Reliability based multidisciplinary design optimization of cooling turbine blade considering uncertainty data statistics

Considering the coupling among aerodynamic, heat transfer and strength, a reliability based multidisciplinary design optimization method for cooling turbine blade is introduced. Multidisciplinary analysis of cooling turbine blade is carried out by sequential conjugated heat transfer analysis and strength analysis with temperature and pressure interpolation. Uncertainty data including the blade wall, rib thickness, elasticity Modulus and rotation speed is collected. Data statistics display the probability models of uncertainty data follow three-parameter Weibull distribution. The thickness of blade wall, thickness and height of ribs are chosen as design variables. Kriging surrogate model is introduced to reduce time-consuming multidisciplinary reliability analysis in RBMDO loop. The reliability based multidisciplinary design optimization of a cooling turbine blade is carried out. Optimization results shows that the RBMDO method proposed in this work improves the performance of cooling turbine blade availably.

     

GPU-based model predictive control for continuous casting spray cooling control system using particle swarm optimization

Model predictive control (MPC) for spray cooling control system requires a repeated online solution of an optimization problem that includes partial differential equations (PDEs). To simulate the future temperature behavior of steel billets, 3D dynamic heat transfer model is used. The special solution domain of PDEs has led to large computation cost, which is the main challenge in the real-time practical application of spray cooling control system. Meanwhile, the heat transfer coefficients need to be identified using the measured surface temperature. This work presents a two-level parallel solution method implemented on a Graphics processing unit (GPU) for MPC of spray cooling control systems and a weighted least squares modified conjugate gradient method (WLS–MCG) for identification of heat transfer coefficients. Two-level parallel solution method consists of parallel-based heat transfer model and stream parallel particle swarm optimization (PSO). PSO is used to solve the optimization problem. WLS–MCG consists of the weighted least squares (WLS) and modified conjugate gradient method (MCG). The experimental results show that the two-level parallel solution method has good computational performance and achieves satisfactory control performance.     

涡轮气冷叶片传热分析数据提取技术研究

涡轮气冷叶片传热管网计算是涡轮气冷叶片传热设计的重要环节,针对涡轮气冷 叶片传热设计需求,提出了涡轮气冷叶片模型传热分析数据提取的方法,具体包括计算单元划 分、流路自动判断、网络图生成和传热数据提取等算法。结合涡轮气冷叶片结构特点,使用UG Open API 工具开发了传热分析数据提取系统,实现了涡轮气冷叶片传热分析数据的提取、管理 和输出功能,以用于后续的分析计算,提高了传热设计管网计算的自动化水平,并通过实例验 证了所提出方法的可行性。     

宽通道换热板片结构的多目标头脑风暴优化设计

换热板片作为构成宽通道板式换热器的核心部件,对其换热效果具有直接影响.为有效提升换热器性能,减少能量损耗,提出一种宽通道换热板片结构的多目标头脑风暴优化设计方法.首先,根据换热板片的形状和布局特点,提取梯形凸台尺寸及其分布间隔构成结构参数,采用正交法,基于Fluent数值模拟软件获得25组换热板片结构样本;然后,采用回归构建换热努塞尔数和压力损失的代理模型,以最大换热效果和最小能量损耗作为优化目标,采用基于网格的多目标头脑风暴优化算法,寻优获得最佳换热板片的结构设计方案.统计实验结果表明,换热性能代理模型可以有效降低评价代价,所提出优化设计方法可以更加高效地获得具有最佳换热效果和能量损耗的换热板片结构.     

邻域自适应粒子群算法求解地源热泵区域 能源系统鲁棒优化调度问题

针对地源热泵区域能源系统中冷热负荷和机组效能的不确定性, 本文提出了一种考虑双重不确定性的鲁棒优化调度方法. 首先, 基于多面体不确定模型描述调度模型中的鲁棒变量. 然后, 针对建筑冷热负荷不确定性, 采用对偶原理将双层优化模型等价为单层优化模型; 对于机组效能不确定性, 采用场景法进行分析. 最后, 采用多目标优化约束处理方法处理鲁棒优化调度模型中的约束条件. 同时, 为更加高效、准确求解所构建的优化调度模型, 提出了一种邻域自适应粒子群优化算法(NAPSO). 实验结果表明, 在制冷和制热工况下, 与经验运行策略相比, 本文所提方法可分别减少7.22%和5.55%的系统运行成本, 是一种解决地源热泵区域能源系统鲁棒优化调度的有效方法.     

