Search results for: tomato yield prediction

Authors: Nikolaos Reppas, Yilin Gui, Ben Wetenhall, Colin Davie

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Injection of CO2 inside wellbore can induce different kind of loadings that can lead to thermal, hydraulic, and mechanical changes on the surrounding rock. A dual-porosity theoretical constitutive model will be presented for the stability analysis of the wellbore during CO2 injection. An elastoplastic damage response will be considered. A bounding yield surface will be presented considering damage effects on sandstone. The main target of the research paper is to present a theoretical constitutive model that can help industries to safely store CO2 in geological rock formations and forecast any changes on the surrounding rock of the wellbore. The fully coupled elasto-plastic damage Thermo-Hydraulic-Mechanical theoretical model will be validated from existing experimental data for sandstone after simulating some scenarios by using FEM on MATLAB software.

Keywords: carbon capture and storage, rock mechanics, THM effects on rock, constitutive model

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Authors: Aaron Aboshio

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Numerical study of the static response of homogeneous clay stratum considering a wide range of cohesion and subject to foundation loads is presented. The linear elastic–perfectly plastic constitutive relation with the von Mises yield criterion were utilised to develop a numerically cost effective finite element model for the soil while imposing a rigid body constrain to the foundation footing. From the analyses carried out, estimate of the bearing capacity factor, Nc as well as the ultimate load-carrying capacities of these soils, effect of cohesion on foundation settlements, stress fields and failure propagation were obtained. These are consistent with other findings in the literature and hence can be a useful guide in design of safe foundations in clay soils for buildings and other structure.

Keywords: bearing capacity factors, finite element method, safe bearing pressure, structure-soil interaction

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Authors: Diego Tancara, Raul Coto, Ariel Norambuena, Hoseein T. Dinani, Felipe Fanchini

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Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlapping between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlapping between quantum states. We observe a good performance of the models.

Keywords: quantum, machine learning, kernel, non-markovianity

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Authors: Chen Xi, Lao Xuerui, Li Junjie, Jiang Yike, Wang Hanwei, Zeng Zihao

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behaviour recognition models, to provide empirical data such as 'pedestrian flow data and human behavioural characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, convolutional neural network

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Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin

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This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Keywords: neural network, self-organizing map, rule extraction, rule insertion

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Authors: Maxim A. Kadatskiy, Konstantin V. Khishchenko

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Iron alloys are widespread components in various types of structural materials which are exposed to intensive thermal and mechanical loads. Various quantum-statistical cell models with the approximation of self-consistent field can be used for the prediction of the behavior of these materials under extreme conditions. The application of these models is even more valid, the higher the temperature and the density of matter. Results of Hugoniot calculation for iron alloys in the framework of three quantum-statistical (the Thomas–Fermi, the Thomas–Fermi with quantum and exchange corrections and the Hartree–Fock–Slater) models are presented. Results of quantum-statistical calculations are compared with results from other reliable models and available experimental data. It is revealed a good agreement between results of calculation and experimental data for terra pascal pressures. Advantages and disadvantages of this approach are shown.

Keywords: alloy, Hugoniot, iron, terapascal pressure

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Authors: Supriyono Supriyono, Sumardiyono Sumardiyono, Peni Pujiastuti, Dian Indriana Hapsari

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Crude palm oil (CPO) is a vegetable oil that came from a palm tree bunch. The productivity of the oil is 12 ton/hectare/year. Thus palm oil tree was known as highest vegetable oil yield. It was grown across Equatorial County, especially in Malaysia and Indonesia. The greenish-red color on CPO was come from carotenoid. Carotenoid is one of the antioxidants that could be extracted. Carotenoid could be used as functional food and other purposes. Another antioxidant that also found in CPO is tocopherol. The aim of the research work is to find antioxidant activity on CPO comparing to the synthetic antioxidant that available in a market. In this research work, antioxidant was extracted by a mixture of acetone and n.hexane, while the activity of the antioxidant extract was determined by DPPH method. Antioxidant activity of the extracted compound about 46% compared to pure tocopherol. While the solvent mixture compose by 90% acetone and 10% n. hexane meet the best on the antioxidant activity.

