| 51. |
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| 52. |
- Abbasiverki, Roghayeh
(författare)
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Numerical modelling considerations for analysis of concrete hydraulic structures subjected to high-frequency seismic loads
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Concrete hydraulic structures are of great importance in today's society. When situated in areas with hard bedrock, these structures may become extra vulnerable to seismic excitations as these here are dominated by high-frequency vibrations which can have disastrous consequences for slender structures. The aim of this thesis was to investigate special considerations that must be made when conducting analyses of such hydraulic structures during high-frequency excitations. Underground and on the ground structures were investigated separately. Underground concrete pipelines and concrete buttress dams were selected for the study because their behaviour when exposed to seismic excitations is dominated by their stiffness. The most effective models and modelling methods for the seismic analyses of such structures were implemented and evaluated. Two-dimensional finite element (FE) models were developed for the dynamic analysis of underground concrete pipelines loaded by seismic waves propagating from bedrock through soil. The interaction between the bedrock and the surrounding soil was investigated with respect to rock geometry and soil properties. The surface of dam foundations is commonly irregular, resulting in nonuniform motions at the dam-foundation interface. The free-field modelling methods for concrete dam foundations were adapted in order to accurately describe the propagation of earthquake vibrations from the source to the ground surface. The implementation of a threedimensional FE model for concrete buttress dams was investigated. Two different methods for free-field modelling are presented, which can be implemented independently of the software used. The seismic loads are applied as effective earthquake forces at non-reflecting boundaries. In the first method, the free-field motions at the non-reflecting boundaries are determined by the so-called domain reduction method using the direct FE calculation. In the second method, the free-field motions are analytically determined based on the onedimensional wave propagation theory. The results are also compared with the massless foundation modelling approach, in which the topographical amplifications are neglected. It was demonstrated that a two-dimensional model can effectively account for pipeline behaviour. The most important aspect of the models is the ability to capture bending deformations, as segmented structures such as pipelines are vulnerable in this respect. Nonuniform bedrock reduces the safety of concrete pipeline, especially because of bending deformations in the pipe and joints. The massless method gave unreliable results for analyses of dams, especially for high-frequency excitations. The analytical method was also unreliable in estimating the non-linear behaviour of the dams. But, a new time domain deconvolution method was developed to transform the earthquake motion from the foundation surface to the corresponding input motion at depth. It wasfound that free-field modelling of foundations using the direct FE method can accurately capture the topographic amplifications of the seismic excitations. It was shown that a three-dimensional model is required for seismic evaluation of concrete buttress dams. The topographic amplification of high-frequency waves at the surface of canyons had a significant effect on the response of this type of dam.
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| 53. |
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| 54. |
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| 55. |
- Abbes, Yacine
(författare)
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Educational studies in heat and power technology : how students learn with multimedia tools and problem-based learning
- 2005
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Higher education is undergoing continuous changes and new learning tools and methods are implemented. Researchers in education do not always agree upon the effectiveness of some of the methods introduced into engineering education. The present thesis consists of two case studies on educational methods introduced at the Department of Energy Technology, at Royal Institute of Technology (KTH), Sweden. The qualitative research methodology has been used in case one and a combination of qualitative and quantitative methodology has been used in the second case. The sources of evidences consisted of: unstructured interviews, analysis of video recording, questionnaires, and analysis of a variety of documents. In the first case, an educational program in heat and power technology was analysed. The second case consists in an in-depth study of group dynamics in a Problem –Based Learning course. These studies showed that the learning approach adopted by students depends strongly on the way they view the particular learning tool or method. The first case study revealed the existence of two types of learners. Surfacelearners follow the structure suggested by the designers of the multimedia program. This category of learners focuses only on the material available in the program. Deep-learners go beyond the information and the structure suggested in the program and combine different learning tools in their learning. These students do not follow the structure of the tutorials’ of the multimedia program. This study showed that students who had a strong view how to learn with a multimedia program or a learning method benefited less from the learning tools available. Students with weak views on how to learn from educational program or leaning tool benefit less from the presentation and engage in more surface learning. Self-motivated learners use the multimedia presentation in novel ways and crosscheck the information given with other material. The second study showed that students have unclear and weak views on how to learn with student-directed Problem- Based Learning model. Four types of learners were identified in Problem-Based Learning project: Leaders, Key Actors, Common Students and Social Loafers. Leaders and Key Actors are self-motivated individuals and participate most in the projects. Students who viewed themselves or were viewed as leaders were held responsible to take most of the decisions and students expected them to work more than the average student. Students who viewed themselves as common team members expected a lower workload than leaders’. Key Actors are self-motivated students who do not view themselves as separate from other group members but who participate more than others. Leaders learned more group and social processes, that they did not fully take part in, while common students learned more from the project management aspects that they did not take part in. The study also found that Problem-Based Learning groups can become very cohesive, and can develop distorted views on how to learn with Problem-Based Learning, and un-common group dynamics phenomena such as groupthink can occur in Problem-Based Learning setting.
