Computational design process modeling

In the conceptual design phase, relatively simple equations and functions (or compiled code) are used to describe the aircraft and to perform trade-off studies. The latter require an optimal execution sequence in order to reduce computational cost and design time, respectively. The focus of this paper is the dynamic derivation of the optimal computational plan for each study so that the designer could focus on designing the aircraft rather than managing the process flow. Two methodologies, the Design Structure Matrix (DSM) and the Incidence Matrix are used for the computational process modeling. The incidence matrix describes the relationship between variables and equations/models. The DSM has been used to express the dependency relationships between the models and also, after manipulation, to produce the solution process. The designer specifies the independent (known) variables first. Then the variable flow is modeled using the Incidence Matrix Method (IMM). It determines how data flows through the models, and also identifies any strongly connected components (SCCs). The second step is to rearrange all equations/models hierarchically in order to reduce the feedback loops in each of the identified SCCs. This is achieved by the application of a genetic-based algorithm. Subsequently all SCCs and noncoupled models are assembled into a macro model which forms a global DSM. The global DSM is further rearranged to obtain an upper triangular matrix which defines the final model execution sequence. A simple aircraft sizing example is presented to illustrate the proposed method and algorithm. Advantages of the method include improved efficiency and the ability to deal with both algebraic and numerical models as well as with multiple outputs per model

Computational workflow management for conceptual design of complex systems : an air-vehicle design perspective

The decisions taken during the aircraft conceptual design stage are of paramount importance since these commit up to eighty percent of the product life cycle costs. Thus in order to obtain a sound baseline which can then be passed on to the subsequent design phases, various studies ought to be carried out during this stage. These include trade-off analysis and multidisciplinary optimisation performed on computational processes assembled from hundreds of relatively simple mathematical models describing the underlying physics and other relevant characteristics of the aircraft. However, the growing complexity of aircraft design in recent years has prompted engineers to substitute the conventional algebraic equations with compiled software programs (referred to as models in this thesis) which still retain the mathematical models, but allow for a controlled expansion and manipulation of the computational system. This tendency has posed the research question of how to dynamically assemble and solve a system of non-linear models. In this context, the objective of the present research has been to develop methods which significantly increase the flexibility and efficiency with which the designer is able to operate on large scale computational multidisciplinary systems at the conceptual design stage. In order to achieve this objective a novel computational process modelling method has been developed for generating computational plans for a system of non-linear models. The computational process modelling was subdivided into variable flow modelling, decomposition and sequencing. A novel method named Incidence Matrix Method (IMM) was developed for variable flow modelling, which is the process of identifying the data flow between the models based on a given set of input variables. This method has the advantage of rapidly producing feasible variable flow models, for a system of models with multiple outputs. In addition, criteria were derived for choosing the optimal variable flow model which would lead to faster convergence of the system. Cont/d.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Enzyme-functionalized thin-cladding long-period fiber grating in transition mode at dispersion turning point for sugar-level and glucose detection

Enzyme-functionalized dual-peak long-period fiber grating (LPFG) inscribed in 80-μm-cladding B/Ge codoped single-mode fiber is presented for sugar-level and specific glucose detection. Before enzyme functionalization, the dual-peak LPFG was employed for refractive index sensing and sugar-level detection and high sensitivities of ∼4298.20  nm/RIU and 4.6696  nm/% were obtained, respectively. Glucose detection probe was attained by surface functionalization of the dual-peak LPFG via covalent binding with aminopropyl triethoxysilane used as a binding site. Optical micrographs confirmed the presence of enzyme. The surface-functionalized dual-peak LPFG was tested with D-(+)-glucose solution of different concentrations. While the peak 2 at the longer wavelength was suitable only to measure lower glucose concentration (0.1 to 1.6  mg/ml) recording a high sensitivity of 12.21±0.19  nm/(mg/ml), the peak 1 at the shorter wavelength was able to measure a wider range of glucose concentrations (0.1 to 3.2  mg/ml) exhibiting a maximum resonance wavelength shift of 7.12±0.12  nm/mg/ml. The enzyme-functionalized dual-peak LPFG has the advantage of direct inscription of highly sensitive grating structures in thin-cladding fibre without etching, and most significantly, its sensitivity improvement of approximately one order of magnitude higher than previously reported LPFG and excessively tilted fibre grating (Ex-TFG) for glucose detection

A fiber optic biosensor for the detection of cholesterol levels based on chitosan coated long period grating.

