As metodologias para a síntese de processos, baseadas em otimização de superestruturas, mostram-se mais poderosas do que as baseadas no projeto em heuríticas, já que levam em consideração as inúmeras possibilidades de construção do processo. É pratica comum a utilização de modelos simplificados (como modelos de rendimento), na representação dos equipamentos dentro destas superestruturas, uma vez que a utilização de modelos fenomenológicos tornar-se-ia inviável, devido aos tempos computacionais e à dificuldade técnica de se programar os módulos e propriedades termodinâmicas de um simulador comercial, dentro de uma plataforma de otimização como o GAMS. Modelos com alta precisão, que não requerem grandes tempos computacionais, reduzidos ou de superfície de resposta, aproximam modelos fenomenológicos e podem ser introduzidos em problemas de síntese, obtendo soluções mais precisas. Neste trabalho, foram construídos modelos reduzidos para dois processos comumente encontrados em sistemas de tratamento de efluentes de refinarias de petróleo: uma torre de resfriamento e um stripper de vapor. Os dados utilizados para a correlação dos modelos reduzidos foram gerados, através da simulação de modelos semi-fenomenológicos ou fenomenológicos destes equipamentos. É proposta uma metodologia original para gerar modelos reduzidos, que diferentemente dos trabalhos anteriores em que se utilizam modelos reduzidos tipo caixa preta, emprega-se um modelo reduzido tipo caixa cinza para a representação da coluna de stripper, o que aumenta a capacidade de correlação do modelo ao conjunto de dados, resultando em menores erros de predição. Dois tipos clássicos de modelos reduzidos foram utilizados dentro dos modelos caixa-cinza: um modelo baseado em redes neurais e um modelo polinomial. Ambos mostraram-se capazes de representar o modelo fenomenológico com pequenos erros e permitindo resolver o problema de síntese em um tempo computacional razoável (da ordem de 10 segundos). Nota-se também que são obtidas várias soluções ótimas locais que diferem ligeiramente de acordo com a abordagem utilizada, mas que qualitativamente correspondem a um mesmo conjunto de soluções. / The superstructure optimization-based methodologies for process synthesis can be more powerful than the heuristic based methodologies, because they cope the many possibilities in order to design the process. It is common practice to use simplified models (efficiency-based models), in the representation of equipment in the superstructures, because the use of phenomenological models would be unfeasible due the high computational time and the technical difficulties in order to program module and thermodynamics properties packages of commercial simulators, into an optimization platform as GAMS. High accuracy models, which do not require large computational time, can be obtained using different types of surrogate or response surface, approximate phenomenological models of the system and can be introduced into the synthesis problems, leading to more precise solutions. In this work, surrogate models were built for two processes usually found in water treatment networks in petroleum refineries: a cooling tower and a steam stripper. The data sets used for fitting the surrogate model were generated by the semi-phenomenological or the first principles model of those equipment. An original methodology for generating surrogate model is proposed that differently of previous works (which make use of black box surrogate models) uses gray box models to represents de stripper column. This approach improves the correlation capacity of the surrogate model to the set data and reduces the prediction errors. Finally, two classic surrogate models were used inside the gray-box models: a neural network based model and a polynomial model. The results showed that both are able to represent the phenomenological model with small errors to solve the synthesis problem in a short computational time (which not exceed in a magnitude of 10 seconds). It is noticed also that many local solutions are obtained, these differ slightly depending on the approach used, but qualitatively represent the same set of solutions.

Title: “I hope to be your uterus” - A multimodal analysis on surrogacy in scenes from The Handmaid’s tale and Friends Surrogacy is a widely discussed subject both internationally and in Sweden. The opinions on whether it should be legalized or not differ from country to country, which is reflected in how they implement their different laws on the subject. The main issues in the debates about surrogacy is whether a legalization of altruistic surrogacy would lead to safe methods within the medical care and stop the expansion of illegal markets or if there’s risks of opening the doors to a bigger commercial market. Parallel to the political discussion and opinions on the subject, the portrayal of surrogacy has appeared within popular culture, as in TV-series, for a period of time. It can be found in different genres like drama and comedy since the 90s, which makes the subject interesting to observe. The purpose of this essay is to study how the topic of surrogacy is portrayed in two different genres in the TV-series Friends and The Handmaid’s tale. For this purpose we use multimodal analysis to answer if there are similarities and/or differences in the portrayal of surrogacy within our research material. The theoretical framework is based on Richard Dyers theories on stereotypes, Carol Patemans ideas of ‘the sexual contract’ and Brooke Weihe Edges studies on the representation of women in different movie genres amongst others. Our study shows that even if the genres vary there are stereotypical structures in how both infertile women and surrogate mothers are presented to the audience.

