzing the effect of reinforcement stiffness on the performance of complete select fill, complete marginal fill, and hybrid-fill (i.e., combination of select and marginal fills) back-to-back RE walls, immediately after construction and upon rainwater infiltration through finite element modelling. A constant width to height (W/H) ratio of 3 and height (H) of 6 m was considered for the numerical analysis and the stiffness of reinforcement layers was varied from 500 kN/m to 10000 kN/m. Results showed that reinforcement stiffness had a noticeable influence on the response of RE wall, subsequent to construction as well as rainwater infiltration. Facing displacement was found to decrease and maximum reinforcement tension and factor of safety were observed to increase with increasing the stiffness of reinforcement. However, beyond a stiffness of 5000 kN/m, no significant reduction in facing displacement was observed. The behavior of fully marginal fill wall considered in this study was found to be reasonable even after rainwater infiltration when the high stiffness reinforcement layers are used.

Keywords: back-to-back reinforced earth wall, finite element modelling, rainwater infiltration, reinforcement stiffness

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Authors: Ahlad Neti, Elisa Arch, Martha Hall

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Studies have shown that individuals who use Ankle-Foot-Orthoses (AFOs) have a high level of dissatisfaction regarding their current AFOs. Studies point to the focus on technical design with little attention given to the user perspective as a source of AFO designs that leave users dissatisfied. To design a new AFO that satisfies users and thereby improves their quality of life, the reasons for their dissatisfaction and their wants and needs for an improved AFO design must be identified. There has been little research into the user perspective on AFO use and desired improvements, so the relationship between AFO design and satisfaction in daily use must be assessed to develop appropriate metrics and constraints prior to designing a novel AFO. To assess the user perspective on AFO design, structured interviews were conducted with 7 individuals (average age of 64.29±8.81 years) who use AFOs. All interviews were transcribed and coded to identify common themes using Grounded Theory Method in NVivo 12. Qualitative analysis of these results identified sources of user dissatisfaction such as heaviness, bulk, and uncomfortable material and overall needs and wants for an AFO. Beyond the user perspective, certain objective factors must be considered in the construction of metrics and constraints to ensure that the AFO fulfills its medical purpose. These more objective metrics are rooted in a common medical device market and technical standards. Given the large body of research concerning these standards, these objective metrics and constraints were derived through a literature review. Through these two methods, a comprehensive list of metrics and constraints accounting for both the user perspective on AFO design and the AFO’s medical purpose was compiled. These metrics and constraints will establish the framework for designing a new AFO that carries out its medical purpose while also improving the user experience. The metrics can be categorized into several overarching areas for AFO improvement. Categories of user perspective related metrics include comfort, discreteness, aesthetics, ease of use, and compatibility with clothing. Categories of medical purpose related metrics include biomechanical functionality, durability, and affordability. These metrics were used to guide an iterative prototyping process. Six concepts were ideated and compared using system-level analysis. From these six concepts, two concepts – the piano wire model and the segmented model – were selected to move forward into prototyping. Evaluation of non-functional prototypes of the piano wire and segmented models determined that the piano wire model better fulfilled the metrics by offering increased stability, longer durability, fewer points for failure, and a strong enough core component to allow a sock to cover over the AFO while maintaining the overall structure. As such, the piano wire AFO has moved forward into the functional prototyping phase, and healthy subject testing is being designed and recruited to conduct design validation and verification.

