2.3–21 GHz broadband and high linearity distributed low noise amplifier

This paper focuses on the design of a 2.3–21 GHz Distributed Low Noise Amplifier (LNA) with low noise figure (NF), high gain (S₂₁), and high linearity (IIP3) for broadband applications. This distributed amplifier (DA) includes S/C/X/Ku/K-band, which makes it very suitable for heterodyne receivers. The proposed DA uses a 0.18 μm GaAs pHEMT process (OMMIC ED02AH) in cascade architecture with lines adaptation and equalization of phase velocity techniques, to absorb their parasitic capacitances into the gate and drain transmission lines in order to achieve wide bandwidth and to enhance gain and linearity. The proposed broadband DA achieved an excellent gain in the flatness of 13.5 ± 0.2 dB, a low noise figure of 3.44 ± 1.12 dB, and a small group delay variation of ±19.721 ps over the range of 2.3–21 GHz. The input and output reflection coefficients S₁₁ and S₂₂ are less than −10 dB. The input compression point (P_1dB) and input third-order intercept point (IIP3) are −1.5 dBm and 11.5 dBm, respectively at 13 GHz. The dissipated power is 282 mW and the core layout size is 2.2 × 0.8 mm².     

An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

Recent activities in the field of Nuclear Operational Management and Nuclear Safety Engineering

Recent activities in the field of Nuclear Operational Management and Nuclear Safety Engineering, the studies related to risk analysis methodology, design, and operational management, physical phenomena, and emergency preparedness and nuclear security, have been progressed. Especially, ‘risk analysis methodology’ and ‘design and operational management’ are the main categories of the field, in which more than half of published articles on Journal of Nuclear Science and Technology are related to these categories.     

Optimal design of Raman fibre amplifier based on terminal value optimization strategy and shuffled frog leaping algorithm

This paper introduces an evolutionary algorithm, Shuffled Frog Leaping Algorithm (SFLA), to solve the optimization problem in designing the multi-pumped Raman Fibre Amplifier (RFA). SFLA is a powerful optimizer tool because of its efficient mathematical expressions and global search capability. We utilize SFLA to determine the optimal pump wavelengths and pump powers by minimizing the gain ripple of RFA. To accelerate calculations, a terminal value optimization strategy (TVOS) is incorporated into the evolution of SFLA. This proposed strategy takes the terminal power values of pumps as the decision variables in optimization. Then, the optimal original power values of the pumps are obtained by solving the Power Coupled Equations once, without using the traditional method of repetitive guesses.The combination of SFLA and TVOS enhances the efficiency of optimization and accelerates calculation, while satisfying the design requirements of RFA.The simulation results show that nearly 65% of computational time has been saved compared with the traditional average power analysis. The 4-pumped C+L band of backward multi-pumped RFA with the average net gain of 0 dB, 1 dB and 2 dB are designed individually, where the gain ripple is less than 0.64 dB. The combination of SFLA and TVOS enhance the optimization efficiency and improve the performance of RFA with good gain profile.     

Computer‐aided design methodology for linearity enhancement of multiwatt GaN HEMT amplifiers using multiple parallel devices

This article presents a design methodology for linearizing GaN HEMT amplifiers based on splitting a large FET into multiple parallel FETs with same total gate periphery and by biasing them individually. By varying the biases, the magnitude and the phase of the IMD3 components at the output of FET changes. A detailed simulation methodology using commercial microwave CAD software is presented. Simulation results show that by biasing one device in Class AB and other(s) in deep Class AB mode, IMD3 components of parallel FETs can be made out of phase to each other leading to cancellation and improvement in linearity. Three prototype circuits were simulated using (a) a single 5 mm FET (1 × 5 mm), (b) two parallel 2.5 mm FETs (2 × 2.5 mm), and (c) four parallel 1.25 mm FETs (4 × 1.25 mm), for a total gate periphery of 5 mm, over the frequency range of 0.8 to 1.0 GHz. IMD3 improvement up to 20 dBc was achieved with the 4 × 1.25 mm circuit when the FET biases were optimized. Measurement results show improvement in linearity up to 20 dBc for 4 × 1.25 mm circuit. The proposed method improves linearity without a substantial penalty on the power consumption and is straightforward to implement.     

涡流空气分级机熵产与分级性能分析

利用热力学第二定律中的熵产理论对涡流空气分级机各不可逆因素引起的熵产进行分析，通过粉料分级试验对其分级性能进行验证，获得了黏性熵产、湍流熵产和壁面熵产分布特点及操作参数对熵产和分级精度的影响规律。熵产分析结果表明，涡流空气分级机内湍流熵产和壁面熵产占总熵产的比例高达56.41%和43.11%，湍流熵产主要产生于转笼叶片间和转笼内部，进风口和细粉出口壁面剪切引起较大壁面熵产；此外，转笼转速和进口风速变化分别仅对转笼区域和切向进风口区域内气流运动熵产影响较大，进口风速-转笼转速处于8.6m/s、 800r/min和18m/s、1200r/min操作工况附近时，涡流空气分级机内总熵产/总能变化率较小，分级流场稳定性较高，对粗、细颗粒分离有利，该工况下分级机的粉料分级试验效果较好，说明熵产理论可用于涡流分级机内流动分析及其操作参数的优化匹配。     

Optimal approximation of asymmetric type fractional-order bandpass Butterworth filter using decomposition technique

In this letter, the optimal rational approximation of fractional-order bandpass Butterworth filter (FOBBF) is presented. The transfer function of the FOBBF is decomposed into a multiplication of first-order and second-order terms. As a result, the design stability conditions can be easily satisfied using only the variable boundary constraints. The proposed technique generalizes the symmetric fractional-order roll-off characteristic as only a special case of the asymmetric one. Several examples are presented to validate the modeling efficacy.     

楼宇型分布式能源系统设备容量和运行策略优化研究

设备容量优化和运行策略优化是分布式能源系统设计，运行的关键问题。为实现分布式能源系统的经济效益，能效水平和环境效益最大化，针对楼宇型分布式能源系统建立了相对普适化的物理模型和数学模型，以粒子群优化算法和线性规划相结合，采用两阶段优化方法计算系统的最优容量配置，并给出运行策略。以某写字楼的分布式能源系统为例，得到最优的系统设备容量和全年逐时运行策略，并采用遍历法验证计算结果的准确性。优化的分布式能源系统与传统供能系统相比，费用年值降低7.79%，年总能耗降低24.18%，污染物排放量减少了62.77 %。     

Thermally driven failure of Nd:YVO4 amplifier crystals

We report on an unusual failure pattern resulting from thermally driven fracture of laser amplifier single crystals. The pattern led to some confusion with regard to the point of initiation due to the coalescence of hackle lines to a region within the fracture surface, rather than the more common divergence of hackle away from the origin. The pattern leads to new fracture terms: Hackle node - the coalescence of hackle marks to a point of prior compression. The feature is produced as a thermally-generated, centrally-located compressive region transforms to tension thereby drawing crack propagation and hackle to the compressive region, forming an internal terminus. Hackle terminus – a hackle node formed by final crack propagation within the component. As part of the analysis, the fracture toughness and slow crack growth constants of neodymium doped, yttrium ortho-vanadate (Nd:YVO₄) were measured. Nd:YVO₄ exhibits slow crack growth and is very brittle with a fracture toughness of only 0.48 MPa√m.