Origami-based design is increasing in popularity as its benefits and advantages become better understood and explored. However, many opportunities still exist for the application of origami principles to engineered designs, especially in the use of non-paper, thick sheet materials. One specific area utilizing thick sheet materials that is especially promising is origami-based mechanisms that require electrical power transfer applications. Many of these opportunities can be met by the use of surrogate folds. This thesis provides methods and frameworks that can be used by engineers to efficiently select and design surrogate folds for use in origami-based mechanisms and products. Surrogate folds are a means of achieving fold-like behavior, offering a simple method for achieving folding motions in thicker materials. A surrogate fold is a localized reduction in stiffness in a given direction allowing the material to function like a fold. A family of surrogate folds is reviewed, and the respective behaviors of the folds discussed. For a specified fold configuration, the material thickness is varied to yield different sizes of surrogate folds. Constraint assumptions drive the design, and the resultant configurations are compared for bending motions. Finite element and analytical models for the folds are also compared. Prototypes are made from different materials. This work creates a base for creating design guidelines for using surrogate folds in thick sheet materials. As mechanisms with origami-like movement increase in popularity, there is a need for conducting electrical power across folds. Surrogate folds can be used to address this need. Current methods and opportunities for conducting across folds are reviewed. A framework for designing conductive surrogate folds that can be adapted to fit specific applications is presented. Equations for calculating the electrical resistance in single surrogate folds as well as arrays are given. Prototypes of several conductive joints are presented and discussed. The framework is then followed in the design and manufacture of a conductive origami-inspired mechanism.

Improving the wind turbine blade design has a significant effect on the efficiency of the wind turbine. This is a challenging multi-disciplinary optimization problem. During the blade design process, the aerodynamic shapes, sizing of the structural members, and material composition must all be determined and optimized. Some previous blade design methods incorporate the wind turbine's static response with an added safety factor to account for neglected dynamic effects. Others incorporate the dynamic response, but in general is limited to a few design cases. By not fully incorporating the dynamic response of the wind turbine, the final turbine blade design is either too conservative by overemphasizing the dynamic effects or infeasible by failing to adequately account for these effects. In this work, we propose two methods which efficiently incorporate the dynamic response into the optimization routine. The first method involves iteratively calculating damage equivalent fatigue that are fixed during the optimization process. We also demonstrate the training and use of a surrogate model to efficiently estimate the fatigue damage and extreme events in the design process. This surrogate model has been generalized to be used for different rated turbines, and can predict the fatigue damage of a wind turbine with less than 5% error. In general, these alternative, more efficient methods have been shown to be an adequate replacement of the more computationally expensive method of calculating the dynamic response of the turbine within the optimization routine.

Finite element analysis (FEA) software is used to obtain linear and non-linear solutions to one, two, and three-dimensional (3-D) geometric problems that will see a particular load and constraint case when put into service. Parametric FEA models are commonly used in iterative design processes in order to obtain an optimum model given a set of loads, constraints, objectives, and design parameters to vary. In some instances it is desirable for a designer to obtain some intuition about how changes in design parameters can affect the FEA solution of interest, before simply sending the model through the optimization loop. This could be accomplished by running the FEA on the parametric model for a set of part family members, but this can be very timeconsuming and only gives snapshots of the models real behavior. The purpose of this thesis is to investigate a method of visualizing the FEA solution of the parametric model as design parameters are changed in real-time by approximating the FEA solution using surrogate modeling methods. The tools this research will utilize are parametric FEA modeling, surrogate modeling methods, and visualization methods. A parametric FEA model can be developed that includes mesh morphing algorithms that allow the mesh to change parametrically along with the model geometry. This allows the surrogate models assigned to each individual node to use the nodal solution of multiple finite element analyses as regression points to approximate the FEA solution. The surrogate models can then be mapped to their respective geometric locations in real-time. Solution contours display the results of the FEA calculations and are updated in real-time as the parameters of the design model change.

