The application of optimization algorithms in the design of many economical and industrial problems currently represents a significant assignment. The development of high-powered computers allows an application of difficult mathematical techniques and physical phenomena to simulate real problems with sufficient accuracy. The optimization techniques used in engineering designs are mostly represented by modified mathematical programming methods with extension of their usability. The aim of the presented thesis "Using Optimization´s Algorithms by Designing of Structures" is to analyze the applicability of optimization procedures which are available in the widely used computing system ANSYS in civil and mechanical engineering practice. The numerical analyses were performed within the frame of multi-extreme, one to three dimensional optimization problems, multi-dimensional problems expressed by minimizing the weight of a truss beam and efficient design of air gap location in wooden studs from the point of view of thermal features of the structure. The analyzed optimization processes are in plurality verified with accurate manual computing and graphical solutions and the accent is put on optimization methods´ possibilities to improve robustness, efficiency and accuracy of the optimization algorithms in civil engineering problems´ designs. The optimization methods represent a suitable approach to improve the efficient design of a wide range of civil and mechanical engineering structures and elements. By combination of their advantages and FEM/FEA method it is possible to achieve very good results, although robustness of the solutions is not guaranteed. The robustness and accuracy of the procedure could be increased by competent exploration of design space and suitable selections of optimization methods´ features.

Im Beitrag werden Forschungsergebnisse zum numerischen Entwurf textilbewehrter Verstärkungsschichten zusammengefasst. Die Ergebnisse resultieren im Wesentlichen aus den Arbeiten der Teilprojekte D2-Numerische Simulation, E3-Sicherheitsbeurteilung und E4-Numerische Langzeitprognose des Sonderforschungsbereichs 528. Zusätzlich wird auf Transferleistungen verwiesen. / The paper provides a summary of research results concerning numerical design approaches for textile reinforced structures. The outcome mainly results from the work done in the subprojects of the Collaborative Research Centre 528: D2-Numerical Simulation, E3-Reliability Assessment und E4-Numerical Long-term Prognosis. Further, the paper also points out the transfer potential.

In this thesis, the authors investigate how machine learning can be utilized for speeding up the design optimization process of gas turbines. The Finite Element Analysis (FEA) steps of the design process are examined if they can be replaced with machine learning algorithms. The study is done using a component with given constraints that are provided by Siemens Energy AB. With this component, two approaches to using machine learning are tested. One utilizes design parameters, i.e. raw floating-point numbers, such as the height and width. The other technique uses a high dimensional mesh as input. It is concluded that using design parameters with surrogate models is a viable way of performing design optimization while mesh input is currently not. Results from using different amount of data samples are presented and evaluated.

<p> </p> <p>Gas sensors have many applications. Volatile organic compound (VOC) sensors are used for monitoring air quality in homes and office spaces, as well as monitoring manufacturing environments where a wide variety of VOCs can be produced. These gases can include formaldehyde, which can be toxic to humans at concentrations as low as 1 ppm. Other applications for gas sensors include flammable refrigerant detection. With the move towards developing more environmentally friendly appliances, many companies have started to use refrigerants such as R600a (isobutane) and R32 (difluoromethane), which have a much lower global warming potential (GWP) than their predecessors, such as R134a and R410a. While this move is beneficial for the environment, steps to ensure their safe usage have not been widely implemented to date. Therefore, sensors to detect VOCs at or below exposure limits, as well as flammable refrigerants at or below lower flammability limits (LFL), should be developed to ensure undue hazards are identified and mitigated. </p>