Keywords: ankle-foot orthotic, assistive technology, human centered design, medical devices

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Authors: M. A. Askari, M. A. Nazari, P. Perrier, Y. Payan

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Growth and remodeling of biological structures have gained lots of attention over the past decades. Determining the response of the living tissues to the mechanical loads is necessary for a wide range of developing fields such as, designing of prosthetics and optimized surgery operations. It is a well-known fact that biological structures are never stress-free, even when externally unloaded. The exact origin of these residual stresses is not clear, but theoretically growth and remodeling is one of the main sources. Extracting body organs from medical imaging, does not produce any information regarding the existing residual stresses in that organ. The simplest cause of such stresses is the gravity since an organ grows under its influence from its birth. Ignoring such residual stresses might cause erroneous results in numerical simulations. Accounting for residual stresses due to tissue growth can improve the accuracy of mechanical analysis results. In this paper, we have implemented a computational framework based on fixed-point iteration to determine the residual stresses due to growth. Using nonlinear continuum mechanics and the concept of fictitious configuration we find the unknown stress-free reference configuration which is necessary for mechanical analysis. To illustrate the method, we apply it to a finite element model of healthy human face whose geometry has been extracted from medical images. We have computed the distribution of residual stress in facial tissues, which can overcome the effect of gravity and cause that tissues remain firm. Tissue wrinkles caused by aging could be a consequence of decreasing residual stress and not counteracting the gravity. Considering these stresses has important application in maxillofacial surgery. It helps the surgeons to predict the changes after surgical operations and their consequences.

Keywords: growth, soft tissue, residual stress, finite element method

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Authors: David C. Ni

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Contemporary models for N-body systems are based on temporal, two-body, and mass point representation of Newtonian mechanics. Other mainstream models include 2D and 3D Ising models based on local neighborhood the lattice structures. In Quantum mechanics, the theories of collective modes are for superconductivity and for the long-range quantum entanglement. However, these models are still mainly for the specific phenomena with a set of designated parameters. We are therefore motivated to develop a new construction directly from the complex-variable N-body systems based on the extended Blaschke functions (EBF), which represent a non-temporal and nonlinear extension of Lorentz transformation on the complex plane – the normalized momentum spaces. A point on the complex plane represents a normalized state of particle momentums observed from a reference frame in the theory of special relativity. There are only two key parameters, normalized momentum and nonlinearity for modelling. An algorithm similar to Jenkins-Traub method is adopted for solving EBF iteratively. Through iteration, the solution sets show a form of σ + i [-t, t], where σ and t are the real numbers, and the [-t, t] shows various distributions, such as 1-peak, 2-peak, and 3-peak etc. distributions and some of them are analog to the canonical distributions. The results of the numerical analysis demonstrate continuum-to-discreteness transitions, evolutional invariance of distributions, phase transitions with conjugate symmetry, etc., which manifest the construction as a potential candidate for the unification of statistics. We hereby classify the observed distributions on the finite convergent domains. Continuous and discrete distributions both exist and are predictable for given partitions in different regions of parameter-pair. We further compare these distributions with canonical distributions and address the impacts on the existing applications.

Keywords: blaschke, lorentz transformation, complex variables, continuous, discrete, canonical, classification

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Authors: Javier Carroquino, Nieves García-Casarejos, Pilar Gargallo, F. Javier García-Ramos

