Optimization of profile and damping layer of plates embedded with acoustic black hole indentations for broadband energy dissipation

Acoustic Black Hole (ABH) plate structure has shown promising potentials of vibration suppression above a cut on frequency. For energy dissipation below the cut on frequency, however, the ABH is less effective due to the absence of wave focusing effect. This work reports a simultaneous optimization of ABH plates for broadband energy dissipation. Two sets of design variables of ABH plates, that is, geometry of the profile and topology of the damping layer, are optimized in an alternatively nested procedure. A novel objective function, namely the upper limit of kinetic energy, is proposed. Modeling of ABH structures is implemented and dynamic characteristic is solved using finite element method. A rectangular plate embedded with two ABH indentations is presented as a numerical example. Influence of frequency ranges in the calculation and mass ratios of the damping layer on results are discussed. The achieved optimal arrangement of the damping layer is found to cover equally, if not more, above the non-ABH (uniform) part of the plate than the ABH area. This is inconsistent with the conventional believe that damping layers should cover as much of the ABH area as possible. Mechanism of the broadband energy dissipation by the optimal solution is demonstrated.

Efficient Computation of Probabilistic Dominance in Multi-objective Optimization

Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach for the comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. The comparison is performed through accurate and efficient calculations of the probability that one solution dominates the other in terms of each uncertain objective. Second, such an operator can be flexibly used and combined with many existing multi-objective optimization frameworks and techniques by just substituting their standard comparison operator, thus easily enabling the Pareto front optimization of problems with multiple uncertain objectives. Third, a new benchmark for evaluating uncertainty-aware optimization techniques is introduced by incorporating different types of uncertainties into a well-known benchmark for multi-objective optimization problems. Fourth, the new comparison operator and benchmark suite are integrated into an existing multi-objective optimization framework that features a selection of multi-objective optimization problems and algorithms. Fifth, the efficiency in terms of performance and execution time of the proposed comparison operator is evaluated on the introduced uncertainty benchmark. Finally, statistical tests are applied giving evidence of the superiority of the new comparison operator in terms of \epsilon -dominance and attainment surfaces in comparison to previously proposed approaches.

Simultaneous Optimization of Excitation Wavelength and Emission Window for Semiconducting Polymer Nanoparticles to Improve Imaging Quality

Optimized excitation wavelength and emission window are essential for fluorescence imaging with high quality. Semiconducting polymer nanoparticles (SPNs) as fluorescent contrast agents have been extensively studied, but their imaging abilities in the second near infrared IIb window (NIR-IIb, 1500 to 1700 nm) with long excitation wavelength have not been reported yet. Herein, as a proof-of-concept, we demonstrate for the first time that an SPN named L1057 nanoparticles (NPs) exhibit intense NIR-IIb signal due to its ultra-high brightness and broad emission spectrum. After screening 915 nm as an optimal excitation wavelength, we applied L1057 NPs to visualize the whole body vessels, cerebral vessels, gastrointestinal tract, and tumor progression in different stages, achieving superior spatial resolution and signal to background ratio in the NIR-IIb window with respect to NIR-II window (1000 to 1700 nm). This study reveals that simultaneous optimization of excitation wavelength and emission window is an efficient strategy to enhance imaging quality and that L1057 NPs can serve as a promising NIR-IIb contrast agent for high-resolution and deep-tissue imaging.

Parametric Design & Analysis of Plastics Fuel Blend by using Taguchi Method in Diesel Engine

Over the past two centuries, energy needs have risen dramatically, particularly due to the transportation and industry sectors. However, the main made fuels like (fossil fuels) are polluting and their reserves are limited. Governments & research organization work together for make the use of renewable resources a priority and reduce irresponsible use of natural supplies through increased conservation. The energy crisis is a broad is biggest problem in world. Most people don't realize to their reality unless the price of fuel at the pump goes up or there are lines at the fuel station. Plastics waste fuel is sustainable and futuristic solution of fossil fuel as well as biggest problem of waste management of plastic waste can solve by this fuel. In thesis we prepare the plastic waste fuel by application of paralysis process in this process use low, medium and high grade of plastic and heated with limited amount of oxygen melt the plastic. The result of paralysis finds of liquid fuel and flammable gas. This fuel can be used as a blend in diesel with a proportion of B0D100, B10D90 B20D80, & B30D70 where B tent to blend of plastic fuel and D tend to diesel as if B0D100 means blend 0% and diesel 100%. These blend run diesel engine. The blends are in 10%, 20% & 30% plastic paralysis oil with standard diesel fuel. For experiment simultaneous optimization used a method called “Taguchi” used in the engine such as injection pressure and load condition. Taguchi Method of Optimization is a simplest method of optimizing experimental parameters in less number of trials.

3 kW Thermoelectric Generator for Natural Gas-Powered Heavy-Duty Vehicles—Holistic Development, Optimization and Validation

Emissions from heavy-duty vehicles need to be reduced to decrease their impact on the climate and to meet future regulatory requirements. The use of a cost-optimized thermoelectric generator based on total cost of ownership is proposed for this vehicle class with natural gas engines. A holistic model environment is presented that includes all vehicle interactions. Simultaneous optimization of the heat exchanger and thermoelectric modules is required to enable high system efficiency. A generator design combining high electrical power (peak power of about 3000 W) with low negative effects was selected as a result. Numerical CFD and segmented high-temperature thermoelectric modules are used. For the first time, the possibility of an economical use of the system in the amortization period of significantly less than 2 years is available, with a fuel reduction in a conventional vehicle topology of already up to 2.8%. A significant improvement in technology maturity was achieved, and the power density of the system was significantly improved to 298 W/kg and 568 W/dm3 compared to the state of the art. A functional model successfully validated the simulation results with an average deviation of less than 6%. An electrical output power of up to 2700 W was measured.