Uncertainties In Boundary Conditions Research Articles

Feasibility of Estimating Sea Surface Height Anomalies from Surface Ocean Currents and Winds

Abstract We propose a method to reconstruct sea surface height anomalies (SSHA) from vector surface currents and winds. This analysis is motivated by the proposed satellite ODYSEA, which is a Doppler scatterometer that measures coincident surface vector winds and currents. If it is feasible to estimate SSHA from these measurements, then ODYSEA could provide collocated fields of SSHA, currents, and winds over a projected wide swath of ∼1700 km. The reconstruction also yields estimates of the low-frequency surface geostrophic, Ekman, irrotational, and nondivergent current components and a framework for separation of balanced and unbalanced motions. The reconstruction is based on a steady-state surface momentum budget including the Ekman drift, Coriolis acceleration, and horizontal advection. The horizontal SSHA gradient is obtained as a residual of these terms, and the unknown SSHA is solved for using a Helmholtz–Hodge decomposition given an imposed SSHA boundary condition. We develop the reconstruction using surface currents, winds, and SSHA off the U.S. West Coast from a 43-day coupled ROMS–WRF simulation. We also consider how simulated ODYSEA measurement and sampling errors and boundary condition uncertainties impact reconstruction accuracy. We find that temporal smoothing of the currents for periods of 150 h is necessary to mitigate large reconstruction errors associated with unbalanced near-inertial motions. For the most realistic case of projected ODYSEA measurement noise and temporal sampling, the reconstructed SSHA fields have an RMS error of 2.1 cm and a model skill (squared correlation) of 0.958 with 150-h resolution. We conclude that an accurate SSHA reconstruction is feasible using information measured by ODYSEA and external SSHA boundary conditions.

Towards spatio-temporal comparison of simulated and reconstructed sea surface temperatures for the last deglaciation

Abstract. An increasing number of climate model simulations is becoming available for the transition from the Last Glacial Maximum to the Holocene. Assessing the simulations' reliability requires benchmarking against environmental proxy records. To date, no established method exists to compare these two data sources in space and time over a period with changing background conditions. Here, we develop a new algorithm to rank simulations according to their deviation from reconstructed magnitudes and temporal patterns of orbital and millennial-scale temperature variations. The use of proxy forward modeling allows for accounting for non-climatic processes that affect the temperature reconstructions. It further avoids the need to reconstruct gridded fields or regional mean temperature time series from sparse and uncertain proxy data. First, we test the reliability and robustness of our algorithm in idealized experiments with prescribed deglacial temperature histories. We quantify the influence of limited temporal resolution, chronological uncertainties, and non-climatic processes by constructing noisy pseudo-proxies. While model–data comparison results become less reliable with increasing uncertainties, we find that the algorithm discriminates well between simulations under realistic non-climatic noise levels. To obtain reliable and robust rankings, we advise spatial averaging of the results for individual proxy records. Second, we demonstrate our method by quantifying the deviations between an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. The ranking of the simulations differs substantially between the considered regions and timescales, which suggests that optimizing for agreement with the temporal patterns of a small set of proxies might be insufficient for capturing the spatial structure of the deglacial temperature variability. We attribute the diversity in the rankings to more regionally confined temperature variations in reconstructions than in simulations, which could be the result of uncertainties in boundary conditions, shortcomings in models, or regionally varying characteristics of reconstructions such as recording seasons and depths. Future work towards disentangling these potential reasons can leverage the flexible design of our algorithm and its demonstrated ability to identify varying levels of model–data agreement. Additionally, the algorithm can be applied to variables like oxygen isotopes and climate transitions such as the penultimate deglaciation and the last glacial inception.

