Orientador: Carlos Teofilo Salinas Sedano / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-27T04:21:17Z (GMT). No. of bitstreams: 1 Armengol_JanMateu_M.pdf: 6999606 bytes, checksum: e35806586b55eb3f8f1b370625009ba8 (MD5) Previous issue date: 2015 / Resumo: Neste trabalho é apresentado um modelo para a formação de gelo poroso entre placas planas paralelas baseado em um novo tratamento bidimensional da taxa de crescimento. O modelo considera as equações de transporte de quantidade de movimento, energia e massa. Para a resolução do modelo, as equações são discretizadas usando o método de volumes finitos em um domínio bidimensional composto por ar e gelo poroso. A interface móvel ar-gelo é tratada utilizando a formulação de malha fixa. O domínio computacional inclui a região frontal das placas planas com a finalidade de estudar o crescimento do gelo poroso nessa região. O código numérico é verificado por partes de acordo com artigos de referência na literatura e o modelo é validado com dados experimentais. Os dados experimentais, reportando temperatura e crescimento de forma locais, coincidem com os resultados numéricos com um erro relativo inferior a 10 \% para o caso intermediário de taxa de umidade / Abstract: In this study, a frost formation model is presented based on a new two-dimensional approach for the growth rate. For modeling the frost formation over parallel cold plates, the basic transport equations of mass, energy and momentum have been discretized using the finite volume method in a two-dimensional domain in which air and frost are considered. A volume tracking method employing a fixed grid formulation is used to deal with the air-frost moving boundary. An extended domain in the inlet boundary has been considered in order to study the frost formation in the leading edge of the plate. The numerical code is gradually verified using benchmarking references. The numerical results have been validated against experimental data in which frost growth and temperature as a function of time are reported as local values. The model predictions of the frost thickness as a function of time agree with the experimental data within 10 \% of deviation for the case of intermediate humidity ratio / Mestrado / Termica e Fluidos / Mestre em Engenharia Mecânica

Bolidens elektrolysverk på Rönnskär hade ett behov av att analysera potentialen för att återvinna och utnyttja spillvärme från kondensat för verket. Ångan som användes för existerande värmeväxlare varåtervunnen från olika processer inom Rönnskär, men även producerad av en oljepanna. Den ännu outnyttjade spillvärme som fanns på elektrolysverket skulle potentiellt kunna assistera existerande värmeväxlare för att minska oljekonsumtionen. Ett av målen var att undersöka om det var möjligt attöka temperaturen på elektrolyten till 63 ℃ med ytterligare en värmeväxlare före den nuvarandevärmeväxlaren. Uppvärmningspotentialen för en värmeväxlare med den kondensatmängd och kondensattemperatur som fanns på elektrolysverket skulle undersökas om temperaturen på kondensatet var lägre än 63 ℃. Projektet analyserade relaterade system inom elektrolysverket och mer specifikt avkoppringens utformning med olika maskiner och system. Analysen inkluderade också rekommendationer inom val av värmeväxlare, dimensionering och beräkningar för konfiguration av rörvärmeväxlaren. Sedan beräknades även kondensatmängd, kondensattemperatur, hållbarhetsanalys och ekonomisk analys. En känslighetsanalys utfördes för de viktigaste variablerna.  Resultaten visade att mängden kondensat var för liten för att motivera ytterligare en värmeväxlare. Detta ledde till två fallstudier för olika scenarion inom elektrolysverket vad gällde mängd kondensat, kondensattemperaturer och uppvärmningspotential för elektrolyten. I första fallet beräknades kravet på kondensattemperaturen för uppvärmning av elektrolyten till 63 ℃. I andra fallet utforskades uppvärmningspotentialen av elektrolyten med den beräknade kondensattemperatur på cirka 60 ℃. För båda fallen så användes varierande volymflöden mellan 53,5 liter/min till 126 liter/min. Det medfördeatt elektrolyttemperaturen kunde värmas upp till ett intervall på mellan 30 och 44 ℃. Elektrolytens uppnådda temperatur var beroende av vilket volymflöde som valdes. För att nå temperaturen 63 ℃ påelektrolyten behövdes en kondensattemperatur på cirka 96 ℃ för maxkapacitet. Bästa valet av värmeväxlare var rörvärmeväxlare. Utformningen av värmeväxlaren gav ett acceptabelt tryckfall och god total värmeöverföringskoefficient. Hållbarhetsanalysen visade att det var gynnsamt med en värmeväxlare för att minska koldioxidutsläppen. Minskade koldioxidkostnader gav en mer gynnsam ekonomi, frånsett eventuella investeringskostnader. Känslighetsanalysen visade att känsliga variabler har låg påverkan på resultatet och att de viktiga variablerna som temperaturer och flöden påverkaderesultatet som förväntat. Slutligen kunde man konstatera att denna lösning inte var gynnsam för tillfället på grund av brist på kondensatmängd, men kan vara relevant för elektrolysverket i framtiden. / Boliden's electrolysis plant at Rönnskär had a need to analyze the potential to reuse waste heat from the condensate. The steam used for existing heat exchangers was recovered by various processes within Rönnskär and/or produced