基于条件生成对抗网络（CGAN），通过在CGAN后附加多层感知机（MLP）检验器，发展了一种目标检验条件生成对抗网络（TT-CGAN）并将其用于翼型反设计。TT-CGAN可以重点检验设计目标的实现效果，增强了CGAN对于附加条件的检验效果。基于UIUC翼型数据库，选取了797个真实翼型，并通过求解基于雷诺平均Navier-Stokes（RANS）方程组计算得到了各翼型对应的气动参数，形成真实翼型数据库；利用类别/形状函数变换（CST）方法对翼型外形进行参数化，将翼型外形从100维几何参数描述为14维CST参数。通过特征级融合方式将升力系数、阻力系数、表面压力分布融合得到多模态气动参数，并与基于升阻力系数的气动参数作对比，分别作为网络的附件条件，进行翼型反设计。结果表明，基于多模态数据TT-CGAN的翼型反设计方法生成结果更为精准，翼型几何外形的平均均方根误差为1.779×10^-3，平均绝对误差为1.351×10^-3。通过求解RANS方程组对生成翼型进行数值模拟验证，结果显示其升力系数的平均相对误差为3.599 8%，阻力系数的平均相对误差为3.723 9%，生成翼型的升阻力系数均满足设计指标，生成结果较精准。通过比较训练样本与测试样本的升阻比分布，发现升阻比在［20，30）区间上的翼型占总测试集的40%，而升阻比在此区间的训练翼型仅占训练集的16%，即使在训练样本较少的区间，该方法也能实现准确的预测，具有一定泛化性。

A Target Testing Conditional Generative Adversarial Network （TT-CGAN） is developed and applied to airfoil inverse design. This network extends the Conditional Generative Adversarial Network （CGAN） by integrating a Multi-Layer Perceptron （MLP） tester， so as to enhance the capability of CGAN in evaluating the impact of additional conditions on target testing. Utilizing the UIUC airfoil database， 797 real airfoils were selected， and their corresponding aerodynamic parameters were calculated by solving the Reynolds-Averaged Navier-Stokes （RANS） equations to construct a comprehensive airfoil database. The airfoil shapes were parameterized using the Class Shape Transformation （CST） method， transforming the geometric parameters from 100 to 14 CST parameters. Multi-modal aerodynamic parameters， including lift coefficient， drag coefficient， and surface pressure distribution， were fused using the feature-level fusion approach. These parameters were compared with the aerodynamic parameters based solely on lift and drag coefficients， which served as auxiliary conditions for the network during the airfoil inverse design process. The results indicate that the TT-CGAN based inverse design method generates more accurate airfoils， with an average root mean square error of 1.779×10^-3 and an average mean absolute error of 1.351×10^-3 in airfoil geometry. The generated airfoils were further validated through numerical simulations by solving the RANS equations， demonstrating an average relative error of 3.599 8% for the lift coefficient and 3.723 9% for the drag coefficient， confirming that the generated airfoils can meet the specified design criteria. Analysis of the lift-to-drag ratio distributions reveals that 40% of the test airfoils achieved lift-to-drag ratios within the ［20， 30） range， compared to only 16% in the training set. This finding highlights the method’s capability to make accurate predictions even within data-sparse regions， showcasing its generalizability.