A conceptual design study of the reusable reentry satellite

Experimentation leading to an understanding of life processes under reduced and extremely low gravitational forces will profoundly contribute to the success of future space missions involving humans. In addition to research on gravitational biology, research on the effects of cosmic radiation and the interruption and change of circadian rhythms on life systems is also of prime importance. Research in space, however, is currently viewed by biological scientists as an arena that is essential, yet largely inaccessible to them for their experimentation. To fulfill this need, a project and spacecraft system described as the Reusuable Reentry Satellite or Lifesat has been proposed by NASA

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Data-Driven Numerical Simulation and Optimization Using Machine Learning, and Artificial Neural Networks Methods for Drilling Dysfunction Identification and Automation

Providing the necessary energy supply to a growing world and market is essential to support human social development in an environmentally friendly. The energy industry is undergoing a digital transformation and rapidly adopting advanced technologies to improve safety and productivity and reduce carbon emissions. Energy companies are convinced that applying data-driven and physics-based technologies is the economical way forward. In drilling engineering, automating components of the drilling process has seen remarkable milestones with considerable efficiency gains. However, more elegant solutions are needed to plan, simulate, and optimize the drilling process for traditional and renewable energy generation. This work contributes to such efforts, specifically in autonomous drilling optimization, real-time drilling simulation, and data-driven methods by developing: 1) a physics-based and data-driven drilling optimization and control methodologies to aid drilling operators in performing more effective decisions and optimizing the Rate of Penetration (ROP) while reducing drilling dysfunctions. 2) developing an integrated real-time drilling simulator, 3) using data-driven methodologies to identify drilling inefficiencies and improve performance. Initially, a novel drilling control systems algorithm using machine learning methods to maximize the performance of manually controlled drilling while advising was investigated. This study employs feasible non-linear control theory and data analysis to assist in data pre-analysis and evaluation. Further emphasis was spent on developing algorithms based on formation identification and Mechanical Specific Energy (MSE), simulation, and validation. Initial drilling tests were performed in a lab-scale drilling rig with improved ROP and dysfunction identification algorithms to validate the simulated performance. Ultimately, the miniaturized drilling machine was fully automated and improved with several systems to improve performance and study the dynamic behavior while drilling by designing and implementing new control algorithms to mitigate dysfunctions and optimize the rate of penetration (ROP). Secondly, to overcome some of the current limitations faced by the industry and the need for the integration of drilling simulation models and software, in which cross-domain physics are uni-fied within a single tool through the proposition and publication of an initial common open-source framework for drilling simulation and modeling. An open-source framework and platform that spans across technical drilling disciplines surpass what any single academic or commercial orga-nization can achieve. Subsequently, a complementary filter for downhole orientation estimation was investigated and developed using numerical modeling simulation methods. In addition, the prospective drilling simulator components previously discussed were used to validate, visualize, and benchmark the performance of the dynamic models using prerecorded high-frequency down-hole data from horizontal wells. Lastly, machine-learning techniques were analyzed using open, and proprietary recorded well logs to identify, derive, and train supervised learning algorithms to quickly identify ongoing or incipient vibration and loading patterns that can damage drill bits and slow the drilling process. Followed by the analysis and implementation feasibility of using these trained models into a con-tained downhole tool for both geothermal and oil drilling operations was analyzed. As such, the primary objectives of this interdisciplinary work build from the milestones mentioned above; in-corporating data-driven, probabilistic, and numerical simulation methods for improved drilling dysfunction identification, automation, and optimization

Understanding morphogenesis in myxobacteria from a theoretical and experimental perspective

Several species of bacteria exhibit multicellular behaviour, with individuals cells cooperatively working together within a colony. Often this has communal benefit since multiple cells acting in unison can accomplish far more than an individual cell can and the rewards can be shared by many cells. Myxobacteria are one of the most complex of the multicellular bacteria, exhibiting a number of different spatial phenotypes. Colonies engage in multiple emergent behaviours in response to starvation culminating in the formation of massive, multicellular fruiting bodies. In this thesis, experimental work and theoretical modelling are used to investigate emergent behaviour in myxobacteria. Computational models were created using FABCell, an open source software modelling tool developed as part of the research to facilitate modelling large biological systems. The research described here provides novel insights into emergent behaviour and suggests potential mechanisms for allowing myxobacterial cells to go from a vegetative state into a fruiting body. A differential equation model of the Frz signalling pathway, a key component in the regulation of cell motility, is developed. This is combined with a three-dimensional model describing the physical characteristics of cells using Monte Carlo methods, which allows thousands of cells to be simulated. The unified model explains how cells can ripple, stream, aggregate and form fruiting bodies. Importantly, the model copes with the transition between stages showing it is possible for the important myxobacteria control systems to adapt and display multiple behaviours