Optimal resilient power grid operation during the course of a progressing wildfire

We study a two-stage stochastic and nonlinear optimization model for operating a power grid exposed to a natural disaster. Although this approach can be generalized to any natural hazard of continuous (and not instantaneous) nature, our focus is on wildfires. We assume that an approaching wildfire impacts the power grid by reducing the transmission capacity of its overhead lines. At the time when proactive decisions have to be taken, the severity of the wildfire is not known. This introduces uncertainty. In this paper, we extend previous work by more realistically capturing this uncertainty and by strengthening the mathematical programming formulation through standard reformulation techniques. With these reformulation techniques, the resulting two-stage, convex mixed-integer quadratically constrained programming formulation can be efficiently solved using commercial quadratic programming solvers as demonstrated on a case study on a modified version of the IEEE 123-bus test system with 100 scenarios. We also quantify the uncertainties through a second case study using the following three standard metrics of two-stage stochastic optimization: the expected value of perfect information, the expected result of using the expected value solution and the value of the stochastic solution.     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

Parameter estimation for a morphochemical reaction-diffusion model of electrochemical pattern formation

The process of electrodeposition can be described in terms of a reaction-diffusion partial differential equation (PDE) system that models the dynamics of the morphology profile and the chemical composition. Here we fit such a model to the different patterns present in a range of electrodeposited and electrochemically modified alloys using PDE constrained optimization. Experiments with simulated data show how the parameter space of the model can be divided into zones corresponding to the different physical patterns by examining the structure of an appropriate cost function. We then use real data to demonstrate how numerical optimization of the cost function can allow the model to fit the rich variety of patterns arising in experiments. The computational technique developed provides a potential tool for tuning experimental parameters to produce desired patterns.     

Corrosion behavior of under‐, peak‐, and over‐aged 6063 alloy: A comparative study

The mechanical property of age‐hardenable Al‐alloys is governed by the state of ageing, which determines the microstructure and consequently, their corrosion behavior which is a vital aspect for a number of applications. This article presents a comparative assessment of corrosion behavior of under‐, peak‐ and over‐aged Al‐Mg‐Si alloy. Corrosion characteristics have been determined via immersion tests in 0.1 M ortho‐phosphoric acid solution and intergranular corrosion (IGC) tests. Corroded surfaces are examined by field emission scanning electron micrographs‐energy dispersive spectroscopy and 3D optical profilometer. The obtained results reveal that the corrosion rate at a specific immersion time as well as the depth of IGC increases in the order for under‐, peak‐, and over‐aged states. Irrespective of the state of ageing, corrosion loss increases linearly but the rate of corrosion decreases rapidly with increasing immersion time. The dominant mode of corrosion in under‐aged alloy is identified as localized pitting, while peak‐aged is highly susceptible to IGC in contrast extensive pitting corrosion is observed for over‐aged alloy. The observed differences in corrosion behavior are explained considering characteristics of precipitates. Formation of β (Mg₂Si) in case of over‐aged alloy and presence of inclusions like AlFeMnSi particles are found to accelerate pitting corrosion.     

An automated treatment planning strategy for highly noncoplanar radiotherapy arc trajectories

Radiation therapy is a technology-driven cancer treatment modality that has experienced significant advances over the last decades, due to multidisciplinary contributions that include engineering and computing. Recent technological developments allow the use of noncoplanar volumetric modulated arc therapy (VMAT), one of the most recent photon treatment techniques, in clinical practice. In this work, an automated noncoplanar arc trajectory optimization framework designed in two modular phases is presented. First, a noncoplanar beam angle optimization algorithm is used to obtain a set of noncoplanar irradiation directions. Then, anchored in these directions, an optimization strategy is proposed to compute an optimal arc trajectory. The computational experiments considered a pool of twelve difficult head-and-neck tumor cases. It was possible to observe that, for some of these cases, the optimized noncoplanar arc trajectories led to significant treatment planning quality improvements, when compared with coplanar VMAT treatment plans. Although these experiments were done in a research environment treatment planning software (matRad), the conclusions can be of interest for a clinical setting: automated procedures can simplify the current treatment workflow, produce high-quality treatment plans, making better use of human resources and allowing for unbiased comparisons between different treatment techniques.     

Revisiting the Parallel Strategy for DOACROSS Loops

Journal of Computer Science and Technology - DOACROSS loops are significant parts in many important scientific and engineering applications, which are generally exploited pipeline/wave-front...     

高效液相色谱法测定多环芳烃的出峰顺序探讨

目的探讨水﹑气﹑土壤中多环芳烃检测标准(HJ 478-2009﹑HJ 647-2013、HJ 784-2016)的正确出峰时间和顺序。方法用高效液相色谱来对苊烯、芴、苊、?、苯并(a)蒽进行定性分析,并与3个标准中的出峰顺序进行比较。结果苊烯、苊、芴、苯并(a)蒽、?的出峰时间分别为6.450、7.923、8.233、17.760、18.740min,与标准HJ478-2009﹑HJ647-2013的出峰顺序存在差异。结论在使用标准HJ478-2009﹑HJ647-2013、HJ 784-2016同时测定16种多环芳烃时,多环芳烃的出峰顺序及时间应以HJ 784-2016为准。     

重力注水过程流动不稳定现象关键影响因素研究

重力驱动注水过程中由于流量较小以及蒸汽的积聚可能导致流动不稳定现象的发生，对核反应堆安全运行具有重要的影响。通过实验研究的方法，搭建了重力注水模拟实验装置，研究了不同蒸汽出口形阻、高位储水箱水位和加热棒初始温度下流动不稳定现象的变化规律。结果表明，重力驱动注水过程流动不稳定现象包含冷却水初次注入阶段、注入水逐出阶段和冷却水再注入阶段等。在一定冷却水初始温度、冷却水入口形阻以及系统压力下，蒸汽排出速度以及实验本体内筒顶部的聚集情况取决于蒸汽出口形阻，减小蒸汽出口形阻可加快蒸汽排放速度，压力峰峰值降低、振荡周期变长，有利于系统稳定；提高高位储水箱水位加快了冷却水注入速率，增加了加热棒被淹没率，降低了流动不稳定现象的发生次数和持续时间；随加热棒初始温度的升高，冷却水流量出现了波动向停滞的转变，流动不稳定现象发生的次数增加且持续时间加长。     

Topology optimization for crashworthiness of thin-walled structures under axial crash considering nonlinear plastic buckling and locations of plastic hinges

Although topology optimization is established for linear static problems, more effort is required for solving nonlinear plastic problems. A new topology optimization approach with equivalent static loads (ESLs) is suggested to find the optimum topologies and locations of plastic hinges of thin-walled crash boxes by considering crash-induced deformation, the main crash energy-absorbing mechanism. Together with finite element method crashworthiness analyses, considering all nonlinearities with rate-dependent plasticity, the method was developed using an appropriate time-incremental scheme of ESLs without removing any high values of loads. Analyses show that the crash boxes with optimum topologies have energy-absorbing capabilities equivalent to the original structure. The proposed method is evaluated for two crashes: a crash box at low speed and a double cell subjected to high-speed collision. The results indicate that this method captures nonlinear crushing behaviours and accurate locations of plastic hinges where, if proper reinforcements are made, energy absorption can be enhanced.