Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 Theory Manual

Dalbey, Keith; Eldred, Michael; Geraci, Gianluca; Jakeman, John Davis; Maupin, Kathryn; Monschke, Jason; Seidl, Daniel; Tran, Anh; Menhorn, Friedrich; Zeng, Xiaobo

doi:10.2172/1814069

Cited by 6 publications

(5 citation statements)

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“…Multiobjective model optimization is achieved by a Metropolis Hastings MCMC via Dakota optimization software (Adams et al., 2019) with the QUESO Bayesian calibration library (Estacio‐Hiroms et al., 2016), first presented by Shadrick et al. (2021).…”

Section: Methodsmentioning

confidence: 99%

“…Successful implementation of this coupled model approach will allow for the constraint of the long‐term geomorphic evolution of rocky coasts over a wide range of coastal cliff environments (and degree of shore platform preservation therein) in a manner that reduces equifinality that often arises from coastal evolution models due to (necessarily) simplified erosional processes (Shadrick et al., 2021). Best fit solutions are achieved via Metropolis Hastings Markov Chain Monte Carlo (MCMC, Adams et al., 2019; Estacio‐Hiroms et al., 2016) simulations that iterate over a wide range of possible input values for the parameters most likely to influence shore platform evolution: material resistance, wave height decay rate, and intertidal weathering rate (Carr & Graff, 1982; Matsumoto et al., 2018), Through a simultaneous optimization of measured 10 Be concentrations and shore platform topography to modeled predictions of both, we estimate late Holocene (∼2 kyr) coastal cliff retreat rates for Del Mar, California and explore the influence of relative sea level rise, waves, and weathering on long‐term coastal erosion at this site.…”

Section: Introductionmentioning

confidence: 99%

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Late Holocene Cliff Retreat in Del Mar, CA, Revealed From Shore Platform ¹⁰Be Concentrations and Numerical Modeling

et al. 2023

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Rocky coast cliff retreat presents a hazard to coastal communities and infrastructure that is potentially amplified under rising sea level conditions, among other factors. Unfortunately, constraints on retreat rates are typically limited to those derived from imagery and maps spanning the last ∼100 years. Here, we use a newly developed coupled model of shore platform profile evolution and cosmogenic radionuclide production that considers the influence of relative sea level (RSL) rise, weathering, material resistance, and wave height decay to model cliff retreat over millennial timescales and its potential drivers in Del Mar, California. We demonstrate the ability to use topographic and bathymetric measurements from a narrow shore platform along with a limited data set of nine cosmogenic 10Be concentrations extending ∼125 m from the cliff base to obtain modeled cliff retreat rates that steadily range from 5.0 to 12.5 cm yr−1 over the last two millennia until 100 years before present. These rates are consistent with modern retreat rates of about 2–19 cm yr−1 here. RSL rise in Southern California remained relatively constant during the late Holocene, potentially explaining the relatively stable modeled cliff retreat rate over this time. We also explore the relative influence of weathering, material resistance, and wave erosion efficacy and find that both weathering and wave‐driven erosion are necessary to replicate the measured data at this location, with the latter exerting a stronger control on model acceptance, suggesting that waves may provide a possible mechanism by which RSL rise may influence coastal cliff erosion in southern California.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Late Holocene Cliff Retreat in Del Mar, CA, Revealed From Shore Platform ¹⁰Be Concentrations and Numerical Modeling

et al. 2023

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“…To automate this process, an uncertainty quantifcation (UQ) framework, is developed by loosely coupling the statistical tool, DAKOTA [31], to the thermal-hydraulic system code, RELAP5/MOD3.4, via a Python script. Te Monte Carlo (MC) random sampling technique along with the Latin hyper-cube (LHC) method is used to defne a combination of input parameters that scan the spectrum of all possible initial and boundary conditions for the thermalhydraulics model.…”

