Numerical calculations were performed over a variety of two-dimensional rib roughness configurations in which the ratio of flow depth to roughness height was varied from 1.1 to 40. Periodically fully developed flow was achieved by employing periodic boundary conditions and the effect of turbulence was accounted for by a two-layer model. These calculations were used to test the hypothesis that any rough wall resistance may be reduced to an equivalent wall shear stress located on a plane wall. The position of the plane wall is determined by a novel method of prediction obtained by consideration of strearnwise force moments. The resistance is then determined by three dynamically significant length scales: the first (yo) specifies the position of the equivalent plane wall, the second is the depth of flow h and the third is similar to Nikuradse's sand grain roughness k,,. The latter length scale is however depth dependent and a universal relationship is postulated: ks y,, -,= F/Tk where ksw is the asymptotic value of ks at very large flow depths. For the calculation of friction factor, a resistance equation is proposed of the form typical of fully rough flows. These postulates are supported by the numerical model results though further work including physical experiments is required to confirm them. Before applying the two-layer model to this problem it was tested on smooth rectangular duct flows and Schlichting's (1936) long angle roughness experiments. The opportunity was taken to further explore these flows, and in addition calculations were carried out for Grass et al's (1991) open channel rib roughness experiments. The periodic boundary conditions were also applied to a larninar counter-flow plate-fin heat exchanger. A novel source-sink arrangement for heat flux was developed in order to implement these boundary conditions.

Orientador: Artur Zaghini Francesconi / Dissertação (mestrado) - Universidade Estadual de Campinas. Faculdade de Engenharia Quimica / Made available in DSpace on 2018-08-04T22:34:23Z (GMT). No. of bitstreams: 1 Galvao_AlessandroCazonatto_M.pdf: 4286598 bytes, checksum: 5343225ecef9d45c563d1805d234599e (MD5) Previous issue date: 2005 / Resumo: Resumo: Este trabalho compreende o estudo experimetal da entalpia molar em excesso em função da composição de soluções líquidas binárias contendo 1-pentanol + acetonitrila e 1- hexanol + acctonitrila às temperaturas de 288,15, 298,15, 313,15 e 323,15K e pressão atmosférica. O método experimental utilizado para determinação da entalpia molar em excesso foi a calorimetria. O ponto de partida foi a determinação experimental do efeito térmico provocado na solução pela mistura de dois reagentes puros. Os modelos ERAS e PFP foram ampliados na tentativa de correlacionar os dados experimentais. Todas as soluções estudadas apresentaram curvas com formato aproximadamente parabólico e valores positivos para entalpia molar em excesso em função da fração molar. Os resultados de entalpia molar em excesso aumentam com o aumento da temperatura e com o aumento da cadeia carbônica do álcool. Os modelos PFP e ERAS foram capazes de correlacionar satisfatoriamente os dados experimentais de entalpia molar em excesso. Utilizou-se alguns conceitos da teoria de forças intermoleculares na tentativa de analisar o comportamento dos dados experimentais e dos modelos matemáticos / Abstract: Abstract: This work is focused on the experimental study of the excess molar enthalpy as a functions of composition of binary liquid solutions containing 1-pentanol + acetonitrile and 1- hexanol + acetonitrile for 288,15, 298,15, 313,15 and 323,15K and atmospheric pressure. The excess molar enthalpy was calculated using colorimetric method. To accomplish this purpose the starting point was calculated using calorimetric method. To accomplish this purpose, the starting point was the experimental measure of the thermal effect that arises when a mixture of two pure components take place. This solution models PFP and ERAS were applied with the attempt of correlating the experimental data. All the studied system showed values of excess molar enthalpy increase with the temperature and with the number of carbons in the alkanol chain. Both models, ERAS and PFP, were able to describe the main features of the solutions and to correlate the experimental data of the excess molar enthalpy. Some concepts of molecular forces were applied as an attempt of analyzing the behaviour of the experimental data and the mathematical models / Mestrado / Sistemas de Processos Quimicos e Informatica / Mestre em Engenharia Química

