Abstract

Aircraft emission targets worldwide and their climatic effects have put pressure in government agencies, aircraft manufacturers and airlines to reduce water vapour, carbon dioxide (CO2CO2) and oxides of nitrogen (NOxNOx) resulting from aircraft emissions. The difficulty of reducing emissions including water vapor, carbon dioxide (CO2CO2) and oxides of nitrogen (NOxNOx) is mainly due to the fact that a commercial aircraft is usually designed for a particular optimal cruise altitude but may be requested or required to operate and deviate at different altitudes and speeds to archive a desired or commanded flight plan, resulting in increased emissions. This is a multi- disciplinary problem with multiple trade-offs such as optimizing engine efficiency, minimizing fuel burnt and emissions while maintaining prescribed aircraft trajectories, altitude profiles and air safety. There are possible attempts to solve such problems by designing new wing/aircraft shape, new efficient engine, ATM technology, or modifying the aircraft flight plan. Based on the rough data provided by an air carrier company, who was willing to assess the methodology, this paper will present the coupling of an advanced optimization technique with mathematical models and algorithms for aircraft emission, and fuel burnt reduction through flight plan optimization. Two different approaches are presented; the first one describes a deterministic optimization of the flight plan and altitude profile in order to reduce the fuel consumption while reducing time and distance. The second approach presents the robust design optimization of the previous case considering uncertainties on several parameters. Numerical results will show that the methods are able to capture a set of useful trade-offs solutions between aircraft range and fuel consumption, as well as fuel consumption and flight time.

Abstract

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

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A multi-fidelity Gaussian process (MF-GP) is presented for the forward uncertainty quantification (UQ) of the performance of an autonomous surface vehicle (ASV) subject to uncertain operating conditions. The ASV is a shallow water autonomous multipurpose platform (SWAMP), designed for the acquisition of the environmental parameters in the extremely shallow waters of wetlands. The quantity of interest (QoI) is the hydrodynamic resistance of the SWAMP subject to variable payload and longitudinal position of its center of mass. The QoI is assessed by a linear potential-flow solver coupled with the rigid body equations of motion. Multiple fidelity levels are defined based on the computational grid size and the level of coupling between hydrodynamic loads and motions. The MF-GP is based on a low-fidelity surrogate, corrected with an additive function, representing the error between higher and lower fidelity solutions. The MF-GP provides the prediction with the associated uncertainty. The latter is used to adaptively train the MF-GP, adding points where the prediction uncertainty is maximum. Finally, the UQ of the QoI is performed by Monte Carlo sampling on the MF-GP surrogate. The first four statistical moments, the 95th percentile, and the probability density function of the QoI are assessed. MF-GP is compared to its single-fidelity (high-fidelity based) counterpart, showing overall better results.

Abstract

The availability of machine learning techniques opens up possibilities in different fields of civil engineering. Their application in conjunction with numerical simulations overcomes the limitations in traditional approaches and pave the road for some new horizons. This communication presents several applications of such a hybrid tool in design and safety assessment of dams and hydraulic structures. They include the generation of behavior prediction models from monitoring data, the identification of behavior patterns with classification models, the analysis of the seismic response of gravity dams with heterogeneous concrete in a probabilistic framework, the investigation of the performance of arch dams, and the estimation of the discharge capacity of arched labyrinth spillways.

Abstract

In this paper, we introduce a bi-fidelity stochastic collocation (SC) method for the linear transport equation with diffusive scaling and high-dimensional random inputs characterized by random variables. For the high-fidelity linear transport model, the asymptotic-preserving Discontinuous Galerkin implicit-explicit method in the micro-macro decomposition framework is used. We discuss different choices of low-fidelity models and conduct the corresponding uniform error estimates for the bi-fidelity method. Extensive numerical tests are presented to validate our bi-fidelity SC method.

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