Keywords: antioxidant, beta carotene, crude palm oil, DPPH, tocopherol

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Authors: H. Arceo, S. Rincon, C. Ben-Youssef, J. Rivera, A. Zepeda

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Biodiesel has emerged as a material with great potential as a renewable energy replacement to current petroleum-based diesel. Recently, biodiesel production is focused on the development of more efficient, sustainable process with lower costs of production. In this sense, a “green” approach to biodiesel production has stimulated the use of sustainable heterogeneous acid catalysts, that are better alternatives to conventional processes because of their simplicity and the simultaneous promotion of esterification and transesterification reactions from low-grade, highly-acidic and water containing oils without the formation of soap. The focus of this methodology is the development of new heterogeneous catalysts that under ordinary reaction conditions could reach yields similar to homogeneous catalysis. In recent years, metal organic frameworks (MOF) have attracted much interest for their potential as heterogeneous acid catalysts. They are crystalline porous solids formed by association of transition metal ions or metal–oxo clusters and polydentate organic ligands. This hybridization confers MOFs unique features such as high thermal stability, larger pore size, high specific area, high selectivity and recycling potential. Thus, MOF application could be a way to improve the biodiesel production processes. In this work, we evaluated the catalytic activity of MOF [(4,4-bipyridine)2(O₂CCH₃)2Zn]n (MOF Zn-I) for the synthesis of biodiesel from canola oil. The reaction conditions were optimized using the response surface methodology with a compound design central with 24. The variables studied were: Reaction temperature, amount of catalyst, molar ratio oil: MetOH and reaction time. The preparation MOF Zn-I was performed by mixing 5 mmol 4´4 dipyridine dissolved in 25 mL methanol with 10 mmol Zn(O₂CCH₃)₂ ∙ 2H₂O in 25 mL water. The crystals were obtained by slow evaporation of the solvents at 60°C for 18 h. The prepared catalyst was characterized using X-ray diffraction (XRD) and Fourier transform infrared spectrometer (FT-IR). The prepared catalyst was characterized using X-ray diffraction (XRD) and Fourier transform infrared spectrometer (FT-IR). Experiments were performed using commercially available canola oil in ace pressure tube under continuous stirring. The reaction was filtered and vacuum distilled to remove the catalyst and excess alcohol, after which it was centrifuged to separate the obtained biodiesel and glycerol. 1H NMR was used to calculate the process yield. GC-MS was used to quantify the fatty acid methyl ester (FAME). The results of this study show that the acid catalyst MOF Zn-I could be used as catalyst for biodiesel production through heterogeneous transesterification of canola oil with FAME yield 82 %. The optimum operating condition for the catalytic reaction were of 142°C, 0.5% catalyst/oil weight ratio, 1:30 oil:MeOH molar ratio and 5 h reaction time.

Keywords: fatty acid methyl ester, heterogeneous acid catalyst, metal organic framework, transesterification

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: Siti Nur Izati Azmi, Zatil Afifah Omar, Kathi Swaran, Navin Kumar

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Organophilic Clays, also known as Organoclays, is used as a viscosifier in Oil based Drilling fluids. Most often, Organophilic clay are produced from modified Sodium and Calcium based Bentonite. Many studies and data show that Organophilic Clay using Hectorite based clays provide the best yield and good fluid loss properties in an oil-based drilling fluid at a higher cost. In terms of the manufacturing process, the two common methods of manufacturing organophilic clays are a Wet Process and a Dry Process. Wet process is known to produce better performance product at a higher cost while Dry Process shorten the production time. Hence, the purpose of this study is to evaluate the various formulation of an organophilic clay and its performance vs. the cost, as well as to determine the most efficient and cost-effective method of manufacturing organophilic clays.

