Search results for: CNN architectures

Authors: Yu-Cheng Chen, Tzu-Ping Lin, Feng-Yi Lin, Chih-Yu Chen

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Due to the urban heat island effect caused by highly development of city, the heat stress increased in recent year rapidly. Resulting in a sharp raise of the energy used in urban area. The heat stress during summer time exacerbated the usage of air conditioning and electric equipment, which caused more energy consumption and anthropogenic heat. Therefore, an accurate and simple method to measure energy used in urban area can be helpful for the architectures and urban planners to develop better energy efficiency goals. This research applies the combination of airborne LiDAR data and thermal imager to provide an innovate method to estimate energy consumption. Owing to the high resolution of remote sensing data, the accurate current volume and total floor area and the surface temperature of building derived from LiDAR and thermal imager can be herein obtained to predict energy used. In the estimate process, the LiDAR data will be divided into four type of land cover which including building, road, vegetation, and other obstacles. In this study, the points belong to building were selected to overlay with the land use information; therefore, the energy consumption can be estimated precisely with the real value of total floor area and energy use index for different use of building. After validating with the real energy used data from the government, the result shows the higher building in high development area like commercial district will present in higher energy consumption, caused by the large quantity of total floor area and more anthropogenic heat. Furthermore, because of the surface temperature can be warm up by electric equipment used, this study also applies the thermal image of building to find the hot spots of energy used and make the estimation method more complete.

Keywords: urban heat island, urban planning, LiDAR, thermal imager, energy consumption

Procedia PDF Downloads 218

Authors: Efe Çukur

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This study aims to discuss and compare i) widespread and generic design, particularly industrial design education in relation to the specific needs of the automotive/transportation industry, and ii) an automotive/transportation design education model within and under to provide the conditions of design education and automotive industry, especially in Turkey and T.R.N.C. The automotive industry is the 11th largest in the world ($1.51 trillion). One of the most important departments in this industry, along with sales, marketing and engineering, is the design department. The automotive industry is known as the locomotive industry, but there is a non-automotive design department on the academic side of Turkey. This suggestion; includes the presentation of a program proposal that meets the needs of the industry for Turkey and T.R.N.C., the second largest automobile manufacturing country in Europe. On the education side, industrial design education has become a generic title. Automotive design studios are divided into several subgroups. Even in the higher graduate education, the automotive design departments get their subgroups like exterior design and interior design. Transportation design, which is a subfield of industrial design, is offered as higher education in transportation design departments, particularly in America and Europe. In these departments, the curriculum is shaped to the needs of the sectors. Higher education transportation design programs began in the mid-20th century. Until those high education programs...Until these high education programs, the industry has adapted architectures and engineers for designer workloads. Still today transportation design graduates are not the majority of the design studios. The content of the study is an in-depth comparison of these institutions and how the requirements, demands of the industry are met in this regard and revealed. Some of the institutions are selected from Europe and US. To be analyzed under the headings of staff, courses, syllabus, University-Industry collaboration, and location selection. The study includes short, mid, and long term proposals and a hypothesis for discussion. In short, the study will not only provide a wide comparative scope of information on generic and specialized aspects of design education in different countries but also propose a higher education model for automotive / transportation design with solid data of requirements, methodology, and structure regarding learning outcomes, and especially industry cooperation.

Keywords: design education, automotive - transportation design programs, transportation design, automotive industry in Turkey /T.R.N.C., automotive design education in Turkey /T.R.N.C.

Procedia PDF Downloads 65

Authors: Gregor Kosec

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Simple and robust numerical approach for solving flow problems is presented, where involved physical fields are represented through the local approximation functions, i.e., the considered field is approximated over a local support domain. The approximation functions are then used to evaluate the partial differential operators. The type of approximation, the size of support domain, and the type and number of basis function can be general. The solution procedure is formulated completely through local computational operations. Besides local numerical method also the pressure velocity is performed locally with retaining the correct temporal transient. The complete locality of the introduced numerical scheme has several beneficial effects. One of the most attractive is the simplicity since it could be understood as a generalized Finite Differences Method, however, much more powerful. Presented methodology offers many possibilities for treating challenging cases, e.g. nodal adaptivity to address regions with sharp discontinuities or p-adaptivity to treat obscure anomalies in physical field. The stability versus computation complexity and accuracy can be regulated by changing number of support nodes, etc. All these features can be controlled on the fly during the simulation. The presented methodology is relatively simple to understand and implement, which makes it potentially powerful tool for engineering simulations. Besides simplicity and straightforward implementation, there are many opportunities to fully exploit modern computer architectures through different parallel computing strategies. The performance of the method is presented on the lid driven cavity problem, backward facing step problem, de Vahl Davis natural convection test, extended also to low Prandtl fluid and Darcy porous flow. Results are presented in terms of velocity profiles, convergence plots, and stability analyses. Results of all cases are also compared against published data.

Keywords: fluid flow, meshless, low Pr problem, natural convection

Procedia PDF Downloads 209

Authors: Monish Sengupta, Benjamin Heydecker, Daniel Woodland

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New train control systems rely mainly on Automatic Train Protection (ATP) and Automatic Train Operation (ATO) dynamically to control the speed and hence performance. The ATP and the ATO form the vital element within the CBTC (Communication Based Train Control) and within the ERTMS (European Rail Traffic Management System) system architectures. Reliable and accurate measurement of train location, speed and acceleration are vital to the operation of train control systems. In the past, all CBTC and ERTMS system have deployed a balise or equivalent to correct the uncertainty element of the train location. Typically a CBTC train is allowed to miss only one balise on the track, after which the Automatic Train Protection (ATP) system applies emergency brake to halt the service. This is because the location uncertainty, which grows within the train control system, cannot tolerate missing more than one balise. Balises contribute a significant amount towards wayside maintenance and studies have shown that balises on the track also forms a constraint for future track layout change and change in speed profile.This paper investigates the causes of the location uncertainty that is currently experienced and considers whether it is possible to identify an effective filter to ascertain, in conjunction with appropriate sensors, more accurate speed, distance and location for a CBTC driven train without the need of any external balises. An appropriate sensor fusion algorithm and intelligent sensor selection methodology will be deployed to ascertain the railway location and speed measurement at its highest precision. Similar techniques are already in use in aviation, satellite, submarine and other navigation systems. Developing a model for the speed control and the use of Kalman filter is a key element in this research. This paper will summarize the research undertaken and its significant findings, highlighting the potential for introducing alternative approaches to train positioning that would enable removal of all trackside location correction balises, leading to huge reduction in maintenances and more flexibility in future track design.