by an oil fired boiler. The waste heat that was available at the electrolysis plant could possibly assist existing heat exchangers to reduce oil consumption. One of the goals was to investigate whether it was possible to increase the temperature of the electrolyte to 63 ℃with an additional heat exchanger before the current heat exchanger. The heating potential of a heat exchanger with the certain amount of condensate and condensate temperature that was present at the electrolysis plant would be investigated if the temperature of the condensate was lower than 63 ℃.The project analyzed related systems within the electrolysis plant and more specifically, the process of interest in terms of design with different machines and systems. The analysis also included recommendations in the selection of heat exchangers, dimensioning and calculations for the configuration of a shell and tube heat exchanger. The amount of condensate, condensate temperature, sustainability analysis and economic analysis were also calculated. The sensitivity analysis was performed on important and sensitive variables. The result showed that the amount of condensate was too small to justify an additional heat exchanger. This led to two case studies for different scenarios within the electrolysis plant in terms of amount of condensate, condensate temperatures and heating potential for the electrolyte. In the first case, the requirement for the condensate temperature for heating the electrolyte to 63 ℃ was calculated. In the second case, the heating potential of the electrolyte with the calculated condensate temperature and the different amounts of condensate were explored. The volume flow varied between 53,5 liters/min to 126 liters/min for the condensate with a temperature of about 60 ℃. This meant that the electrolyte temperature could be heated between a range 30 to 44 ℃. The temperature of the electrolyte was depended on the volume flow chosen. To reach the temperature of 63 ℃ on the electrolyte, a condensate temperature of approximately 96 ℃ was needed for maximum capacity. The best choices of heat exchangers were a shell and tube heat exchanger. The design of the heat exchanger gave an acceptable pressure drop and a good overall heat transfer coefficient. The sustainability analysis showed that it was beneficial to have a heat exchanger to reduce carbon dioxide emissions. By reducing the carbon emissions, it resulted in less cost for EUA (European union allowance), but the investment cost was not included in these calculations. The sensitivity analysis showed that sensitive variables have a low impact on the result and that the important variables such as temperatures and flows affected the result as expected. Finally, it could be stated that this solution was not favorable at the moment due to a lack of condensate but may be relevant for the electrolysis plant in the future.

The purpose of this research project is the design and optimization of complex chemical engineering problems, by employing evolutionary algorithms (EAs). EAs are optimization techniques which mimic the principles of genetics and natural selection. Given their population-based approach, EAs are well suited for solving multiobjective optimization problems (MOOPs) to determine Pareto-optimal solutions. The Pareto front refers to the set of non-dominated solutions which highlight trade-offs among the different objectives. A broad range of applications have been studied, all of which are drawn from the chemical engineering field. The design of an industrial packed bed styrene reactor is initially studied with the goal of maximizing the productivity, yield and selectivity of styrene. The dual population evolutionary algorithm (DPEA) was used to circumscribe the Pareto domain of two and three objective optimization case studies for three different configurations of the reactor: adiabatic, steam-injected and isothermal. The Pareto domains were then ranked using the net flow method (NFM), a ranking algorithm that incorporates the knowledge and preferences of an expert into the optimization routine. Next, a multiobjective optimization of the heat transfer area and pumping power of a shell-and-tube heat exchanger is considered to provide the designer with multiple Pareto-optimal solutions which capture the trade-off between the two objectives. The optimization was performed using the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) on two case studies from the open literature. The algorithm was also used to determine the impact of using discrete standard values of the tube length, diameter and thickness rather than using continuous values to obtain the optimal heat transfer area and pumping power. In addition, a new hybrid algorithm called the FP-NSGA-II, is developed in this thesis by combining a front prediction algorithm with the fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II). Due to the significant computational time of evaluating objective functions in real life engineering problems, the aim of this hybrid approach is to better approximate the Pareto front of difficult constrained and unconstrained problems while keeping the computational cost similar to NSGA-II. The new algorithm is tested on benchmark problems from the literature and on a heat exchanger network problem.