Keywords: organophilic clay, viscosifier, wet process, dry process

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Authors: Fahad Suleiman

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Agriculture has been the mainstay of the nation’s economy in Nigeria. It provides food for the ever rapidly increasing population and raw materials for the industries. People especially the rural dwellers are gainfully employed on their crop farms and small-scale livestock farms for income earning. In farming, availability of funds and time management are one of the major factors that influence the system of farming in Nigeria in which mathematical science knowledge was highly required in order for farms to be managed effectively. Farmers often applied mathematics, almost every day for a variety of tasks, ranging from measuring and weighing, to land marking. This paper, therefore, explores some of the ways math is used in farming. For instance, farmers use arithmetic variety of farm activities such as seed planting, harvesting crop, cultivation and mulching. It is also important in helping farmers to know how much their livestock weighs, how much milk their cows produce and crop yield per acres, among others.

Keywords: agriculture, application, economic, farming, mathematics

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Authors: Zhaojun Wang, Zongdi Sun, Zhiyuan Liu

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First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.

Keywords: Shanghai carbon trading, carbon trading price, carbon trading volume, wavelet analysis, BP neural network model

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Authors: Mario Pérez-Won, Vilbett Briones L., Guido Trautmann, María José Bugueño, Gipsy Tabilo-Munizaga, Luis Gonzalez-Cavieres

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The fishing industry generates a significant amount of shrimp byproducts, which often result in environmental contamination. Protein extraction from these by-products is a potential solution to minimize waste and revalue the by-products. To improve the extraction of proteins (by chemical method) from shrimp (Pleuroncodes monodon) by-products, the emerging technologies of ohmic heating (OH), microwaves (MW) and pulsed electric fields (PEF) were used. The results show that microwaves, electrical pulses, and ohmic heating improved performance by 28.19%, 19.25%, and 3.65%, respectively. Furthermore, conformational changes were studied by DSC and FTIR. Subsequently, the use of these proteins in electrospinning technology was evaluated. In conclusion, this study demonstrates that the application of emerging technologies, can significantly improve the extraction yield of proteins from shrimp by-products.

Keywords: electrospinning, emerging technologies, improving extraction, shrimp by-products

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Authors: Maryam Mirzaei, Sinisa Krajnovic´

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The paper reports a study about the prediction of flows around simplified vehicles using Partially-Averaged Navier-Stokes (PANS). Numerical simulations are performed for two simplified vehicles: A slanted-back Ahmed body at Re=30 000 and a square back Ahmed body at Re=300 000. A comparison of the resolved and modeled physical flow scales is made with corresponding LES and experimental data for a better understanding of the performance of the PANS model. The PANS model is compared for coarse and fine grid resolutions and it is indicated that even a coarse-grid PANS simulation is able to produce fairly close flow predictions to those from a well-resolved LES simulation. The results indicate the possibility of improvement of the predictions by employing a finer grid resolution.

Keywords: partially-averaged Navier-Stokes, large eddy simulation, PANS, LES, Ahmed body

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Authors: Waqas Ali

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Concrete is broadly used as a structural material in the construction of buildings. When the concrete is exposed to elevated temperature, its strength evaluation is very necessary in the existing structure. In this study, the effect of temperature and the reliability of the core test has been evaluated. For this purpose, the cylindrical cores were extracted from High strength concrete (HSC) specimens that were exposed to the temperature ranging from 300 ℃ to 900 ℃ with a constant duration of 4 hr. This study compares the difference between the standard heated cylinders and the cores taken from them after curing of 90 days. The difference of cylindrical control and binary mix samples and extracted cores revealed that there is 12.19 and 12.38% difference at 300℃, while this difference was found to increase up to 12.89%, 13.03% at 500 ℃. Furthermore, this value is recorded as 12.99%, 13.57% and 14.40%, 14.38% at 700 ℃ and 900 ℃, respectively. A total of four equations were developed through a regression model for the prediction of the strength of concrete for both standard cylinders and extracted cores whose R square values were 0.9733, 0.9627 and 0.9473, 0.9452, respectively.