Keywords: ERTMS, CBTC, ATP, ATO

Procedia PDF Downloads 386

Authors: P. Caimmi, E. Bele, A. Abolfathi

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Lattice materials are cellular structures composed of a periodic network of beams. They offer high weight-specific mechanical properties and lend themselves to numerous weight-sensitive applications. The periodic internal structure responds to external vibrations through characteristic frequency bandgaps, making these materials suitable for the reduction of noise and vibration. However, the deviation from architectural homogeneity, due to, e.g., manufacturing imperfections, has a strong influence on the mechanical properties and vibration response of these materials. In this work, we present results on the influence of geometric imperfections on the vibration response of hexagonal lattices. Three classes of geometrical variables are used: the characteristics of the architecture (relative density, ligament length/cell size ratio), imperfection type (degree of non-periodicity, cracks, hard inclusions) and defect morphology (size, distribution). Test specimens with controlled size and distribution of imperfections are manufactured through selective laser sintering. The Frequency Response Functions (FRFs) in the form of accelerance are measured, and the modal shapes are captured through a high-speed camera. The finite element method is used to provide insights on the extension of these results to semi-infinite lattices. An updating procedure is conducted to increase the reliability of numerical simulation results compared to experimental measurements. This is achieved by updating the boundary conditions and material stiffness. Variations in FRFs of periodic structures due to changes in the relative density of the constituent unit cell are analysed. The effects of geometric imperfections on the dynamic response of periodic structures are investigated. The findings can be used to open up the opportunity for tailoring these lattice materials to achieve optimal amplitude attenuations at specific frequency ranges.

Keywords: lattice architectures, geometric imperfections, vibration attenuation, experimental modal analysis

Procedia PDF Downloads 99

Authors: Sonam Chorol, Sharvan Kumar, Pritam Mukhopadhyay

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In biology, supramolecular systems require the use of chemical fuels to stay in sustained nonequilibrium steady states termed dissipative self-assembly in contrast to synthetic self-assembly. Biomimicking these natural dynamic systems, some studies have demonstrated artificial self-assembly under nonequilibrium utilizing various forms of energies (fuel) such as chemical, redox, and pH. Naphthalene diimides (NDIs) are well-known organic molecules in supramolecular architectures with high electron affinity and have applications in controlled electron transfer (ET) reactions, etc. Herein, we report the endergonic ET from tetraphenylborate to highly electron-deficient phosphonium NDI²+ dication to generate NDI•+ radical. The formation of radicals was confirmed by UV-Vis-NIR absorption spectroscopy. Electron-donor and electron-acceptor energy levels were calculated from experimental electrochemistry and theoretical DFT analysis. The HOMO of the electron donor locates below the LUMO of the electro-acceptor. This indicates that electron transfer is endergonic (ΔE°ET = negative). The endergonic ET from NaBPh₄ to NDI²+ dication was achieved thermodynamically by the formation of coupled biphenyl product confirmed by GC-MS analysis. NDI molecule bearing octyl phosphonium at the core and H-bond forming imide moieties at the axial position forms a gel. The rheological properties of purified radical ion NDI⦁+ gels were evaluated. The atomic force microscopy studies reveal the formation of large branching-type networks with a maximum height of 70-80 nm. The endergonic ET from NaBPh₄ to NDI²+ dication was used to design the assembly and disassembly redox reaction cycle using reducing (NaBPh₄) and oxidizing agents (Br₂) as chemical fuels. A part of NaBPh₄ is used to drive assembly, while a fraction of the NaBPh₄ is dissipated by forming a useful product. The system goes back to the disassembled NDI²+ dication state with the addition of Br₂. We think bioinspired dissipative self-assembly is the best approach to developing future lifelike materials with autonomous behavior.

Keywords: Ionic-gel, redox-cycle, self-assembly, useful product

Procedia PDF Downloads 47

Authors: Youtao Gao, Bingyu Jin, Tanran Zhao, Bo Xu

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The history of satellite navigation can be dated back to the 1960s. From the U.S. Transit system and the Russian Tsikada system to the modern Global Positioning System (GPS) and the Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS), performance of satellite navigation has been greatly improved. Nowadays, the navigation accuracy and coverage of these existing systems have already fully fulfilled the requirement of near-Earth users, but these systems are still beyond the reach of deep space targets. Due to the renewed interest in space exploration, a novel high-precision satellite navigation system is becoming even more important. The increasing demand for such a deep space navigation system has contributed to the emergence of a variety of new constellation architectures, such as the Lunar Global Positioning System. Apart from a Walker constellation which is similar to the one adopted by GPS on Earth, a novel constellation architecture which consists of libration point satellites in the Earth-Moon system is also available to construct the lunar navigation system, which can be called accordingly, the libration point satellite navigation system. The concept of using Earth-Moon libration point satellites for lunar navigation was first proposed by Farquhar and then followed by many other researchers. Moreover, due to the special characteristics of Libration point orbits, an autonomous orbit determination technique, which is called ‘Liaison navigation’, can be adopted by the libration point satellites. Using only scalar satellite-to-satellite tracking data, both the orbits of the user and libration point satellites can be determined autonomously. In this way, the extensive Earth-based tracking measurement can be eliminated, and an autonomous satellite navigation system can be developed for future space exploration missions. The method of state estimate is an unnegligible factor which impacts on the orbit determination accuracy besides type of orbit, initial state accuracy and measurement accuracy. We apply the extended Kalman filter(EKF) and the unscented Kalman filter(UKF) to determinate the orbits of Lagrangian navigation satellites. The autonomous orbit determination errors are compared. The simulation results illustrate that UKF can improve the accuracy and z-axis convergence to some extent.