Effects of hygrothermal properties and boundary condition uncertainties in heat and mass transfer simulations of cave walls

In the Mogao Grottoes, hygrothermal cycling is the main cause for the observed mural deterioration development. A coupled heat and mass transfer (HMT) model in cave walls is constructed; it describes the hygrothermal behavior in porous wall construction and materials. This model allows us to evaluate their abilities to regulate microclimate conditions in caves and ensure a suitable protective environment. In this paper, finite variation effects in boundary conditions and material characteristics on the HMT model response are investigated. To determine the most influential hygrothermal parameters, further uncertainties in the parameters (density, heat capacity, thermal conductivity, adsorption isotherm and vapor permeability) and boundary conditions of the model are discussed. Additionally, both quantitative local sensitivity analysis (LSA) and global sensitivity analysis (GSA) was conducted to analyze the input parameters of the numerical model to identify the primary input parameters influencing the model's results. The results show that density and heat capacity do not significantly influence the simulated results when these properties are changed to values 2.5 times greater than those of the reference case. A lower vapor permeability or thermal conductivity and an improved adsorption isotherm are better for maintaining a stable humidity environment and reducing the mural salt disease risk. Note that the boundary conditions significantly affect the simulated results, especially reducing the average air relative humidity value; the amplitude air temperature value seems more beneficial for suppressing the salt damage risk to mural paintings.

Dynamic Modeling of Coastal Compound Flooding Hazards Due to Tides, Extratropical Storms, Waves, and Sea-Level Rise: A Case Study in the Salish Sea, Washington (USA)

The Puget Sound Coastal Storm Modeling System (PS-CoSMoS) is a tool designed to dynamically downscale future climate scenarios (i.e., projected changes in wind and pressure fields and temperature) to compute regional water levels, waves, and compound flooding over large geographic areas (100 s of kilometers) at high spatial resolutions (1 m) pertinent to coastal hazard assessments and planning. This research focuses on advancing robust and computationally efficient approaches to resolving the coastal compound flooding components for complex, estuary environments and their application to the Puget Sound region of Washington State (USA) and the greater Salish Sea. The modeling system provides coastal planners with projections of storm hazards and flood exposure for recurring flood events, spanning the annual to 1-percent annual chance of flooding, necessary to manage public safety and the prioritization and cost-efficient protection of critical infrastructure and valued ecosystems. The tool is applied and validated for Whatcom County, Washington, and includes a cross-shore profile model (XBeach) and overland flooding model (SFINCS) and is nested in a regional tide–surge model and wave model. Despite uncertainties in boundary conditions, hindcast simulations performed with the coupled model system accurately identified areas that were flooded during a recent storm in 2018. Flood hazards and risks are expected to increase exponentially as the sea level rises in the study area of 210 km of shoreline. With 1 m of sea-level rise, annual flood extents are projected to increase from 13 to 33 km2 (5 and 13% of low-lying Whatcom County) and flood risk (defined in USD) is projected to increase fifteenfold (from 14 to USD 206 million). PS-CoSMoS, like its prior iteration in California (CoSMoS), provides valuable coastal hazard projections to help communities plan for the impacts of sea-level rise and storms.

VALIDATION OF COMPUTER SIMULATION OF COMPLEX THERMAL TRANSPORT PROBLEMS: A TRIBUTE TO PROFESSOR DARRELL PEPPER

An important consideration in mathematical modeling and numerical simulation is that of validation of the models. This is particularly critical in thermal systems and processes because of the simplifications and idealizations usually needed to make the problem amenable to a solution, lack of accurate material property data, combined mechanisms, uncertainty in boundary conditions, and other complexities in the process. Modeling is needed for a basic understanding of the processes involved, as well as for providing accurate inputs for system design, control, and optimization. However, it is important to ensure that the numerical code performs satisfactorily for the chosen method and that the model is an accurate representation of the physical problem. These aspects are sometimes referred to as verification and validation, respectively, or simply as validation of the mathematical/numerical model employed. Unless the models are satisfactorily validated and the accuracy of the predictions established, the models cannot be used as basis for design and for choosing operating conditions to obtain desired system or product characteristics. Validation of the models is based on a consideration of the physical behavior of the results obtained, elimination of the effects of arbitrary parameters like grid and time step, comparisons with available analytical or numerical results for similar problems, and comparisons with experimental results on a physical model or a prototype. This paper considers some of the major considerations involved in validation of numerical simulation models for complex thermal problems. A few examples of practical problems are also presented. This topic is chosen because it was of particular interest to Professor Darrell Pepper and the author has had several interactions with him on complex thermal problems.

