Autonomous Trajectory Design Considering the Limitation of Torque and Drag Forces

In the oil and gas industry, designing well trajectories is an important part of drilling operations that affect well construction, completion, and production. But the current trajectory planning process works in isolation and does not take many engineering constraints into account, which leads to inefficiency, manual iterations, and less-than-ideal results. This study aims to solve the problem by making an automated system for designing 3D trajectories that uses engineering calculations and focuses on torque & drag analysis. The objectives of this research include the development of algorithms to automate and optimize trajectory design, the integration of torque and drag calculations to avoid drill string damage through buckling or over torque, and the evaluation of the system’s performance through case studies. The research also explores the kick-off point optimization and trajectory optimization between target points to enhance well placement and planning efficiency. The significance of this research lies in its potential to revolutionize well planning processes and minimize the cost associated with planning complex wells. It will help the industry by making trajectory planning easier, saving time and money, and minimizing the risks of drilling operations

Model-based systems engineering approach in phased antenna array design and optimization

Abstract. This thesis introduces the concept of Model-based systems engineering by providing an example on how the hardware aspects of a phased antenna array can be modeled with a system modeling language SysML in a modeling software application Cameo Systems modeler, and demonstrates how the resulting system model is used as a central hub for integration with the analysis of a phased antenna array. This thesis covers the creation of analysis models at three different fidelity levels for a phased antenna array that operates in the frequency range from 3.4 to 4 gigahertz. The models are created with the electromagnetic modeling and simulation software Ansys HFSS, and the programming and numeric computing platform MATLAB, which is used to create a script that handles post-processing of the simulation results. The lowest fidelity analysis model is automated in the Multi-disciplinary analysis and optimization tool ModelCenter. The result is an analysis workflow with configurable design parameters as its inputs and performance evaluation parameters as its outputs. The workflow combines and automates the consequent execution of the HFSS electromagnetic model and the post-processing MATLAB script. Afterwards, the workflow is integrated with the system model, which enables the use of requirements in the analysis, and the ability to upload designs achieved with the analysis to the system model. This connected workflow is used to perform a Design of experiments and a machine learning algorithm driven optimization on the phased antenna array, with the goal of finding the best possible spacings between the individual radiating elements in the array. The Design of experiments produces graphs that visualize statistical relationships between the antenna array’s design variables and its performance evaluation parameters. The optimization produces a graph that visualizes a pareto front between different performance evaluation parameters. In other words, the graph shows the design alternatives that cannot be further improved in any parameter without degrading another. This graph is used to make an informed decision on the best radiating element spacings in the antenna array. This results in 50 millimetres for the vertical spacing and 40 millimetres for the horizontal spacing in this example. Finally, the design option is uploaded to the system model, which concludes the demonstration of system and analysis modeling and their integrated usage in the design and optimization of a phased antenna array.Tiivistelmä. Tässä diplomityössä käydään läpi mallipohjaisen järjestelmäsuunnittelun konsepti, sekä osoitetaan esimerkin avulla, kuinka sitä käytetään vaiheistettujen antenniryhmien mallintamiseen SysML-järjestelmänmallinnuskielellä, Cameo Systems Modeler -työkalussa. Tämän lisäksi työssä esitetään, kuinka mallinuksesta syntyvää järjestelmämallia käytetään integroinnin keskuksena vaiheistetun antenniryhmän suunnittelun analysoinnille. Työssä käydään läpi kolmen eri tarkkuustason mallin luonti vaiheistetulle antenniryhmälle, jonka toimintataajuusalue ulottuu 3.4:stä gigahertsistä neljään gigahertsiin. Mallit luodaan käyttämällä sähkömagneettista mallinnus- ja simulointi ohjelmistoa nimeltään Ansys HFSS, sekä numeerista laskenta- ja ohjelmointialustaa nimeltään MATLAB, jolla luodaan skripti simuloinnin tulosten jälkikäsittelyä varten. Tämän jälkeen alimman tarkkuustason analyysimalli automatisoidaan monitieteisellä analyysi- ja optimointi työkalulla nimeltään ModelCenter. Tämä tehdään rakentamalla analyysin työnkulku, jonka tulona on antenniryhmän suunnittelumuuttujia ja lähtönä sen suorituskykyä kuvaavia parametreja. Analyysin työnkulku yhdistää sekä automatisoi sähkömagneettisen HFSS-mallin ja MATLAB-jälkikäsittelyskriptin peräkkäisen ajamisen ModelCenterissä. Tämän jälkeen analyysin työnkulku integroidaan järjestelmämallin kanssa. Tämä mahdollistaa vaatimusten käyttämisen analyyseissa sekä kyvyn ladata analyysin perusteella saatuja suunnitteluvaihtoehtoja järjestelmämalliin. Tätä kytkettyä analyysin työnkulkua käytetään vaiheistetun antenniryhmän kokeelliseen suunnitteluun sekä koneoppimisen algoritmeja käyttävään optimointiin, joiden tavoitteena on löytää parhaat mahdolliset antenniryhmän yksittäisten säteilevien elementtien väliset etäisyydet. Kokeellinen suunnittelu tuottaa kuvaajia, jotka visualisoivat antenniryhmän suunnittelumuuttujien ja suorituskykyä kuvaavien parametrien välisiä tilastollisia riippuvuussuhteita, tehden niiden ymmärtämisestä helppoa. Optimointi tuottaa kuvaajan, joka visualisoi eri suunnitteluvaihtoehdoilla saatavien suorituskykyä kuvaavien parametrien välistä pareto-tehokkuutta. Toisin sanoen kuvaajasta nähdään parhaat suunnitteluvaihtoehdot, joissa minkään suorituskykyä kuvaavan parametrin arvoa ei voida enää parantaa huonontamatta jonkun toisen arvoa. Tämän kuvaajan perusteella tehdään päätös parhaista mahdollisista antenniryhmän säteilevien elementtien välisistä etäisyyksistä. Tulos johon esimerkissä päädytään on 50 millimetriä korkeussuunnassa ja 40 millimetriä sivusuunnassa. Lopuksi tämä suunnitteluvaihtoehto ladataan järjestelmämalliin, joka päättää havainnollistavan esimerkin järjestelmä ja analyysimallinnuksesta, sekä niiden yhdistetystä käytöstä vaiheistettujen antenniryhmien suunnittelussa ja optimoinnissa