Numerical calculations were performed over a variety of two-dimensional rib roughness configurations in which the ratio of flow depth to roughness height was varied from 1.1 to 40. Periodically fully developed flow was achieved by employing periodic boundary conditions and the effect of turbulence was accounted for by a two-layer model. These calculations were used to test the hypothesis that any rough wall resistance may be reduced to an equivalent wall shear stress located on a plane wall. The position of the plane wall is determined by a novel method of prediction obtained by consideration of strearnwise force moments. The resistance is then determined by three dynamically significant length scales: the first (yo) specifies the position of the equivalent plane wall, the second is the depth of flow h and the third is similar to Nikuradse's sand grain roughness k,,. The latter length scale is however depth dependent and a universal relationship is postulated: ks y,, -,= F(Tkwhere ksw is the asymptotic value of ks at very large flow depths. For the calculation of friction factor, a resistance equation is proposed of the form typical of fully rough flows. These postulates are supported by the numerical model results though further work including physical experiments is required to confirm them. Before applying the two-layer model to this problem it was tested on smooth rectangular duct flows and Schlichting's (1936) long angle roughness experiments. The opportunity was taken to further explore these flows, and in addition calculations were carried out for Grass et al's ( 199 1) open channel rib roughness experiments. The periodic boundary conditions were also applied to a larninar counter-flow plate-fin heat exchanger.A novel source-sinka rrangemenfto r heat flux was developedi n order to implement these boundary conditions.

Building an efficient and robust robot that does not use appendages for locomotion requires inspiration and a thorough understanding of the working principles of limbless animals’ locomotion. In these animals, the passive properties of their morphology and material allow them to dwell in complex terrains at different animals’ scales by using only a simple mode of locomotion, i.e., undulatory locomotion. A better understanding of these animals can inspire efficient locomotion strategies and lead to multi-gait terrain adaptation that exploits their physical intelligence. This study endeavors to model undulatory locomotion in various environments and study the effect of endogenous and exogenous dynamics in limbless bodies. First, undulatory locomotion is modeled analytically using the Lagrangian mechanics approach in a dry frictional environment. A discrete multi-bar system is set to get the propulsive force through frictional anisotropy. The system is then non-dimensionalized to determine the factors representing material and environmental properties. The principal components of the model are body stiffness, internal damping, moment of inertia, and frictional anisotropy. Simulations showed the interdependence of these quantities to achieve the desired speed. The results also highlighted the interdependence of endogenous and exogenous dynamics to achieve different locomotion gaits. Swimming, crawling, and polychaete-like locomotion are characterized based on stiffness factor, frictional factor, and frictional coefficient ratio. The model is validated by inputting the required parameters of the corn snake from the literature. Then undulatory locomotion is modeled in a viscous environment, and the results are compared with the dry environment. It is found that the optimum weight of dry and viscous frictional factors can be found in a hybrid environment to achieve better speed performance. Finally, the experimental validation is carried out in a dry friction environment. The results from experimental and physical models are compared. The physical robot is a wheel-based modular system with flexible joints moving on different substrates. The influence of the spatial distribution of the body stiffness on the speed performance is also explored. Findings suggest that the environment affects the performance of undulatory locomotion based on the body stiffness distribution. Although quantitatively the stiffness varies with the environment, we obtained a qualitative constitutive law for all environments. Specifically, we expect the stiffness distribution to exhibit either an ascending-descending or an ascending-plateau pattern along the length of the object, from head to tail. Furthermore, undulatory locomotion showed sensitivity to contact mechanics: solid-solid or solid-viscoelastic contact produced different locomotion dynamics. Our findings elucidate how terrestrial limbless animals achieve undulatory locomotion performance by exploiting the passive properties of the environment and the body. Application of the obtained results can lead to better-performing long-segmented robots exploiting the aptness of passive body dynamics and the characteristics of the environment where they need to move.

