Applications of Additive Manufacturing for Norwegian Oil and Gas Industries

The additive manufacturing or 3D printing (3DP) technologies have undergone exponential expansion, particularly in the previous couple of decades. Additive manufacturing technologies have paved the way for easy component manufacturing in large-scale and high-performance businesses. The introduction of desktop 3D printers has established 3DP as a reliable technique for generating prototypes and direct parts from CAD files. This technology is employed in an industrial setting for a range of purposes, including the invention and manufacture of customized and task-specific tools. This thesis looks at the benefits and drawbacks of deploying a 3D printer on an offshore facility to encourage on-site part manufacture, save operating costs, and reduce downtime. The thesis proposes ways for speeding and simplifying the creation of customized products. The approaches utilized were aimed to discover flaws and opportunities in offshore platforms' 3D printing processes. It also includes a comparative examination of production procedures, which will aid in decision-making. Furthermore, the technical structure of the proposed method would outline a path for developing prototype designs and tools to address identified difficulties. The proposed ideas and produced technologies could have a positive impact on the oil and gas industries' operations. The thesis also goes over the equipment needed for post-processing printed parts, as well as their availability on offshore platforms. The reliability issues associated with 3D printed parts are also addressed, which will improve RAMS analysis of printed parts

Prediction of airflow for automotive cooling applications using openFOAM

This research focuses on studying OpenFOAM\u27s capability of underhood thermal simulations and investigating the performance of various fan modeling techniques in comparison to other commercial software packages. An isolated fan is modeled in OpenFOAM using Moving Reference Frame (MRF) and Actuator Disk techniques. To evaluate their performances, the simulation results are compared to the experimental data which was provided by a fan testing facility and the available simulation results from Star-CCM+ and ACE+. The pressure rise is the main parameter that is used for comparisons. To further investigate OpenFOAM\u27s capabilities, a full vehicle model using MRF technique is studied and the airflow rate across the radiator from simulation results was compared to experimental data and ACE+. The simulation results showed that OpenFOAM has a promising performance on solving the pressure rise across an isolated fan using MRF and Actuator Disk Model. Within the scope of this study, both fan modeling techniques in OpenFOAM gave more accurate results than Star-CCM+ and ACE+, while the Actuator Disk Model predicted the pressure rise more precisely than the MRF model. By modeling the fan using MRF technique in a full vehicle simulation, the predicted airflow rate across the radiator in OpenFOAM was less accurate than ACE+

Effects of Heat Transfer on Vehicle Front-end Cooling Airflow Simulation

This research focuses on virtual simulation techniques for vehicle underhood airflow. The main objective is to gain a better understanding of heat transfer effects on vehicle underhood cooling airflow and provide correction methods to increase the accuracy of simulations early in the vehicle development phase. Simulations are carried out for a stand-alone radiator setup, based on three different flow assumptions; constant density iso-thermal, constant density with heat transfer, and variable density with heat transfer. It was observed that, in some cases, corrected heat exchanger porous resistance terms need to be adopted for each simulation case in order to provide good correlation with test data. Similar flow assumptions are carried over to a full vehicle underhood simulation, for which additional components, such as a transmission oil cooler, condenser, and fan were modeled. It was observed that mass flow rates at the radiator inlet are over-estimated with the assumption of an incompressible iso-thermal flow; by 2% with respect to the incompressible simulation with temperature effects, and by 10% with respect to the variable density simulation with temperature effects. It is suggested that in order to capture the local increase in velocity field at the heat exchangers, it is necessary to perform simulations with a variable density. However, to establish confidence in the quantitative results, further studies regarding the impact of fan modelling and variable density effects should be performed

14th International Conference on Turbochargers and Turbocharging

14th International Conference on Turbochargers and Turbocharging addresses current and novel turbocharging system choices and components with a renewed emphasis to address the challenges posed by emission regulations and market trends. The contributions focus on the development of air management solutions and waste heat recovery ideas to support thermal propulsion systems leading to high thermal efficiency and low exhaust emissions. These can be in the form of internal combustion engines or other propulsion technologies (eg. Fuel cell) in both direct drive and hybridised configuration. 14th International Conference on Turbochargers and Turbocharging also provides a particular focus on turbochargers, superchargers, waste heat recovery turbines and related air managements components in both electrical and mechanical forms

Numerical Prediction of Automotive Underhood Airflows using an Uncalibrated Fan Body Force Model

