References
- Alswaitti, M., M. Albughdadi, and N. A. M. Isa. 2018. “Density-based Particle Swarm Optimization Algorithm for Data Clustering.” Expert Systems with Applications 91: 170–186. doi:https://doi.org/10.1016/j.eswa.2017.08.050.
- Ba, S., W. R. Myers, and W. A. Brenneman. 2015. “Optimal Sliced Latin Hypercube Designs.” Technometrics 57 (4): 479–487. doi:https://doi.org/10.1080/00401706.2014.957867.
- Chen, X., T. Zhang, P. Ying, M. Zheng, W. Wu, L. Xia, T. Li, et al. 2002. “A Novel Catalyst for Hydrazine Decomposition: Molybdenum Carbide Supported on γ-Al 2 O 3”. Chemical Communications 3: 288–289. doi:https://doi.org/10.1039/b109400a.
- Diwekar, U. M., and J. R. Kalagnanam. 1996. “Robust Design Using an Efficient Sampling Technique.” Computers & Chemical Engineering 20: S389–S394. doi:https://doi.org/10.1016/0098-1354(96)00075-0.
- ElRafey, A., and J. Wojtusiak. 2017. “Recent Advances in Scaling‐down Sampling Methods in Machine Learning.” Wiley Interdisciplinary Reviews: Computational Statistics 9 (6): e1414. doi:https://doi.org/10.1002/wics.1414.
- Fowlkes, W. Y., C. M. Creveling, and J. Derimiggio. 1995. Engineering Methods for Robust Product Design: Using Taguchi Methods in Technology and Product Development. Reading, MA: Addison-Wesley.
- Hu, X., X. Chen, G. T. Parks, and W. Yao. 2016. “Review of Improved Monte Carlo Methods in Uncertainty-based Design Optimization for Aerospace Vehicles.” Progress in Aerospace Sciences 86: 20–27. doi:https://doi.org/10.1016/j.paerosci.2016.07.004.
- Hwang, C. H., S. N. Lee, S. W. Baek, C. Y. Han, S. K. Kim, and M. J. Yu. 2012. “Effects of Catalyst Bed Failure on Thermochemical Phenomena for a Hydrazine Monopropellant Thruster Using Ir/Al2O3 Catalysts.” Industrial & Engineering Chemistry Research 51 (15): 5382–5393. doi:https://doi.org/10.1021/ie202347f.
- Iooss, B., and P. Lemaître. 2015. “A Review on Global Sensitivity Analysis Methods.” In Uncertainty Management in Simulation-optimization of Complex Systems, 101–122. Springer, Boston.
- Jilak, A., E. Assareh, and M. Nedaei. 2017. “Application of a Novel Multi-objective Optimisation Method Integrated with the Artificial Neural Networks for Optimum Design of a Plate Heat Exchanger.” Australian Journal of Mechanical Engineering 1–15. doi:https://doi.org/10.1080/14484846.2017.1359897.
- Kroese, D. P., T. Taimre, and Z. I. Botev. 2013. Handbook of Monte Carlo Methods. John Wiley & Sons, Inc., Hoboken, New Jersey
- Liu, Z.-Z., W. Li, and M. Yang. 2015. “Two General Extension Algorithms of Latin Hypercube Sampling.” Mathematical Problems in Engineering 492-450.
- Makled, A., and H. Belal. 2009. “Modeling of Hydrazine Decomposition for Monopropellant Thrusters.” 13th International Conference on Aerospace Sciences & Aviation Technology, Military Technical College, Kobry Elkobbah, Cairo, Egypt.
- McKay, M. D. 1992. “Latin Hypercube Sampling as a Tool in Uncertainty Analysis of Computer Models.” Proceedings of the 24th conference on Winter simulation, 557–564. ACM, New York.
- Meibody, M., H. Naseh, and F. Ommi. 2019. “A Kriging Based Multi Objective Gray Wolf Optimization for Hydrazine Catalyst Bed.” Engineering Solid Mechanics 7 (3): 179–192. doi:https://doi.org/10.5267/j.esm.2019.5.005.