Yes / In this work a simulation of detailed steady state model of an industrial fluid catalytic cracking (FCC) unit with a newly proposed six-lumped kinetic model which cracks gas oil into diesel, gasoline, liquefied petroleum gas (LPG), dry gas and coke. Frequency factors, activation energies and heats of reaction for the catalytic cracking kinetics and a number of model parameters were estimated using a model based parameter estimation technique along with data from an industrial FCC unit in Sudan. The estimated parameters were used to predict the major riser fractions; diesel as 0.1842 kg-lump/kg-feed with a 0.81% error while gasoline as 0.4863 kg-lump/kg-feed with a 2.71% error compared with the plant data. Thus, with good confidence, the developed kinetic model is able to simulate any type of FCC riser with six-lump model as catalyst-to-oil (C/O) ratios were varied and the results predicted the typical riser profiles.

<p dir="ltr">In recent years, Federated Learning (FL) has emerged as a promising approach for training machine learning models across distributed data sources while preserving privacy. However, traditional FL faces significant challenges in reliabilities, including the risk of the single point of failure and vulnerabilities to adversarial attacks. </p><p dir="ltr">This research proposes an innovative framework, Blockchain-based FL(BCFL), leveraging blockchain to decentralize the FL system and enhance its reliability. To optimize BCFL in resource-constrained environments, we design incentive mechanisms and resource allocation schemes to maximize computational efficiency for clients engaging in both training and mining tasks. Additionally, we introduce a dual-task resource allocation scheme specifically tailored for Mobile Edge Computing (MEC), enabling edge servers to manage both BCFL and offloading tasks efficiently. To address the inherent risk of client dropout in distributed learning, we propose the HieAvg algorithm within a decentralized hierarchical FL framework, mitigating the impact of stragglers through historical weight-based aggregation. This research also introduces the Faker attack, a novel model poisoning approach that exploits weaknesses in similarity metrics commonly used in FL defenses. In response, we develop the Similarity of Partial Parameters (SPP) defense, a random parameter selection strategy that disrupts the predictability of similarity evaluations, offering robust protection against adaptive attacks.</p><p dir="ltr">Our research provides practical strategies to fortify FL systems against reliability vulnerabilities. This work lays the foundation for more secure, reliable, and efficient FL in various environments through decentralized architectures and novel fault </p>

<p dir="ltr">Understanding disease transmission is a complex and challenging task as it encompasses a wide range of intricate interactions involving pathogens, hosts, and the environment. Numerous factors, including genetics, behavior, immunity, social dynamics, and environmental conditions, contribute to the complexity. Furthermore, diseases exhibit significant variability in transmission patterns, including variations in the mode of transmission (e.g., respiratory, oral, touch-based, vector-borne), incubation period, and infectiousness. The dynamic nature of disease transmission compounds the existing challenges by introducing temporal variability and environmental variations, thereby intensifying the complexity of the study. Therefore, understanding disease transmission requires comprehensive research, integrated models, and a multidisciplinary approach to decipher the intricate web of interactions and factors involved. This dissertation aims to bridge the use and scalability gap between different levels of transmission models through the utilization of multi-paradigm modeling methods, incorporating varying levels of abstraction, to gain comprehensive insights into disease transmission. The first goal focuses on enhancing pandemic resiliency by analyzing the impact of varying parameters of heating, ventilation, and air conditioning (HVAC) on the dynamics of exhaled droplets and aerosols in the indoor environment using computational fluid dynamics (CFD) modeling. This goal operates at a micro-level of modeling, examining the detailed fluid dynamics and particle dispersion within indoor spaces. By simulating the movement of droplets under different HVAC configurations, this goal provides insights into the effectiveness of ventilation systems and optimizes parameter configurations in controlling disease transmission. The second goal of this dissertation is to aid organizations in evaluating potential policies to mitigate contact-caused risks in indoor spaces during a pandemic. This goal utilizes an ensemble of agent-based simulation (ABS) models, which operate at a higher level of abstraction. These models consider the behaviors and interactions of individuals within indoor environments, such as classrooms or meeting rooms, while incorporating physical distancing guidelines and seating policies. The third goal aims to improve pandemic prediction capabilities by developing a multivariate, spatiotemporal, deep-learning model that predicts COVID-19 hospitalization based on historical cases and evaluates the impact of state-level policy changes. This goal operates at the highest level of abstraction by utilizing deep learning techniques to analyze large-scale, publicly available data. The model captures temporal dependencies using long short-term memory (LSTM) networks and spatial dependencies using graph convolutional networks (GCN), graph attention networks (GAT), and graph transformer networks (GTN). By considering variables such as daily hospitalization and various policy changes, this approach provides a comprehensive framework for forecasting hospitalization cases and assessing policy impacts at the state level. This integrated, abstraction-based approach provides a more holistic understanding of disease transmission, allowing for the exploration of complex scenarios and the assessment of intervention impacts across different scales. This integrated architecture enables policymakers and public health professionals to develop targeted, effective strategies to mitigate the spread of diseases, allocate resources efficiently, and minimize the overall impact on public health. </p>