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The energy used in agriculture is a source of global emissions of greenhouse gases. The two main types of this energy are electricity for pumping and diesel for agricultural machinery. In order to reduce these emissions, the European project LIFE REWIND addresses the supply of this demand from renewable sources. First of all, comprehensive data on energy demand and available renewable resources have been obtained in several case studies. Secondly, a set of simulations and optimizations have been performed, in search of the best configuration and sizing, both from an economic and emission reduction point of view. For this purpose, it was used software based on genetic algorithms. Thirdly, a prototype has been designed and installed, that it is being used for the validation in a real case. Finally, throughout a year of operation, various technical and economic parameters are being measured for further analysis. The prototype is not connected to the utility grid, avoiding the cost and environmental impact of a grid extension. The system includes three kinds of photovoltaic fields. One is located on a fixed structure on the terrain. Another one is floating on an irrigation raft. The last one is mounted on a two axis solar tracker. Each has its own solar inverter. The total amount of nominal power is 44 kW. A lead acid battery with 120 kWh of capacity carries out the energy storage. Three isolated inverters support a three phase, 400 V 50 Hz micro-grid, the same characteristics of the utility grid. An advanced control subsystem has been constructed, using free hardware and software. The electricity produced feeds a set of seven pumps used for purification, elevation and pressurization of water in a drip irrigation system located in a vineyard. Since the irrigation season does not include the whole year, as well as a small oversize of the generator, there is an amount of surplus energy. With this surplus, a hydrolyser produces on site hydrogen by electrolysis of water. An off-road vehicle with fuel cell feeds on that hydrogen and carries people in the vineyard. The only emission of the process is high purity water. On the one hand, the results show the technical and economic feasibility of stand-alone renewable energy systems to feed seasonal pumping. In this way, the economic costs, the environmental impacts and the landscape impacts of grid extensions are avoided. The use of diesel gensets and their associated emissions are also avoided. On the other hand, it is shown that it is possible to replace diesel in agricultural machinery, substituting it for electricity or hydrogen of 100% renewable origin and produced on the farm itself, without any external energy input. In addition, it is expected to obtain positive effects on the rural economy and employment, which will be quantified through interviews.

Keywords: drip irrigation, greenhouse gases, hydrogen, renewable energy, vineyard

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Authors: Didier Auroux, Vladimir Groza

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This work is part of STEEP Marie-Curie ITN project, and it focuses on the identification of unknown parameters of the proposed generic Abrasive WaterJet Milling (AWJM) PDE model, that appears as an ill-posed inverse problem. The necessity of studying this problem comes from the industrial milling applications where the possibility to predict and model the final surface with high accuracy is one of the primary tasks in the absence of any knowledge of the model parameters that should be used. In this framework, we propose the identification of model parameters by minimizing a cost function, measuring the difference between experimental and numerical solutions. The adjoint approach based on corresponding Lagrangian gives the opportunity to find out the unknowns of the AWJM model and their optimal values that could be used to reproduce the required trench profile. Due to the complexity of the nonlinear problem and a large number of model parameters, we use an automatic differentiation software tool (TAPENADE) for the adjoint computations. By adding noise to the artificial data, we show that in fact the parameter identification problem is highly unstable and strictly depends on input measurements. Regularization terms could be effectively used to deal with the presence of data noise and to improve the identification correctness. Based on this approach we present results in 2D and 3D of the identification of the model parameters and of the surface prediction both with self-generated data and measurements obtained from the real production. Considering different types of model and measurement errors allows us to obtain acceptable results for manufacturing and to expect the proper identification of unknowns. This approach also gives us the ability to distribute the research on more complex cases and consider different types of model and measurement errors as well as 3D time-dependent model with variations of the jet feed speed.

Keywords: Abrasive Waterjet Milling, inverse problem, model parameters identification, regularization

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Authors: Rukayya Ibrahim Muazu, Aiduan Li Borrion, Julia A. Stegemann

Abstract:

Biomass briquette gasification is regarded as a promising route for efficient briquette use in energy generation, fuels and other useful chemicals, however, previous research work has focused on briquette gasification in fixed bed gasifiers such as updraft and downdraft gasifiers. Fluidised bed gasifier has the potential to be effectively sized for medium or large scale. This study investigated the use of fuel briquettes produced from blends of rice husks and corn cobs biomass residues, in a bubbling fluidised bed gasifier. The study adopted a combination of numerical equations and Aspen Plus simulation software to predict the product gas (syngas) composition based on briquette's density and biomass composition (blend ratio of rice husks to corn cobs). The Aspen Plus model was based on an experimentally validated model from the literature. The results based on a briquette size of 32 mm diameter and relaxed density range of 500 to 650 kg/m3 indicated that fluidisation air required in the gasifier increased with an increase in briquette density, and the fluidisation air showed to be the controlling factor compared with the actual air required for gasification of the biomass briquettes. The mass flowrate of CO2 in the predicted syngas composition, increased with an increase in the air flow rate, while CO production decreased and H2 was almost constant. The H2/CO ratio for various blends of rice husks and corn cobs did not significantly change at the designed process air, but a significant difference of 1.0 for H2/CO ratio was observed at higher air flow rate, and between 10/90 to 90/10 blend ratio of rice husks to corn cobs. This implies the need for further understanding of biomass variability and hydrodynamic parameters on syngas composition in biomass briquette gasification.