Thesis (M. A.(Social Work)) --University of Limpopo, 2018 / Involuntary childlessness and infertility affects women from various cultural and religious backgrounds. Childless women suffer from social and psychological ailments because of their circumstances. Previous research reveals that women who suffer from infertility and childlessness experience social exclusion and ridicule from their women folk who have children. In South Africa reproduction is a human right and everyone is allowed to make decisions on whether or not they should have children. Surrogacy as an assisted reproductive technique is allowed and governed through the Children’s Act 38 of 2005. However, the knowledge of childless women on surrogacy, their belief system and willingness to take up surrogacy as a way to have children is unknown to the researcher. The aim of this study was to explore the perceptions of childless women on surrogacy as an assisted reproductive technique. The study was exploratory and qualitative in nature. The participants were identified through purposive and snowball sampling. Data was collected until saturation point and seven participants were interviewed. Unstructured, face to face interviews were conducted. An audio recorder was utilised during the interviews. Thematic analysis was employed in data analysis and trustworthiness was used to establish the credibility, transferability, dependability and conformability of the study. Most women in this study mentioned that they had heard and were aware of surrogacy but they were not well-informed about the relevant legislation. Women in this study were willing to take up surrogacy as an option to have their own children.

The present work presents a framework for multiscale modeling of multimaterial flows using surrogate modeling techniques in the particular context of shocks interacting with clusters of particles. The work builds a framework for bridging scales in shock-particle interaction by using ensembles of resolved mesoscale computations of shocked particle laden flows. The information from mesoscale models is “lifted” by constructing metamodels of the closure terms - the thesis analyzes several issues pertaining to surrogate-based multiscale modeling frameworks. First, to create surrogate models, the effectiveness of several metamodeling techniques, viz. the Polynomial Stochastic Collocation method, Adaptive Stochastic Collocation method, a Radial Basis Function Neural Network, a Kriging Method and a Dynamic Kriging Method is evaluated. The rate of convergence of the error when used to reconstruct hypersurfaces of known functions is studied. For sufficiently large number of training points, Stochastic Collocation methods generally converge faster than the other metamodeling techniques, while the DKG method converges faster when the number of input points is less than 100 in a two-dimensional parameter space. Because the input points correspond to computationally expensive micro/meso-scale computations, the DKG is favored for bridging scales in a multi-scale solver. After this, closure laws for drag are constructed in the form of surrogate models derived from real-time resolved mesoscale computations of shock-particle interactions. The mesoscale computations are performed to calculate the drag force on a cluster of particles for different values of Mach Number and particle volume fraction. Two Kriging-based methods, viz. the Dynamic Kriging Method (DKG) and the Modified Bayesian Kriging Method (MBKG) are evaluated for their ability to construct surrogate models with sparse data; i.e. using the least number of mesoscale simulations. It is shown that unlike the DKG method, the MBKG method converges monotonically even with noisy input data and is therefore more suitable for surrogate model construction from numerical experiments. In macroscale models for shock-particle interactions, Subgrid Particle Reynolds’ Stress Equivalent (SPARSE) terms arise because of velocity fluctuations due to fluid-particle interaction in the subgrid/meso scales. Mesoscale computations are performed to calculate the SPARSE terms and the kinetic energy of the fluctuations for different values of Mach Number and particle volume fraction. Closure laws for SPARSE terms are constructed using the MBKG method. It is found that the directions normal and parallel to those of shock propagation are the principal directions of the SPARSE tensor. It is also found that the kinetic energy of the fluctuations is independent of the particle volume fraction and is 12-15% of the incoming shock kinetic energy for higher Mach Numbers. Finally, the thesis addresses the cost of performing large ensembles of resolved mesoscale computations for constructing surrogates. Variable fidelity techniques are used to construct an initial surrogate from ensembles of coarse-grid, relative inexpensive computations, while the use of resolved high-fidelity simulations is limited to the correction of initial surrogate. Different variable-fidelity techniques, viz the Space Mapping Method, RBFs and the MBKG methods are evaluated based on their ability to correct the initial surrogate. It is found that the MBKG method uses the least number of resolved mesoscale computations to correct the low-fidelity metamodel. Instead of using 56 high-fidelity computations for obtaining a surrogate, the MBKG method constructs surrogates from only 15 resolved computations, resulting in drastic reduction of computational cost.

