Optimization of adaptive test design methods for the determination of steady-state data-driven models in terms of combustion engine calibration

This thesis deals with the development of a model-based adaptive test design strategy with a focus on steady-state combustion engine calibration. The first research topic investigates the question how to handle limits in the input domain during an adaptive test design procedure. The second area of scope aims at identifying the test design method providing the best model quality improvement in terms of overall model prediction error. To consider restricted areas in the input domain, a convex hull-based solution involving a convex cone algorithm is developed, the outcome of which serves as a boundary model for a test point search. A solution is derived to enable the application of the boundary model to high-dimensional problems without calculating the exact convex hull and cones. Furthermore, different data-driven engine modeling methods are compared, resulting in the Gaussian process model as the most suitable one for a model-based calibration. To determine an appropriate test design method for a Gaussian process model application, two new strategies are developed and compared to state-of-the-art methods. A simulation-based study shows the most benefit applying a modified mutual information test design, followed by a newly developed relevance-based test design with less computational effort. The boundary model and the relevance-based test design are integrated into a multicriterial test design strategy that is tailored to match the requirements of combustion engine test bench measurements. A simulation-based study with seven and nine input parameters and four outputs each offered an average model quality improvement of 36 % and an average measured input area volume increase of 65 % compared to a non-adaptive space-filling test design. The multicriterial test design was applied to a test bench measurement with seven inputs for verification. Compared to a space-filling test design measurement, the improvement could be confirmed with an average model quality increase of 17 % over eight outputs and a 34 % larger measured input area

Optimization of adaptive test design methods for the determination of steady-state data-driven models in terms of combustion engine calibration

This thesis deals with the development of a model-based adaptive test design strategy with a focus on steady-state combustion engine calibration. The first research topic investigates the question how to handle limits in the input domain during an adaptive test design procedure. The second area of scope aims at identifying the test design method providing the best model quality improvement in terms of overall model prediction error. To consider restricted areas in the input domain, a convex hull-based solution involving a convex cone algorithm is developed, the outcome of which serves as a boundary model for a test point search. A solution is derived to enable the application of the boundary model to high-dimensional problems without calculating the exact convex hull and cones. Furthermore, different data-driven engine modeling methods are compared, resulting in the Gaussian process model as the most suitable one for a model-based calibration. To determine an appropriate test design method for a Gaussian process model application, two new strategies are developed and compared to state-of-the-art methods. A simulation-based study shows the most benefit applying a modified mutual information test design, followed by a newly developed relevance-based test design with less computational effort. The boundary model and the relevance-based test design are integrated into a multicriterial test design strategy that is tailored to match the requirements of combustion engine test bench measurements. A simulation-based study with seven and nine input parameters and four outputs each offered an average model quality improvement of 36 % and an average measured input area volume increase of 65 % compared to a non-adaptive space-filling test design. The multicriterial test design was applied to a test bench measurement with seven inputs for verification. Compared to a space-filling test design measurement, the improvement could be confirmed with an average model quality increase of 17 % over eight outputs and a 34 % larger measured input area

RETROSPECTIVE AND EXPLORATORY ANALYSES FOR ENHANCING THE SAFETY OF ROTORCRAFT OPERATIONS

From recent safety reports, the accident rates associated with helicopter operations have reached a plateau and even have an increasing trend. More attention needs to be directed to this domain, and it was suggested to expand the use of flight data recorders on board for monitoring the operation. With the expected growth of flight data records in the coming years, it is essential to conduct analyses and provide the findings to the operator for risk mitigation. In this thesis, a retrospective analysis is proposed to detect potential anomalies in the fight data for rotorcraft operations. In the study, an algorithm is developed to detect the phases of flight for segmenting the flights into homogeneous entities. The anomaly detection is then performed on the flight segments within the same flight phases, and it is implemented through a sequential approach. Aside from the retrospective analysis, the exploratory analysis aims to efficiently find the safety envelope and predict the recovery actions for a hazardous event. To facilitate the exploration of the corresponding operational space, we provide a framework consisting of surrogate modeling and the design of experiments for tackling the tasks. In the study, the autorotation, a maneuver used to land the vehicle under power loss, is treated as a used case to test and validate the proposed framework.Ph.D

Physical layer security in wireless networks: intelligent jamming and eavesdropping

This work aims at addressing two critical security issues residing in the physical layer of wireless networks, namely intelligent jamming and eavesdropping. In the first two chapters we study the problem of jamming in a fixed-rate transmission system with fading, under the general assumption that the jammer has no knowledge about either the codebook used by the legitimate communication terminals, or the source’s output. Both transmitter and jammer are subject to power constraints which can be enforced over each codeword (peak) or over all codewords (average). All our jamming problems are formulated as zero-sum games, having the probability of outage as pay-off function and power control functions as strategies. We provide a comprehensive coverage of these problems, under fast and slow fading, peak and average power constraints, pure and mixed strategies, with and without channel state information (CSI) feedback. Contributions to the eavesdropping problem include a novel feedback scheme for transmitting secret messages between two legitimate parties, over an eavesdropped communication link, presented in Chapter 4. Relative to Wyner’s traditional encoding scheme, our feedback-based encoding often yields larger rate-equivocation regions and achievable secrecy rates. More importantly, by exploiting the channel randomness inherent in the feedback channels, our scheme achieves a strictly positive secrecy rate even when the eavesdropper’s channel is less noisy than the legitimate receiver’s channel. In Chapter 5 we study the problem of active eavesdropping in fast fading channels. The active eavesdropper is a more powerful adversary than the classical eavesdropper. It can choose between two functional modes: eavesdropping the transmission between the legitimate parties (Ex mode), and jamming it (Jx mode) – the active eavesdropper cannot function in full duplex mode. We consider two scenarios: the best-case scenario, when the transmitter knows the eavesdropper’s strategy in advance – and hence can adaptively choose an encoding strategy – and the worst-case scenario, when the active eavesdropper can choose its strategy based on the legitimate transmitter-receiver pair’s strategy. For the second scenario, we introduce a novel encoding scheme, based on very limited and unprotected feedback – the Block-Markov Wyner (BMW) encoding scheme – which outperforms any schemes currently available

