{"paper_id":"2fd006c8-4d2c-4ba3-9ced-d18d93490dea","body_text":"Surrogate-Based Optimization of an Adaptively Refined-Mesh Multiphase Granular Flow Model for Landslide-Induced Waves | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Surrogate-Based Optimization of an Adaptively Refined-Mesh Multiphase Granular Flow Model for Landslide-Induced Waves Fulkan Kafilah Al Husein, Novan Tofany, Muhammad Syazali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7405476/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Subaerial landslides can generate destructive tsunami waves that threaten coastal and reservoir environments. High-fidelity multiphase CFD models provide an accurate representation of these events but demand considerable computational resources, especially when simulating granular flows and complex wave interactions. Adaptive mesh refinement (AMR) has shown promise in improving simulation efficiency; however, its effectiveness is highly sensitive to parameter settings that are often tuned heuristically. This study introduces a surrogate-based sequential approximation optimization (SAO) framework using Radial Basis Function (RBF) models to systematically optimize AMR parameters within a multiphase granular flow solver. The method is applied to two benchmark cases representing small- and large-scale laboratory scenarios. In the small-scale case, SAO achieves a 56% reduction in time with negligible loss in accuracy. The optimized configuration is successfully transferred to the large-scale case, yielding a 30% reduction, and is further validated through independent optimization. The framework demonstrates stable convergence across different sampling sets and remains sample-efficient under limited simulation samples. A detailed analysis highlights the influence of mesh dynamics, domain complexity, and early-stage refinement behavior on AMR performance. The results confirm that surrogate-assisted AMR optimization offers a scalable and efficient alternative to traditional sensitivity analysis. The proposed framework offers a practical approach for improving the efficiency of CFD-based landslide-induced tsunami simulations and shows potential for future extension to large-scale, more realistic topographies and early-stage reconstructing studies Computational Mathematics Ocean Engineering Geophysics Granular landslide Tsunami Multiphase CFD Adaptive Mesh Refinement Surrogate Optimization Full Text Additional Declarations The authors declare potential competing interests as follows: This research was supported by funding from Universiti Brunei Darussalam (UBD) and BRIN-Indonesia. The authors declare that, aside from this funding, they have no financial or non-financial competing interests that could have influenced the work reported in this article. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7405476\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":502374568,\"identity\":\"78326a10-ef63-4797-8fc8-278aa0f878e4\",\"order_by\":0,\"name\":\"Fulkan Kafilah Al Husein\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Mathematics Department, The Republic of Indonesia Defense University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fulkan\",\"middleName\":\"Kafilah Al\",\"lastName\":\"Husein\",\"suffix\":\"\"},{\"id\":502374569,\"identity\":\"6ab3d768-c7d1-4540-89bd-e477a8409df7\",\"order_by\":1,\"name\":\"Novan Tofany\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYNCDDwwHGBsYGAwYQCRRgHEGyVqYeYjRYs7eY/i5gKEucb577+HXtm13ZBvYm7dJMO64g1OLZc8ZY+kZDIcTN545l2ad2/bMuIHnWJkE45lnOLUY3EhLkAa6J3HjjBwz49y2w4kNEjlmEoxth/FpSf7NA3TYxvlvzIwtQVrk3xDSknwMaAtz4nwJHuPHjGBbeAhoOXP4mDWPwWHjDTw5Zow9554Zt/GkFVsknsGj5Xhj822eijrZ+e1njD/8KLsj289+eOONjztwa4FqBKIDDGwSIDYbiEggoAEM5BsYmD8Qo3AUjIJRMApGHgAAEKxaLSgxKgwAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Mathematical Sciences Programme, Faculty of Science, Universiti Brunei Darussalam (UBD)\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Novan\",\"middleName\":\"\",\"lastName\":\"Tofany\",\"suffix\":\"\"},{\"id\":502374570,\"identity\":\"1dec3b70-944d-4a3b-bf8f-0decee49b340\",\"order_by\":2,\"name\":\"Muhammad Syazali\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Mathematics Department, The Republic of Indonesia Defense University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Muhammad\",\"middleName\":\"\",\"lastName\":\"Syazali\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-08-19 07:12:17\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":true,\"conflictsOfInterestStatement\":true,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":true},\"doi\":\"10.21203/rs.3.rs-7405476/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7405476/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":89441984,\"identity\":\"354a841d-1d86-4645-acf2-6b343b321252\",\"added_by\":\"auto\",\"created_at\":\"2025-08-20 03:39:43\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":8004027,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"2025SAOLandslideTsunamiSpringerPrePrint.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7405476/v1_covered_8fd91396-ca5e-4c94-b6b9-69d788583580.pdf\"}],\"financialInterests\":\"The authors declare potential competing interests as follows: This research was supported by funding from Universiti Brunei Darussalam (UBD) and BRIN-Indonesia. 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High-fidelity multiphase CFD models provide an accurate representation of these events but demand considerable computational resources, especially when simulating granular flows and complex wave interactions. Adaptive mesh refinement (AMR) has shown promise in improving simulation efficiency; however, its effectiveness is highly sensitive to parameter settings that are often tuned heuristically. This study introduces a surrogate-based sequential approximation optimization (SAO) framework using Radial Basis Function (RBF) models to systematically optimize AMR parameters within a multiphase granular flow solver. The method is applied to two benchmark cases representing small- and large-scale laboratory scenarios. In the small-scale case, SAO achieves a 56% reduction in time with negligible loss in accuracy. The optimized configuration is successfully transferred to the large-scale case, yielding a 30% reduction, and is further validated through independent optimization. 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