Les produits mécatroniques sont complexes et multidisciplinaires par nature. Les exigences pour les concevoir sont souvent contradictoires et doivent être validées par les différentes équipes d'ingénierie disciplinaire (ID). Pour répondre à cette complexité et réduire le temps de conception, les ingénieurs disciplinaires ont besoin de collaborer dynamiquement, de résoudre les conflits interdisciplinaires et de réutiliser les connaissances de projets antérieurs. De plus, ils ont besoin de collaborer en permanence avec l’équipe d’ingénierie systèmes (IS) pour avoir un accès direct aux exigences et l’équipe d’optimisation multidisciplinaire (OMD) pour valider le système dans sa globalité.Nous proposons d'utiliser des techniques de gestion des connaissances pour structurer les connaissances générées lors des activités de collaboration afin d'harmoniser le cycle de conception. Notre principale contribution est une approche d'unification qui explique comment IS, ID et OMD se complètent et peuvent être utilisés en synergie pour un cycle de conception intégré et continu. Notre méthodologie permet de centraliser les connaissances nécessaires à la collaboration et au suivi des exigences. Elle assure également la traçabilité des échanges entre les ingénieurs grâce à la théorie des graphes. Cette connaissance formalisée du processus de collaboration permet de définir automatiquement un problème OMD. / Mechatronic products are complex and multidisciplinary in nature. The requirements to design them are often contradictory and must be validated by the various disciplinary engineering (DE) teams. To address this complexity and reduce design time, disciplinary engineers need to collaborate dynamically, resolve interdisciplinary conflicts, and reuse knowledge from previous projects. In addition, they need to work seamlessly with the Systems Engineering (SE) team to have direct access to requirements and the Multidisciplinary Design Optimization (MDO) team for global validation. We propose to use Knowledge Management techniques to structure the knowledge generated during collaboration activities and harmonize the overall design cycle. Our primary contribution is a unification approach, elaborating how SE, DE, and MDO complement each-other and can be used in synergy for an integrated and continuous design cycle. Our methodology centralizes the product knowledge necessary for collaboration. It ensures traceability of the exchange between disciplinary engineers using graph theory. This formalized process knowledge facilitates MDO problem definition.

In order to enhance accessibility to microgravity research, the design process for experiments on the ISS must be streamlined and accessible to all scientific disciplines, not just aerospace engineers. Thus, a general design and analysis toolbox with accompanying best practices manual for microgravity liquid containment is proposed. The work presented in this thesis improves the design process by introducing a modular liquid tank design which can be filled, drained, or act as a passive liquid-gas separation device. It can also be pressurized, and used for aerosol spray. This tank can be modified to meet the design requirements of various experimental setups and liquids. Furthermore, rough simulations of this tank are presented and available to the user for modification. The simulation and design methodology for other general cases is discussed as well. After reading this thesis, the user should have a basic understanding of how liquids behave in microgravity. She will be able to run simple simulations, design, build, test, and fly a liquid management device which has been modified to suit the requirements of her specific experiment. The general tank design can be manufactured using 3-D printing, traditional CNC milling, or a combination thereof. The design methodology and best practices presented here have been used to design tanks used in experiments on the International Space Station for Budweiser and Lambda Vision. Both tanks functioned nominally on orbit. While the specific data from these experiments cannot be presented due to proprietary restrictions, using this thesis as a design guide for new experiments should yield favorable results when applied to new tank designs. If the reader has any questions or would like an updated design process, the author’s preferred contact information can be found using the Orcid iD: 0000-0002-2617-2957 .

No description available.

This dissertation presents an investigation and design optimization of energy absorbent protective systems that protect the brain. Specifically, the energy absorption characteristics of the bighorn sheep skull-horn system were quantified and used to inform a topology optimization performed on a football helmet facemask leading to reduced values of brain injury indicators. The horn keratin of a bighorn sheep was experimentally characterized in different stress states, strain rates, and moisture contents. Horn keratin demonstrated a clear strain rate dependence in both tension and compression. As the strain rate increased, the flow stress increased. Also, increased moisture content decreased the strength and increased ductility. The hydrated horn keratin energy absorption increased at high strain rates when compared to quasi-static data. The keratin experimental data was then used to inform constitutive models employed in the simulation of bighorn sheep head impacts at 5.5 m/s. Accelerations values as high as 607 G’s were observed in finite element simulations for rams butting their heads, which is an order of magnitude higher than predicted brain injury threshold values. In the most extreme case, maximum tensile pressure and maximum shear strains in the ram brain were 245 kPa and 0.28, respectively. These values could serve as true injury metrics for human head impacts. Finally, a helmeted human head Finite Element (FE) model is created, validated, and used to recreate impacts from a linear impactor. The results from these simulations are used to train a surrogate model, which is in turn utilized in multi-objective design optimization. Brain injury indicators were significantly reduced by performing multi-objective design optimization on a football helmet facemask. In particular, the tensile pressure and maximum shear strain in the brain decreased 7.5 % and 39.5 %, respectively when comparing the optimal designs to the baseline design. While the maximum tensile pressure and maximum shear strain values in the brain for helmeted head impacts (30.2 kPa and 0.011) were far less than the ram impacts (245 kPa and 0.28), helmet impacts up to 12.3 m/s have been recorded, and could easily surpass these thresholds.

