Research on acceleration methods for performance simulation of adaptive variable cycle engines based on principle-data fusion drive
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摘要:
自适应变循环发动机（ACE）基于各部件调节机构的协同调节，面向复合型飞行任务需求具备综合性能优势。然而，ACE多调节机构和工作状态变化剧烈的特点导致其性能仿真模型在收敛性和计算效率方面存在严重不足，难以满足飞机-发动机协同设计中对ACE开展大规模性能优化设计的需求。提出一种原理-数据联合驱动的性能仿真加速方法，构建基于原理分析的通用全局初猜值预测模型与基于数据的自扩展初猜值变可信度代理模型，形成原理-数据联合驱动的发动机初猜值预测框架，解决多构型、多设计方案下，ACE性能仿真模型难以合理选择初猜值所造成的收敛性差和计算效率低的问题。针对ACE特有的等进口流量节流状态控制规律优化问题开展数值仿真验证，结果表明：所提出方法相较于传统方法的收敛能力提高38%以上，仿真计算速度提高45%以上。该方法可有效支撑飞/发协同设计中针对各类ACE设计参数和控制规律的大规模仿真优化，具有重要的工程应用价值。
Abstract:The adaptive cycle engine (ACE) achieves comprehensive performance advantages for various flight missions through the collaborative regulation of variable mechanisms. However, the numerous variable mechanisms and intense changes in the operating states of ACE results in significant shortcomings in the convergence and computational efficiency of the existing performance simulation model. These shortcomings make it difficult to meet the demands for large-scale performance optimization design of ACE in the aircraft-engine co-design. A performance simulation acceleration method driven by both principles and data is proposed. This method constructs a general global initial guess prediction model based on principle analysis and a data-driven self-expanding initial guess variable fidelity surrogate model, forming a principle-data joint-driven engine initial guess prediction framework. This framework addresses the problems of poor convergence and low computational efficiency caused by the difficulty in reasonably selecting initial guesses for the performance simulation models under various configurations and design schemes. Numerical simulation verification is conducted to optimize the ACE control laws at equal-inlet flow throttle state, and the results demonstrate that the proposed method achieves more than a 38% improvement in convergence capability and a 45% increase in simulation computation speed compared to traditional methods. This method effectively supports large-scale simulation optimization of ACE design parameters and control laws in aircraft-engine co-design, holding significant engineering application value.
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图 1 带核心机驱动风扇级的ACE结构示意图
Figure 1. Schematic diagram of the ACE with core-driven fan stage
图 2 N-R法迭代求解示意图
Figure 2. Schematic diagram of the iterative solution using the Newton-Raphson method
图 3 N-R法迭代求解陷入局部最优示意图
Figure 3. Schematic diagram of the Newton-Raphson method converging to a local optimum
图 4 DYNGEN初猜值预测流程
Figure 4. Prediction process of DYNGEN initial guess value estimation
图 5 ACE等流量节流发动机推力和主要猜值变化规律
Figure 5. Variation trends of engine thrust and main guess values during the ACE constant-flow throttling process
图 6 高通用初猜值预测模型样本点拟合情况
Figure 6. Fitting results of sample points in the high-universality initial guess value prediction model
图 7 高通用全局初猜值预测公式应用流程
Figure 7. Application process of the high-universality global initial guess value prediction formula
图 8 高通用初猜值预测方法效果对比
Figure 8. Performance comparison of high-universality initial value prediction methods
图 9 样本分布对预测效果影响示意图
Figure 9. Schematic diagram illustrating the impact of sample distribution on prediction performance
图 10 基于K-D树最近邻搜索的代理模型自扩展仿真流程
Figure 10. Self-expanding simulation process of the surrogate model based on K-D tree nearest neighbor search
图 11 典型工况下控制规律优化过程性能对比
Figure 11. Performance comparison of control law optimization processes under typical operating conditions
图 12 不同优化工况下联合驱动加速方法样本库数据更新情况
Figure 12. Data update status of the sample library using the joint-driven acceleration method under different optimization conditions
表 1 ACE总体性能模型部件性能猜值
Table 1. Guess values of component performance in the ACE overall performance model
变量 含义 Nr 转子相对物理转速 Tt4 主燃烧室出口总温 LFFAN FFAN辅助线值 LRFAN RFAN辅助线值 LCDFS CDFS辅助线值 LHPC HPC辅助线值 LHPT HPT辅助线值 LLPT LPT辅助线值 
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表 2 用于训练初猜值预测模型的发动机参数方案
Table 2. Engine parameter scheme for training the initial value prediction model
序号 构型 高度/km 马赫数 涡轮前
总温/K总涵道比 低压压缩
部件压比高压压缩
部件压比低压压缩
部件数量高压压缩
部件数量1 三外涵ACE 11 1.5 1833 0.981 2.88 6.37 2 2 2 三股流ACE 0 0 1900 0.6 4.72 6.8 2 1 3 双外涵VCE 0 0 1650 0.656 3.86 6 1 2 4 常规混排涡扇发动机 0 0 1800 0.5 4 7.5 1 1
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表 3 初猜值预测方法收敛能力对比
Table 3. Convergence capability comparison of initial value prediction methods
初猜值预测方法 三外涵ACE 双外涵VCE 三股流ACE 三股流ACE（改变设计参数）* 高通用预测方法 628 674 1049 1141 DYNGEN预测方法 439 485 648 691 设计点初猜值 331 377 583 577 注：*设计点高度为0 km, 马赫数为0，总涵道比为0.55，涡轮前总温为 2100 K，低压压气机总压比为5.75，高压压缩部件压比为7。
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表 4 不同初猜值预测方法对控制规律优化仿真计算量的影响对比
Table 4. Comparison of the impact of different initial value prediction methods on the computational cost of control law optimization simulation
序号 发动机工况 优化目标 总流路计算次数 设计点初猜值 DYNGEN 联合驱动加速方法 1 H=11 km, Ma=0.8* 45%中间推力，耗油率最低 无法计算** 750483 378225 2 H=11 km, Ma=0.8* 60%中间推力，耗油率最低 无法计算** 734517 318528 3 H=11 km, Ma=1.5* 80%中间推力，耗油率最低 543789 742779 267534 4 H=11 km, Ma=2* 中间状态推力最大 629793 517131 279603 5 H=0 km, Ma=0* 中间状态推力最大 699651 705834 241929 注：* H为飞行高度，Ma为飞行马赫数；**给定设计点猜值作为仿真初猜值，由于偏离收敛猜值过多，发动机总体性能仿真无法收敛。
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表 5 典型仿真任务下联合驱动加速方法效果验证
Table 5. Validation of the acceleration effect of the joint-driven acceleration method under typical simulation tasks
初猜值
预测方法速度高度特性流路
计算次数节流特性流路
计算次数DYNGEN 9 630 2 565 联合驱动加速方法 6 363 882
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