Keywords: high strength, temperature, core, reliability

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Authors: Adeyemi, Samuel Olusegun

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The paper reports on the result of experimental gill net assessment of the fishery of Gbedikere Lake in Kogi State between October 2006 and September 2008. Three stations A-C were sampled. Twelve species from ten families were represented in the experimental gill-net catches. These composed of families Protopteridae, Mormyridae, Clariidae, Mochokidae, Cichlidae, Cyprinidae, Malapteruridae, Osteoglossidae, Gymnarchidae, and Citharinidae. The Cichlids dominated the catches. This is made up of Oreochromis niloticus (17.90%), and Tilapia zilli (13.01%). These combined to make up 30.91% of the total number of fish caught. Also, the Cichlids formed 30.91% of the total catch by weight followed Heterotis niloticus (15.56%), Clarias gariepinus (13.16%), Gmynarchus niloticus (8.78%), Heterobranchus bidorsalis (7.14%), Synodontis nigrita (6.69%), Mormyrus rume (5.68%), Citharinus citharus (3.91%), Labeo senegalensis (2.93%), and Protopterus annectens (2.74%), respectively.

Keywords: experimental gill net, species diversity, abundance, distribution, Oxbow Lake and yield

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Authors: Oluwagbemi Victor Aladeokin

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In the last decade, the global peanut cultivation has seen increased demand, which is attributed to their health benefits, rising to ~ 41.4 MMT in 2019/2020. Peanut and other nutshells are considered as waste in various parts of the world and are usually used for their fuel value. However, this agricultural by-product can be converted to a higher value product such as activated carbon. For many years, due to the highly porous structure of activated carbon, it has been widely and effectively used as an adsorbent in the purification and separation of gases and liquids. Those used for commercial purposes are primarily made from a range of precursors such as wood, coconut shell, coal, bones, etc. However, due to difficulty in regeneration and high cost, various agricultural residues such as rice husk, corn stalks, apricot stones, almond shells, coffee beans, etc, have been explored to produce activated carbons. In the present study, the potential of peanut shells as precursors in the production of activated carbon and their adsorption capacity is investigated. Usually, precursors used to produce activated carbon have carbon content above 45 %. A typical raw peanut shell has 42 wt.% carbon content. To increase the yield, this study has employed chemical activation method using zinc chloride. Zinc chloride is well known for its effectiveness in increasing porosity of porous carbonaceous materials. In chemical activation, activation temperature and impregnation ratio are parameters commonly reported to be the most significant, however, this study has also studied the influence of activation time on the development of activated carbon from peanut shells. Activated carbons are applied for different purposes, however, as the application of activated carbon becomes more specific, an understanding of the influence of activation variables to have a better control of the quality of the final product becomes paramount. A traditional approach to experimentally investigate the influence of the activation parameters, involves varying each parameter at a time. However, a more efficient way to reduce the number of experimental runs is to apply design of experiment. One of the objectives of this study is to optimize the activation variables. Thus, this work has employed response surface methodology of design of experiment to study the interactions between the activation parameters and consequently optimize the activation parameters (temperature, impregnation ratio, and activation time). The optimum activation conditions found were 485 °C, 15 min and 1.7, temperature, activation time, and impregnation ratio respectively. The optimum conditions resulted in an activated carbon with relatively high surface area ca. 1700 m2/g, 47 % yield, relatively high density, low ash, and high fixed carbon content. Impregnation ratio and temperature were found to mostly influence the final characteristics of the produced activated carbon from peanut shells. The results of this study, using response surface methodology technique, have revealed the potential and the most significant parameters that influence the chemical activation process, of peanut shells to produce activated carbon which can find its use in both liquid and gas phase adsorption applications.

Keywords: chemical activation, fixed carbon, impregnation ratio, optimum, surface area

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Authors: Badiy Elmabrouk, Abdelhamid Boujarif, Zhiguo Zeng, Stephane Borrel, Robert Heidsieck

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In an era where sustainability is paramount, this paper introduces a machine learning-driven testing protocol to accurately predict diode failures, merging reliability engineering with failure physics to enhance repair operations efficiency. Our approach refines the burn-in process, significantly curtailing its duration, which not only conserves energy but also elevates productivity and mitigates component wear. A case study from GE HealthCare’s repair center vividly demonstrates the method’s effectiveness, recording a high prediction of diode failures and a substantial decrease in energy consumption that translates to an annual reduction of 6.5 Tons of CO2 emissions. This advancement sets a benchmark for environmentally conscious practices in the electronics repair sector.

