Optimization study of third harmonic generation in quantum cascade lasers.

We present THz surface emission by difference frequency generation in nonlinear quantum cascade lasers operating at room temperature. The device comprises two separate, transversely superimposed gratings, one for selective feedback of the mid-infrared modes and one for surface emission of the generated THz radiation. The mid-infrared grating is realized as a higher order, loss-coupled distributed feedback grating for single mode emission at two distinct wavelengths. It defines the spatial distribution of the nonlinear polarization wave, which allows for precise placement of the second order top grating for surface emission.

We report room-temperature terahertz sources based on Čerenkov difference-frequency generation in dual-wavelength mid-infrared quantum cascade lasers with giant resonant optical nonlinearities originating from intersubband transitions. A Čerenkov difference-frequency generation scheme allows for extraction of THz radiation along the whole length of the laser waveguide and provides directional terahertz emission. Experimentally, our sources demonstrate a conversion efficiency of up to 70 μW/W2 approximately an order of magnitude improvement over the previous reports.

Terahertz generation in quantum-cascade lasers through down-conversion optical nonlinearity: Design and modeling

Milliwatt second harmonic power at 4.45 µm was achieved in InP lattice-matched quantum cascade lasers with monolithically-integrated resonant nonlinear cascades and modal phase matching. The use of an InP top-cladding layer and high-reflectance coating on the laser back facet enabled high linear laser power and consequently record nonlinear power.

In this paper, a multi-objective particle swarm optimization algorithm based on the relative distance strategy is proposed to realize hydrodynamic coefficient identification, which can improve the accuracy of parameter identification without determining the weight manually. Firstly, single-objective particle swarm optimization (PSO) is used to identify the parameters of the model with a low coupling degree. Then, the parameters of the strongly coupled sway and yaw motion model are regarded as multi-objective, and a set of unbiased Pareto optimal solutions are obtained by the multi-objective particle swarm optimization (MOPSO) algorithm. Then the distance function of the Pareto solution is constructed to determine the parametric solution of the model. Finally, experimental identification is carried out by using the data set collected from the real ship. Experimental results show that the accuracy of model parameters obtained by this identification method is about 91.6%, and there is no need to set the weighting coefficient.

A method for designing miniaturized frequency selective surface (FSS) using equivalent circuit model (ECM) and multi-objective particle swarm optimization (MOPSO) algorithm is proposed in this paper. In the proposed method, ECM describing the desired frequency response is established, and parameters of the ECM are determined using the MOPSO algorithm. Combining the ECM analysis and the MOPSO algorithm, the requirements of desired frequency response can be converted into the constraints of equivalent circuit parameters, which simplifies the optimization process of FSS design. For verification, a single-layer bandstop-bandpass FSS composed of convoluted elements with the unit cell size of 0.073λ 0 × 0.073λ 0 is designed. Simulation results show that the −15 dB bandwidth is from 2.62 to 2.83 GHz, and the −0.5 dB bandwidth is from 3.84 to 5 GHz. Meanwhile, the resonant frequency is stable at different incident angles (up to 80°) under TE and TM polarization. Finally, the proposed FSS prototype is fabricated and measured. The measured results agree well with the simulated ones.

This study employs a multi-objective particle swarm optimization (MOPSO) algorithm to address the dual-objective challenge in the robotic polishing of Ti-6Al-4V. The aim is to determine optimal parameters that minimize surface roughness while maximizing the material removal rate (MRR), thereby improving both surface quality and processing efficiency. First, a material removal depth model for end-face polishing is established based on Preston's equation and theoretical analysis, from which the MRR model is derived. Subsequently, orthogonal experiments are conducted to investigate the influence of process parameters and their interactions on surface roughness, followed by the development of a quadratic polynomial roughness prediction model. Analysis of variance (ANOVA) and model validation confirm the model's reliability. Finally, the MOPSO algorithm is applied to obtain the Pareto optimal solution set, yielding the optimal parameter combination. Experimental results demonstrate that at a normal contact force of 7.58 N, a feed rate of 4.52 mm/s, and a spindle speed of 5851 rpm, the achieved MRR and Ra values are 0.2197 mm3/s and 0.291 μm, respectively. These results exhibit errors of only 5.64% and 2.65% compared to model predictions, validating the proposed method's effectiveness.

A solution of the nonlinear Eulerian fluid-dynamical equation of a helix traveling-wave tube was developed by the method of successive approximation. The nonlinearity was restricted to the second order that could account for the second harmonic generation in the device and could estimate the sensitivity of the second harmonic to the beam and circuit parameters. The second harmonic RF output power relative to the fundamental was found to be controlled by the beam voltage and current; was less for a negative than for a positive dispersion helical slow-wave structure of the device; and could be minimized by optimizing the distributed loss put along the structure. Unlike the Lagrangian approach, the present Eulerian approach gives a closed-form solution, avoiding a large-scale numerical integration of the problem. Hence the present approach is less encumbered and takes less computertime than the Lagrangian approach. Also, the present analysis, though it is exemplified in this paper with particular reference to the second-order nonlinearity for second harmonic generation, is rather general. One can easily extend the analysis to a higher-order nonlinearity, say, to the third order for the study of third harmonic generation, intermodulation distortion etc. The method however has the limitation in that it cannot account for the saturation that is caused by the defocussing of the electron bunch from the RF phase. However, it can be extended to account for the saturation of the fundamental that is caused by the third-order nonlinearity.