Keywords: extended Kalman filter, autonomous orbit determination, unscented Kalman filter, navigation constellation

Procedia PDF Downloads 260

Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

Procedia PDF Downloads 73

Authors: Ahmed G. Mahgoub, Dahlia H. Hafez, Mostafa A. Abu Kiefa

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The Cone Penetration Test (CPT) is a common in-situ test which generally investigates a much greater volume of soil more quickly than possible from sampling and laboratory tests. Therefore, it has the potential to realize both cost savings and assessment of soil properties rapidly and continuously. The principle objective of this paper is to demonstrate the feasibility and efficiency of using artificial neural networks (ANNs) to predict the soil angle of internal friction (Φ) and the soil modulus of elasticity (E) from CPT results considering the uncertainties and non-linearities of the soil. In addition, ANNs are used to study the influence of different parameters and recommend which parameters should be included as input parameters to improve the prediction. Neural networks discover relationships in the input data sets through the iterative presentation of the data and intrinsic mapping characteristics of neural topologies. General Regression Neural Network (GRNN) is one of the powerful neural network architectures which is utilized in this study. A large amount of field and experimental data including CPT results, plate load tests, direct shear box, grain size distribution and calculated data of overburden pressure was obtained from a large project in the United Arab Emirates. This data was used for the training and the validation of the neural network. A comparison was made between the obtained results from the ANN's approach, and some common traditional correlations that predict Φ and E from CPT results with respect to the actual results of the collected data. The results show that the ANN is a very powerful tool. Very good agreement was obtained between estimated results from ANN and actual measured results with comparison to other correlations available in the literature. The study recommends some easily available parameters that should be included in the estimation of the soil properties to improve the prediction models. It is shown that the use of friction ration in the estimation of Φ and the use of fines content in the estimation of E considerable improve the prediction models.

Keywords: angle of internal friction, cone penetrating test, general regression neural network, soil modulus of elasticity

Procedia PDF Downloads 394

Authors: C. Busuioc, I. Stancu, A. Nicoara, A. Zamfirescu, A. Evanghelidis

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The field of tissue engineering has expanded its potential due to the use of composite biomaterials belonging to increasingly complex systems, leading to bone substitutes with properties that are continuously improving to meet the patient's specific needs. Furthermore, the development of biomaterials based on ceramic and polymeric phases is an unlimited resource for future scientific research, with the final aim of restoring the original tissue functionality. Thus, in the first stage, composite scaffolds based on polycaprolactone (PCL) or polylactic acid (PLA) and inorganic powders were prepared by employing the electrospinning technique. The targeted powders were: commercial and laboratory synthesized hydroxyapatite (HAp), as well as barium titanate (BT). By controlling the concentration of the powder within the precursor solution, together with the processing parameters, different types of three-dimensional architectures were achieved. In the second stage, both the mineral powders and hybrid composites were investigated in terms of composition, crystalline structure, and microstructure so that to demonstrate their suitability for tissue engineering applications. Regarding the scaffolds, these were proven to be homogeneous on large areas and loaded with mineral particles in different proportions. The biological assays demonstrated that the addition of inorganic powders leads to modified responses in the presence of simulated body fluid (SBF) or cell cultures. Through SBF immersion, the biodegradability coupled with bioactivity were highlighted, with fiber fragmentation and surface degradation, as well as apatite layer formation within the testing period. Moreover, the final composites represent supports accepted by the cells, favoring implant integration. Concluding, the purposed fibrous materials based on bioresorbable polymers and mineral powders, produced by the electrospinning technique, represent candidates with considerable potential in the field of tissue engineering. Future improvements can be attained by optimizing the synthesis process or by simultaneous incorporation of multiple inorganic phases with well-defined biological action in order to fabricate multifunctional composites.

Keywords: barium titanate, electrospinning, fibre networks, hydroxyapatite, smart scaffolds

Procedia PDF Downloads 83

Authors: Santiago Silva de Andrade

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In recent decades, Marx's ecological theory of the value-form and the theory of metabolic rift have represented fundamental methodological innovations for social scientists interested in environmental transformations and their relationships with the development of the capital system. However, among Latin American environmental historians, such theoretical and methodological instruments have been used infrequently and very cautiously. This investigation aims to demonstrate how the concepts of metabolic rift and ecological value-form are important for understanding the environmental, economic and social transformations in the Amazon region between the second half of the 19th century and the end of the 20th century. Such transformations manifested themselves mainly in two dimensions: the first concerns the link between the manufacture of tropical substances for export and scientific developments in the fields of botany, chemistry and agriculture. This link was constituted as a set of social, intellectual and economic relations that condition each other, configuring an asymmetrical field of exchanges and connections between the demands of the industrialized world - personified in scientists, naturalists, businesspeople and bureaucrats - and the agencies of local social actors, such as indigenous people, riverside dwellers and quilombolas; the second dimension concerns the imperative link between the historical development of the capitalist world-system and the restructuring of the natural world, its landscapes, biomes and social relations, notably in peripheral colonial areas. The environmental effects of capitalist globalization were not only seen in the degradation of exploited environments, although this has been, until today, its most immediate and noticeable aspect. There was also, in territories subject to the logic of market accumulation, the reformulation of patterns of authority and institutional architectures, such as property systems, political jurisdictions, rights and social contracts, as a result of the expansion of commodity frontiers between the 16th and 21st centuries. . This entire set of transformations produced impacts on the ecological landscape of the Amazon. This demonstrates the need to investigate the histories of local configurations of power, spatial and ecological - with their institutions and social actors - and their role in structuring the capitalist world-system , under the lens of the ecological theory of value-form and metabolic rift.

Keywords: amazon, ecology, form-value, metabolic rift

Procedia PDF Downloads 35

Authors: Nan Hu, Chao Wang, Xi Li, Xuehai Zhou

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The field-programmable gate array (FPGA) has been widely adopted in the high-performance computing domain. In recent years, the embedded system-on-a-chip (SoC) contains coarse granularity multi-core CPU (central processing unit) and mobile GPU (graphics processing unit) that can be used as general-purpose accelerators. The motivation is that algorithms of various parallel characteristics can be efficiently mapped to the heterogeneous architecture coupled with these three processors. The CPU and GPU offload partial computationally intensive tasks from the FPGA to reduce the resource consumption and lower the overall cost of the system. However, in present common scenarios, the applications always utilize only one type of accelerator because the development approach supporting the collaboration of the heterogeneous processors faces challenges. Therefore, a systematic approach takes advantage of write-once-run-anywhere portability, high execution performance of the modules mapped to various architectures and facilitates the exploration of design space. In this paper, A servant-execution-flow model is proposed for the abstraction of the cooperation of the heterogeneous processors, which supports task partition, communication and synchronization. At its first run, the intermediate language represented by the data flow diagram can generate the executable code of the target processor or can be converted into high-level programming languages. The instantiation parameters efficiently control the relationship between the modules and computational units, including two hierarchical processing units mapping and adjustment of data-level parallelism. An embedded system of a three-dimensional waveform oscilloscope is selected as a case study. The performance of algorithms such as contrast stretching, etc., are analyzed with implementations on various combinations of these processors. The experimental results show that the heterogeneous computing system with less than 35% resources achieves similar performance to the pure FPGA and approximate energy efficiency.