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| 56. |
- Abbott, Andrew, et al.
(författare)
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Ionic liquids at interfaces : general discussion
- 2018
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Ingår i: Faraday discussions. - : Royal Society of Chemistry (RSC). - 1359-6640 .- 1364-5498. ; 206, s. 549-586
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 57. |
- Abburu, Sai Kausik, et al.
(författare)
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Analyzing interaction effects in a vehicle model using network theory
- 2021
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Ingår i: Resource Efficient Vehicles Conference, rev2021.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The vehicle industry is moving towards developing more sustainable and efficient solutions. This movement towards sustainable and efficient solutions brings up the need to develop and integrate new subsystem technologies that are beneficial for the overall vehicle system. However, introducing new technology into an existing vehicle architecture may have knock-on effects on the dependent subsystems. Furthermore, there can be a bias towards the existing technological solutions as a large part of the architecture is developed pertaining to the established solutions. Therefore, sufficient knowledge is required to understand the level of impact the interdependencies, both direct and indirect, can have at a subsystem level and at the overall vehicle system level. To address and assess these interdependencies that arise during the conceptual design phase, a bottom-up design model is proposed. The model, utilizing network theory could represent each subsystem as nodes and their interaction effects on each other as edges. Thus, the interaction effects between different subsystems and their complex influence on the overall vehicle system are considered. This model could serve to evaluate an optimal solution in terms of functional density and economic benefits thus providing the opportunity to avoid any unintended negative indirect effects. Furthermore, it could help in identifying the technological limits in the current vehicle system and thus, identifying the areas that can be developed to further enhance the vehicle system performance. The method of implementation, its advantages, disadvantages, applications, and challenges in implementation are discussed.
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| 58. |
- Abburu, Sai Kausik
(författare)
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Vehicle Conceptualisation, Compactness, and Subsystem Interaction : A network approach to design and analyse the complex interdependencies in vehicles
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The conventional approach to vehicle design is restrictive, limited, andbiased. This often leads to sub-optimal utilisation of vehicle capabilities and allocated resources and ultimately entails the repercussions of designing andlater on an using an inefficient vehicle. To overcome these limitations, it is important to gain a deeper understanding of the interaction effects at component,subsystem, and system level. In this thesis, the research is focused on identifying appropriate methods and developing robust models to facilitate the interaction analysis.To scrutinise and identify appropriate methods, criteria were developed.Initially, the Design Structure Matrix (DSM) and its variations were examined.While DSM proved to be fundamental for capturing interaction effects,it lacked the ability to answer questions about the structure and behaviour ofinteractions and to predict unintended effects. Therefore, network theory wasexplored as a complementary method to DSM which was capable of providing insights into interaction structures and identifying influential variables.Subsequently, two criteria were established to identify subsystems significant to interaction analysis: high connectivity to other subsystems and multidisciplinary composition. The traction motor was observed to satisfyboth criteria as it had higher connectivity with other subsystems and was composed of multiple disciplines. Therefore, a detailed model of an induction motor was developed to enable the interaction analysis.The induction motor model was integrated into a cross-scalar design tool.The tool employed a two-step process: translating operational parametersto motor inputs using Newtonian equations and deriving physical attributes,performance characteristics, and performance attributes of the motor. Comparing the obtained performance characteristics curve against existing studiesvalidated the model’s reliability and capabilities. The design tool demonstrated adaptability to different drive cycles and the ability to modify motor performance without affecting operational parameters. Thus validating the capability of the design tool to capture cross-scalar and intra-subsystem interaction effects. To examine inter-subsystem interaction, a thermal model of an inverter was developed, capturing temperature variations in the power electronics based on motor inputs. The design tool successfully captured interaction effects between motor and inverter designs, highlighting the interplay with operational parameters.Thus, this thesis identifies methods for interaction analysis and develops robust subsystem models. The integrated design tool effectively captures intra-subsystem, inter-subsystem, and cross-scalar interaction effects. The research presented contributes to the overarching project goal of developing methods and models that capture interaction effects and in turn serve as a guiding tool for designers to understand the consequences of their design choices.