基于FINE/Turbo的高压涡轮叶片流热耦合分析

为验证FINE/Turbo软件对高压涡轮流热耦合求解问题的准确性,将Mark Ⅱ型燃气涡轮叶片作为分析对象,选用不同的湍流模型和转捩模型进行数值模拟,得到叶片表面压力分布,B2B面的压力、温度、马赫数和湍流动能分布,叶片内部温度分布以及叶片表面传热系数分布,并与试验数据进行比较.结果表明:对于流热耦合问题,FINE/T...     

MPTL板翅式换热器性能的数值分析

研究机械泵驱动的两相冷却回路(MPTL)换热器传热效能优化问题,由于换热器的应用环境极为复杂,目前的实验数据极度匮乏,而且结构特殊,现有的标准叉流板式换热器的设计方法并不适用。为了提出可行的设计优化方法,针对某典型MPTL板翅式换热器,建立一种简化的三维物理数学模型,采用有限体积法及流固耦合方法深入研究该换热器在典型结构及设计工况下的流场特性,随后研究其在不同翅高及进口雷诺数时的换热性能变化规律。数值仿真结果表明,换热器换热效率较高,压力损失较小,且随着翅高的降低、进口雷诺数的增加,换热性能逐渐提高。结果证明,可为换热器的进一步设计与优化提供有益的参考。     

Cooling Load Density Optimization of an Irreversible Simple Brayton Refrigerator

The performance optimization of an irreversible simple Brayton refrigerator coupled to constant-temperature heat reservoirs is carried out by taking the cooling load density, i.e., the ratio of cooling load to the maximum specific volume in the cycle, as the optimization objective using finite-time thermodynamics (FTT) or entropy generation minimization (EGM) in this paper. The analytical formulae about the relations between cooling load density and pressure ratio, as well as between coefficient of performance (COP) and pressure ratio are derived with the heat resistance losses in the hot- and cold-side heat exchangers, and the irreversible compression and expansion losses in the compressor and expander. The influences of the effectiveness of the heat exchangers, the temperature ratio of the reservoirs, and the efficiencies of the compressor and expander on the cooling load density versus COP are provided by numerical examples. The cooling load density optimization is performed by searching the optimum pressure ratio of the compressor, and searching the optimum distribution of heat conductance of the hot- and cold-side heat exchangers for the fixed total heat exchanger inventory. The influences of some design parameters, including the effectiveness of the heat exchangers between the working fluid and heat reservoirs, the efficiencies of compressor and expander, the temperature ratio of heat reservoirs, on the maximum cooling load density, the optimum heat conductance distribution and the optimum pressure ratio are provided by numerical examples. The refrigeration plant design with optimization leads to a smaller size including the compressor, expander, and the hot- and cold-side heat exchangers.     

利用神经网络控制连续浇铸过程中的热传导

In continuous casting, the cooling-solidification process must be based on the adaptation of heat transfer, which is directly connected to casting conditions such as casting speed, casting temperature, and cooling parameters. Most control schemes are based on the static relation between casting speed and water flow rate in each cooling zone; this constitutes an open loop that does not consider the dynamic surface temperature, which is an important parameter for the final slab quality. In steelmaking, the casting-speed changes affect the global heat transfer. An optimal operation requires an adjustment of the process control variables, i.e., global heat transfer. A learning neural network (NN) allows the identification and the control of a nonlinear heat transfer model in the continuous casting process. A heat transfer model was developed using the dynamic heat balance. A comparison between the experimental open loop results and those of the model simulation is considered. Following adaptation, the model is used for controlling the slab surface temperature in closed loop, using NN technology and PID controllers. The NN identification and control strategy gives a stable temperature closed loop control comparatively to the conventional PID.     

压气机叶片扭曲规律的多目标三维气动优化

为了提高轴流压气机的等熵效率和总压比,采用基于人工神经网络及遗传算法的叶轮机械叶片三维优化设计方法,开发了一种高性能的动叶片。优化目标是在流量不减小的情况下,尽可能的提高转子叶片的总压比和等熵效率。优化仿真结果显示,优化后所获得的扭曲叶片可以有效地改善叶根处的流动分离,流动分离区明显后移,损失显著降低,在整个工作范围,等熵效率提高了1.27%-7.08%,流量和总压比也都得到了大幅度的提高。结果表明,对亚音叶片进行扭曲规律优化效果很明显,优化方法是获得高性能转子叶片的有效途径。     

基于最优控制策略的逆变器散热结构优化

为了提高电动汽车用永磁同步电机逆变器IGBT模块的可靠性,文章从热损耗和散热两方面对逆变器可靠性进行研究。首先通过对比分析永磁同步电机在同一工况下d轴电枢电流为零(id=0)和最大转矩电流比(MTPA)两种控制方式下逆变器中IGBT模块的损耗,发现MTPA控制策略优于id=0控制策略;接着,基于MTPA控制策略,设计了一种热管和风冷相结合的散热结构,相较原风冷散热结构,采用新型散热方式可使芯片最高工作温度降低8.49℃;最后,采用最优拉丁超立方抽样构建响应面代理模型(RSM),并采用多岛遗传算法(MIGA)对代理模型进行优化处理。经仿真验证,优化处理后的“热管+风冷”散热结构使得芯片最高温度又降低了15.12℃,有效提升了IGBT模块的热可靠性。     