A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response

Room-temperature, narrow-linewidth terahertz quantum cascade laser sources based on intracavity Cherenkov difference-frequency generation with emission at 3.5 THz, nearly 60µW power output, and conversion efficiency of approximately 0.55 mW/W2 are reported.

This paper deals with the optimal placement of distributed generation (DG) units in distribution systems via an enhanced multi-objective particle swarm optimization (EMOPSO) algorithm. To pursue a better simulation of the reality and provide the designer with diverse alternative options, a multi-objective optimization model with technical and operational constraints is constructed to minimize the total power loss and the voltage fluctuation of the power system simultaneously. To enhance the convergence of MOPSO, special techniques including a dynamic inertia weight and acceleration coefficients have been integrated as well as a mutation operator. Besides, to promote the diversity of Pareto-optimal solutions, an improved non-dominated crowding distance sorting technique has been introduced and applied to the selection of particles for the next iteration. After verifying its effectiveness and competitiveness with a set of well-known benchmark functions, the EMOPSO algorithm is employed to achieve the optimal placement of DG units in the IEEE 33-bus system. Simulation results indicate that the EMOPSO algorithm enables the identification of a set of Pareto-optimal solutions with good tradeoff between power loss and voltage stability. Compared with other representative methods, the present results reveal the advantages of optimizing capacities and locations of DG units simultaneously, and exemplify the validity of the EMOPSO algorithm applied for optimally placing DG units.

This paper deals with a multi-objective unequal sized dynamic facility layout problem (DFLP) with pickup/drop-off locations. First, a mathematical model to obtain optimal solutions for small size instances of the problem is developed. Then, a multi-objective particle swarm optimisation (MOPSO) algorithm is implemented to find near optimal solutions. Two new heuristics to prevent overlapping of the departments and to reduce ‘unused gaps’ between the departments are introduced. The performance of the MOPSO is examined using some sets of available test problems in the literature and various random test problems in small, medium, and large sizes. The percentage of improvements on the initial solutions is calculated for small, medium and large size instances. Also, the generation metric and the space metric for non-dominated solutions are examined. These experiments show the good performance of the developed MOPSO and sensitivity analysis show the robustness of the obtained solutions.

In order to enhance the convergence and distribution of multi-objective particle swarm algorithm, an improved multi-objective particle swarm optimization algorithm was proposed. Linear decreasing inertia weight was used to update. The method can improve the deficiency that the algorithm falls into local optimal easily. The improved Logistic mapping was used to increase ergodicity of the particles. The method can expand the search scope. At the same time, the elite archiving mechanism and the mutation probability were introduced to increase the disturbance. The method can improve the local optimal. Compared with the real Pareto front, NSGA-II and MOEAD algorithm, the simulation shows that the algorithm proposed in the paper is effective.

The current paper presents a novel multi-objective particle swarm optimization (MOPSO) algorithm for solving no-wait flow shop scheduling problems with makespan and maximum tardiness criteria. First, in the algorithm, particles are represented as job permutations and updated directly in the discrete domain. Second, the concept of Pareto dominance is applied to compare different solutions of multi-objective optimization, and a set is employed to hold and to update the obtained non-dominated solutions, where a randomly selected non-dominated solution is assigned as the global best particle to maintain the diversity of the searching direction and to speed up the convergence process to the Pareto front. Third, a new multi-objective heuristic, named the PWQ (Pan—Wang—Qian) heuristic, is proposed to produce a population of initial particles with relatively good performances. Fourth, a simple but effective multi-objective local search algorithm is developed to embed in the MOPSO algorithm for stressing the balance between global exploration and local exploitation. In addition, two speed-up methods are devised to evaluate a job permutation and its insert neighbourhood respectively. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed algorithm is superior to a recently published multi-objective hybrid differential evolution (MHDE) algorithm in terms of searching quality, diversity level, robustness, and efficiency.

Terahertz (THz) frequency sources such as quantum cascade lasers (QCLs) offer a variety of new applications in far-infrared frequency bands. For better understanding of their behavior, a device-specific modeling approach is used to predict the optical output power under current modulation and chip temperature. This letter reports the numerical simulation of gain switching to evaluate temperature dependence for short pulse generation in QCLs. Modified rate equations are used for self-consistent computation of emitted THz optical power from QCLs. The device emits at 116 µm and is driven by various inputs such as haversine, tangential hyperbolic and square electrical pulses. From the simulation it is evident that the square input pulse requires the least magnitude current (0.83 A) at 15 K to generate pulses with minimum full width half maximum (FWHM). Moreover, when the peak amplitude of the square input signal is 1.01 A, it generates optical pulses with the least FWHM of 19.42 ps at 45 K. In the case of the haversine input with peak amplitude of 1.8A, a peak power of 27.86 mW is generated at 15 K.