Publications Acknowledging KAUST Support

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  • Article

    Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors

    (Elsevier BV, 2024-03-23) Fang, Zhice; Wang, Yi; van Westen, Cees; Lombardo, Luigi; School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China; University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), PO Box 217, Enschede, AE 7500, Netherlands; Key Laboratory of Ocean Space Resource Management Technology, Ministry of Natural Resources, Hangzhou 310012, China; State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China

    Landslide spatial prediction using data-driven models has predominantly concentrated on predicting where landslides may occur. Nevertheless, few researchers have turned to jointly modeling how large and when landslides will be for a given terrain unit. This study proposes a data-driven model capable of estimating how large landslides may be, for the entire Taiwan main island in a fourteen-year time window. To address this task, we implement a space–time generalized additive model to fit the complex relationships between environmental factors and landslide size. In addition to incorporating static and dynamic covariates into the modeling process, the model takes into account spatial and temporal interactions to elucidate the spatiotemporal variations affecting landslide size. To test the effectiveness of the model, we employ a comprehensive set of cross-validation (CV) procedures, includes a randomized 10fold-CV, a spatially constrained CV, a temporal leave-one-year-out CV, and a spatio-temporal CV. The experimental results demonstrate that the space–time model delivers acceptable and interpretable prediction outcomes, demonstrating the ability to predict landslide area for a given slope unit within a specified time period. We believe that the space–time landslide modeling will lay the foundation for landslide community to analyze landslide characteristic within a dynamic context. Furthermore, given its inherent spatio-temporal nature, we anticipate that this approach may pave the way for simulation studies exploring diverse climate scenarios.

  • Conference Paper

    Cryogenic Carbon Capture™ Technoeconomic Analysis

    (Elsevier BV, 2021-04-08) Hoeger, Christopher; Burt, Stephanie; Baxter, Larry; Sustainable Energy Solutions a Chart Company, 1489 West 105 North, Orem, UT 84057 USA; Brigham Young University, Provo, UT 84602 USA

    The Cryogenic Carbon Capture™ (CCC) process significantly decreases cost and energy demands for CO2 separation and pressurization to 150 bar compared to alternatives. The process is a post-combustion technology that cools CO2-laden flue gas to desublimation temperatures (−100 to −135 °C), separates solid CO2-that forms from the flue gas-from the light gases, uses the cold products to cool incoming gases in a recuperative heat exchanger, compresses the solid/liquid CO2 to final pressures (100-200 bar), and delivers a compressed CO2 stream separated from an atmospheric pressure light-gas stream. The overall energy and economic costs are about 30-50% lower than most competing processes that involve air separation units (ASUs), solvents, or similar technologies. In addition, the CCC process enjoys several ancillary benefits, including (a) it is a minimally invasive bolt-on technology, (b) it provides highly efficient removal of most pollutants (Hg, SOx, NO2, HCl, etc.), and (c) possible energy storage capacity. This report outlines the process details and economic and energy comparisons relative to other well-documented alternatives. This paper presents the results of a detailed techno-economic comparison of CCC with amine-based systems. The comparison uses identical financial and economic assumptions similar process assumptions as the detailed analyses published by US DOE in the greenfield analysis. Specifically, the comparison assumes power plants that produce the same net output, one equipped with and a second without carbon capture. Separately, the paper compares similar analyses for retrofitting existing systems using typical plant characteristics in the US (initial capital costs have been paid, high plant utilization), though there are no DOE estimates available for direct comparison. Financial and technical assumptions for all comparisons are maintained as close to the DOE reference studies as possible. The results demonstrate about 30-50% lower costs and energy demands for capture from greenfield coal plants. Natural gas plants produce substantially lower CO2 concentrations which makes the cost of capturing a ton of CO2 at the same capture rate as the coal plant higher for all processes while the cost of CO2 capture per unit of power generation is lower. However, CCC maintains about the same absolute energy and cost advantages for NG as for coal compared to amine systems. Finally, the costs of retrofitting a station are compared to those of building a new station with and without capture. The retrofit costs are comparable to (slightly lower than) new plant costs without capture. In all cases operating and capital cost comparisons show that the CCC process can be retrofitted to a variety of plants to cost effectively reduce CO2 emissions. Further process integration into the upstream processes and unique process features like water recovery, and integrated energy storage bring the effective cost of carbon capture using the CCC process down further and increase its advantages over alternatives. This technoeconomic analysis shows that the CCC process has the potential to the be lowest cost carbon capture technology under development today.