Local heat transfer process and pressure drop in a micro-channel integrated with arrays of temperature and pressure sensors

One of the most important components in micro-fluidic system is the micro-channel which involves complicated flow and transport process. This study presents micro-scale thermal fluid transport process inside a micro-channel with a height of 37 μm. The channel can be heated on the bottom wall and is integrated with arrays of pressure and temperature sensors which can be used to measure and determine the local heat transfer and pressure drop. A more simplified model with modification of Young’s Modulus from the experimental test is used to design and fabricate the arrays of pressure sensors. Both the pressure sensors and the channel wall use polymer materials which greatly simplify the fabrication process. In addition, the polymer materials have a very low thermal conductivity which significantly reduces the heat loss from the channel to the ambient that the local heat transfer can be accurately measured. The air flow in the micro-channel can readily become compressible even at a very low Reynolds number condition. Therefore, simultaneous measurement of both the local pressure drop and the temperature on the heated wall is required to determine the local heat transfer. Comparison of the local heat transfer for a compressible air flow in micro-channel is made with the theoretical prediction based on incompressible air flow in large-scale channel. The comparison has clarified many of the conflicting results among different works.     

Microscale thermal fluid transport process in a microchannel integrated with arrays of temperature and pressure sensors

One of the most important components in a microfluidic system is the microchannel which involves complicated flow and transport process. This work presents microscale thermal fluid transport process inside a microchannel with a height of 37 μm. The channel can be heated on the bottom wall and is integrated with arrays of pressure and temperature sensors which can be used to measure and determine the local heat transfer and pressure drop. A more simplified model with modification of Young’s Modulus from the experimental test is used to design and fabricate the arrays of pressure sensors. Both the pressure sensors and the channel wall use polymer materials which greatly simplifies the fabrication process. In addition, the polymer materials have a very low thermal conductivity which significantly reduces the heat loss from the channel to the ambient that the local heat transfer can be accurately measured. The airflow in the microchannel can readily become compressible even at a very low Reynolds number condition. Therefore, simultaneous measurement of both the local pressure drop and the temperature on the heated wall are required to determine the local heat transfer. Comparison of the local heat transfer for a compressible airflow in microchannel is made with the theoretical prediction based on incompressible airflow in large scale channel. The comparison has clarified many of the conflicting results among different works.     

Development of a structural optimization strategy for the design of next generation large thermoplastic wind turbine blades

This paper presents the development of a structural optimization process for the design of future large thermoplastic wind turbine blades. The optimization process proposed in this paper consists of three optimization steps. The first step is a topology optimization of a short untwisted and non tapered section of the blade, with the inner volume used as the design domain. The second step is again a topology optimization, but on the first half of a blade to study the effect of non symmetry of the structure due to blade twist and taper. Results of this optimization step are then interpreted to build a shell model of the complete blade structure to perform composite size optimization based on a minimum mass objective subjected to constraints on deflection, composite strength and structural stability. Different blade models using ribs are then optimized and compared against conventional blade structure (box spar structure without ribs and single web structure without ribs). The use of ribs in wind turbine blade structures, which is more adapted to thermoplastic composite manufacturing than for thermoset composites, leads to slightly lighter blades than conventional blade structures.     

Hierarchical decomposition and optimization of thermal transformer performances

An optimization under constraints of heat transformer systems is presented. The hierarchical decomposition and endoreversibility principle are integrated to avoid the great number of variables in the analyzed optimization problem. The analysis method is applied on a solar absorption refrigerator in the purpose to define the optimum design parameters. The decomposition levels of an equivalent model are defined. The equation system governing the behavior of the chosen model is established using Lagrange method for optimization under constraints. These constraints are defined according to thermodynamic laws. The couplings between the optimal functional and conceptual parameters are defined. The temperature and pinch distributions through heat exchangers are determined. The performance limits of an endoreversible cycle are defined for different values of the cooling load. The contribution of the different component heat transfer areas and the times of transfer are deducted according to technical an economic analysis.     