Keywords: aspen plus, briquettes, fluidised bed, gasification, syngas

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: A. A. Purohit, M. M. Vaidya, M. D. Kudale

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Among the various developing countries in the world like China, Malaysia, Korea etc., India is also developing its infrastructures in the form of Road/Rail/Airports and Waterborne facilities at an exponential rate. Mumbai, the financial epicenter of India is overcrowded and to relieve the pressure of congestion, Navi Mumbai suburb is being developed on the east bank of Thane creek near Mumbai. The government due to limited space at existing Mumbai Airports (domestic and international) to cater for the future demand of airborne traffic, proposes to build a new international airport near Panvel at Navi Mumbai. Considering the precedence of extreme rainfall on 26^th July 2005 and nearby townships being in a low-lying area, wherein new airport is proposed, it is inevitable to study this complex confluence area from a hydrodynamic consideration under both tidal and extreme events (predicted discharge hydrographs), to avoid inundation of the surrounding due to the proposed airport reclamation (1160 hectares) and to determine the safe grade elevation (SGE). The model studies conducted using the application of unstructured mesh to simulate the Panvel estuarine area (93 km²), calibration, validation of a model for hydraulic field measurements and determine the maxima water levels around the airport for various extreme hydrodynamic events, namely the simultaneous occurrence of highest tide from the Arabian Sea and peak flood discharges (Probable Maximum Precipitation and 26^th July 2005) from five rivers, the Gadhi, Kalundri, Taloja, Kasadi and Ulwe, meeting at the proposed airport area revealed that: (a) The Ulwe River flowing beneath the proposed airport needs to be diverted. The 120m wide proposed Ulwe diversion channel having a wider base width of 200 m at SH-54 Bridge on the Ulwe River along with the removal of the existing bund in Moha Creek is inevitable to keep the SGE of the airport to a minimum. (b) The clear waterway of 80 m at SH-54 Bridge (Ulwe River) and 120 m at Amra Marg Bridge near Moha Creek is also essential for the Ulwe diversion and (c) The river bank protection works on the right bank of Gadhi River between the NH-4B and SH-54 bridges as well as upstream of the Ulwe River diversion channel are essential to avoid inundation of low lying areas. The maxima water levels predicted around the airport keeps SGE to a minimum of 11m with respect to Chart datum of Ulwe Bundar and thus development is not only technologically-economically feasible but also sustainable. The unstructured mesh modeling is a promising tool to simulate complex extreme hydrodynamic events and provides a reliable solution to evolve optimal SGE of airport.

Keywords: airport, hydrodynamics, safe grade elevation, tides

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Authors: Sohrab Jafarpour, Hamed Farokhi, Mohammad Rahmati, Alireza Gholipour

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In the current study, a nonlinear fluid-structure interaction (FSI) biomechanical model of atherosclerosis in the left anterior descending (LAD) coronary artery is developed to perform a detailed sensitivity analysis of the geometrical features of an atheroma plaque. In the development of the numerical model, first, a 3D geometry of the diseased artery is developed based on patient-specific dimensions obtained from the experimental studies. The geometry includes four influential geometric characteristics: stenosis ratio, plaque shoulder-length, fibrous cap thickness, and eccentricity intensity. Then, a suitable strain energy density function (SEDF) is proposed based on the detailed material stability analysis to accurately model the hyperelasticity of the arterial walls. The time-varying inlet velocity and outlet pressure profiles are adopted from experimental measurements to incorporate the pulsatile nature of the blood flow. In addition, a computationally efficient type of structural boundary condition is imposed on the arterial walls. Finally, a non-Newtonian viscosity model is implemented to model the shear-thinning behaviour of the blood flow. According to the results, the structural responses in terms of the maximum principal stress (MPS) are affected more compared to the fluid responses in terms of wall shear stress (WSS) as the geometrical characteristics are varying. The extent of these changes is critical in the vulnerability assessment of an atheroma plaque.