  • Article

    Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era

    (Elsevier BV, 2024-03-08) Koronaki, Eleni D.; Evangelou, Nikolaos; Martin-Linares, Cristina P.; Titi, Edriss S.; Kevrekidis, Ioannis G.; Faculté des Sciences, de la Technologie et de la Communication, Université de Luxembourg, Maison du Nombre, Avenue de la Fonte 6, L-4364 Esch-sur-Alzette, Luxembourg; Department of Chemical and Biomolecular Engineering and Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA; Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, USA; Department of Mathematics, Texas A & M University, College Station, TX 77843, USA; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, UK

    This study presents a collection of purely data-driven workflows for constructing reduced-order models (ROMs) for distributed dynamical systems. The ROMs we focus on, are data-assisted models inspired by, and templated upon, the theory of Approximate Inertial Manifolds (AIMs); the particular motivation is the so-called post-processing Galerkin method of Garcia-Archilla, Novo and Titi. Its applicability can be extended: the need for accurate truncated Galerkin projections and for deriving closed-formed corrections can be circumvented using machine learning tools. When the right latent variables are not a priori known, we illustrate how autoencoders as well as Diffusion Maps (a manifold learning scheme) can be used to discover good sets of latent variables and test their explainability. The proposed methodology can express the ROMs in terms of (a) theoretical (Fourier coefficients), (b) linear data-driven (POD modes) and/or (c) nonlinear data-driven (Diffusion Maps) coordinates. Both Black-Box and (theoretically-informed and data-corrected) Gray-Box models are described; the necessity for the latter arises when truncated Galerkin projections are so inaccurate as to not be amenable to post-processing. We use the Chafee-Infante reaction-diffusion and the Kuramoto-Sivashinsky dissipative partial differential equations to illustrate and successfully test the overall framework.

  • Article

    Multicomponent Droplet Evaporation in a Geometric Volume-Of-Fluid Framework

    (Elsevier BV, 2024-03) Cipriano, Edoardo; Essamade Saufi, Abd; Frassoldati, Alessio; Faravelli, Tiziano; Popinet, Stéphane; Cuoci, Alberto; CRECK Modeling Lab, Department of Chemistry, Materials, and Chemical Engineering “G. Natta”, Politecnico di Milano, Piazza Leonardo da Vinci, 32, Milano, 20133, Italy; Institut Jean Le Rond d'Alembert, CNRS UMR 7190, Sorbonne Université, Paris, 75005, France

    This work proposes an innovative model for multicomponent phase change in interface-resolved simulations. The two-phase system is described by a geometric Volume-Of-Fluid (VOF) approach, and considers multiple components in non-isothermal environments, relaxing the hypothesis of pure liquid droplets usually studied in the literature. The model includes the Stefan flow and implements the following solutions for the complications that arise when studying liquid mixtures: i) a coupled approach for solving the interface jump conditions; ii) a proper strategy to obtain a liquid velocity for the advection of the volume fraction field, also applicable to static droplets with strong density ratio; iii) and a geometric approach to discretize the scalar fields transport equations. This model was implemented in the open-source code Basilisk, and it was tested on a number of benchmark phase change problems, such as the fixed flux evaporation, the Stefan problem, Epstein Plesset, and the Scriven problem. These test cases demonstrate the convergence of the numerical methods to the analytical solutions. More complex configurations, such as multicomponent isothermal and non-isothermal droplets are compared using numerical benchmark solutions obtained assuming spherical symmetry. The code, as well as the simulation setups, are released on the Basilisk website, making it the first model and open-source implementation of multicomponent phase change in a VOF framework.

  • Article

    Matching maternal and paternal experiences underpin molecular thermal acclimation

    (Wiley, 2024-03-23) Bonzi, L. C.; Donelson, J. M.; Spinks, R. K.; Munday, P. L.; Ravasi, T.; Schunter, C.; The Swire Institute of Marine Science, School of Biological Sciences The University of Hong Kong Hong Kong Hong Kong SAR; ARC Centre of Excellence for Coral Reef Studies James Cook University Townsville Queensland Australia; College of Science and Engineering James Cook University Townsville Queensland Australia; Blue Carbon Section, Australian Government Department of Climate Change, Energy, the Environment and Water Canberra Australian Capital Territory Australia; Marine Climate Change Unit Okinawa Institute of Science and Technology Graduate University Okinawa Japan; State Key Laboratory of Marine Pollution and Department of Chemistry City University of Hong Kong Hong Kong Hong Kong SAR

    The environment experienced by one generation has the potential to affect the sub -sequent one through non- genetic inheritance of parental effects. Since both mothers and fathers can influence their offspring, questions arise regarding how the mater -nal, paternal and offspring experiences integrate into the resulting phenotype. We aimed to disentangle the maternal and paternal contributions to transgenerational thermal acclimation in a reef fish, Acanthochromis polyacanthus, by exposing two gen -erations to elevated temperature (+1.5°C) in a fully factorial design and analysing the F2 hepatic gene expression. Paternal and maternal effects showed not only common but also parent-specific components, with the father having the largest influence in shaping the offspring's transcriptomic profile. Fathers contributed to transcriptional transgenerational response to warming through transfer of epigenetically controlled stress–response mechanisms while mothers influenced increased gene expression as -sociated with lipid metabolism regulation. However, the key to acclimation potential was matching thermal experiences of the parents. When both parents were exposed to the same condition, offspring showed increased expression of genes related to structural RNA production and transcriptional regulation, whereas environmentalmismatch in parents resulted in maladaptive parental condition transfer, revealed by translation suppression and endoplasmic reticulum stress. Interestingly, the offspring'sown environmental experience had the smallest influence on their hepatic transcrip-tion profiles. Taken together, our results show the complex nature of the interplay among paternal, maternal and offspring cue integration, and reveal that acclimation