A fiber optic sensor for the measurement of total cholesterol is designed and developed. The developed chitosan coated long period grating (LPG) sensor shows a sensitivity of 5.025×106 pm·mL/g in the measurement range of the sensor. The sensor also shows a linear response in the measured range of cholesterol levels, which is highly desirable for exploitation as a commercial cholesterol sensor

Computational workflow management for conceptual design of complex systems: an air-vehicle design perspective

The decisions taken during the aircraft conceptual design stage are of paramount importance since these commit up to eighty percent of the product life cycle costs. Thus in order to obtain a sound baseline which can then be passed on to the subsequent design phases, various studies ought to be carried out during this stage. These include trade-off analysis and multidisciplinary optimisation performed on computational processes assembled from hundreds of relatively simple mathematical models describing the underlying physics and other relevant characteristics of the aircraft. However, the growing complexity of aircraft design in recent years has prompted engineers to substitute the conventional algebraic equations with compiled software programs (referred to as models in this thesis) which still retain the mathematical models, but allow for a controlled expansion and manipulation of the computational system. This tendency has posed the research question of how to dynamically assemble and solve a system of non-linear models. In this context, the objective of the present research has been to develop methods which significantly increase the flexibility and efficiency with which the designer is able to operate on large scale computational multidisciplinary systems at the conceptual design stage. In order to achieve this objective a novel computational process modelling method has been developed for generating computational plans for a system of non-linear models. The computational process modelling was subdivided into variable flow modelling, decomposition and sequencing. A novel method named Incidence Matrix Method (IMM) was developed for variable flow modelling, which is the process of identifying the data flow between the models based on a given set of input variables. This method has the advantage of rapidly producing feasible variable flow models, for a system of models with multiple outputs. In addition, criteria were derived for choosing the optimal variable flow model which would lead to faster convergence of the system. Cont/d

Multidisciplinary design optimization framework for the pre design stage

Presented is a novel framework for performing flexible computational design studies at preliminary design stage. It incorporates a workflow management device (WMD) and a number of advanced numerical treatments, including multi-objective optimization, sensitivity analysis and uncertainty management with emphasis on design robustness. The WMD enables the designer to build, understand, manipulate and share complex processes and studies. Results obtained after applying the WMD on various test cases, showed a significant reduction of the iterations required for the convergence of the computational system. The tests results also demonstrated the capabilities of the advanced treatments as follows: The novel procedure for global multi-objective optimization has the unique ability to generate well-distributed Pareto points on both local and global Pareto fronts simultaneously. The global sensitivity analysis procedure is able to identify input variables whose range of variation does not have significant effect on the objectives and constraints. It was demonstrated that fixing such variables can greatly reduce the computational time while retaining a satisfactory quality of the resulting Pareto front. The novel derivative-free method for uncertainty propagation, which was proposed for enabling multi-objective robust optimization, delivers a higher accuracy compared to the one based on function linearization, without altering significantly the cost of the single optimization step

Image processing and machine learning approaches for the automatic diagnosis of endometrial cancer: A comprehensive review

In many medical imaging based diagnosis, Deep Learning (DL) algorithms play an essential role. A DL algorithm can be used to identify abnormal and normal cells in the uterus's endometrium in order to discover Endometrial Cancer (EC) cells. EC is difficult to diagnose since it develops without causing any symptoms. DL algorithm can distinguish between normal, abnormal, and malignant cells, producing more accurate findings than screening by hand procedures such as liquid cytology and Pap smear test. For the accurate and easier detection of EC cells, DL employs multiple architectures. The findings of an analysis and survey of the many forms of DL architecture, as well as their accuracy and performance, are addressed in this work. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the most modalities of advanced imaging used for the non-invasive diagnosis of EC. EC is considered as the fourth most prevalent malignancy in women and one of the common most gynaecological cancers. Diagnostic imaging in clinical evaluation with has not been proven yet to be precise enough to substitute surgical staging in determining the spread of cancer. It may allow for improved surgical process optimization and a more customised therapeutic plan

Design and Development of Fiber Grating Based Chemical and Bio-Sensors

Cochin university of science and TechnologyInternational School of Photonic

Computational design process modelling

In the conceptual design phase, relatively simple equations and functions (or compiled code) are used to describe the aircraft and to perform trade-off studies. The latter require an optimal execution sequence in order to reduce computational cost and design time, respectively. The focus of this paper is the dynamic derivation of the optimal computational plan for each study so that the designer could focus on designing the aircraft rather than managing the process flow. Two methodologies, the Design Structure Matrix (DSM) and the Incidence Matrix are used for the computational process modeling. The incidence matrix describes the relationship between variables and equations/models. The DSM has been used to express the dependency relationships between the models and also, after manipulation, to produce the solution process. The designer specifies the independent (known) variables first. Then the variable flow is modeled using the Incidence Matrix Method (IMM). It determines how data flows through the models, and also identifies any strongly connected components (SCCs). The second step is to rearrange all equations/models hierarchically in order to reduce the feedback loops in each of the identified SCCs. This is achieved by the application of a genetic-based algorithm. Subsequently all SCCs and noncoupled models are assembled into a macro model which forms a global DSM. The global DSM is further rearranged to obtain an upper triangular matrix which defines the final model execution sequence. A simple aircraft sizing example is presented to illustrate the proposed method and algorithm. Advantages of the method include improved efficiency and the ability to deal with both algebraic and numerical models as well as with multiple outputs per model