The efficient study of liquid droplets ranging from micrometers to a few centimeters by levitation is usually hindered by conventional design limitations. This is due to continuous droplet deformation in the test section. This research discusses the development of a robust design methodology for large droplet-stabilization (d > Capillary Number (Ca)) vertical wind tunnels. A modeling and simulation design environment has been developed that involves component sizing and integration at a central ANSYS-Fluent platform, followed by design optimization. The work inculcates numerical analysis of guide vanes to minimize the viscous losses and, subsequently, the wind tunnel dimensions. The process is followed by the design of honeycomb and wire screens and their analyses for a given geometry. A multi-variable design optimization problem has been optimized with response surface approximations. Statistical modeling of the expensive functions obtained from the solution of Navier-stokes equations has been accomplished in order to deal with non-linear and discontinuous behavior. Numerical optimization of the meta-model can help to find the most feasible wind tunnel design with computational efficiency. A non-conventional design with varying test area cross-sections has been introduced to investigate the droplet stability in constantly changing velocity profiles. Longitudinal as well as lateral velocity variations in the test section, creating velocity buckets with minimum turbulence intensity, has been introduced and analyzed using novel concept designs. The research highlights a systematic design methodology and an alternate configuration for liquid droplet wind tunnels while focusing on stable droplet levitation.

<p>Autoinjectors are pen-like devices that typically deliver drug products of 2 mL or less. They shield the needle before and after use, reducing patient anxiety from needle phobia and mitigating the risk of needlestick injuries and accidental contamination. Additionally, automatic delivery ensures more consistent needle penetration depth and injection force than manual injection methods. </p> <p><br></p> <p>To optimize autoinjector design, this thesis presents experimentally validated computational models that describe the processes of needle insertion, drug delivery, and transport of subcutaneously administered therapeutic proteins in the body. A multi-objective optimization framework is also proposed to guide the design of autoinjectors.</p> <p><br></p> <p>This thesis focuses on spring-driven autoinjectors, the most common type of autoinjector. It begins with an overview of the interactions between the spring-driven autoinjector, tissue, and therapeutic proteins. Moving on to Chapter 2, a computational model is presented to accurately predict the kinematics of the syringe barrel and plunger during the needle insertion process.</p> <p><br></p> <p>In Chapter 3, we present a quasi-steady model for the drug delivery process, which considers the rheology of therapeutic proteins. The Carreau model is adopted to describe protein viscosity, and explicit relationships between flow rate and pressure drop in the needle are derived. Furthermore, the applicable regime for the power-law model for protein viscosity is identified.</p> <p><br></p> <p>Chapter 4 quantifies the impact of sloshing and cavitation on therapeutic proteins in the syringe. Additionally, a workflow is presented to integrate available simulation tools to predict the performance of spring-driven autoinjectors. The influence of each design parameter of spring-driven autoinjectors on their performance is also discussed. </p> <p><br></p> <p>The spring-driven autoinjector delivers therapeutic proteins through subcutaneous administration. To gain insights into the transport process of therapeutic proteins, Chapter 5 presents a physiologically-based pharmacokinetic model that has been validated against experimental data for humans and rats. The lymph flow rate significantly affects the bioavailability of therapeutic proteins. This finding highlights the importance of studying the transport of therapeutic proteins in the lymphatic system in future research.</p> <p><br></p> <p>Chapter 6 provides a multi-objective design optimization framework for the spring-driven autoinjector. The computational model is replaced with an accurate deep neural network surrogate to improve the computational efficiency.  Using this surrogate model, we conduct a sensitivity analysis to identify essential design parameters. After that, we perform multi-objective optimization to find promising design candidates.</p> <p><br></p> <p>Chapter 7 presents a model for bubble dynamics in a protein solution. An explicit expression for the bubble dissolution rate is derived, enabling extraction of the interfacial properties of the protein-coated interface from the measured bubble radii. Moreover, analytical solutions for the response of a protein-coated bubble to an imposed acoustic pressure are derived. This work provides insight into protein-coated bubbles, which are used as vehicles to deliver drugs, as active miniature tracers to probe the rheology of soft and biological materials, or as contrast agents to enhance the ultrasound backscatter in ultrasonic imaging.</p> <p><br></p> <p>At last, in Chapter 8, we introduce a model for laser-induced cavitation that considers several key factors, such as liquid compressibility, heat transfer, and non-equilibrium evaporation and condensation. Our model's predictions for the time-course of bubble radii have been validated with experimental data. Moreover, our model reveals that the reduction of the bubble's oscillation amplitude is primarily due to a decrease in the number of vapor molecules inside the bubble, highlighting the crucial role of phase change in laser-induced cavitation bubbles.</p> <p><br></p> <p>The developed computational models and framework provide crucial insights into the development of spring-driven autoinjectors and cavitation bubbles. These studies can also enhance the efficacy and safety of the delivery of therapeutic proteins, ultimately improving patient outcomes.</p>