The purpose of this research work is to demonstrate the feasibility of creating a quick response decision platform for middle management in industry. It utilises the strengths of current, but more importantly creates a leap forward in the theory and practice of Supervisory and Data Acquisition (SCADA) systems and Discrete Event Simulation and Modelling (DESM). The proposed research platform uses real-time data and creates an automatic platform for real-time and predictive system analysis, giving current and ahead of time information on the performance of the system in an efficient manner. Data acquisition as the backend connection of data integration system to the shop floor faces both hardware and software challenges for coping with large scale real-time data collection. Limited scope of SCADA systems does not make them suitable candidates for this. Cost effectiveness, complexity, and efficiency-orientation of proprietary solutions leave space for more challenge. A Flexible Data Input Layer Architecture (FDILA) is proposed to address generic data integration platform so a multitude of data sources can be connected to the data processing unit. The efficiency of the proposed integration architecture lies in decentralising and distributing services between different layers. A novel Sensitivity Analysis (SA) method called EvenTracker is proposed as an effective tool to measure the importance and priority of inputs to the system. The EvenTracker method is introduced to deal with the complexity systems in real-time. The approach takes advantage of event-based definition of data involved in process flow. The underpinning logic behind EvenTracker SA method is capturing the cause-effect relationships between triggers (input variables) and events (output variables) at a specified period of time determined by an expert. The approach does not require estimating data distribution of any kind. Neither the performance model requires execution beyond the real-time. The proposed EvenTracker sensitivity analysis method has the lowest computational complexity compared with other popular sensitivity analysis methods. For proof of concept, a three tier data integration system was designed and developed by using National Instruments’ LabVIEW programming language, Rockwell Automation’s Arena simulation and modelling software, and OPC data communication software. A laboratory-based conveyor system with 29 sensors was installed to simulate a typical shop floor production line. In addition, EvenTracker SA method has been implemented on the data extracted from 28 sensors of one manufacturing line in a real factory. The experiment has resulted 14% of the input variables to be unimportant for evaluation of model outputs. The method proved a time efficiency gain of 52% on the analysis of filtered system when unimportant input variables were not sampled anymore. The EvenTracker SA method compared to Entropy-based SA technique, as the only other method that can be used for real-time purposes, is quicker, more accurate and less computationally burdensome. Additionally, theoretic estimation of computational complexity of SA methods based on both structural complexity and energy-time analysis resulted in favour of the efficiency of the proposed EvenTracker SA method. Both laboratory and factory-based experiments demonstrated flexibility and efficiency of the proposed solution.

