Detailed design of a lattice composite fuselage structure by a mixed optimization method

In this paper, a procedure for designing a lattice fuselage barrel has been developed and it comprises three stages: first, topology optimization of an aircraft fuselage barrel has been performed with respect to weight and structural performance to obtain the conceptual design. The interpretation of the optimal result is given to demonstrate the development of this new lattice airframe concept for the fuselage barrel. Subsequently, parametric optimization of the lattice aircraft fuselage barrel has been carried out using Genetic Algorithms on metamodels generated with Genetic Programming from a 101-point optimal Latin hypercube design of experiments. The optimal design has been achieved in terms of weight savings subject to stability, global stiffness and strain requirements and then was verified by the fine mesh finite element simulation of the lattice fuselage barrel. Finally, a practical design of the composite skin complying with the aircraft industry lay-up rules has been presented. It is concluded that the mixed optimization method, combining topology optimization with the global metamodel-based approach, has allowed to solve the problem with sufficient accuracy as well as provided the designers with a wealth of information on the structural behaviour of the novel anisogrid composite fuselage design

Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

Conventional engineering design optimization requires a large amount of expensive experimental tests from prototypes or computer simulations, which may result in an inefficient and unaffordable design process. In order to overcome these disadvantages, a surrogate model may be used to replace the prototype tests. To construct a surrogate model of sufficient accuracy from limited number of tests/simulations, a multi-level surrogate modeling strategy is introduced in this article. First, a chosen number of points determined by optimal Latin Hypercube Design of Experiments are used to generate global-level surrogate models with genetic programming and the fitness landscape can be explored by genetic algorithms for near-optimal solutions. Local-level surrogate models are constructed then from the extended-optimal Latin Hypercube samples in the vicinity of global optimum on the basis of a much smaller number of chosen points. As a result, an improved optimal design is achieved. The efficiency of this strategy is demonstrated by the parametric optimization design of a piezoelectric flex transducer energy harvester. The optimal design is verified by finite element simulations and the results show that the proposed multi-level surrogate modeling strategy has the advantages of faster convergence and more efficiency in comparison with the conventional single-single level surrogate modeling technique

Statistical analysis of high-speed jet flows

The spatiotemporal dynamics of pressure fluctuations of a turbulent jet flow is examined from the viewpoints of symbolic permutations theory and Kolmogorov-Smirnov statistics. The methods are applied to unveil hidden structures in the near-field of the two jets corresponding to the NASA SHJAR SP3 and SP7 experiments. Large Eddy Simulations (LES) are performed using the high-resolution Compact Accurately Boundary-Adjusting high-REsolution Technique (CABARET) accelerated on Graphics Processing Units (GPUs). It is demonstrated that the decomposition of the LES pressure solutions into symbolic patterns of simpler temporal structure reveals the existence of some orderly structures in the jet flows. To separate the non-linear dynamics of the revealed structures from the linear part, the results based on the pressure signals obtained from LES are compared with the surrogate dataset constructed from the original data

Application of Genetic Programming and Artificial Neural Network Approaches for Reconstruction of Turbulent Jet Flow Fields

Two Machine Learning (ML) methods are considered for reconstruction of turbulet signals corresponding to the Large Eddy Simulation database obtained by application of the high-resolution CABARET method accelerated on GPU cards for flow solutions of NASA Small Hot Jet Acoustic Rig (SHJAR) jets. The first method is the Feedforward Neural Networks technique, which was successfully implemented for a turbulent flow over a plunging aerofoil in (Lui and Wolf, 2019). The second method is based on the application of Genetic Programming, which is well-known in optimisation research, but has not been applied for turbulent flow reconstruction before. The reconstruction of local flow velocity and pressure signals as well as timedependent principle coefficients of the Spectral Proper Orthogonal Decomposition of turbulent pressure fluctuations are considered. Stability and dependency of the ML algorithms on the smoothness property and the sampling rate of the underlying turbulent flow signals are discussed

Metamodel-assisted design optimization of piezoelectric flex transducer for maximal bio-kinetic energy conversion

Energy Harvesting Devices (EHD) have been widely used to generate electrical power from the bio-kinetic energy of human body movement. A novel Piezoelectric Flex Transducer (PFT) based on the Cymbal device has been proposed by Daniels et al. (2013) for the purpose of energy harvesting. To further improve the efficiency of the device, optimal design of the PFT for maximum output power subject to stress and displacement constraints is carried out in this paper. Sequential Quadratic Programming (SQP) on metamodels generated with Genetic Programming from a 140-point optimal Latin hypercube design of experiments is used in the optimization. Finally, the optimal design is validated by finite element simulations. The simulations show that the magnitude of the electrical power generated from this optimal PFT harvesting device can be up to 6.5 mw when a safety design factor of 2.0 is applied

Bulk Transfer Coefficients Estimated From Eddy-Covariance Measurements Over Lakes and Reservoirs

Peer reviewe

Toward Automatic Label-Free Whispering Gallery Modes Biodetection with a Quantum Dot-Coated Microsphere Population

We explore a new calibration-free approach to biodetection based on whispering gallery modes (WGMs) without a reference measure and relative shifts. Thus, the requirement to keep track of the sensor position is removed, and a freely moving population of fluorophore-doped polystyrene microspheres can now fulfill this role of sensing resonator. Breaking free from fixed surface-based biosensing promotes adhesion between the microsphere sensors and the analytes since both can now be thoroughly mixed. The 70-nm-wide spectrum of green fluorescent microbeads allows us to monitor over 20 WGMs simultaneously without needing evanescent light coupling into the microspheres, hence enabling remote sensing. Since the exact radius of each microsphere is unknown a priori, it requires algorithmic analyses to obtain a reliable result for the refractive index of a solution. We first test our approach with different solutions of alcohol in water obtaining 3 × 10−4 precision on the refractive index at lower concentrations. Then, the solutions of bacterial spores in water yield clear evidence of biodetection in the statistical analysis of WGMs from 50 microspheres. To extend the fluorescence spectral range of our WGM sensors, we present preliminary results on coating microspheres with CdSe/ZnS quantum dots