Nondeterministic analyses were performed on a one-dimensional, steady, chemically reacting, reduced-order model for hydrogen peroxide flow through a monopropellant catalyst bed. Polynomial chaos expansion was implemented to decrease sampling time in order to perform the probabilistic analyses including quantification of global sensitivities using Sobol indices, construction of axial property uncertainty envelopes for random inputs, and construction of posterior probability distributions with credible intervals for variation in chemical tuning parameters. Results from the study show that model behavior is primarily governed by propellant mass fraction and activation energy of the global reaction. Additionally, posterior distributions generated via Bayesian inference indicate that activation energy and the number of active sites per volume are related by a logarithmic family of solutions as a result of the reaction advancement gradient form in the model. A region of high probability activation energy and number of active sites per volume values are identified using Markov chain Monte Carlo to compare model predictions with experimental data across five individual test cases.

对过氧化氢流经单推进剂催化剂床层的一维，稳定，化学反应，降序模型进行了不确定性分析。进行多项式混沌扩展以减少采样时间，以进行概率分析，包括使用Sobol指数量化整体灵敏度，构建随机输入的轴向属性不确定性包络以及构建具有可靠区间的后验概率分布，以调节化学调谐参数的变化。研究结果表明，模型行为主要受推进剂质量分数和整体反应的活化能支配。另外，通过贝叶斯推断产生的后验分布表明，由于模型中的反应进行梯度形式，活化能和每体积活性位点的数量与溶液的对数族相关。使用马尔可夫链蒙特卡罗（Monte Carlo）将模型预测与五个独立测试案例中的实验数据进行比较，从而确定了一个高概率激活能量区域和每体积值活动位置的数量。