- Mokarram, V., and M. R. Banan. 2017. “A New PSO-based Algorithm for Multi-objective Optimization with Continuous and Discrete Design Variables.” Structural and Multidisciplinary Optimization, journal article. July, 57, 509–533.
- Most, T., and J. Will. 2008. “Metamodel of Optimal Prognosis-an Automatic Approach for Variable Reduction and Optimal Metamodel Selection.” Proc. Weimarer Optimierungs-und Stochastiktage 5: 20–21.
- Qian, P. Z. 2012. “Sliced Latin Hypercube Designs.” Journal of the American Statistical Association 107 (497): 393–399. doi:https://doi.org/10.1080/01621459.2011.644132.
- Rekab, K., and M. Shaikh. 2005. Statistical Design of Experiments with Engineering Applications. CRC Press, Taylor & Francis Group, New York.
- Robinson, D., and C. Atcitty. 1999. “Comparison of Quasi-and pseudo-Monte Carlo Sampling for Reliability and Uncertainty Analysis.” 40th Structures, Structural Dynamics, and Materials Conference and Exhibit, 1589, 12-15 April 1999, St. Louis, MO, U.S.A.
- Sallaberry, C. J., J. C. Helton, and S. C. Hora. 2008. “Extension of Latin Hypercube Samples with Correlated Variables.” Reliability Engineering & System Safety 93 (7): 1047–1059. doi:https://doi.org/10.1016/j.ress.2007.04.005.
- Schuëller, G. I., and H. A. Jensen. 2008. “Computational Methods in Optimization considering Uncertainties–an Overview.” Computer Methods in Applied Mechanics and Engineering 198 (1): 2–13. doi:https://doi.org/10.1016/j.cma.2008.05.004.
- Sheikholeslami, R., and S. Razavi. 2017. “Progressive Latin Hypercube Sampling: An Efficient Approach for Robust Sampling-based Analysis of Environmental Models.” Environmental Modelling & Software 93: 109–126. doi:https://doi.org/10.1016/j.envsoft.2017.03.010.
- Taguchi, G. 1986. Introduction to Quality Engineering: Designing Quality into Products and Processe. Asian productivity organization.
- Tong, C. 2006. “Refinement Strategies for Stratified Sampling Methods.” Reliability Engineering & System Safety 91 (10–11): 1257–1265. doi:https://doi.org/10.1016/j.ress.2005.11.027.
- Tsui, K.-L. 1992. “An Overview of Taguchi Method and Newly Developed Statistical Methods for Robust Design.” Iie Transactions 24 (5): 44–57. doi:https://doi.org/10.1080/07408179208964244.
- Van Nguyen, N., J.-W. Lee, Y.-D. Lee, and H.-U. Park. 2014. “A Multidisciplinary Robust Optimisation Framework for UAV Conceptual Design.” The Aeronautical Journal 118 (1200): 123–142. doi:https://doi.org/10.1017/S0001924000009027.
- Williamson, D. 2015. “Exploratory Ensemble Designs for Environmental Models Using K‐extended Latin Hypercubes.” Environmetrics 26 (4): 268–283. doi:https://doi.org/10.1002/env.v26.4.
- Yin, Y., D. K. Lin, and M.-Q. Liu. 2014. “Sliced Latin Hypercube Designs via Orthogonal Arrays.” Journal of Statistical Planning and Inference 149: 162–171. doi:https://doi.org/10.1016/j.jspi.2014.02.008.
- Youn, B. D., K. K. Choi, L. Du, and D. Gorsich. 2007. “Integration of Possibility-based Optimization and Robust Design for Epistemic Uncertainty.” Journal of Mechanical Design 129 (8): 876–882. doi:https://doi.org/10.1115/1.2717232.
- Zuiani, F., M. Vasile, and A. Gibbings. 2012. “Evidence-based Robust Design of Deflection Actions for near Earth Objects.” Celestial Mechanics and Dynamical Astronomy 114 (1–2): 107–136. doi:https://doi.org/10.1007/s10569-012-9423-1.