The purpose of this research project is the design and optimization of complex chemical engineering problems, by employing evolutionary algorithms (EAs). EAs are optimization techniques which mimic the principles of genetics and natural selection. Given their population-based approach, EAs are well suited for solving multiobjective optimization problems (MOOPs) to determine Pareto-optimal solutions. The Pareto front refers to the set of non-dominated solutions which highlight trade-offs among the different objectives. A broad range of applications have been studied, all of which are drawn from the chemical engineering field. The design of an industrial packed bed styrene reactor is initially studied with the goal of maximizing the productivity, yield and selectivity of styrene. The dual population evolutionary algorithm (DPEA) was used to circumscribe the Pareto domain of two and three objective optimization case studies for three different configurations of the reactor: adiabatic, steam-injected and isothermal. The Pareto domains were then ranked using the net flow method (NFM), a ranking algorithm that incorporates the knowledge and preferences of an expert into the optimization routine. Next, a multiobjective optimization of the heat transfer area and pumping power of a shell-and-tube heat exchanger is considered to provide the designer with multiple Pareto-optimal solutions which capture the trade-off between the two objectives. The optimization was performed using the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) on two case studies from the open literature. The algorithm was also used to determine the impact of using discrete standard values of the tube length, diameter and thickness rather than using continuous values to obtain the optimal heat transfer area and pumping power. In addition, a new hybrid algorithm called the FP-NSGA-II, is developed in this thesis by combining a front prediction algorithm with the fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II). Due to the significant computational time of evaluating objective functions in real life engineering problems, the aim of this hybrid approach is to better approximate the Pareto front of difficult constrained and unconstrained problems while keeping the computational cost similar to NSGA-II. The new algorithm is tested on benchmark problems from the literature and on a heat exchanger network problem.

Traditionally Shell-and-tube heat exchangers are designed using correlation based approaches like Kern method and Bell-Delaware method. With the advances in Computational Fluid Dynamics (CFD) software, it is now possible to design small heat exchangers using CFD. In this thesis, shell-and-tube heat exchangers are modeled and numerically analyzed using a commercial finite volume package. The modeled heat exchangers are relatively small, have single shell and tube passes. The leakage effects are not taken into account in the design process. Therefore, there is no leakage from baffle orifices and no gap between baffles and the shell. This study is focused on shell side flow phenomena. First, only shell side is modeled and shell side heat transfer and flow characteristics are analyzed with a series of CFD simulations. Various turbulence models are tried for the first and second order discretization schemes using different mesh densities. CFD predictions of the shell side pressure drop and the heat transfer coefficient are obtained and compared with correlation based method results. After selecting the best modeling approach, the sensitivity of the results to the flow rate, the baffle spacing and baffle cut height are investigated. Then, a simple double pipe heat exchanger is modeled. For the double pipe heat exchanger, both the shell (annulus) side and the tube side are modeled. Last, analyses are performed for a full shell-and-tube heat exchanger model. For that last model, a small laminar educational heat exchanger setup is used. The results are compared with the available experimental results obtained from the setup. Overall, it is observed that the flow and temperature fields obtained from CFD simulations can provide valuable information about the parts of the heat exchanger design that need improvement. The correlation based approaches may indicate the existence of a weakness in design, but CFD simulations can also pin point the source and the location of the weakness.