Risk Assessment Method for Analyzing Borehole Instability Considering Formation Heterogeneity

In the study of borehole instability, the majority of input parameters often rely on the average values that are treated as fixed values. However, in practical engineering scenarios, these input parameters are often accompanied by a high degree of uncertainty. To address this limitation, this paper establishes a borehole stability model considering the uncertainty of input parameters, adopts the Monte Carlo method to calculate the borehole stability reliability at different drilling fluid densities, evaluates the sensitivity of borehole instability to a single parameter, and studies the safe drilling fluid density window at different borehole stability reliability values under multi-parameter uncertainties. The results show that the uncertainty of rock cohesion has a great influence on the fracture pressure of the vertical and horizontal wells. The minimum horizontal stress has the greatest influence on the fracture pressure of the vertical and horizontal wells, followed by pore pressure. In the analysis of borehole stability, the accuracy of cohesion and minimum horizontal stress parameters should be improved. In scenarios involving multiple parameter uncertainties, while the overall trend of the analysis results remains consistent with the conventional borehole stability outcomes, there is a noteworthy narrowing of the safe drilling fluid density window. This suggests that relying on conventional borehole stability analysis methods for designing the safe drilling fluid density window can considerably increase the risks of borehole instability. Uncertainty assessment is crucial to determine the uncertainties associated with the minimum required mud pressure, thereby ensuring more informed decision-making during drilling operations. To meet practical application demands, structure and boundary condition uncertainties should be implemented for a more comprehensive assessment of borehole stability.

Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) – Science plan and experimental design

The Pliocene Model Intercomparison Project (PlioMIP) was initiated in 2008. Over two phases PlioMIP has helped co-ordinate the experimental design and publication strategy of the community, which has included an increasing number of climate models and modelling groups from around the world. It has engaged with palaeoenvironmental scientists to foster new data synthesis supporting the construction of new model boundary conditions, as well as to facilitate new data-model comparisons. The work has advanced our understanding of Pliocene climates and environments, enhanced our knowledge regarding the ability of complex climate and Earth System models to accurately simulate climate change, and helped to refine our estimates of how sensitive the climate system is to forcing conditions.In this community protocol paper, we outline the scientific plan for PlioMIP Phase 3 (PlioMIP3). This plan provides the required guidance to participating modelling groups from around the world to successfully set up and perform PlioMIP3 climate model experiments. The project is open to new participants from the scientific community (both from the climate modelling and geosciences communities).In PlioMIP3, we retain the PlioMIP2 Core experiments (Eoi400, E280) and extend the Core requirements to include either an experiment focussed on the Early Pliocene or an alternative Late Pliocene simulation (or both). These additions (a) allow a comparison of Early and Late Pliocene warm intervals and help build research connections and synergy with the MioMIP (Miocene Model Intercomparison Project - also known as DeepMIP-Miocene) and PlioMioVAR projects (Pliocene-Miocene Variability Working Group), and (b) create an alternative time slice simulation for 3.205 Ma (MIS KM5c) through removal of some of the largest palaeogeographic differences introduced between PlioMIP1 and 2 resulting in minimal land-sea mask variations from the modern. In addition, we present ten optional experiments designed to enhance our assessment of climate sensitivity and to explore the uncertainty in greenhouse gas-related forcing. For the first time, we introduce orbital sensitivity experiments into the science plan, as well as simulations incorporating dynamic vegetation-climate feedbacks and an experiment designed to examine the potential significance of East Antarctic Ice Sheet boundary condition uncertainty. These changes enhance palaeo-to-future scientific connections and enable an exploration of the significance of palaeogeographic uncertainties on climate simulations.