Keywords: atherosclerosis, fluid-Structure interaction modeling, material stability analysis, and nonlinear biomechanics

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Authors: Rony Tawk, Boutros Ghannam, Maroun Nemer

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Topology optimization technique presents a potential tool for the design and optimization of structures involved in mass and heat transfer. The method starts with an initial intermediate domain and should be able to progressively distribute the solid and the two fluids exchanging heat. The multi-objective function of the problem takes into account minimization of total pressure loss and maximization of heat transfer between solid and fluid subdomains. Existing methods account for the presence of only one fluid, while the actual work extends optimization distribution of solid and two different fluids. This requires to separate the channels of both fluids and to ensure a minimum solid thickness between them. This is done by adding a third objective function to the multi-objective optimization problem. This article uses density approach where each cell holds two local design parameters ranging from 0 to 1, where the combination of their extremums defines the presence of solid, cold fluid or hot fluid in this cell. Finite volume method is used for direct solver coupled with a discrete adjoint approach for sensitivity analysis and method of moving asymptotes for numerical optimization. Several examples are presented to show the ability of the method to find a trade-off between minimization of power dissipation and maximization of heat transfer while ensuring the separation and continuity of the channel of each fluid without crossing or mixing the fluids. The main conclusion is the possibility to find an optimal bi-fluid domain using topology optimization, defining a fluid to fluid heat exchanger device.

Keywords: topology optimization, density approach, bi-fluid domain, laminar steady state regime, fluid-to-fluid heat exchanger

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Authors: Nousheen Hashmi, Shoab Ahmad Khan

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Active collaboration among the green energy sources and the load demand leads to serious issues related to power quality and stability. The growing number of green energy resources and Distributed-Generators need newer strategies to be incorporated for their operations to keep the power energy stability among green energy resources and micro-grid/Utility Grid. This paper presents a novel technique for energy power management in Grid-Connected Photovoltaic with energy storage system under set of constraints including weather conditions, Load Shedding Hours, Peak pricing Hours by using rule-based fuzzy smart grid controller to schedule power coming from multiple Power sources (photovoltaic, grid, battery) under the above set of constraints. The technique fuzzifies all the inputs and establishes fuzzify rule set from fuzzy outputs before defuzzification. Simulations are run for 24 hours period and rule base power scheduler is developed. The proposed fuzzy controller control strategy is able to sense the continuous fluctuations in Photovoltaic power generation, Load Demands, Grid (load Shedding patterns) and Battery State of Charge in order to make correct and quick decisions.The suggested Fuzzy Rule-based scheduler can operate well with vague inputs thus doesn’t not require any exact numerical model and can handle nonlinearity. This technique provides a framework for the extension to handle multiple special cases for optimized working of the system.

Keywords: photovoltaic, power, fuzzy logic, distributed generators, state of charge, load shedding, membership functions

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Authors: Tomohiko Utsuki, Kyoka Sato