Underhood vehicle airflow simulations are an important part of the overall vehicle thermal management process, especially in the preliminary stages of the vehicle development program when performing experimental work on cooling system prototypes can prove to be expensive, time-consuming, or simply impossible due to the absence of any physical vehicle prototypes. Accurate prediction of the automotive fan performance, which forms a critical component of the cooling module, is a prerequisite for the optimum sizing and design of heat exchangers, and the rest of the under-hood installations. The coupled and complex nature of the under-hood flow environment necessitates consideration of the entire front-end cooling module, and preferably the entire vehicle, in a single simulation to judge the fan performance. Direct modelling of the rotating fan blades in a full vehicle simulation can yield unacceptably long run times, hence the norm is to use simplified numerical models which can capture the general fan behaviour at a reduced cost. Industrial practice is to calibrate these fan models with experimental or high-fidelity simulated fan performance data, which slows down the design process and is expensive. This work solves this problem by using an uncalibrated body force fan modelling approach, which only requires fan geometry information and no a-priori fan performance data. The approach has previously shown promising results for aircraft engine fan applications, however it’s suitability for automotive fan applications is tested for the first time. The model performs with a comparable accuracy as the current state-of-the-art calibrated fan modelling techniques. It predicts the radiator airflow rate to within 8% of the experimentally-measured value at idle. At high vehicle speed, the accuracy improves to 1%. Success in this project facilitates a low-cost, reliable and rapid aerothermal analysis tool for designing vehicle cooling systems

Design and Optimization of an FSAE Vehicle

The purpose of this project was to develop a race car for the Formula SAE Michigan Design Competition. For this year\u27s team, the focus was to transition the car from 13 in. diameter wheels to 10 in. wheels, while maintaining current powertrain output and drivability of the previous car. Rationale includes reducing unsprung mass of the vehicle and transitioning to Hoosier\u27s LC0 tire compound; a tire compound with a theoretically better temperature range for the type of driving done in FSAE. In order to do this successfully, a new suspension system, braking system, frame, and accompanying parts needed to be designed. The final goals of this car were to achieve a reduced or similar weight to the 2019 car, while having improved handling

Optimisation of the Flow Process in Engine Bays - 3D Modelling of Cooling Airflow

The focus of today’s automotive industry is to reduce emissions and fuel consumption of all vehicles. Concentrating on the truck industry, the last 20 years have focused largely on cutting emissions of particulate matter and nitrogen oxides. For the future, attention will be on fuel consumption and emissions of carbon dioxide. Significant changes have been made to fulfill new emission legislations, but the basic vehicle architecture has been kept. New after treatment systems that increases the thermal loading of the cooling system have been added within the same packaging envelope as before. This means that there is less space to evacuate cooling airflow today and more airflow than ever is required. Furthermore, project costs have increased over the years, focus is also on cutting cost and lead times. Thus virtual development early in the project is highly desirable. Long before any prototypes are available, companies must now answer the question; will this truck have competitive performance? As the project progresses, redesigns become more expensive. Development time is becoming more and more limited, meaning any changes tend to become major changes. This has lead to a new focus of detailed and accurate simulations of vehicle performance. For these reasons, in the context of underhood thermal management, this project has been carried out; to improve and optimise the flow process in engine bays. 3D CFD supported by 1D models and measurements has been studied to predict the cooling airflow in the engine bay of trucks. The conclusions are that there are good opportunities to simulate the flow process in engine bays early in development projects. This research project presents several different methods that, for different degrees of effort deliver different accuracy and indications are that simulation can replicate measurements. This is though, with advanced simulation models and a lot of computational effort, at least seen from today’s perspective

The Design, Manufacturing, and Testing of the 2014 Side-Pods for the Global Formula Racing Vehicle

For many years, engine overhearing has been the major cause for vehicle breakdowns and engine malfunctions for most teams in Endurance events at Formula Society of Automotive Engineers (FSAE) Series competitions. Additionally, with a growing importance of aerodynamics to the overall success of the Global Formula Racing at these events, in addition to an internal need for engine cooling, the side-pods have been a large focus of redesign for the 2014 season. The side-pods, which house oil and water radiators for engine cooling, are responsible for providing adequate airflow to maintain stable engine operating temperatures, while increasing downforce and reducing drag on the vehicle. This thesis details the complete design and manufacturing processes, followed by testing simulations and physical validation of the 2014 side-pods on the Global Formula Racing combustion and electric vehicles