Keywords: FPGA-CPU-GPU collaboration, design space exploration, heterogeneous computing, intermediate language, parameterized instantiation

Procedia PDF Downloads 80

Authors: Nalla Somaiah, Praveen Kumar

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Scaling of transistors and, hence, interconnects is very important for the enhanced performance of microelectronic devices. Scaling of devices creates significant complexity, especially in the multilevel interconnect architectures, wherein current crowding occurs at the corners of interconnects. Such a current crowding creates hot-spots at the respective corners, resulting in non-uniform temperature distribution in the interconnect as well. This non-uniform temperature distribution, which is exuberated with continued scaling of devices, creates a temperature gradient in the interconnect. In particular, the increased current density at corners and the associated temperature rise due to Joule heating accelerate the electromigration induced failures in interconnects, especially at corners. This has been the classic reliability issue associated with metallic interconnects. Herein, it is generally understood that electromigration induced damages can be avoided if the length of interconnect is smaller than a critical length, often termed as Blech length. Interestingly, the effect of non-negligible temperature gradients generated at these corners in terms of thermomigration and electromigration-thermomigration coupling has not attracted enough attention. Accordingly, in this work, the interplay between the electromigration and temperature gradient induced mass transport was studied using standard Blech structure. In this particular sample structure, the majority of the current is forcefully directed into the low resistivity metallic film from a high resistivity underlayer film, resulting in current crowding at the edges of the metallic film. In this study, 150 nm thick Cu metallic film was deposited on 30 nm thick W underlayer film in the configuration of Blech structure. Series of Cu thin strips, with lengths of 10, 20, 50, 100, 150 and 200 μm, were fabricated. Current density of ≈ 4 × 1010 A/m² was passed through Cu and W films at a temperature of 250ºC. Herein, along with expected forward migration of Cu atoms from the cathode to the anode at the cathode end of the Cu film, backward migration from the anode towards the center of Cu film was also observed. Interestingly, smaller length samples consistently showed enhanced migration at the cathode end, thus indicating the existence of inverse Blech length effect in presence of temperature gradient. A finite element based model showing the interplay between electromigration and thermomigration driving forces has been developed to explain this observation.

Keywords: Blech structure, electromigration, temperature gradient, thin films

Procedia PDF Downloads 232

Authors: Syed Irfan Raza Naqvi, Zheng Jiangbin, Ahmad Moshin, Pervez Akhter

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Currently, the IoT system depends on a centralized client-servant architecture that causes various scalability and privacy vulnerabilities. Distributed ledger technology (DLT) introduces a set of opportunities for the IoT, which leads to practical ideas for existing components at all levels of existing architectures. Blockchain Technology (BCT) appears to be one approach to solving several IoT problems, like Bitcoin (BTC) and Ethereum, which offer multiple possibilities. Besides, IoTs are resource-constrained devices with insufficient capacity and computational overhead to process blockchain consensus mechanisms; the traditional BCT existing challenge for IoTs is poor scalability, energy efficiency, and transaction fees. IOTA is a distributed ledger based on Direct Acyclic Graph (DAG) that ensures M2M micro-transactions are free of charge. IOTA has the potential to address existing IoT-related difficulties such as infrastructure scalability, privacy and access control mechanisms. We proposed an architecture, SLDBI: A Scalable, lightweight DAG-based Blockchain Design for Intelligent IoT Systems, which adapts the DAG base Tangle and implements a lightweight message data model to address the IoT limitations. It enables the smooth integration of new IoT devices into a variety of apps. SLDBI enables comprehensive access control, energy efficiency, and scalability in IoT ecosystems by utilizing the Masked Authentication Message (MAM) protocol and the IOTA Smart Contract Protocol (ISCP). Furthermore, we suggest proof-of-work (PoW) computation on the full node in an energy-efficient way. Experiments have been carried out to show the capability of a tangle to achieve better scalability while maintaining energy efficiency. The findings show user access control management at granularity levels and ensure scale up to massive networks with thousands of IoT nodes, such as Smart Connected Buildings (SCBDs).

Keywords: blockchain, IOT, direct acyclic graphy, scalability, access control, architecture, smart contract, smart connected buildings

Procedia PDF Downloads 83

Authors: Chukwuka Justus Iwegbu, Monye Christopher Prayer

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Propeller aircraft engines, and more generally engines with a large rotating part (turboprops, high bypass ratio turbojets, etc.) are widely used in the industry and are subject to numerous developments in order to reduce their fuel consumption. In this context, unconventional architectures such as open rotors or distributed propulsion appear, and it is necessary to consider the influence of these systems on the aircraft's stability in flight. Indeed, the tendency to lengthen the blades and wings on which these propulsion devices are fixed increases their flexibility and accentuates the risk of whirl flutter. This phenomenon of aeroelastic instability is due to the precession movement of the axis of rotation of the propeller, which changes the angle of attack of the flow on the blades and creates unsteady aerodynamic forces and moments that can amplify the motion and make it unstable. The whirl flutter instability can ultimately lead to the destruction of the engine. We note the existence of a critical speed of the incident flow. If the flow velocity is lower than this value, the motion is damped and the system is stable, whereas beyond this value, the flow provides energy to the system (negative damping) and the motion becomes unstable. A simple model of whirl flutter is based on the work of Houbolt & Reed who proposed an analytical expression of the aerodynamic load on a rigid blade propeller whose axis orientation suffers small perturbations. Their work considered a propeller subjected to pitch and yaw movements, a flow undisturbed by the blades and a propeller not generating any thrust in the absence of precession. The unsteady aerodynamic forces were then obtained using the thin airfoil theory and the strip theory. In the present study, the unsteady aerodynamic loads are expressed for a general movement of the propeller (not only pitch and yaw). The acceleration and rotation of the flow by the propeller are modeled using a Blade Element Momentum Theory (BEMT) approach, which also enable to take into account the thrust generated by the blades. It appears that the thrust has a stabilizing effect. The aerodynamic model is further developed using Theodorsen theory. A reduced order model of the aerodynamic load is finally constructed in order to perform linear stability analysis.