Section: Bepu Approach Formentioning

confidence: 99%

Time‐Series Forecasting of a Typical PWR Undergoing Large Break LOCA

Kaminski,

Diab

2024

Science and Technology of Nuclear Installations

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In this work, a machine learning (ML) metamodel is developed for the time-series forecasting of a typical nuclear power plant response undergoing a loss of coolant accident (LOCA). The plant model of choice is based on the APR1400 nuclear reactor. The key systems and components of APR1400 relevant to the investigated scenario are modelled using the thermal-hydraulic code, RELAP5/MOD3.4, following the description published in the design control document. The model is tested under a spectrum of initial and boundary conditions via propagation of key uncertain parameters (UPs) which are derived from the phenomena identification and ranking table (PIRT). This is achieved by loosely coupling RELAP5/MOD3.4 with the statistical tool, Dakota. The most probable nuclear power plant (NPP) response was calculated using the best estimate plus uncertainty (BEPU) approach. Next, the database generated from the NPP system response was used as an input for the ML model. The NPP system response was represented by peak cladding temperature (PCT), safety injection system (SIT), mass flow rate, reactor power, and primary system pressure. In this research, two regression models were tested with reasonably good performance, namely, the gated recurrent unit (GRU) and the long short-term memory (LSTM).

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“…In addition to the simulations studied for the inversion procedure (see Section 4.3), we performed a total of 900 simulations (S-Dak-see Table 3) to study the sensitivity of mass loading at the 75 sampling sites to the different parameters of Table 2. For this analysis, we have used the open source DAKOTA toolkit [69]. At each iteration, the Latin hypercube sampling method has been adopted to properly cover the parameter space [70].…”

Section: Parametric Analysismentioning

confidence: 99%

Particle Sedimentation in Numerical Modelling: A Case Study from the Puyehue-Cordón Caulle 2011 Eruption with the PLUME-MoM/HYSPLIT Models

et al. 2022

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Numerical modelling of tephra fallout is a fast-developing research area in volcanology. Several models are currently available both to forecast the dispersion of volcanic particles in the atmosphere and to calculate the particles deposited at different locations on the ground. Data from these simulations can then be used both to manage volcanic crises (e.g., protect air traffic) or perform long-term hazard assessment studies (e.g., through hazard maps). Given the importance of these tasks, it is important that each model is thoroughly tested in order to assess advantages and limitations, and to provide useful information for quantifying the model uncertainty. In this study we tested the coupled PLUME-MoM/HYSPLIT models by applying them to the Puyehue–Cordon Caulle 2011 sub-Plinian eruption. More specifically, we tested new features recently introduced in these well-established models (ash aggregation, external water addition, and settling velocity models), we implemented a new inversion procedure, and we performed a parametric analysis. Our main results reaffirm the pivotal role played by mass eruption rate on the final deposit and show that some choices for the input parameters of the model can lead to the large overestimation in total deposited mass (which can be reduced with our inversion procedure). The parametric analysis suggests a most likely value of the mass eruption rate in the range 2.0–6.3 × 106 kg/s. More studies with a similar approach would be advisable in order to provide final users with useful indications about the parameters that should be carefully evaluated before being used as input for this kind of model.

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Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 Theory Manual

Cited by 6 publications

References 0 publications

Late Holocene Cliff Retreat in Del Mar, CA, Revealed From Shore Platform ¹⁰Be Concentrations and Numerical Modeling

Late Holocene Cliff Retreat in Del Mar, CA, Revealed From Shore Platform ¹⁰Be Concentrations and Numerical Modeling

Time‐Series Forecasting of a Typical PWR Undergoing Large Break LOCA

Particle Sedimentation in Numerical Modelling: A Case Study from the Puyehue-Cordón Caulle 2011 Eruption with the PLUME-MoM/HYSPLIT Models

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Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 Theory Manual

Cited by 6 publications

References 0 publications

Late Holocene Cliff Retreat in Del Mar, CA, Revealed From Shore Platform 10Be Concentrations and Numerical Modeling

Late Holocene Cliff Retreat in Del Mar, CA, Revealed From Shore Platform 10Be Concentrations and Numerical Modeling

Time‐Series Forecasting of a Typical PWR Undergoing Large Break LOCA

Particle Sedimentation in Numerical Modelling: A Case Study from the Puyehue-Cordón Caulle 2011 Eruption with the PLUME-MoM/HYSPLIT Models

Contact Info

Product

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Late Holocene Cliff Retreat in Del Mar, CA, Revealed From Shore Platform ¹⁰Be Concentrations and Numerical Modeling

Late Holocene Cliff Retreat in Del Mar, CA, Revealed From Shore Platform ¹⁰Be Concentrations and Numerical Modeling