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| 59. |
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| 60. |
- Abdalmoaty, Mohamed, 1986-
(författare)
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Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. During the last decade, several numerical methods have been developed to approximately solve the maximum likelihood problem. A class of algorithms that attracted considerable attention is based on sequential Monte Carlo algorithms (also known as particle filters/smoothers) and particle Markov chain Monte Carlo algorithms. These algorithms were able to obtain impressive results on several challenging benchmark problems; however, their application is so far limited to cases where fundamental limitations, such as the sample impoverishment and path degeneracy problems, can be avoided.This thesis introduces relatively simple alternative parameter estimation methods that may be used for fairly general stochastic nonlinear dynamical models. They are based on one-step-ahead predictors that are linear in the observed outputs and do not require the computations of the likelihood function. Therefore, the resulting estimators are relatively easy to compute and may be highly competitive in this regard: they are in fact defined by analytically tractable objective functions in several relevant cases. In cases where the predictors are analytically intractable due to the complexity of the model, it is possible to resort to {plain} Monte Carlo approximations. Under certain assumptions on the data and some conditions on the model, the convergence and consistency of the estimators can be established. Several numerical simulation examples and a recent real-data benchmark problem demonstrate a good performance of the proposed method, in several cases that are considered challenging, with a considerable reduction in computational time in comparison with state-of-the-art sequential Monte Carlo implementations of the ML estimator.Moreover, we provide some insight into the asymptotic properties of the proposed methods. We show that the accuracy of the estimators depends on the model parameterization and the shape of the unknown distribution of the outputs (via the third and fourth moments). In particular, it is shown that when the model is non-Gaussian, a prediction error method based on the Gaussian assumption is not necessarily more accurate than one based on an optimally weighted parameter-independent quadratic norm. Therefore, it is generally not obvious which method should be used. This result comes in contrast to a current belief in some of the literature on the subject. Furthermore, we introduce the estimating functions approach, which was mainly developed in the statistics literature, as a generalization of the maximum likelihood and prediction error methods. We show how it may be used to systematically define optimal estimators, within a predefined class, using only a partial specification of the probabilistic model. Unless the model is Gaussian, this leads to estimators that are asymptotically uniformly more accurate than linear prediction error methods when quadratic criteria are used. Convergence and consistency are established under standard regularity and identifiability assumptions akin to those of prediction error methods.Finally, we consider the problem of closed-loop identification when the system is stochastic and nonlinear. A couple of scenarios given by the assumptions on the disturbances, the measurement noise and the knowledge of the feedback mechanism are considered. They include a challenging case where the feedback mechanism is completely unknown to the user. Our methods can be regarded as generalizations of some classical closed-loop identification approaches for the linear time-invariant case. We provide an asymptotic analysis of the methods, and demonstrate their properties in a simulation example.
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