Performance assessment of heat exchanger using intelligent decision making tools

Process and manufacturing industries today are under pressure to deliver high quality outputs at lowest cost. The need for industry is therefore to implement cost savings measures immediately, in order to remain competitive. Organizations are making strenuous efforts to conserve energy and explore alternatives. This paper explores the development of an intelligent system to identify the degradation of heat exchanger system and to improve the energy performance through online monitoring system. The various stages adopted to achieve energy performance assessment are through experimentation, design of experiments and online monitoring system. Experiments are conducted as per full factorial design of experiments and the results are used to develop artificial neural network models. The predictive models are used to predict the overall heat transfer coefficient of clean/design heat exchanger. Fouled/real system value is computed with online measured data. Overall heat transfer coefficient of clean/design system is compared with the fouled/real system and reported. It is found that neural net work model trained with particle swarm optimization technique performs better comparable to other developed neural network models. The developed model is used to assess the performance of heat exchanger with the real/fouled system. The performance degradation is expressed using fouling factor, which is derived from the overall heat transfer coefficient of design system and real system. It supports the system to improve the performance by asset utilization, energy efficient and cost reduction in terms of production loss. This proposed online energy performance system is implemented into the real system and the adoptability is validated.     

Evaluation of heat transfer coefficients during upward and downward transient directional solidification of Al–Si alloys

Aluminum alloys with silicon as a major alloying element consist of a class of alloys which provides the most significant part of all shaped castings manufactured. This is mainly due to the outstanding effect of silicon in the improvement of casting characteristics, combined with other physical properties such as mechanical properties and corrosion resistance. In general, an optimum range of silicon content can be assigned to casting processes. For slow cooling rate processes (sand, plaster, investment), the range is 5 to 7 wt%; for permanent molds, 7 to 9%; and for die castings, 8 to 12%. Since most casting parts are produced considering there is no dominant heat flow direction during solidification, it seems to be adequate to examine both upward and downward growth directions to better understand foundry systems. The way the heat flows across the metal/mold interface strongly affects the evaluation of solidification and plays a remarkable role in the structural integrity of castings. Gravity or pressure die casting, continuous casting, and squeeze casting are some of the processes where product quality is more directly affected by the interfacial heat transfer conditions. Once information in this area is accurate, foundrymen can effectively optimize the design of their chilling systems to produce sound castings. The present work focuses on the determination and evaluation of transient heat transfer coefficients from the experimental cooling curves during solidification of Al 5, 7, and 9 wt% Si alloys. The method used is based on comparisons between experimental data and theoretical temperature profiles furnished by a numerical solidification model, which applies finite volume techniques. In other words, the resulting data were compared with a solution for the inverse heat conduction problem. The necessary solidification thermodynamic input data were obtained by coupling the software ThermoCalc Fortran interface with the solidification model. A comparison between upward and downward transient metal/mold heat transfer coefficients is conducted.     

HPS整流管芯片散热效率计算仿真研究

针对传统散热效率计算方法存在计算时间过长、计算效率过低以及计算误差偏高等问题,提出了一种新的散热效率计算方法———基于傅里叶导热方程的散热效率计算方法。通过对高功率半导体整流管芯片进行分析,引用傅里叶导热方程计算出整流管芯片的传热热阻,根据传热热阻随着温度的变化,获取高功率半导体整流管芯片散热系数。根据高功率半导体整流管芯片散热系数,构建高功率半导体整流管芯片散热模型,利用建立的模型分析转速、冷气流入口的压力和速度以及冷却孔的分布等对转子温度场的以及散热效率的影响,优化散热路径,完成高功率半导体整流管芯片散热计算。实验结果表明,所提方法有效减少了计算时间,提高了计算效率,与此同时,降低了计算误差,使计算结果更为准确。     

Cooling Load Density Analysis and Optimization for an Endoreversible Air Refrigerator

The performance analysis and optimization of an endoreversible air refrigerator is carried out by taking the cooling load density, which is defined as the ratio of cooling load to the maximum specific volume in the cycle, as the optimization objective in this paper. The results obtained are different from those with the cooling load objective. Numerical examples show the effects of pressure ratio and allocation of heat exchanger inventory on the cooling load density of the refrigerator.     

Multi-disciplinary constrained optimization of wind turbines

We describe procedures for the multi-disciplinary design optimization of wind turbines, where design parameters are optimized by maximizing a merit function, subjected to constraints that translate all relevant design requirements. Evaluation of merit function and constraints is performed by running simulations with a parametric high-fidelity aero-servo-elastic model; a detailed cross-sectional structural model is used for the minimum weight constrained sizing of the rotor blade. To reduce the computational cost, the multi-disciplinary optimization is performed by a multi-stage process that first alternates between an aerodynamic shape optimization step and a structural blade optimization one, and then combines the two to yield the final optimum solution. A complete design loop can be performed using the proposed algorithm using standard desktop computing hardware in one-two days. The design procedures are implemented in a computer program and demonstrated on the optimization of multi-MW horizontal axis wind turbines and on the design of an aero-elastically scaled wind tunnel model.