Keywords: advancing, entrepreneurial, knowledge, industralization

Procedia PDF Downloads 66

Authors: Zeina Bou Diab, Arij Daou

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The neural mechanisms of sequential behaviors are intensively studied, with songbirds a focus for learned vocal production. We are studying the premotor nucleus HVC at a nexus of multiple pathways contributing to song learning and production. The HVC consists of multiple classes of neuronal populations, each has its own cellular, electrophysiological and functional properties. During singing, a large subset of motor cortex analog-projecting HVCRA neurons emit a single 6-10 ms burst of spikes at the same time during each rendition of song, a large subset of basal ganglia-projecting HVCX neurons fire 1 to 4 bursts that are similarly time locked to vocalizations, while HVCINT neurons fire tonically at average high frequency throughout song with prominent modulations whose timing in relation to song remains unresolved. This opens the opportunity to define models relating explicit HVC circuitry to how these neurons work cooperatively to control learning and singing. We developed conductance-based Hodgkin-Huxley models for the three classes of HVC neurons (based on the ion channels previously identified from in vitro recordings) and connected them in several physiologically realistic networks (based on the known synaptic connectivity and specific glutaminergic and gabaergic pharmacology) via different architecture patterning scenarios with the aim to replicate the in vivo firing patterning behaviors. We are able, through these networks, to reproduce the in vivo behavior of each class of HVC neurons, as shown by the experimental recordings. The different network architectures developed highlight different mechanisms that might be contributing to the propagation of sequential neural activity (continuous or punctate) in the HVC and to the distinctive firing patterns that each class exhibits during singing. Examples of such possible mechanisms include: 1) post-inhibitory rebound in HVCX and their population patterns during singing, 2) different subclasses of HVCINT interacting via inhibitory-inhibitory loops, 3) mono-synaptic HVCX to HVCRA excitatory connectivity, and 4) structured many-to-one inhibitory synapses from interneurons to projection neurons, and others. Replication is only a preliminary step that must be followed by model prediction and testing.

Keywords: computational modeling, neural networks, temporal neural sequences, ionic currents, songbird

Procedia PDF Downloads 38

Authors: Katherine Lapierre

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The research engages with the notion of the irrational in the conception of digital architectural design. Following this line, various projects are developed and proposed explorations in the form of plans and 3D models that expose the precarious balance between feasibility, viability, and impossibility due to standards and physical and technical constraints. These different experiments are a testing ground for new practices and media, such as the exploration of different techniques of 3D printing for building construction as well as the assistance of artificial intelligence. The work focuses on outsider architecture, directly referring to the expression "Outsider Art," first used by Roger Cardinal in 1971 to translate the expression "Art Brut," defined and imagined by Jean Paulhan and Dubuffet in 1945. It represents an unprecedented potential sphere of action, broadening the debate on the status and possible contribution of Art Brut notions to architecture and design in constructing a global architectural discourse. The project focuses on the design of models/sculptures inspired by empirical practices culled from the Catalog raisonné of outsider architecture, a database. Our works use enlarged-scale versions of chosen artifacts of nature that have been scanned and transformed. Outsider architectures revive Bachelardian theories silenced by the paradigm shift that accompanied modernity. Our research-creation work follows this line of thought, linking the unconscious with the Bachelardian concept of material imagination. The model and plan studies are based on a process halfway between sculpture and architecture, used to create living units and focus on the potential of digital architecture as a trigger for imagination. The different representation techniques used in these creations contribute to developing various skills in computer modeling, 3D scanning and 3D printing. Outsider creates the opportunity to investigate methods of representation sidelined by architectural practice. The logic of construction shifts, emphasizing the creation of spaces with numeric tools of representation (Rhino, PrusaSlicer, ThinkerCAD, RevoScan, MFStudio, AutoCAD) that follow empirical design methods drawn from the field of architecture. Model-making becomes a laboratory for the formal exploration of various numeric transformations: anamorphism, scaling, piercing, extruding, and erasing.

Keywords: singular architecture, outsider art, anarchitecture, archisculpture, maquette-sculpture, photomount, collage, 3d print

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: Mamouni Mohammed Dhiya Eddine

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This work aims at studying the exploitation of high-speed networks of clusters for distributed storage. Parallel applications running on clusters require both high-performance communications between nodes and efficient access to the storage system. Many studies on network technologies led to the design of dedicated architectures for clusters with very fast communications between computing nodes. Efficient distributed storage in clusters has been essentially developed by adding parallelization mechanisms so that the server(s) may sustain an increased workload. In this work, we propose to improve the performance of distributed storage systems in clusters by efficiently using the underlying high-performance network to access distant storage systems. The main question we are addressing is: do high-speed networks of clusters fit the requirements of a transparent, efficient and high-performance access to remote storage? We show that storage requirements are very different from those of parallel computation. High-speed networks of clusters were designed to optimize communications between different nodes of a parallel application. We study their utilization in a very different context, storage in clusters, where client-server models are generally used to access remote storage (for instance NFS, PVFS or LUSTRE). Our experimental study based on the usage of the GM programming interface of MYRINET high-speed networks for distributed storage raised several interesting problems. Firstly, the specific memory utilization in the storage access system layers does not easily fit the traditional memory model of high-speed networks. Secondly, client-server models that are used for distributed storage have specific requirements on message control and event processing, which are not handled by existing interfaces. We propose different solutions to solve communication control problems at the filesystem level. We show that a modification of the network programming interface is required. Data transfer issues need an adaptation of the operating system. We detail several propositions for network programming interfaces which make their utilization easier in the context of distributed storage. The integration of a flexible processing of data transfer in the new programming interface MYRINET/MX is finally presented. Performance evaluations show that its usage in the context of both storage and other types of applications is easy and efficient.