The purpose of this research project is the design and optimization of complex chemical engineering problems, by employing evolutionary algorithms (EAs). EAs are optimization techniques which mimic the principles of genetics and natural selection. Given their population-based approach, EAs are well suited for solving multiobjective optimization problems (MOOPs) to determine Pareto-optimal solutions. The Pareto front refers to the set of non-dominated solutions which highlight trade-offs among the different objectives. A broad range of applications have been studied, all of which are drawn from the chemical engineering field. The design of an industrial packed bed styrene reactor is initially studied with the goal of maximizing the productivity, yield and selectivity of styrene. The dual population evolutionary algorithm (DPEA) was used to circumscribe the Pareto domain of two and three objective optimization case studies for three different configurations of the reactor: adiabatic, steam-injected and isothermal. The Pareto domains were then ranked using the net flow method (NFM), a ranking algorithm that incorporates the knowledge and preferences of an expert into the optimization routine. Next, a multiobjective optimization of the heat transfer area and pumping power of a shell-and-tube heat exchanger is considered to provide the designer with multiple Pareto-optimal solutions which capture the trade-off between the two objectives. The optimization was performed using the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) on two case studies from the open literature. The algorithm was also used to determine the impact of using discrete standard values of the tube length, diameter and thickness rather than using continuous values to obtain the optimal heat transfer area and pumping power. In addition, a new hybrid algorithm called the FP-NSGA-II, is developed in this thesis by combining a front prediction algorithm with the fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II). Due to the significant computational time of evaluating objective functions in real life engineering problems, the aim of this hybrid approach is to better approximate the Pareto front of difficult constrained and unconstrained problems while keeping the computational cost similar to NSGA-II. The new algorithm is tested on benchmark problems from the literature and on a heat exchanger network problem.

This master’s thesis deals with the design of heat recovery system from melting furnace waste gases. The first part is devoted to a brief description of heat exchangers, with the special importance being placed on the shell-and-tube heat exchanger. The second part contains a calculation of stoichiometric combustion, design of geometrical dimensions, calculation of pressure drops and power. At the end of the thesis there are various possibilities of utilization of the obtained waste heat and their basic economic assessment.

The major challenge associated with renewable-energy systems especially solar, is the supply intermittency. One effective solution is to incorporate thermal energy storage components utilizing phase change materials (PCMs). These materials have the potential to store large amounts of energy in relatively small volumes and within nearly an isothermal storage process. The primary drawback of today’s PCMs is that their low thermal conductivity values critically limit their energy storage applications. Also, this grossly reduces the melting/ solidification rates, thus making the system response time to be too long. So, the application of heat transfer enhancement is very important. To improve the PCM storage performance, an efficient performing containment vessel (triplex-tube) along with applications of various heat transfer enhancement techniques was investigated. The techniques were; (i) dispersion of solid nanoparticles, (ii) incorporation of metal foam with nanoparticle dispersion, and (iii) insertion of longitudinal fins with nanoparticle dispersion. Validated simulation models were developed to examine the effects of implementing these techniques on the PCM phase-change rate during the energy storage and recovery modes. The results are presented with detailed model description, analysis, and conclusions. Results show that the use of nanoparticles with metal foam or fins is more efficient than using nanoparticles alone within the same volume usage. Also, employing metal foam or fins alone results in much better improvement for the same system volume.