The objective of this study is to propose an efficient sampling-based RBDO using a new classification method to reduce the computational cost. In addition, accuracy improvement strategies for the Kriging method are proposed to reduce the number of expensive computer experiments. Current research effort involves: (1) developing a new classification method that is more efficient than conventional surrogate modeling methods while maintaining required accuracy level; (2) developing a sequential adaptive sampling method that inserts samples near the limit state function; (3) improving the efficiency of the RBDO process by using a fixed hyper-spherical local window with an efficient uniform sampling method and identification of active/violated constraints; and (4) improving the accuracy of the Kriging method by introducing several strategies. In the sampling-based RBDO, only accurate classification information is needed instead of accurate response surface. On the other hand, in general, surrogates are constructed using all available DoE samples instead of focusing on the limit state function. Therefore, the computational cost of surrogates can be relatively expensive; and the accuracy of the limit state (or decision) function can be sacrificed in return for reducing the error on unnecessary regions away from the limit state function. On the contrary, the support vector machine (SVM), which is a classification method, only uses support vectors, which are located near the limit state function, to focus on the decision function. Therefore, the SVM is very efficient and ideally applicable to sampling-based RBDO, if the accuracy of SVM is improved by inserting virtual samples near the limit state function. The proposed sequential sampling method inserts new samples near the limit state function so that the number of DoE samples is minimized. In many engineering problems, expensive computer simulations are used and thus the total computational cost needs to be reduced by using less number of DoE samples. Several efficiency strategies such as: (1) launching RBDO at a deterministic optimum design, (2) hyper-spherical local windows with an efficient uniform sampling method, (3) filtering of constraints, (4) sample reuse, (5) improved virtual sample generation, are used for the proposed sampling-based RBDO using virtual SVM. The number of computer experiments is also reduced by implementing accuracy improvement strategies for the Kriging method. Since the Kriging method is used for generating virtual samples and generating response surface of the cost function, the number of computer experiments can be reduced by introducing: (1) accurate correlation parameter estimation, (2) penalized maximum likelihood estimation (PMLE) for small sample size, (3) correlation model selection by MLE, and (4) mean structure selection by cross-validation (CV) error.

Net pay (NP) and net-to-gross ratio (NGR) are often crucial quantities to characterize a reservoir and assess the amount of hydrocarbons in place. Numerous methods in the industry have been developed to evaluate NP and NGR, depending on the intended purposes. These methods usually involve the use of cut-off values of one or more surrogate variables to discriminate non-reservoir from reservoir rocks. This study investigates statistical issues related to the selection of such cut-off values by considering the specific case of using porosity () as the surrogate. Four methods are applied to permeability-porosity datasets to estimate porosity cut-off values. All the methods assume that a permeability cut-off value has been previously determined and each method is based on minimizing the prediction error when particular assumptions are satisfied. The results show that delineating NP and evaluating NGR require different porosity cut-off values. In the case where porosity and the logarithm of permeability are joint normally distributed, NP delineation requires the use of the Y-on-X regression line to estimate the optimal porosity cut-off while the reduced major axis (RMA) line provides the optimal porosity cut-off value to evaluate NGR. Alternatives to RMA and regression lines are also investigated, such as discriminant analysis and a data-oriented method using a probabilistic analysis of the porosity-permeability crossplots. Joint normal datasets are generated to test the ability of the methods to predict accurately the optimal porosity cut-off value for sampled sub datasets. These different methods have been compared to one another on the basis of the bias, standard error and robustness of the estimates. A set of field data has been used from the Travis Peak formation to test the performance of the methods. The conclusions of the study have been confirmed when applied to field data: as long as the initial assumptions concerning the distribution of data are verified, it is recommended to use the Y-on-X regression line to delineate NP while either the RMA line or discriminant analysis should be used for evaluating NGR. In the case where the assumptions on data distribution are not verified, the quadrant method should be used.

In this paper we present an approach to recover the dynamics from recurrences of a system and then generate (multivariate) twin surrogate (TS) trajectories. In contrast to other approaches, such as the linear-like surrogates, this technique produces surrogates which correspond to an independent copy of the underlying system, i. e. they induce a trajectory of the underlying system visiting the attractor in a different way. We show that these surrogates are well suited to test for complex synchronization, which makes it possible to systematically assess the reliability of synchronization analyses. We then apply the TS to study binocular fixational movements and find strong indications that the fixational movements of the left and right eye are phase synchronized. This result indicates that there might be one centre only in the brain that produces the fixational movements in both eyes or a close link between two centres.