Keywords: distributed storage, remote file access, cluster, high-speed network, MYRINET, zero-copy, memory registration, communication control, event notification, application programming interface

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Authors: Natarajan Ramasamy, Gurulingamurthy Haralur, Ramesh Nivarthu, Nikhil Kumar Singha

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Elastomers are polymeric materials with varied backbone architectures ranging from linear to dendrimeric structures and wide varieties of monomeric repeat units. These elastomers show strongly viscous and weakly elastic when it is not cross-linked. But when crosslinked, based on the extent the properties of these elastomers can range from highly flexible to highly stiff nature. Lightly cross-linked systems are well studied and reported. Understanding the nature of highly cross-linked rubber based upon chemical structure and architecture is critical for varieties of applications. One of the critical parameters is cross-link density. In the current work, we have studied the highly cross-linked state of linear, lightly branched to star-shaped branched elastomers and determined the cross-linked density by using different models. Change in hardness, shift in Tg, change in modulus and swelling behavior were measured experimentally as a function of the extent of curing. These properties were analyzed using varied models to determine cross-link density. We used hardness measurements to examine cure time. Hardness to the extent of curing relationship is determined. It is well known that micromechanical transitions like Tg and storage modulus are related to the extent of crosslinking. The Tg of the elastomer in different crosslinked state was determined by DMA, and based on plateau modulus the crosslink density is estimated by using Nielsen’s model. Usually for lightly crosslinked systems, based on equilibrium swelling ratio in solvent the cross link density is estimated by using Flory–Rhener model. When it comes to highly crosslinked system, Flory-Rhener model is not valid because of smaller chain length. So models based on the assumption of polymer as a Non-Gaussian chain like 1) Helmis–Heinrich–Straube (HHS) model, 2) Gloria M.gusler and Yoram Cohen Model, 3) Barbara D. Barr-Howell and Nikolaos A. Peppas model is used for estimating crosslink density. In this work, correction factors are determined to the existing models and based upon it structure-property relationship of highly crosslinked elastomers was studied.

Keywords: dynamic mechanical analysis, glass transition temperature, parts per hundred grams of rubber, crosslink density, number of networks per unit volume of elastomer

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: I. Valdez, J. Perdomo, M. Colindres, N. Castro

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Today, digital servo systems are extensively used in industrial manufacturing processes, robotic applications, vehicles and other areas. In such control systems, control action is provided by digital controllers with different compensation algorithms, which are designed to meet specific requirements for a given application. Due to the constant search for optimization in industrial processes, it is of interest to design digital controllers that offer ease of realization, improved computational efficiency, affordable return rates, and ease of tuning that ultimately improve the performance of the controlled actuators. There is a vast range of options of compensation algorithms that could be used, although in the industry, most controllers used are based on a PID structure. This research article compares different types of digital compensators implemented in a servo system for DC motor position control. PID compensation is evaluated on its two most common architectures: PID position form (1 DOF), and PID speed form (2 DOF). State feedback algorithms are also evaluated, testing two modern control theory techniques: discrete state observer for non-measurable variables tracking, and a linear quadratic method which allows a compromise between the theoretical optimal control and the realization that most closely matches it. The compared control systems’ performance is evaluated through simulations in the Simulink platform, in which it is attempted to model accurately each of the system’s hardware components. The criteria by which the control systems are compared are reference tracking and disturbance rejection. In this investigation, it is considered that the accurate tracking of the reference signal for a position control system is particularly important because of the frequency and the suddenness in which the control signal could change in position control applications, while disturbance rejection is considered essential because the torque applied to the motor shaft due to sudden load changes can be modeled as a disturbance that must be rejected, ensuring reference tracking. Results show that 2 DOF PID controllers exhibit high performance in terms of the benchmarks mentioned, as long as they are properly tuned. As for controllers based on state feedback, due to the nature and the advantage which state space provides for modelling MIMO, it is expected that such controllers evince ease of tuning for disturbance rejection, assuming that the designer of such controllers is experienced. An in-depth multi-dimensional analysis of preliminary research results indicate that state feedback control method is more satisfactory, but PID control method exhibits easier implementation in most control applications.

Keywords: control, DC motor, discrete PID, discrete state feedback

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Authors: Sara A. Ben Lashihar

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The heritage building information modelling (HBIM) of the historical masonry buildings has expanded lately to meet the urgent needs for conservation and structural analysis. The masonry structures are unique features for ancient building architectures worldwide that have special cultural, spiritual, and historical significance. However, there is a research gap regarding the reliability of the HBIM modeling process of these structures. The HBIM modeling process of the masonry structures faces significant challenges due to the inherent complexity and uniqueness of their structural systems. Most of these processes are based on tracing the point clouds and rarely follow documents, archival records, or direct observation. The results of these techniques are highly abstracted models where the accuracy does not exceed LOD 200. The masonry assemblages, especially curved elements such as arches, vaults, and domes, are generally modeled with standard BIM components or in-place models, and the brick textures are graphically input. Hence, future investigation is necessary to establish a methodology to generate automatically parametric masonry components. These components are developed algorithmically according to mathematical and geometric accuracy and the validity of the survey data. The main aim of this paper is to provide a comprehensive review of the state of the art of the existing researches and papers that have been conducted on the HBIM modeling of the masonry structural elements and the latest approaches to achieve parametric models that have both the visual fidelity and high geometric accuracy. The paper reviewed more than 800 articles, proceedings papers, and book chapters focused on "HBIM and Masonry" keywords from 2017 to 2021. The studies were downloaded from well-known, trusted bibliographic databases such as Web of Science, Scopus, Dimensions, and Lens. As a starting point, a scientometric analysis was carried out using VOSViewer software. This software extracts the main keywords in these studies to retrieve the relevant works. It also calculates the strength of the relationships between these keywords. Subsequently, an in-depth qualitative review followed the studies with the highest frequency of occurrence and the strongest links with the topic, according to the VOSViewer's results. The qualitative review focused on the latest approaches and the future suggestions proposed in these researches. The findings of this paper can serve as a valuable reference for researchers, and BIM specialists, to make more accurate and reliable HBIM models for historic masonry buildings.

Keywords: HBIM, masonry, structure, modeling, automatic, approach, parametric

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Authors: M. Maleczek, Leszek Bogdan, Kazimierz Drabczyk, Agnieszka Iwan