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2019-03-13T13:59:33Z No. of bitstreams: 1 Clemar Trentin Oliveira_.pdf: 4154239 bytes, checksum: 0abf1d28fa69ea97d15e72a7624615f5 (MD5) / Made available in DSpace on 2019-03-13T13:59:33Z (GMT). No. of bitstreams: 1 Clemar Trentin Oliveira_.pdf: 4154239 bytes, checksum: 0abf1d28fa69ea97d15e72a7624615f5 (MD5) Previous issue date: 2018-12-11 / Nenhuma / Neste trabalho, desenvolve-se uma nova abordagem de otimização do projeto de um trocador de calor casco e tubos. O algoritmo Otimizador por Lobo Cinzento (GWO) é aplicado para minimizar a função objetivo custo total do trocador de calor proposto. As variáveis de otimização adotadas são: número de passes nos tubos, diâmetro externo dos tubos, diâmetro interno do casco, espaçamento dos defletores e corte dos defletores. O método Bell-Delaware e o método de Kern são utilizados para calcular o coeficiente de transferência de calor e a perda de pressão para o lado do casco. Os resultados da otimização são comparados com o projeto original do trocador de calor e também com os resultados alcançados por outros algoritmos de otimização da literatura. Além disso, o algoritmo GWO é comparado com outras meta-heurísticas de otimização em três funções de teste distintas. Os resultados da comparação do algoritmo GWO nas funções de teste mostram um desempenho competitivo, comparado com o Algoritmo Genético, Otimização por Enxame de Partículas, algoritmo Evolução Diferencial e Algoritmo do Vaga-Lume. Os resultados da otimização do trocador de calor utilizando o método de Kern mostra um bom desempenho, com a redução do investimento de capital em 11,95%, 7,93%, 6,24%, 2,37% e 0,19% comparado ao projeto original, GA, ICA, PSO e GSA respectivamente. Além disso, o custo operacional total descontado foi menor que o projeto original e o restante das metaheurísticas exceto para o algoritmo GSA onde o algoritmo GWO obteve resultado 22,33 %, superior. No geral, a redução combinada do investimento de capital e do custo operacional total descontado obtido pela aplicação do algoritmo GWO levaram a uma redução de custo total de 20,80%, 7,28%, 6,07% e 4,06% comparado ao projeto original, GA, ICA e PSO respectivamente. Por fim, os resultados da otimização do trocador de calor utilizando o método Bell-Delaware comparado ao projeto original mostram desempenho satisfatório com redução do investimento de capital em 13,32%. O custo operacional total foi menor em 32,56%. Neste caso, a redução combinada do investimento de capital e do custo operacional total descontado com o GWO levaram a uma redução de custo total de 17,19%. / In this work, a new approach to optimizing the design of a shell and tube heat exchanger is developed. The Grey Wolf Optimizer algorithm (GWO) is applied to minimize the objective total cost function of the proposed heat exchanger. The optimization variables adopted are: number of tubes passes, tube outside diameter, shell inside diameter, baffles spacing and baffle cut. The Bell-Delaware method and the Kern method are used to calculate the heat transfer coefficient and the pressure drop from the shell side. The results of the optimization are compared with the original design and with other optimization algorithms in the literature. In addition, the GWO algorithm is compared with other optimization meta-heuristics in three different test functions. The results of the comparison of the GWO algorithm in the test functions show a competitive performance compared to the Genetic Algorithm, Particle Swarm Optimization, Differential Evolution algorithm and Firefly Algorithm. Already, the results of the optimization of the heat exchanger using the Kern method shows a good performance, with the reduction of capital investment by 11.95%, 7.93%, 6.24%, 2.37% and 0.19% compared to the original project, GA, ICA, PSO and GSA, respectively. In addition, the total discounted operating cost was lower than the original project and the rest of the metaheuristics except for the GSA algorithm where the GWO algorithm obtained 22.33% higher result. Overall, the combined reduction in capital investment and total discounted operating cost obtained by applying the GWO algorithm led to a total cost reduction of 20.80%, 7.28%, 6.07% and 4.06% compared to the original project, GA, ICA and PSO, respectively. Finally, the results of optimization of the heat exchanger using the Bell-Delaware method compared to the original design show satisfactory performance with reduction of capital investment by 13.32 %. The total discounted operating cost was lower by 32.56%. In this case, the combined reduction in capital investment and discounted total operating cost obtained by applying the GWO algorithm led to a total cost reduction of 17.19%.

The work presents investigations into the development of a fuel-powered compact SMA actuator system. For the final SMA actuator, the K-alloy SMA strip (0.9 mm x 2.5 mm), actuated by a forced convection heat transfer mechanism, was embedded in a rectangular channel. In this channel, a rectangular piston, with a slot to accommodate the SMA strip, ran along the strip and was utilized to prevent mixing between the hot and the cold fluid in order to increase the energy density of the system. The fuel, such as propane, was utilized as main energy source in order to achieve high energy and power densities of the SMA actuator system. Numerical analysis was carried out to determine optimal channel geometry and to estimate maximum available force, strain and actuation frequency. Multi-channel combustor/heat exchanger and micro-tube heat exchanger were designed and tested to achieve high heat transfer rate and high compactness. The final SMA actuator system was composed of pumps, valves, bellows, multi-channel combustor/heat exchanger, micro-tube heat exchanger and control unit. The experimental tests of the final system resulted in 250 N force with 2 mm displacement and 1.0 Hz actuation frequency in closed-loop operation, in which the hot and the cold fluid were re-circulated by pumps.