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In emergency medicine, it is recognized that brain resuscitation is very important for the reduction of mortality rate and neurological sequelae. Especially, the control of brain temperature (BT), intracranial pressure (ICP), and cerebral blood flow (CBF) are most required for stabilizing brain’s physiological state in the treatment for such as brain injury, stroke, and encephalopathy. However, the manual control of BT, ICP, and CBF frequently requires the decision and operation of medical staff, relevant to medication and the setting of therapeutic apparatus. Thus, the integration and the automation of the control of those is very effective for not only improving therapeutic effect but also reducing staff burden and medical cost. For realizing such integration and automation, a mathematical model of brain physiological state is necessary as the controlled object in simulations, because the performance test of a prototype of the control system using patients is not ethically allowed. A model of cerebral blood circulation has already been constructed, which is the most basic part of brain physiological state. Also, a migration model of extracellular fluid in brain has been constructed, however the condition that the total volume of intracranial cavity is almost changeless due to the hardness of cranial bone has not been considered in that model. Therefore, in this research, the dynamic migration model of extracellular fluid in brain was constructed on the consideration of the changelessness of intracranial cavity’s total volume. This model is connectable to the cerebral blood circulation model. The constructed model consists of fourteen compartments, twelve of which corresponds to perfused area of bilateral anterior, middle and posterior cerebral arteries, the others corresponds to cerebral ventricles and subarachnoid space. This model enable to calculate the migration of tissue fluid from capillaries to gray matter and white matter, the flow of tissue fluid between compartments, the production and absorption of cerebrospinal fluid at choroid plexus and arachnoid granulation, and the production of metabolic water. Further, the volume, the colloid concentration, and the tissue pressure of/in each compartment are also calculable by solving 40-dimensional non-linear simultaneous differential equations. In this research, the obtained model was analyzed for its validation under the four condition of a normal adult, an adult with higher cerebral capillary pressure, an adult with lower cerebral capillary pressure, and an adult with lower colloid concentration in cerebral capillary. In the result, calculated fluid flow, tissue volume, colloid concentration, and tissue pressure were all converged to suitable value for the set condition within 60 minutes at a maximum. Also, because these results were not conflict with prior knowledge, it is certain that the model can enough represent physiological state of brain under such limited conditions at least. One of next challenges is to integrate this model and the already constructed cerebral blood circulation model. This modification enable to simulate CBF and ICP more precisely due to calculating the effect of blood pressure change to extracellular fluid migration and that of ICP change to CBF.

Keywords: dynamic model, cerebral extracellular migration, brain resuscitation, automatic control

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Authors: Wen Luo, Phil J. Vardon, Anne-Catherine Dieudonne

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One key process that partly controls the success of geothermal projects is fluid reinjection, which benefits in dealing with waste water, maintaining reservoir pressure, and supplying heat-exchange media, etc. Thus, sustaining the injectivity is of great importance for the efficiency and sustainability of geothermal production. However, the injectivity is sensitive to the reinjection process. Field experiences have illustrated that the injectivity can be damaged or improved. In this paper, the focus is on how the injectivity is improved. Since the injection pressure is far below the formation fracture pressure, hydraulic fracturing cannot be the mechanism contributing to the increase in injectivity. Instead, thermal stimulation has been identified as the main contributor to improving the injectivity. For low-enthalpy geothermal reservoirs, which are not fracture-controlled, thermal fracturing, instead of thermal shearing, is expected to be the mechanism for increasing injectivity. In this paper, field data from the sedimentary low-enthalpy geothermal reservoirs in the Netherlands were analysed to show the occurrence of thermal fracturing due to the cooling shock during reinjection. Injection data were collected and compared to show the effects of the thermal fractures on injectivity. Then, a thermo-hydro-mechanical (THM) model for the near field formation was developed and solved by finite element method to simulate the observed thermal fractures. It was then compared with the HM model, decomposed from the THM model, to illustrate the thermal effects on thermal fracturing. Finally, the effects of operational parameters, i.e. injection temperature and pressure, on the changes in injectivity were studied on the basis of the THM model. The field data analysis and simulation results illustrate that the thermal fracturing occurred during reinjection and contributed to the increase in injectivity. The injection temperature was identified as a key parameter that contributes to thermal fracturing.

