A Formal Machine Learning or Multi Objective Decision Making System for Determination of Weights

Abstract: Decision-making typically needs the mechanisms to compromise among opposing norms. Once multiple objectives square measure is concerned of machine learning, a vital step is to check the weights of individual objectives to the system-level performance. Determinant, the weights of multi-objectives is associate in analysis method, associated it's been typically treated as a drawback. However, our preliminary investigation has shown that existing methodologies in managing the weights of multi-objectives have some obvious limitations like the determination of weights is treated as one drawback, a result supporting such associate improvement is limited, if associated it will even be unreliable, once knowledge concerning multiple objectives is incomplete like an integrity caused by poor data. The constraints of weights are also mentioned. Variable weights square measure is natural in decision-making processes. Here, we'd like to develop a scientific methodology in determinant variable weights of multi-objectives. The roles of weights in a creative multi-objective decision-making or machine-learning of square measure analyzed, and therefore the weights square measure determined with the help of a standard neural network

Approximate Modeling of Unsteady Aerodynamics for Hypersonic Aeroelasticity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83554/1/AIAA-52860-446.pd

Global Aerostructural Design Optimization of More Flexible Wings for Commercial Aircraft

In the German Aerospace Center's Virtual Aircraft Technology Integration Platform project, a process for aerostructural wing optimization based on high-fidelity simulation methods is continuously developed. Based upon a parametric geometry, flight performance under transonic flight conditions and maneuver loads are computed by solving the Reynolds-averaged Navier-Stokes equations. Structural mass and elastic characteristics of the wing are determined from structural sizing of the composite wing box for essential maneuver load cases using computational structural mechanics. Global aerostructural wing optimizations are performed for wings with a conventional composite wing-box structure and for more flexible wings. The minimization of the fuel consumption for three flight missions represents the objective function. The optimizations are performed for variable and constant wing planforms as well as with and without consideration of active maneuver load alleviation. A significant mass reduction of the wing box is obtained with the more flexible wing concept, resulting in a decrease in fuel consumption of about 3%. For the optimizations with active maneuver load alleviation, the more flexible wing concept shows an additional reduction of the fuel consumption on the order of 2%. The more flexible wing concept results in optimized wing geometries with increased aspect ratios and reduced taper ratios

Reduced-Order Aerothermoelastic Framework for Hypersonic Vehicle Control Simulation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83643/1/AIAA-2010-7928-117.pd

Simulation, optimization and instrumentation of agricultural biogas plants

During the last two decades, the production of renewable energy by anaerobic digestion (AD) in biogas plants has become increasingly popular due to its applicability to a great variety of organic material from energy crops and animal waste to the organic fraction of Municipal Solid Waste (MSW), and to the relative simplicity of AD plant designs. Thus, a whole new biogas market emerged in Europe, which is strongly supported by European and national funding and remuneration schemes. Nevertheless, stable and efficient operation and control of biogas plants can be challenging, due to the high complexity of the biochemical AD process, varying substrate quality and a lack of reliable online instrumentation. In addition, governmental support for biogas plants will decrease in the long run and the substrate market will become highly competitive. The principal aim of the research presented in this thesis is to achieve a substantial improvement in the operation of biogas plants. At first, a methodology for substrate inflow optimization of full-scale biogas plants is developed based on commonly measured process variables and using dynamic simulation models as well as computational intelligence (CI) methods. This methodology which is appliquable to a broad range of different biogas plants is then followed by an evaluation of existing online instrumentation for biogas plants and the development of a novel UV/vis spectroscopic online measurement system for volatile fatty acids. This new measurement system, which uses powerful machine learning techniques, provides a substantial improvement in online process monitoring for biogas plants. The methodologies developed and results achieved in the areas of simulation and optimization were validated at a full-scale agricultural biogas plant showing that global optimization of the substrate inflow based on dynamic simulation models is able to improve the yearly profit of a biogas plant by up to 70%. Furthermore, the validation of the newly developed online measurement for VFA concentration at an industrial biogas plant showed that a measurement accuracy of 88% is possible using UV/vis spectroscopic probes