The large-break LOCA uncertainty analysis in a VVER-1000 reactor using TRACE and DAKOTA

An uncertainty analysis is inevitable in simulating reactor performance, particularly in the accident analysis for the estimation of safety borders. In this paper, the application of the uncertainty analysis of a Large Break Loss of Coolant Accident (LBLOCA) from main coolant pipeline at the reactor inlet and outlet, and also pressurizer (PRZ) connecting pipe are presented. First, the model developed in TRACE is verified in steady-state conditions and LBLOCA using the Final Safety Analysis Report (FSAR) of Bushehr nuclear power plant. By the verified model, the uncertainty analysis is made respecting uncertainty in boundary conditions. Finally, the analysis is achieved using the surrogate models and Wilks’ method in DAKOTA. The 95/95 uncertainty band of the peak cladding temperature (PCT) and main reactor parameters are obtained and analyzed. The results indicate that the PCT uncertainty in LBLOCA at the reactor inlet is large, the PCT varies in the range of 910 to 970 °C. The drop temperature time of fuel cladding is in the range of 270 to 600 s for LBLOCA at the reactor inlet. The PCT uncertainty in LBLOCA at the reactor outlet and PRZ connecting pipe is low.

Unsteady flow enhancement on an airfoil using sliding window weak-constraint four-dimensional variational data assimilation

This study establishes a continuous sliding window weak-constraint four-dimensional variational approach for reproducing a complete instantaneous flow from sparse spatiotemporal velocity observations. The initial condition, boundary condition, and model-form uncertainties are corrected simultaneously by a spatiotemporally varying additive forcing, coupled with the large eddy simulation (LES) framework, which reinforces subgrid-scale viscosity stresses and simplifies gradient computation. The additive force undergoes a Stokes–Helmholtz decomposition to ensure divergence-free projection and natural pressure determination. The model is theoretically derived to minimize discrepancies between the sparse velocity observations and the numerical predictions of the primary-adjoint system, enabling optimal contribution of the additive force. Synthetic data from a fine-grid LES of the vortical flow over an NACA0012 airfoil are used as observations. The algorithm is evaluated on a benchmark case, where observations are subsampled at 1/400 000 spatiotemporal resolution required for an LES. The sliding window strategy expands the dependence domain of the observations and mitigates the impact of primary-adjoint chaos, achieving over 90% pointwise correlation for filtered parameters and 80% spectral correlation for all of the resolved wavenumbers. Despite the lack of near-wall observations, streaks are accurately recovered due to the convective sensitivity of the observations from the outer flow. While the pressure fluctuation in the inflow region is not as well excited as in LES, recovery is augmented downstream. In both the inner and outer wall layers, the pressure distributions are obtained reasonably well by capturing the signatures of the vortical structure and their downstream convection. The robustness of the algorithm to observation noise is demonstrated. Finally, the impact of temporal resolution on estimation is evaluated, establishing a resolution threshold for successful reconstruction.

Toward the Design of a Representative Heater for Boiling Flow Characterization under PWR’s Prototypical Thermalhydraulic Conditions

In order to improve the understanding of the phenomena underlying the boiling occurrence, CEA Cadarache (France) is designing a new experimental setup, intended to operate for pressures ranging from atmospheric to PWR conditions. This will allow optical access to the convective boiling flow as well as thermal imaging (infrared thermography) of the heated surface. A two-step methodology for designing the heater (particularly its thickness since it directly influences the boiling mechanisms) was developed. This approach is based on solving the heat conduction problem within the heater, considering realistic time-dependent boundary conditions representative of the boiling process. Since those boundary conditions are measured on the external face of the heater, this heat transfer problem is known as an inverse problem that is difficult to solve because of its ill-posedness and high sensitivity to boundary condition uncertainties. In the first stage, we considered one-dimensional modeling to determine the order of magnitude of the heater’s thickness that guaranteed a correct reconstruction of the wet temperature from the measured dry temperature in terms of uncertainties. This value was confirmed in the second stage using a two-dimensional model that accounted for the presence of multiple bubbles on the wet side.