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Crystalline silicon (Si) solar cells are the main product in the market among the various photovoltaic technologies concerning such advantages as: material richness, high carrier mobilities, broad spectral absorption range and established technology. However, photovoltaic technology on the stiff substrates are heavier, more fragile and less cost-effective than devices on the flexible substrates to be applied in special applications. The main goal of our work was to incorporate silicon solar cells into various fabric, without any change of the electrical and mechanical parameters of devices. This work is realized for the GEKON project (No. GEKON2/O4/268473/23/2016) sponsored by The National Centre for Research and Development and The National Fund for Environmental Protection and Water Management. In our work, the polyamide or polyester fabrics were used as a flexible substrate in the created devices. Applied fabrics differ in tensile and tear strength. All investigated polyamide fabrics are resistant to weathering and UV, while polyester ones is resistant to ozone, water and ageing. The examined fabrics are tight at 100 cm water per 2 hours. In our work, commercial silicon solar cells with the size 156 × 156 mm were cut into nine parts (called single solar cells) by diamond saw and laser. Gap and edge after cutting of solar cells were checked by transmission electron microscope (TEM) to study morphology and quality of the prepared single solar cells. Modules with the size of 160 × 70 cm (containing about 80 single solar cells) were created and investigated by electrical and mechanical methods. Weight of constructed module is about 1.9 kg. Three types of solar cell architectures such as: -fabric/EVA/Si solar cell/EVA/film for lamination, -backsheet PET/EVA/Si solar cell/EVA/film for lamination, -fabric/EVA/Si solar cell/EVA/tempered glass, were investigated taking into consideration type of fabric and lamination process together with the size of solar cells. In investigated devices EVA, it is ethylene-vinyl acetate, while PET - polyethylene terephthalate. Depend on the lamination process and compatibility of textile with solar cell an efficiency of investigated flexible silicon solar cells was in the range of 9.44-16.64 %. Multi folding and unfolding of flexible module has no impact on its efficiency as was detected by Instron equipment. Power (P) of constructed solar module is 30 W, while voltage about 36 V. Finally, solar panel contains five modules with the polyamide fabric and tempered glass will be produced commercially for different applications (dual use).

Keywords: flexible devices, mechanical properties, silicon solar cells, textiles

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Authors: Paolo Grasso, Sai Kalyan Yelike, Federico Benzi, Mathieu Le Cam

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The cabin air quality (CAQ) in commercial aircraft is of prime interest, especially in the context of the COVID-19 pandemic. Current Environmental Control Systems (ECS) rely on a prescribed fresh airflow per passenger to dilute contaminants. An adaptive ECS strategy is proposed, leveraging air sensing and filtration technologies to ensure a better CAQ. This paper investigates the CAQ level achieved in commercial aircraft’s cabin during various flight scenarios. The modeling and simulation analysis is performed in a Modelica-based environment describing the dynamic behavior of the system. The model includes the following three main systems: cabin, recirculation loop and air-conditioning pack. The cabin model evaluates the thermo-hygrometric conditions and the air quality in the cabin depending on the number of passengers and crew members, the outdoor conditions and the conditions of the air supplied to the cabin. The recirculation loop includes models of the recirculation fan, ordinary and novel filtration technology, mixing chamber and outflow valve. The air-conditioning pack includes models of heat exchangers and turbomachinery needed to condition the hot pressurized air bled from the engine, as well as selected contaminants originated from the outside or bled from the engine. Different ventilation control strategies are modeled and simulated. Currently, a limited understanding of contaminant concentrations in the cabin and the lack of standardized and systematic methods to collect and record data constitute a challenge in establishing a causal relationship between CAQ and passengers' comfort. As a result, contaminants are neither measured nor filtered during flight, and the current sub-optimal way to avoid their accumulation is their dilution with the fresh air flow. However, the use of a prescribed amount of fresh air comes with a cost, making the ECS the most energy-demanding non-propulsive system within an aircraft. In such a context, this study shows that an ECS based on a reduced and adaptive fresh air flow, and relying on air sensing and filtration technologies, provides promising results in terms of CAQ control. The comparative simulation results demonstrate that the proposed adaptive ECS brings substantial improvements to the CAQ in terms of both controlling the asymptotic values of the concentration of the contaminant and in mitigating hazardous scenarios, such as fume events. Original architectures allowing for adaptive control of the inlet air flow rate based on monitored CAQ will change the requirements for filtration systems and redefine the ECS operation.

Keywords: cabin air quality, commercial aircraft, environmental control system, ventilation

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Authors: Suruchi Saini, Hitender Kumar Tyagi

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With the rapid progress of the nanotechnology industry, it is becoming increasingly important to have compact semiconductor devices to function and offer the best results at various technology nodes. While performing the scaling of the device, several short-channel effects occur. To minimize these scaling limitations, some device architectures have been developed in the semiconductor industry. FinFET is one of the most promising structures. Also, the double-gate 2D Fin field effect transistor has the benefit of suppressing short channel effects (SCE) and functioning well for less than 14 nm technology nodes. In the present research, the MuGFET simulation tool is used to analyze and explain the electrical behaviour of a double-gate 2D Fin field effect transistor. The drift-diffusion and Poisson equations are solved self-consistently. Various models, such as Fermi-Dirac distribution, bandgap narrowing, carrier scattering, and concentration-dependent mobility models, are used for device simulation. The transfer and output characteristics of the double-gate 2D Fin field effect transistor are determined at 10 nm technology node. The performance parameters are extracted in terms of threshold voltage, trans-conductance, leakage current and current on-off ratio. In this paper, the device performance is analyzed at different structure parameters. The utilization of the Id-Vg curve is a robust technique that holds significant importance in the modeling of transistors, circuit design, optimization of performance, and quality control in electronic devices and integrated circuits for comprehending field-effect transistors. The FinFET structure is optimized to increase the current on-off ratio and transconductance. Through this analysis, the impact of different channel widths, source and drain lengths on the Id-Vg and transconductance is examined. Device performance was affected by the difficulty of maintaining effective gate control over the channel at decreasing feature sizes. For every set of simulations, the device's features are simulated at two different drain voltages, 50 mV and 0.7 V. In low-power and precision applications, the off-state current is a significant factor to consider. Therefore, it is crucial to minimize the off-state current to maximize circuit performance and efficiency. The findings demonstrate that the performance of the current on-off ratio is maximum with the channel width of 3 nm for a gate length of 10 nm, but there is no significant effect of source and drain length on the current on-off ratio. The transconductance value plays a pivotal role in various electronic applications and should be considered carefully. In this research, it is also concluded that the transconductance value of 340 S/m is achieved with the fin width of 3 nm at a gate length of 10 nm and 2380 S/m for the source and drain extension length of 5 nm, respectively.