Healthcare problems are complex; they exhibit both detail and dynamic complexity. It has been argued that Discrete Event Simulation (DES), with its ability to capture detail, is ideal for problems exhibiting this type of complexity. On the other hand, System Dynamics (SD) with its focus on feedback and nonlinear relationships lends itself naturally to comprehend dynamic complexity. Although these modelling paradigms provide valuable insights, neither of them are proficient in capturing both detail and dynamic complexity to the same extent. It has been argued in literature that a hybrid approach, wherein SD and DES are integrated symbiotically, will provide more realistic picture of complex systems with fewer assumptions and less complexity. In spite of wide recognition of healthcare as a complex multi- dimensional system, there has not been any reported study which utilises hybrid simulation. This could be attributed to the fact that due to fundamental differences, the mixing of methodologies is quite challenging. In order to overcome these challenges a generic theoretical framework for hybrid simulation is required. However, there is presently no such generic framework which provides guidance about integration of SD and DES to form hybrid models. This research has attempted to provide such a framework for hybrid simulation which can be utilised in healthcare domain. On the basis of knowledge induced from literature, three requirements for the generic framework have been established. It is argued that the framework for hybrid simulation should be able to provide answers to Why (why hybrid simulation is required), What (what information is exchanged between SD and DES models) and How (how SD and DES models are going to interact with each other over the time to exchange information) within the context of implementation of hybrid simulation to different problem scenarios. In order to meet these requirements, a three-phase generic framework for hybrid simulation has been proposed. Each phase of the framework is mapped to an established requirement and provides guidelines for addressing that requirement. The proposed framework is then evaluated theoretically based on its ability to meet these requirements by using multiple cases, and accordingly modified. It is further evaluated empirically with a single case study comprising of Accident and Emergency department of a London district general hospital. The purpose of this empirical evaluation is to identify the limitations of the framework with regard to the implementation of hybrid models. It is realised during implementation that the modified framework has certain limitations pertaining to the exchange of information between SD and DES models. These limitations are reflected upon and addressed in the final framework. The main contribution of this thesis is the generic framework for hybrid simulation which has been applied within healthcare context. Through an extensive review of existing literature in hybrid simulation, the thesis has also contributed to knowledge in multi-method approaches. A further contribution is that this research has attempted to quantify the impact of intangible benefits of information systems into tangible business process improvements. It is expected that this work will encourage those engaged in simulation (e.g., researchers, practitioners, decision makers) to realise the potential of cross-fertilisation of the two simulation paradigms.

This thesis is about modelling of the combustion and emissions of dual fuel HCCI engines for design of “engine combustion system”. For modelling the combustion first the laminar flamelet model and a hybrid Lagrangian / Eulerian method are developed and implemented to provide a framework for incorporating detailed chemical kinetics. This model can be applied to an engine for the validation of the chemical kinetic mechanism. The chemical kinetics, reaction rates and their equations lead to a certain formula for which the coefficients can be obtained from different sources, such as NASA polynomials [1]. This is followed by study of the simulation results and significant findings. Finally, for investigation of the knock phenomenon some characteristics such as compression ratio, fuel equivalence ratio, spark timing and their effects on the performance of an engine are examined and discussed. The OH radical concentration (which is the main factor for production of knock) is evaluated with regard to adjustment of the above mentioned characteristic parameters. In the second part of this work the specification of the sample engine is given and the results obtained from simulation are compared with experimental results for this sample engine, in order to validate the method applied in AVL Fire software. This method is used to investigate and optimize the effects of parameters such as inlet temperature, fuels ratio, diesel fuel injection timing, engine RPM and EGR on combustion in a dual fuel HCCI engine. For modelling the dual fuel HCCI engine AVL FIRE software is applied to simulate the combustion and study the optimization of a combustion chamber design. The findings for the dual fuel HCCI engine show that the mixture of methane and diesel fuel has a great influence on an engine's power and emissions. Inlet air temperature has also a significant role in the start of combustion so that inlet temperature is a factor in auto-ignition. With an increase of methane fuel, the burning process will be more rapid and oxidation becomes more complete. As a result, the amounts of CO and HC emissions decrease remarkably. With an increase of premixed ratio beyond a certain amount, NOX emissions decrease. With pressure increases markedly and at high RPM, knock phenomenon is observed in HCCI combustion.