Inferring surface energy fluxes using drone data assimilation in large eddy simulations

Abstract. Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and satellite remote sensing algorithms. The scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure. Direct field measurements often apply the eddy covariance method on stationary towers, but recently, drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent fluxes of sensible (H) and latent heat (LE). A data assimilation framework to infer uncertainty-aware surface flux estimates from sparse and noisy drone-based observations is developed and tested using a turbulence-resolving large eddy simulation (LES) as a forward model to connect surface fluxes to drone observations. The proposed framework explicitly represents the sequential collection of drone data, accounts for sensor noise, includes uncertainty in boundary and initial conditions, and jointly estimates the posterior distribution of a multivariate parameter space. Assuming typical flight times and observational errors of light-weight, multi-rotor drone systems, we first evaluate the information gain and performance of different ensemble-based data assimilation schemes in experiments with synthetically generated observations. It is shown that an iterative ensemble smoother outperforms both the non-iterative ensemble smoother and the particle batch smoother in the given problem, yielding well-calibrated posterior uncertainty with continuous ranked probability scores of 12 W m−2 for both H and LE, with standard deviations of 37 W m−2 (H) and 46 W m−2 (LE) for a 12 min vertical step profile by a single drone. Increasing flight times, using observations from multiple drones, and further narrowing the prior distributions of the initial conditions are viable for reducing the posterior spread. Sampling strategies prioritizing space–time exploration without temporal averaging, instead of hovering at fixed locations while averaging, enhance the non-linearities in the forward model and can lead to biased flux results with ensemble-based assimilation schemes. In a set of 18 real-world field experiments at two wetland sites in Norway, drone data assimilation estimates agree with independent eddy covariance estimates, with root mean square error values of 37 W m−2 (H), 52 W m−2 (LE), and 58 W m−2 (H+LE) and correlation coefficients of 0.90 (H), 0.40 (LE), and 0.83 (H+LE). While this comparison uses the simplifying assumptions of flux homogeneity, stationarity, and flat terrain, it is emphasized that the drone data assimilation framework is not confined to these assumptions and can thus readily be extended to more complex cases and other scalar fluxes, such as for trace gases in future studies.

The impact of microphysical uncertainty conditional on initial and boundary condition uncertainty under varying synoptic control

Abstract. The relative impact of individual and combined uncertainties of cloud condensation nuclei (CCN) concentration and the shape parameter of the cloud droplet size distribution (CDSD) in the presence of initial and boundary condition uncertainty (IBC) on convection forecasts is quantified using the convection-permitting model ICON-D2 (ICOsahedral Non-hydrostatic). We performed 180-member ensemble simulations for five real case studies representing different synoptic forcing situations over Germany and inspected the precipitation variability on different spatial and temporal scales. During weak synoptic control, the relative impact of combined microphysical uncertainty on daily area-averaged precipitation accounts for about one-third of the variability caused by operational IBC uncertainty. The effect of combined microphysical perturbations exceeds the impact of individual CCN or CDSD perturbations and is twice as large during weak control. The combination of IBC and microphysical uncertainty affects the extremes of daily spatially averaged rainfall of individual members by extending the tails of the forecast distribution by 5 % in weakly forced conditions. The responses are relatively insensitive in strong forcing situations. Visual inspection and objective analysis of the spatial variability in hourly rainfall rates reveal that IBC and microphysical uncertainties alter the spatial variability in precipitation forecasts differently. Microphysical perturbations slightly shift convective cells but affect precipitation intensities, while IBC perturbations scramble the location of convection during weak control. Cloud and rainwater contents are more sensitive to microphysical uncertainty than precipitation and less dependent on synoptic control.