Keywords: current on-off ratio, FinFET, short-channel effects, transconductance

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Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Debabrata Adhikari, Mikhail Matveev, Louise Brown, Andy Long, Jan Kočí

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A good understanding of mesoscale permeability of complex architectures in fibrous porous preforms is of particular interest in order to achieve efficient and cost-effective resin impregnation of liquid composite molding (LCM). Fabrics used in structural reinforcements are typically woven or stitched. However, 3D fabric reinforcement is of particular interest because of the versatility in the weaving pattern with the binder yarn and in-plain yarn arrangements to manufacture thick composite parts, overcome the limitation in delamination, improve toughness etc. To predict the permeability based on the available pore spaces between the inter yarn spaces, unit cell-based computational fluid dynamics models have been using the Stokes Darcy model. Typically, the preform consists of an arrangement of yarns with spacing in the order of mm, wherein each yarn consists of thousands of filaments with spacing in the order of μm. The fluid flow during infusion exchanges the mass between the intra and inter yarn channels, meaning there is no dead-end of flow between the mesopore in the inter yarn space and the micropore in the yarn. Several studies have employed the Brinkman equation to take into account the flow through dual-scale porosity reinforcement to estimate their permeability. Furthermore, to reduce the computational effort of dual scale flow, scale separation criteria based on the ratio between yarn permeability to the yarn spacing was also proposed to quantify the dual scale and negligible micro-scale flow regime for the prediction of mesoscale permeability. In the present work, the key parameter to identify the influence of intra yarn permeability on the mesoscale permeability has been investigated with the systematic study of weft and warp yarn spacing on the plane weave as well as the position of binder yarn and number of in-plane yarn layers on 3D weave fabric. The permeability tensor has been estimated using an OpenFOAM-based model for the various weave pattern with idealized geometry of yarn implemented using open-source software TexGen. Additionally, scale separation criterion has been established based on the various configuration of yarn permeability for the 3D fabric with both the isotropic and anisotropic yarn from Gebart’s model. It was observed that the variation of mesoscale permeability Kxx within 30% when the isotropic porous yarn is considered for a 3D fabric with binder yarn. Furthermore, the permeability model developed in this study will be used for multi-objective optimizations of the preform mesoscale geometry in terms of yarn spacing, binder pattern, and a number of layers with an aim to obtain improved permeability and reduced void content during the LCM process.

Keywords: permeability, 3D fabric, dual-scale flow, liquid composite molding

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Authors: Efrain Rodriguez, Cristhian Riano, Alberto Alvares

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In recent years, robots manipulators with parallel architectures are used in additive manufacturing processes – 3D printing. These robots have advantages such as speed and lightness that make them suitable to help with the efficiency and productivity of these processes. Consequently, the interest for the development of parallel robots for additive manufacturing applications has increased. This article deals with the conceptual design and dimensional synthesis of the linear delta robot for additive manufacturing. Firstly, a methodology based on structured processes for the development of products through the phases of informational design, conceptual design and detailed design is adopted: a) In the informational design phase the Mudge diagram and the QFD matrix are used to aid a set of technical requirements, to define the form, functions and features of the robot. b) In the conceptual design phase, the functional modeling of the system through of an IDEF0 diagram is performed, and the solution principles for the requirements are formulated using a morphological matrix. This phase includes the description of the mechanical, electro-electronic and computational subsystems that constitute the general architecture of the robot. c) In the detailed design phase, a digital model of the robot is drawn on CAD software. A list of commercial and manufactured parts is detailed. Tolerances and adjustments are defined for some parts of the robot structure. The necessary manufacturing processes and tools are also listed, including: milling, turning and 3D printing. Secondly, a dimensional synthesis method applied on design of the linear delta robot is presented. One of the most important key factors in the design of a parallel robot is the useful workspace, which strongly depends on the joint space, the dimensions of the mechanism bodies and the possible interferences between these bodies. The objective function is based on the verification of the kinematic model for a prescribed cylindrical workspace, considering geometric constraints that possibly lead to singularities of the mechanism. The aim is to determine the minimum dimensional parameters of the mechanism bodies for the proposed workspace. A method based on genetic algorithms was used to solve this problem. The method uses a cloud of points with the cylindrical shape of the workspace and checks the kinematic model for each of the points within the cloud. The evolution of the population (point cloud) provides the optimal parameters for the design of the delta robot. The development process of the linear delta robot with optimal dimensions for additive manufacture is presented. The dimensional synthesis enabled to design the mechanism of the delta robot in function of the prescribed workspace. Finally, the implementation of the robotic platform developed based on a linear delta robot in an additive manufacturing application using the Fused Deposition Modeling (FDM) technique is presented.

Keywords: additive manufacturing, delta parallel robot, dimensional synthesis, genetic algorithms

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Authors: Bander Alzahrani, Mohammed Alreshoodi

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Information-centric networking (ICN) using architectures such as the Publish-Subscribe Internet Technology (PURSUIT) has been proposed as a new networking model that aims at replacing the current used end-centric networking model of the Internet. This emerged model focuses on what is being exchanged rather than which network entities are exchanging information, which gives the control plane functions such as routing and host location the ability to be specified according to the content items. The forwarding plane of the PURSUIT ICN architecture uses a simple and light mechanism based on Bloom filter technologies to forward the packets. Although this forwarding scheme solve many problems of the today’s Internet such as the growth of the routing table and the scalability issues, it is vulnerable to brute force attacks which are starting point to distributed- denial-of-service (DDoS) attacks. In this work, we design and analyze a novel source-routing and information delivery technique that keeps the simplicity of using Bloom filter-based forwarding while being able to deter different attacks such as denial of service attacks at the ingress of the network. To achieve this, special forwarding nodes called Edge-FW are directly attached to end user nodes and used to perform a security test for malicious injected random packets at the ingress of the path to prevent any possible attack brute force attacks at early stage. In this technique, a core entity of the PURSUIT ICN architecture called topology manager, that is responsible for finding shortest path and creating a forwarding identifiers (FId), uses a cryptographically secure hash function to create a 64-bit hash, h, over the formed FId for authentication purpose to be included in the packet. Our proposal restricts the attacker from injecting packets carrying random FIds with a high amount of filling factor ρ, by optimizing and reducing the maximum allowed filling factor ρm in the network. We optimize the FId to the minimum possible filling factor where ρ ≤ ρm, while it supports longer delivery trees, so the network scalability is not affected by the chosen ρm. With this scheme, the filling factor of any legitimate FId never exceeds the ρm while the filling factor of illegitimate FIds cannot exceed the chosen small value of ρm. Therefore, injecting a packet containing an FId with a large value of filling factor, to achieve higher attack probability, is not possible anymore. The preliminary analysis of this proposal indicates that with the designed scheme, the forwarding function can detect and prevent malicious activities such DDoS attacks at early stage and with very high probability.

Keywords: forwarding identifier, filling factor, information centric network, topology manager

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