We suggest a numerical method to calculate molecular weight distribution from linear viscoelastic data. The calculation method consists of three components: (1) a viscoelastic model of a monodisperse polymer as a function of molecular weight; (2) the mixing rule connecting viscoelastic data of monodisperse and polydisperse polymers through molecular weight distribution; (3) an algorithm which calculates the molecular weight distribution from the chosen mixing rule. Since we cannot measure the relaxation modulus of all monodisperse samples, we need an accurate monodisperse model for any molecular weight. It is known that a dynamic test is more reliable than a relaxation test, while the mixing rule needs relaxation modulus. Hence, we should have a smart numerical method that can convert dynamic data to relaxation modulus with the minimum conversion error. If we use the numerical method, then we have to generate numerical data from the model. Then it takes quite a long time. On the other hand, if we have a monodisperse model with the analytical relaxation spectrum, then calculation time can be reduced dramatically. Since the conversion from relaxation modulus to dynamic modulus suffers from smaller errors than the reverse conversion because of ill-posedness of the interconversion, the analytical conversion can be implemented more quickly at an acceptable level of errors. This paper proposes a new method satisfying the requirements.

我们建议一种数值方法，可从线性粘弹性数据计算分子量分布。该计算方法包括三个部分：（1）单分散聚合物的粘弹性模型与分子量的关系；（2）通过分子量分布连接单分散和多分散聚合物的粘弹性数据的混合规则；（3）一种算法，可根据所选的混合规则计算分子量分布。由于我们无法测量所有单分散样品的弛豫模量，因此我们需要针对任何分子量的准确单分散模型。众所周知，动态测试比松弛测试更可靠，而混合规则则需要松弛模量。因此，我们应该有一个聪明的数值方法，可以以最小的转换误差将动态数据转换为张弛模量。如果使用数值方法，则必须从模型生成数值数据。则需要相当长的时间。另一方面，如果我们具有带有分析弛豫谱的单分散模型，则可以大大减少计算时间。由于互变不良，从松弛模量到动态模量的转换比逆转换具有较小的误差，因此可以在可接受的误差水平上更快地实现分析转换。本文提出了一种满足要求的新方法。如果我们有一个具有解析弛豫谱的单分散模型，那么可以大大减少计算时间。由于互变不良，从松弛模量到动态模量的转换比逆转换具有较小的误差，因此可以在可接受的误差水平上更快地实现分析转换。本文提出了一种满足要求的新方法。如果我们有一个具有解析弛豫谱的单分散模型，那么可以大大减少计算时间。由于互变不良，从松弛模量到动态模量的转换比逆转换具有较小的误差，因此可以在可接受的误差水平上更快地实现分析转换。本文提出了一种满足要求的新方法。