Keywords: injectivity, reinjection, thermal fracturing, thermo-hydro-mechanical model

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Authors: Amir Abbas Fatemi, Zahra Tabrizian, Kabir Sadeghi

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To find the location and severity of damage that occurs in a structure, characteristics changes in dynamic and static can be used. The non-destructive techniques are more common, economic, and reliable to detect the global or local damages in structures. This paper presents a non-destructive method in structural damage detection and assessment using GA and static data. Thus, a set of static forces is applied to some of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. An analytical model of the truss structure is developed based on the available specification and the properties derived from static data. The damages in structure produce changes to its stiffness so this method used to determine damage based on change in the structural stiffness parameter. Changes in the static response which structural damage caused choose to produce some simultaneous equations. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the static load vector of damaged and healthy structure. Several scenarios defined for damage detection (single scenario and multiple scenarios). The static damage identification methods have many advantages, but some difficulties still exist. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. This strategy is applied to a plane truss. This method is used for a plane truss. Numerical results demonstrate the ability of this method in detecting damage in given structures. Also figures show damage detections in multiple damage scenarios have really efficient answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.

Keywords: damage detection, finite element method, static data, non-destructive, genetic algorithm

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Authors: Vishnu A., Ashesh Saha

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The operation of high-speed rotating machinery in industries is accompanied by rotor vibrations due to many factors. One of the primary instability mechanisms in a rotor system is the centrifugal force induced due to the eccentricity of the center of mass away from the center of rotation. These unwanted vibrations may lead to catastrophic fatigue failure. So, there is a need to control these rotor vibrations. In this work, control of rotor vibrations by using a 4-pole Radial Active Magnetic Bearing (RAMB) as an actuator is analysed. A continuous rotor system model is considered for the analysis. Several important factors, like the gyroscopic effect and rotary inertia of the shaft and disc, are incorporated into this model. The large deflection of the shaft and the restriction to axial motion of the shaft at the bearings result in nonlinearities in the system governing equation. The rotor system is modeled in such a way that the system dynamics can be related to the geometric and material properties of the shaft and disc. The mathematical model of the rotor system is developed by incorporating the control forces generated by the RAMB. A simple PD controller is used for the attenuation of system vibrations. An analytical expression for the amplitude and phase equations is derived using the Method of Multiple Scales (MMS). Analytical results are verified with the numerical results obtained using an ‘ode’ solver in-built into MATLAB Software. The control force is found to be effective in attenuating the system vibrations. The multi-valued solutions leading to the jump phenomenon are also eliminated with a proper choice of control gains. Most interestingly, the shape of the backbone curves can also be altered for certain values of control parameters.

Keywords: rotor dynamics, continuous rotor system model, active magnetic bearing, PD controller, method of multiple scales, backbone curve

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Authors: Feyza Şahi̇n, Harun Deni̇zli̇, Mustafa Zabun, Hüseyi̇n OnbaşIoğli

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Heat exchangers are devices that are widely used to transfer heat between fluids due to their temperature differences. As a type of heat exchanger, air coolers are heat exchangers that cool the air as it passes through the fins of the heat exchanger by transferring heat to the refrigerant in the coil tubes of the heat exchanger. An assembled fin and tube heat exchanger consists of a coil block and a casing with a fan mounted on it. The term “Fan hood” is used to define the distance between the fan and the coil block. Air coolers play a crucial role in cooling systems, and their heat transfer performance can vary depending on design parameters. These parameters can be related to the air side or the internal fluid side. For airside efficiency, the distance between the fan and the coil block affects the performance by creating dead zones at the corners of the casing and maldistribution of airflow. Therefore, a detailed study of the effect of the fan hood on the evaporator and the optimum fan hood distance is necessary for an efficient air cooler design. This study aims to investigate the value of the fan hood in a fin and tube-type air cooler heat exchanger through computational fluid dynamics (CFD) simulations and experimental investigations. CFD simulations will be used to study the airflow within the fan hood. These simulations will provide valuable insights to optimize the design of the fan hood. In addition, experimental tests will be carried out to validate the CFD results and to measure the performance of the fan hood under real conditions. The results will help us to understand the effect of fan hood design on evaporator efficiency and contribute to the development of more efficient cooling systems. This study will provide essential information for evaporator design and improving the energy efficiency of cooling systems.