Keywords: maintenance, burn-in, failure physics, reliability testing

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Authors: J. Martin Leal-Graciano, Juan J. Pérez-Gavilán, A. Reyes-Salazar, J. H. Castorena, J. L. Rivera-Salas

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The experimental results about the global behavior of twelve 1:2 scaled reinforced concrete frame subject to in-plane lateral load are presented. The main objective was to generate experimental evidence about the use of steel bars within mortar bed-joints as shear reinforcement in infill walls. Similar to the Canadian and New Zealand standards, the Mexican code includes specifications for this type of reinforcement. However, these specifications were obtained through experimental studies of load-bearing walls, mainly confined walls. Little information is found in the existing literature about the effects of joint reinforcement on the seismic behavior of infill masonry walls. Consequently, the Mexican code establishes the same equations to estimate the contribution of joint reinforcement for both confined walls and infill walls. A confined masonry construction and a reinforced concrete frame infilled with masonry walls have similar appearances. However, substantial differences exist between these two construction systems, which are mainly related to the sequence of construction and to how these structures support vertical and lateral loads. To achieve the objective established, ten reinforced concrete frames with masonry infill walls were built and tested in pairs, having both specimens in the pair identical characteristics except that one of them included joint reinforcement. The variables between pairs were the type of units, the size of the columns of the frame and the aspect ratio of the wall. All cases included tie-columns and tie-beams on the perimeter of the wall to anchor the joint reinforcement. Also, two bare frame with identical characteristic to the infilled frames were tested. The purpose was to investigate the effects of the infill wall on the behavior of the system to in-plane lateral load. In addition, the experimental results were compared with the prediction of the Mexican code. All the specimens were tested in cantilever under reversible cyclic lateral load. To simulate gravity load, constant vertical load was applied on the top of the columns. The results indicate that the contribution of the joint reinforcement to lateral strength depends on the size of the columns of the frame. Larger size columns produce a failure mode that is predominantly a sliding mode. Sliding inhibits the production of new inclined cracks, which are necessary to activate (deform) the joint reinforcement. Regarding the effects of joint reinforcement in the performance of confined masonry walls, many facts were confirmed for infill walls: this type of reinforcement increases the lateral strength of the wall, produces a more distributed cracking and reduces the width of the cracks. Moreover, it reduces the ductility demand of the system at maximum strength. The prediction of the lateral strength provided by the Mexican code is property in some cases; however, the effect of the size of the columns on the contribution of joint reinforcement needs to be better understood.

Keywords: experimental study, Infill wall, Infilled frame, masonry wall

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Authors: Cristina Diaz, Lucia Perez-Gandarillas, Gonzalo Garcia-Fuentes, Simone Visigalli, Roberto Canziani, Giuseppe Di Florio, Paolo Gronchi