Surgical Planning and Optimization of Patient-Specific Fontan Grafts With Uncertain Post-Operative Boundary Conditions and Anastomosis Displacement

Fontan surgical planning involves designing grafts to perform optimized hemodynamic performance for the patient's long-term health benefit. The uncertainty of post-operative boundary conditions (BC) and graft anastomosis displacements can significantly affect optimized graft designs and lead to undesirable outcomes, especially for hepatic flow distribution (HFD). We aim to develop a computation framework to automatically optimize patient-specific Fontan grafts with the maximized possibility of keeping post-operative results within clinical acceptable thresholds. The uncertainties of BC and anastomosis displacements were modeled using Gaussian distributions according to prior research studies. By parameterizing the Fontan grafts, we built surrogate models of hemodynamic parameters taking the design parameters and BC as input. A two-phase reliability-based robust optimization (RBRO) strategy was developed by combining deterministic optimization (DO) and optimization under uncertainty (OUU) to reduce computational cost. We evaluated the performance of the RBRO framework by comparing it with the DO method in four cases of Fontan patients. The results showed that the surgical plans computed from the proposed method yield up to 79.2% improvement in the reliability of the HFD than those of the DO method ( ). The mean values of indexed power loss (iPL) and the percentage of non-physiologic wall shear stress (%WSS) for the optimized surgical plans met the clinically acceptable thresholds. This study demonstrated the effectiveness of our RBRO framework to address the uncertainties of BC and anastomosis displacements for Fontan surgical planning. The technique developed in this paper demonstrates a significant improvement in the reliability of the predicted post-operative outcomes for Fontan surgical planning. This planning technique is immediately applicable as a building block to enable technology for optimal long-term outcomes for pediatric Fontan patients and can also be used in other pediatric and adult cardiac surgeries.

Network Uncertainty Quantification for Analysis of Multi-Component Systems

Abstract In order to impact physical mechanical system design decisions and realize the full promise of high-fidelity computational tools, simulation results must be integrated at the earliest stages of the design process. This is particularly challenging when dealing with uncertainty and optimizing for system-level performance metrics, as full-system models (often notoriously expensive and time-consuming to develop) are generally required to propagate uncertainties to system-level quantities of interest. Methods for propagating parameter and boundary condition uncertainty in networks of interconnected components hold promise for enabling design under uncertainty in real-world applications. These methods avoid the need for time consuming mesh generation of full-system geometries when changes are made to components or subassemblies. Additionally, they explicitly tie full-system model predictions to component/subassembly validation data which is valuable for qualification. These methods work by leveraging the fact that many engineered systems are inherently modular, being comprised of a hierarchy of components and subassemblies that are individually modified or replaced to define new system designs. By doing so, these methods enable rapid model development and the incorporation of uncertainty quantification earlier in the design process. The resulting formulation of the uncertainty propagation problem is iterative. We express the system model as a network of interconnected component models, which exchange solution information at component boundaries. We present a pair of approaches for propagating uncertainty in this type of decomposed system and provide implementations in the form of an open-source software library. We demonstrate these tools on a variety of applications and demonstrate the impact of problem-specific details on the performance and accuracy of the resulting UQ analysis. This work represents the most comprehensive investigation of these network uncertainty propagation methods to date.

Reducing Geometric Uncertainty in Computational Hemodynamics by Deep Learning-Assisted Parallel-Chain MCMC.

Computational hemodynamic modeling has been widely used in cardiovascular research and healthcare. However, the reliability of model predictions is largely dependent on the uncertainties of modeling parameters and boundary conditions, which should be carefully quantified and further reduced with available measurements. In this work, we focus on propagating and reducing the uncertainty of vascular geometries within a Bayesian framework. A novel deep learning (DL)-assisted parallel Markov chain Monte Carlo (MCMC) method is presented to enable efficient Bayesian posterior sampling and geometric uncertainty reduction. A DL model is built to approximate the geometry-to-hemodynamic map, which is trained actively using online data collected from parallel MCMC chains and utilized for early rejection of unlikely proposals to facilitate convergence with less expensive full-order model evaluations. Numerical studies on two-dimensional aortic flows are conducted to demonstrate the effectiveness and merit of the proposed method.