A systems approach to evolutionary multiobjective structural optimization and beyond

Jin Y, Sendhoff B. A systems approach to evolutionary multiobjective structural optimization and beyond. IEEE Computational Intelligence Magazine. 2009;4(3):62-76.Multiobjective evolutionary algorithms (MOEAs) have shown to be effective in solving a wide range of test problems. However, it is not straightforward to apply MOEAs to complex real-world problems. This paper discusses the major challenges we face in applying MOEAs to complex structural optimization, including the involvement of time-consuming and multi-disciplinary quality evaluation processes, changing environments, vagueness in formulating criteria formulation, and the involvement of multiple sub-systems. We propose that the successful tackling of all these aspects give birth to a systems approach to evolutionary design optimization characterized by considerations at four levels, namely, the system property level, temporal level, spatial level and process level. Finally, we suggest a few promising future research topics in evolutionary structural design that consist in the necessary steps towards a life-like design approach, where design principles found in biological systems such as self-organization, self-repair and scalability play a central role

Typogenetic design - aesthetic decision support for architectural shape generation

Typogenetic Design is an interactive computational design system combining generative design, evolutionary search and architectural optimisation technology. The active tool for supporting design decisions during architectural shape generation uses an aesthetic system to guide the search process. This aesthetic system directs the search process toward preferences expressed interactively by the designer. An image input as design reference is integrated by means of shape comparison to provide direction to the exploratory search. During the shape generation process, the designer can choose solutions interactively in a graphical user interface. Those choices are then used to support the selection process as part of the fitness function by online classification. Enhancing human decision making capabilities in human-in-the-loop design systems addresses the complexity of architecture in respect to aesthetic requirements. On the strength of machine learning, the integral performance trade-off during multi-criteria optimisation was extended to address aesthetic preferences. The tacit knowledge and subjective understanding of designers can be used in the shape generation process based on interactive mechanisms. As a result, an integrated support system for performance-based design was developed and tested. Closing the loop from design to construction using design optimisation of structural nodes in a set of case studies confirmed the need for intuitive design systems, interfaces and mechanisms to make architectural optimisation more accessible and intuitive to handle. This dissertation investigated Typogenetic Design as a tool for initial morphological search. Novel instruments for human interaction with design systems were developed using mixed-method research. The present investigation consists of an in-depth technological enquiry into the use of interactive generative design for exploratory search as an integrated support system for performance-based design. Associated project-based research on the design potential of Typogenetic Design showcases the application of the design system for architecture. Generative design as an expressive tool to produce architectural geometries was investigated in regard to its ability to drive initial morphological search of complex geometries. The reinterpretation of processes and boosting of productivity by artificial intelligence was instrumental in exploring a holistic approach combining quantitative and qualitative criteria in a human-in-the-loop system. The shift in focus from an objective to a subjective understanding of computational design processes indicates a perspective change from optimisation to learning as a computational paradigm. Integrating learning capabilities in architectural optimisation enhances the capability of architects to explore large design spaces of emergent representations using evolutionary search. The shift from design automation to interactive generative design introduces the possibility for designers to evaluate shape solutions based on their knowledge and expertise to the computational system. At the same time, the aesthetic system is trained in adaptation to the choices made by the designer. Furthermore, an initial image input allows the designer to add a design reference to the Typogenetic Design process. Shape comparison using a similarity measure provides additional guidance to the architectural shape generation using grammar evolution. Finally, a software prototype was built and tested by means of user-experience evaluation. These participant experiments led to the specification of custom software requirements for the software implementation of a parametric Typogenetic tool. I explored semi-automated design in application to different design cases using the software prototype of Typogenetic Design. Interactive mass-customisation is a promising application of Typogenetic Design to interactively specify product structure and component composition. The semi-automated design paradigm is one step on the way to moderating the balance between automation and control of computational design systems