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During their lifetime, industrial machines are often subjected to chemical, mechanical and thermal extreme conditions. In some cases, the loss of efficiency comes from the degradation of the surface as a result of its exposition to abrasive environments that can cause wear. This is a common problem to be solved in industries of diverse nature such as food, paper or concrete industries, among others. For this reason, a good selection of the material is of high importance. In the machine design context, stainless steels such as AISI 304 and 316 are widely used. However, the severity of the external conditions can require additional protection for the steel and sometimes coating solutions are demanded in order to extend the lifespan of these materials. Therefore, the development of effective coatings with high wear resistance is of utmost technological relevance. In this research, bilayer coatings made of Titanium-Tantalum, Titanium-Niobium, Titanium-Hafnium, and Titanium-Zirconium have been developed using magnetron sputtering configuration by PVD (Physical Vapor Deposition) technology. Their tribological behavior has been measured and evaluated under different environmental conditions. Two kinds of steels were used as substrates: AISI 304, AISI 316. For the comparison with these materials, titanium alloy substrate was also employed. Regarding the characterization, wear rate and friction coefficient were evaluated by a tribo-tester, using a pin-on-ball configuration with different lubricants such as tomato sauce, wine, olive oil, wet compost, a mix of sand and concrete with water and NaCl to approximate the results to real extreme conditions. In addition, topographical images of the wear tracks were obtained in order to get more insight of the wear behavior and scanning electron microscope (SEM) images were taken to evaluate the adhesion and quality of the coating. The characterization was completed with the measurement of nanoindentation hardness and elastic modulus. Concerning the results, thicknesses of the samples varied from 100 nm (Ti-Zr layer) to 1.4 µm (Ti-Hf layer) and SEM images confirmed that the addition of the Ti layer improved the adhesion of the coatings. Moreover, results have pointed out that these coatings have increased the wear resistance in comparison with the original substrates under environments of different severity. Furthermore, nanoindentation hardness results showed an improvement of the elastic strain to failure and a high modulus of elasticity (approximately 200 GPa). As a conclusion, Ti-Ta, Ti-Zr, Ti-Nb, and Ti-Hf are very promising and effective coatings in terms of tribological behavior, improving considerably the wear resistance and friction coefficient of typically used machine materials.

Keywords: coating, stainless steel, tribology, wear

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Authors: Mohammad Afkhami, Ali Hassanpour, Michael Fairweather

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The work described in this paper was undertaken to gain insight into fundamental aspects of turbulent gas-particle flows with relevance to processes employed in a wide range of applications, such as oil and gas flow assurance in pipes, powder dispersion from dry powder inhalers, and particle resuspension in nuclear waste ponds, to name but a few. In particular, the influence of particle interaction and fluid phase behavior in turbulent flow on particle dispersion in a horizontal channel is investigated. The mathematical modeling technique used is based on the large eddy simulation (LES) methodology embodied in the commercial CFD code FLUENT, with flow solutions provided by this approach coupled to a second commercial code, EDEM, based on the discrete element method (DEM) which is used for the prediction of particle motion and interaction. The results generated by LES for the fluid phase have been validated against direct numerical simulations (DNS) for three different channel flows with shear Reynolds numbers, Reτ = 150, 300 and 590. Overall, the LES shows good agreement, with mean velocities and normal and shear stresses matching those of the DNS in both magnitude and position. The research work has focused on the prediction of those conditions favoring particle aggregation and deposition within turbulent flows. Simulations have been carried out to investigate the effects of particle size, density and concentration on particle agglomeration. Furthermore, particles with different surface properties have been simulated in three channel flows with different levels of flow turbulence, achieved by increasing the Reynolds number of the flow. The simulations mimic the conditions of two-phase, fluid-solid flows frequently encountered in domestic, commercial and industrial applications, for example, air conditioning and refrigeration units, heat exchangers, oil and gas suction and pressure lines. The particle size, density, surface energy and volume fractions selected are 45.6, 102 and 150 µm, 250, 1000 and 2159 kg m-3, 50, 500, and 5000 mJ m-2 and 7.84 × 10-6, 2.8 × 10-5, and 1 × 10-4, respectively; such particle properties are associated with particles found in soil, as well as metals and oxides prevalent in turbulent bounded fluid-solid flows due to erosion and corrosion of inner pipe walls. It has been found that the turbulence structure of the flow dominates the motion of the particles, creating particle-particle interactions, with most of these interactions taking place at locations close to the channel walls and in regions of high turbulence where their agglomeration is aided both by the high levels of turbulence and the high concentration of particles. A positive relationship between particle surface energy, concentration, size and density, and agglomeration was observed. Moreover, the results derived for the three Reynolds numbers considered show that the rate of agglomeration is strongly influenced for high surface energy particles by, and increases with, the intensity of the flow turbulence. In contrast, for lower surface energy particles, the rate of agglomeration diminishes with an increase in flow turbulence intensity.

