Study of fuel systems for LH2-fueled subsonic transport aircraft, volume 1

Several engine concepts examined to determine a preferred design which most effectively exploits the characteristics of hydrogen fuel in aircraft tanks received major emphasis. Many candidate designs of tank structure and cryogenic insulation systems were evaluated. Designs of all major elements of the aircraft fuel system including pumps, lines, valves, regulators, and heat exchangers received attention. Selected designs of boost pumps to be mounted in the LH2 tanks, and of a high pressure pump to be mounted on the engine were defined. A final design of LH2-fueled transport aircraft was established which incorporates a preferred design of fuel system. That aircraft was then compared with a conventionally fueled counterpart designed to equivalent technology standards

ALS liquid hydrogen turbopump

This report summarized analysis, design, and experimental work done on the liquid hydrogen turbopump for the ALS main engine

Design Optimization of Permanent Magnet Machines Over a Target Operating Cycle Using Computationally Efficient Techniques

The common practices of large-scale finite element (FE) model-based design optimization of permanent magnet synchronous machines (PMSMs) oftentimes aim at improving the machine performance at the rated operating conditions, thus overlooking the performance treatment over the entire range of operation in the constant torque and extended speed regions. This is mainly due to the computational complexities associated with several aspects of such large-scale design optimization problems, including the FE-based modeling techniques, large number of load operating points for load-cycle evaluation of the design candidates, and large number of function evaluations required for identification of the globally optimal design solutions. In this dissertation, the necessity of accommodating the entire range of operation in the design optimization of PMSMs is demonstrated through joint application of numerical techniques and mathematical or statistical analyses. For this purpose, concepts such as FE analysis (FEA), design of experiments (DOE), sensitivity analysis, response surface methodology (RSM), and regression analysis are extensively used throughout this work to unscramble the correlations between various factors influencing the design of PMSMs. Also in this dissertation, computationally efficient methodologies are developed and employed to render unprohibitive the problems associated with large-scale design optimization of PMSMs over the entire range of operation of such machines. These include upgrading an existing computationally efficient FEA to solve the electromagnetic field problem at any load operating point residing anywhere in the torque-speed plane, developing a new stochastic search algorithm for effectively handling the constrained optimization problem (COP) of design of electric machines so as to reduce the number of function evaluations required for identifying the global optimum, implementing a k-means clustering algorithm for efficient modeling of the motor load profile, and devising alternative computationally efficient techniques for calculation of strand eddy current losses or characterization of the mechanical stress due to the centrifugal forces on the rotor bridges. The developed methodologies in this dissertation are applicable to the wide class of sine-wave driven PM and synchronous reluctance machines. Here, they were successfully utilized for optimization of two existing propulsion traction motors over predefined operating cycles. Particularly, the well-established benchmark design provided by the Toyota Prius Gen. 2 V-type interior PM (IPM) motor, and a challenging high power density spoke-type IPM for a formula E racing car are treated

Mathematical Modelling of Energy Systems and Fluid Machinery

The ongoing digitalization of the energy sector, which will make a large amount of data available, should not be viewed as a passive ICT application for energy technology or a threat to thermodynamics and fluid dynamics, in the light of the competition triggered by data mining and machine learning techniques. These new technologies must be posed on solid bases for the representation of energy systems and fluid machinery. Therefore, mathematical modelling is still relevant and its importance cannot be underestimated. The aim of this Special Issue was to collect contributions about mathematical modelling of energy systems and fluid machinery in order to build and consolidate the base of this knowledge

Experimental and Theoretical Analysis of Pressure Coupled Infusion Gyration for Fibre Production

In this work, we uncover the science of the combined application of external pressure, controlled infusion of polymer solution and gyration in the field of nanofiber preparation. This novel application takes gyration-based method into another new arena through enabling the mass production of exceedingly fine (few nanometres upwards) nanofibres in a single step. Polyethylene oxide (PEO) was used as a model polymer in the experimental study, which shows the use of this novel method to fabricate polymeric nanofibres and nanofibrous mats under different combinations of operating parameters, including working pressure, rotational speed, infusion rate and collection distance. The morphologies of the nanofibres were characterised using scanning electron microscopy, and the anisotropy of alignment of fibre was studied using two dimensional fast Fourier transform analysis. A correlation between the product morphology and the processing parameters is established. The response surface models of the experimental process were developed using the least squares fitting. A systematic description of the PCIG spinning was developed to help us obtain a clear understanding of the fibre formation process of this novel application. The input data we used are the conventional mean of fibre diameter measurements obtained from our experimental works. In this part, both linear and nonlinear fitting formats were applied, and the successes of the fitted models were mainly evaluated using Adjusted R2 and Akaike Information Criterion (AIC). The correlations and effects of individual parameters and their interactions were explicitly studied. The modelling results indicated the polymer concentration has the most significant impact on fibre diameters. A self-defined objective function was studied with the best-fitted model to optimise the experimental process for achieving the desired nanofibre diameters and narrow standard deviations. The experimental parameters were optimised by several algorithms, and the most favoured sets of parameters recommended by the non-linear interior point methods were further validated through a set of additional experiments. The results of validation indicated that pressure coupled infusion gyration offers a facile way for forming nanofibres and nanofibre assemblies, and the developed model has a good prediction power of experimental parameters that are possible to be useful for achieving the desirable PEO nanofibres

Report of activities of the advanced coal extraction systems definition project, 1979 - 1980

During this period effort was devoted to: formulation of system performance goals in the areas of production cost, miner safety, miner health, environmental impact, and coal conservation, survey and in depth assessment of promising technology, and characterization of potential resource targets. Primary system performance goals are to achieve a return on incremental investment of 150% of the value required for a low risk capital improvement project and to reduce deaths and disability injuries per million man-hour by 50%. Although these performance goals were developed to be immediately applicable to the Central Appalachian coal resources, they were also designed to be readily adaptable to other coals by appending a geological description of the new resource. The work done on technology assessment was concerned with the performance of the slurry haulage system

Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine

Optimal performance of the electric machine/drive system is mandatory to improve the energy consumption and reliability. To achieve this goal, mathematical models of the electric machine/drive system are necessary. Hence, this motivated the editors to instigate the Special Issue “Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine”, aiming to collect novel publications that push the state-of-the art towards optimal performance for the electric machine/drive system. Seventeen papers have been published in this Special Issue. The published papers focus on several aspects of the electric machine/drive system with respect to the mathematical modelling. Novel optimization methods, control approaches, and comparative analysis for electric drive system based on various electric machines were discussed in the published papers

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable

Small Engine Component Technology (SECT)

A study of small gas turbine engines was conducted to identify high payoff technologies for year-2000 engines and to define companion technology plans. The study addressed engines in the 186 to 746 KW (250 to 1000 shp) or equivalent thrust range for rotorcraft, commuter (turboprop), cruise missile (turbojet), and APU applications. The results show that aggressive advancement of high payoff technologies can produce significant benefits, including reduced SFC, weight, and cost for year-2000 engines. Mission studies for these engines show potential fuel burn reductions of 22 to 71 percent. These engine benefits translate into reductions in rotorcraft and commuter aircraft direct operating costs (DOC) of 7 to 11 percent, and in APU-related DOCs of 37 to 47 percent. The study further shows that cruise missile range can be increased by as much as 200 percent (320 percent with slurry fuels) for a year-2000 missile-turbojet system compared to a current rocket-powered system. The high payoff technologies were identified and the benefits quantified. Based on this, technology plans were defined for each of the four engine applications as recommended guidelines for further NASA research and technology efforts to establish technological readiness for the year 2000