Keywords: heat exchanger, fan hood, heat exchanger performance, air flow performance

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Authors: Muhammad Naveed Tahir, Wang Yingkuan, Huang Wenjiang, Raheel Osman

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Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time.

Keywords: ANN, chlorophyll content, chlorophyll indices, satellite images, wheat

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Authors: Mohammadreza Khanmohammadi, Noureddin Nakhostin Ansari, Soofia Naghdi

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Purpose: Our study aimed to validate single item presenteeism question (SIPQ) into the Persian language for patients with low back pain. Background information: low back pain is a common health problem, and it is one of the most prevalent disorder in working people. There are the different subjective way to evaluate the effect of back pain on work productivity that one of them is by implementing single item presenteeism question. This question has not been validated into the Persian language. Method: Patients were asked to answer SIPQ and pain from 0 to 10 according to numerical rating scale (NRS). The functional rating index was administrated to evaluate construct validity. For test-retest reliability, almost 50 patients re-completed the Persian SIPQ. The construct validity of SIPQ was assessed by analyzing Spearman rank correlation between this question and the Persian version of Functional rating index questionnaire. To analyze test-retest reliability, we assessed intraclass correlation coefficient (agreement) (ICC agreement) (two-way random effects model, single measure). Results: The SIPQ score of two groups of patients (84 males, 16 females, mean age ±SD: 33.85±11.16 years, range: 19-67 years) and healthy subjects (48 male, 2 female ones, mean age ±SD: 24.24 ±8.07 years) was statistically significant. (Mann-Whitney U =198.00, P<.001). The Spearman correlation of data showed that there is a significant correlation between Persian SIPQ score and Persian FRI band (r= .559, P<.001). The ICC was .62. So, the analysis indicated good, test-retest reliability. Conclusion: This study showed that Persian version of SIPQ is reliable and valid when applied to back pain patients.

Keywords: cross cultural adaptation, economic burden, low back pain, Persian language, translation

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Authors: G. De Matteis, L. Martina, V. Turco

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The work is focused on determining and comparing special nonlinear static configurations in cholesteric liquid crystals (CLCs), confined between two parallel plates and in the presence of an external static electric/magnetic field. The solutions are stabilised by topological and non-topological conservation laws since they are described in terms of integrable or partially integrable nonlinear boundary value problems. In cholesteric liquid crystals which are subject to geometric frustration; anchoring conditions at boundaries, i.e., homeotropic conditions, are incompatible with the cholesteric twist. This aspect turns out to be essential in the admissible classes of solutions, allowing also for disclination type singularities. Within the framework of Frank-Oseen theory, we study the static configurations for CLCs. First, we find numerical solutions for isolated axisymmetric states in confined CLCs with weak homeotropic anchoring at the boundaries. These solutions describe 3-dimensional modulations, namely spherulites or cholesteric bubbles, actually observed in these systems, of standard baby skyrmions. Relations with well-known nonlinear integrable systems are found and are used to explore the asymptotic behavior of the solutions. Then we turn our attention to extended periodic static configurations called Helicoids or cholesteric fingers, described by an elliptic sine-Gordon model with appropriate boundary conditions, showing how their period and energies are determined by both the thickness of the cell and the intensity of the external electric/magnetic field. We explicitly show that helicoids with π or 2π of rotations of the molecular director are different in many aspects and are not simply algebraically related. The behaviour of the solutions, their energy and the properties of the associated disclinations are discussed in detail, both analytically and numerically.

Keywords: cholesteric liquid crystals, geometric frustration, helicoids, skyrmions

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