Keywords: agglomeration, channel flow, DEM, LES, turbulence

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Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: Dahmane Mohamed, Tab Asma, Trari Mohamed

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BaMnO₃ nanoparticles were synthesized by a nitrate route. Its structure and physical properties were characterized by means of X-ray powder diffraction, radio crystallographic analysis, ultraviolet-visible absorption spectroscopy in diffuse reflectance mode, infrared spectroscopy, and electrochemical measurements. The optical study showed that barium manganese oxide presents a direct transition with band energy 2.13 eV. The electrochemical study allowed us to identify the redox peaks and the corrosion parameters. Capacitance measurement clearly showed n-type conductivity. The photodegradation of paracetamol by BaMnO₃ was followed by UV-visible spectrophotometry; the results were then confirmed by HPLC. BaMnO₃ has shown its photocatalytic efficiency in the photodegradation of 10 mg/L paracetamol under solar irradiation, with a yield of ≈ 88%. The kinetic study has shown that paracetamol degrades with first-order kinetics.

Keywords: BaMnO₃, photodegradation, paracetamol, electrochemical measurements, solar light

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Authors: Xi Yang, Bulent Chavdar, Alan Vonseggern, Taylan Altan

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Fracture in hot precision forging of engine valves was investigated in this paper. The entire valve forging procedure was described and the possible cause of the fracture was proposed. Finite Element simulation was conducted for the forging process, with commercial Finite Element code DEFORMTM. The effects of material properties, the effect of strain rate and temperature were considered in the FE simulation. Two fracture criteria were discussed and compared, based on the accuracy and reliability of the FE simulation results. The selected criterion predicted the fracture location and shows the trend of damage increasing with good accuracy, which matches the experimental observation. Additional modification of the punch shapes was proposed to further reduce the tendency of fracture in forging. Finite Element comparison shows a great potential of such application in the mass production.

Keywords: hotforging, engine valve, fracture, tooling

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Authors: Harpreet Kaur, Er. Arjun, Kirandeep Kaur, P. K. Mittal

Abstract:

Simulation of a human body from a 20% gelatin & 80% water mixture is examined from a wound ballistics point of view. Parameters such as incapacitation energy & temporary to permanent cavity size & tools of hydrodynamics have been employed to arrive at a model of the human body similar to the one adopted by NATO. Calculations using equations of motion yield a value of 339 µs in which a temporary cavity with maximum size settles down to a permanent cavity. This occurs for 10mm size bullets & settles down to a permanent cavity in the case of 4 different bullets, i.e., 5.45, 5.56, 7.62,10 mm sizes. The obtained results are in excellent agreement with the body as a right circular cylinder of 15 cm height & 10 cm diameter. An effort is made here in this work to present a sound theoretical base to parameters commonly used in wound ballistics from field experience discussed by Col Coats & Major Beyer.

Keywords: gelatine, gunshot, hydrodynamic model, oscillation time, temporary and permanent cavity, wound ballistics

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Authors: Daniel S. Brox

Abstract:

Starting from earthquake fault dynamic equations, a correspondence between earthquake occurrence statistics in a seismic region before a major earthquake and eigenvalue statistics of a differential operator whose bound state eigenfunctions characterize the distribution of stress in the seismic region is derived. Modeling these eigenvalue statistics with a 2D Coulomb gas statistical physics model, previously reported deviation of seismic activation earthquake occurrence statistics from Gutenberg-Richter statistics in time intervals preceding the major earthquake is derived. It also explains how statistical physics modeling predicts a finite-dimensional nonlinear dynamic system that describes real-time velocity model evolution in the region undergoing seismic activation and how this prediction can be tested experimentally.

Keywords: seismic activation, statistical physics, geodynamics, signal processing

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Authors: Evy Van Puymbroeck, Wim Nagy, Ken Schotte, Heng Fang, Hans De Backer

Abstract:

The knowledge of residual stresses for welded bridge components is essential to determine the effect of the residual stresses on the fatigue life behavior. The residual stresses of an orthotropic bridge deck are determined by simulating the welding process with finite element modelling. The stiffener is placed on top of the deck plate before welding. A chained thermal-mechanical analysis is set up to determine the distribution of residual stresses for the bridge deck. First, a thermal analysis is used to determine the temperatures of the orthotropic deck for different time steps during the welding process. Twin wire submerged arc welding is used to construct the orthotropic plate. A double ellipsoidal volume heat source model is used to describe the heat flow through a material for a moving heat source. The heat input is used to determine the heat flux which is applied as a thermal load during the thermal analysis. The heat flux for each element is calculated for different time steps to simulate the passage of the welding torch with the considered welding speed. This results in a time dependent heat flux that is applied as a thermal loading. Thermal material behavior is specified by assigning the properties of the material in function of the high temperatures during welding. Isotropic hardening behavior is included in the model. The thermal analysis simulates the heat introduced in the two plates of the orthotropic deck and calculates the temperatures during the welding process. After the calculation of the temperatures introduced during the welding process in the thermal analysis, a subsequent mechanical analysis is performed. For the boundary conditions of the mechanical analysis, the actual welding conditions are considered. Before welding, the stiffener is connected to the deck plate by using tack welds. These tack welds are implemented in the model. The deck plate is allowed to expand freely in an upwards direction while it rests on a firm and flat surface. This behavior is modelled by using grounded springs. Furthermore, symmetry points and lines are used to prevent the model to move freely in other directions. In the thermal analysis, a mechanical material model is used. The calculated temperatures during the thermal analysis are introduced during the mechanical analysis as a time dependent load. The connection of the elements of the two plates in the fusion zone is realized with a glued connection which is activated when the welding temperature is reached. The mechanical analysis results in a distribution of the residual stresses. The distribution of the residual stresses of the orthotropic bridge deck is compared with results from literature. Literature proposes uniform tensile yield stresses in the weld while the finite element modelling showed tensile yield stresses at a short distance from the weld root or the weld toe. The chained thermal-mechanical analysis results in a distribution of residual weld stresses for an orthotropic bridge deck. In future research, the effect of these residual stresses on the fatigue life behavior of welded bridge components can be studied.

Keywords: finite element modelling, residual stresses, thermal-mechanical analysis, welding simulation

Procedia PDF Downloads 171

Authors: Jiahe Liu, Hongyang Yu, Miao Luo, Feng Qian

Abstract:

The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction.

Keywords: SMPLX, mobileNet, gender classification, 3D human reconstruction

Procedia PDF Downloads 99

Authors: Aneri Mehta, Neeti Chaudhary

Abstract:

Governments of the countries around the world have started utilizing the potential of technology to deliver online information and services to their citizens. Using ICT in the form of e-governance could yield great benefits in the reform and modernization of the public sector. The experience of e-governance in a number of developed and developing countries has shown that ICT can be a tool for greater service delivery with the goal of improving service quality. E-governance can also promote ‘good governance’, greater civic engagement can increase opportunities for direct representation and voice, and support for increased democracy. This paper discusses and presents the survey findings that seek to test the role of e-governance in improving service delivery by altering the principal-agent relationship. It further seeks to elucidate the quality aspects of public service. Strong leadership is required to implement e-governance to capture and internalize the benefits of quality services and satisfied citizens.

Keywords: public service, service delivery, e-governance, good governance

Procedia PDF Downloads 479

Authors: Aziza Barrek, Mohamed Haythem Msaddek, Ismail Chenini

Abstract:

Excessive groundwater pumping due to the increasing water demand, especially in the agricultural sector, causes groundwater scarcity. Groundwater recharge is the most important process that contributes to the water's durability. This paper is based on the Analytic Hierarchy Process multicriteria analysis to establish a groundwater recharge susceptibility map. To delineate aquifer suitability for groundwater recharge, eight parameters were used: soil type, land cover, drainage density, lithology, NDVI, slope, transmissivity, and rainfall. The impact of each factor was weighted. This method was applied to the El Fahs plain shallow aquifer. Results suggest that 37% of the aquifer area has very good and good recharge suitability. The results have been validated by the Receiver Operating Characteristics curve. The accuracy of the prediction obtained was 89.3%.

Keywords: AHP, El Fahs aquifer, empirical formula, groundwater recharge zone, remote sensing, semi-arid region

Procedia PDF Downloads 121