Artificial intelligence for digital twins in energy systems and turbomachinery: development of machine learning frameworks for design, optimization and maintenance

The expression Industry4.0 identifies a new industrial paradigm that includes the development of Cyber-Physical Systems (CPS) and Digital Twins promoting the use of Big-Data, Internet of Things (IoT) and Artificial Intelligence (AI) tools. Digital Twins aims to build a dynamic environment in which, with the help of vertical, horizontal and end-to-end integration among industrial processes, smart technologies can communicate and exchange data to analyze and solve production problems, increase productivity and provide cost, time and energy savings. Specifically in the energy systems field, the introduction of AI technologies can lead to significant improvements in both machine design and optimization and maintenance procedures. Over the past decade, data from engineering processes have grown in scale. In fact, the use of more technologically sophisticated sensors and the increase in available computing power have enabled both experimental measurements and highresolution numerical simulations, making available an enormous amount of data on the performance of energy systems. Therefore, to build a Digital Twin model capable of exploring these unorganized data pools collected from massive and heterogeneous resources, new Artificial Intelligence and Machine Learning strategies need to be developed. In light of the exponential growth in the use of smart technologies in manufacturing processes, this thesis aims at enhancing traditional approaches to the design, analysis, and optimization phases of turbomachinery and energy systems, which today are still predominantly based on empirical procedures or computationally intensive CFD-based optimizations. This improvement is made possible by the implementation of Digital Twins models, which, being based primarily on the use of Machine Learning that exploits performance Big-Data collected from energy systems, are acknowledged as crucial technologies to remain competitive in the dynamic energy production landscape. The introduction of Digital Twin models changes the overall structure of design and maintenance approaches and results in modern support tools that facilitate real-time informed decision making. In addition, the introduction of supervised learning algorithms facilitates the exploration of the design space by providing easy-to-run analytical models, which can also be used as cost functions in multi-objective optimization problems, avoiding the need for time-consuming numerical simulations or experimental campaings. Unsupervised learning methods can be applied, for example, to extract new insights from turbomachinery performance data and improve designers’ understanding of blade-flow interaction. Alternatively, Artificial Intelligence frameworks can be developed for Condition-Based Maintenance, allowing the transition from preventive to predictive maintenance. This thesis can be conceptually divided into two parts. The first reviews the state of the art of Cyber-Physical Systems and Digital Twins, highlighting the crucial role of Artificial Intelligence in supporting informed decision making during the design, optimization, and maintenance phases of energy systems. The second part covers the development of Machine Learning strategies to improve the classical approach to turbomachinery design and maintenance strategies for energy systems by exploiting data from numerical simulations, experimental campaigns, and sensor datasets (SCADA). The different Machine Learning approaches adopted include clustering algorithms, regression algorithms and dimensionality reduction techniques: Autoencoder and Principal Component Analysis. A first work shows the potential of unsupervised learning approaches (clustering algorithms) in exploring a Design of Experiment of 76 numerical simulations for turbomachinery design purposes. The second work takes advantage of a nonsequential experimental dataset, measured on a rotating turbine rig characterized by 48 blades divided into 7 sectors that share the same baseline rotor geometry but have different tip designs, to infer and dissect the causal relationship among different tip geometries and unsteady aero-thermodynamic performance via a novel Machine-Learning procedure based on dimensionality reduction techniques. The last application proposes a new anomaly detection framework for gensets in DH networks, based on SCADA data that exploits and compares the performance of regression algorithms such as XGBoost and Multi-layer Perceptron

Enabling Electric Aircraft_Applications and Approaches

Climate concerns have instigated serious research for weight-critical batteries to enable what are termed EVs, electric vehicles, initially for ground transportation. This research has advanced to the point where, at the system level, parity with combustion engines, via vehicle weight and drag reductions to reduce battery requirements, along with continued battery research, can conceivably be achieved in less than 10 years.. Concomitantly, renewable electric generation, which would enable essentially emission-less transportation, has via cost reductions, efficiency improvements and storage research advanced to the point of producing 25% of current electrical generation and 62% of new generation capability with continued rapid cost reductions and consequent rapid further adoption projected. The present report examines the resultant electric aircraft possibilities and opportunities including technologies to reduce requisite battery size and weight via airframe performance improvements, the benefits of electric propulsion and the enablement of a massive new aeronautics market for affordable, safe personal air vehicles that operate off of local streets