Surrogate Models Coupled with Machine Learning to Approximate Complex Physical Phenomena Involving Aerodynamic and Aerothermal Simulations

Numerical simulations provide a key element in aircraft design process, complementing physical tests and flight tests. They could take advantage of innovative methods, such as artificial intelligence technologies spreading in aviation. Simulating the full flight mission for various disciplines pose important problems due to significant computational cost coupled to varying operating conditions. Moreover, complex physical phenomena can occur. For instance, the aerodynamic field on the wing takes different shapes and can encounter shocks, while aerothermal simulations around nacelle and pylon are sensitive to the interaction between engine flows and external flows. Surrogate models can be used to substitute expensive high-fidelitysimulations by mathematical and statistical approximations in order to reduce overall computation cost and to provide a data-driven approach. In this thesis, we propose two developments: (i) machine learning-based surrogate models capable of approximating aerodynamic experiments and (ii) integrating more classical surrogate models into industrial aerothermal process. The first approach mitigates aerodynamic issues by separating solutions with very different shapes into several subsets using machine learning algorithms. Moreover, a resampling technique takes advantage of the subdomain decomposition by adding extra information in relevant regions. The second development focuses on pylon sizing by building surrogate models substitutingaerothermal simulations. The two approaches are applied to aircraft configurations in order to bridge the gap between academic methods and real-world applications. Significant improvements are highlighted in terms of accuracy and cost gain

Methodology and Software for Interactive Decision Support

These Proceedings report the scientific results of an International Workshop on "Methodology and Software for Interactive Decision Support" organized jointly by the System and Decision Sciences Program of IIASA and The National Committee for Applied Systems Analysis and Management in Bulgaria. Several other Bulgarian institutions sponsored the workshop -- The Committee for Science to the Council of Ministers, The State Committee for Research and Technology and The Bulgarian Industrial Association. The workshop was held in Albena, on the Black Sea Coast. In the first section, "Theory and Algorithms for Multiple Criteria Optimization," new theoretical developments in multiple criteria optimization are presented. In the second section, "Theory, Methodology and Software for Decision Support Systems," the principles of building decision support systems are presented as well as software tools constituting the building components of such systems. Moreover, several papers are devoted to the general methodology of building such systems or present experimental design of systems supporting certain class of decision problems. The third section addresses issues of "Applications of Decision Support Systems and Computer Implementations of Decision Support Systems." Another part of this section has a special character. Beside theoretical and methodological papers, several practical implementations of software for decision support have been presented during the workshop. These software packages varied from very experimental and illustrative implementations of some theoretical concept to well developed and documented systems being currently commercially distributed and used for solving practical problems

Journal of Telecommunications and Information Technology, 2003, nr 3

kwartalni

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

B

Sequential multi-objective target value optimization

In engineering processes the specification of optimization targets is usually reduced to minimization or maximization problems. The specfication of challenging multivariate target structures is excluded, due to the lack of algorithms that are able to handle them. Often however the optimum is a precise target instead of a minimum or maximum and it would be helpful if the deviation from the target could be penalized asymmetrically. In this thesis a new heuristic named mtEGO for multi-objective target value sequential optimization has been developed. A small initial spacefilling design is used to fit a surrogate model for each objective of the optimization problem. Based on the predictions and prediction errors of the surrogate model for the whole parameter space virtual observations with dfferent (1 ..ff) confidence levels are constructed. These virtual observations are used to roughly simulate the effect of the model uncertainty on the capability of each setting in the parameter space to be the global optimum. A transformation with desirability functions and the aggregation to a joint desirability index turns the multi-objective target value prob- lem in a simple single-objective maximization problem. Improvements are determined for this single-objective maximization problem then, which are maximized tofind the global optimum. mtEGO therefore works in a hybrid way, which means for each combination of (1...ff) confidence levels an own candidate for the global optimum is determined simultaneously. The candidates are reduced to a small number of updating points using hierarchical clustering. Finally, the model is refined with the observations from the updating points and the algorithm proceeds to generate and add new updating points until the stopping criterion is fulfilled. The mtEGO algorithm is validated successfully by means of extensive simulation studies and two case studies from mechanical engineering. Beside the fact that the two case studies demonstrate the applicability of mtEGO to real applications, they show that mtEGO even works successfully if basic conditions change in an ongoing optimization process. Further, an improved variant of mtEGO, named mtEGOimp, is developed. It does a pre-selection of reasonable confidence levels before cross-combining them. As a consequence, the computation time of the mtEGO approach is strongly reduced, which relaxes time limitations. The incorporation of a convex hull restriction method for failure points and an imputation of missing values into the mtEGO approach, finally extends it to a powerful tool for optimization problems even in the presence of unknown constraints