Investigation of the uncertainty of initial and boundary conditions in the hindcasts of flow fields over urban areas using a mesoscale numerical weather prediction model

AbstractNumerous studies have been conducted using mesoscale numerical weather prediction models to analyze the thermal and wind environments of urban areas around the world. However, weather predictions are highly sensitive to initial conditions. This is the so‐called “butterfly effect.” In this study, the effects of initial and boundary conditions upon the reproduction accuracy for wind environments (particularly for the sea breeze) over the Fukuoka metropolitan area were investigated by varying (i) the objective analysis data used for the initial and boundary conditions and (ii) the length of the spin‐up calculation period. The results of the simulation were compared with surface observations and Doppler LIDAR observations of the airflow field. The accuracy is thought to decrease under an increase in the length of the spin‐up calculation period; however, this was not the case in this study. In terms of the differences in the objective analysis data, no clear trend was observed from the comparisons with surface observations; however, slight differences were observed when comparing with the Doppler LIDAR. The lagged average forecasting method (which reduces the uncertainty of the initial conditions by shifting the calculation start time) was also adopted; the ensemble‐averaged results showed a good correspondence with the observations.

Impact of Boundary Condition and Kinetic Parameter Uncertainties on NOx Predictions in Methane–Air Stagnation Flame Experiments

Abstract A comprehensive understanding of uncertainty sources in experimental measurements is required to develop robust thermochemical models for use in industrial applications. Due to the complexity of the combustion process in gas turbine engines, simpler flames are generally used to study fundamental combustion properties and measure concentrations of important species to validate and improve modeling. Stable, laminar flames have increasingly been used to study nitrogen oxide (NOx) formation in lean-to-rich compositions in low-to-high pressures to assess model predictions and improve accuracy to help develop future low-emissions systems. They allow for nonintrusive diagnostics to measure sub-ppm concentrations of pollutant molecules, as well as important precursors, and provide well-defined boundary conditions to directly compare experiments with simulations. The uncertainties of experimentally measured boundary conditions and the inherent kinetic uncertainties in the nitrogen chemistry are propagated through one-dimensional stagnation flame simulations to quantify the relative importance of the two sources and estimate their impact on predictions. Measurements in lean, stoichiometric, and rich methane–air flames are used to investigate the production pathways active in those conditions. Various spectral expansions are used to develop surrogate models with different levels of accuracy to perform the uncertainty analysis for 15 important reactions in the nitrogen chemistry and the six boundary conditions (ϕ, Tin, uin, du/dzin, Tsurf, P) simultaneously. After estimating the individual parametric contributions, the uncertainty of the boundary conditions are shown to have a relatively small impact on the prediction of NOx compared to kinetic uncertainties in these laboratory experiments. These results show that properly calibrated laminar flame experiments can, not only, provide validation targets for modeling, but also accurate indirect measurements that can later be used to infer individual kinetic rates to improve thermochemical models.

Improving indoor air flow and temperature prediction with local measurements based on CFD-EnKF data assimilation

Mastering detailed multiphysics distribution information is an important prerequisite for creating suitable building environments. This study considered the simulation errors caused by the uncertainty of boundary conditions in computational fluid dynamics (CFD) and aimed to correct simulations with limited observational data. Based on the Ensemble Kalman Filter (EnKF), which is a sequential data assimilation algorithm, a technical framework for accurate indoor airflow and temperature field simulations was established. We evaluated the performance of this method through reduced-scale model experiments and verifying that simulation errors were significantly reduced, and that this approach is applicable to both mechanical ventilation and natural convection conditions. On this basis, further research explored the impact of the measuring point arrangement scheme and ensemble size on assimilation performance, and the optimal setting principles of the above parameters are presented in this paper. The proposed method can effectively weaken the negative impact caused by the uncertainty of boundary conditions in the CFD simulation, thereby improving the prediction accuracy and reliability and providing a positive impetus for realizing a global monitoring of the physical field of a building space.

Newton trust-region methods with primary variable switching for simulating high temperature multiphase porous media flow

Coupling multiphase flow with energy transport due to high temperature heat sources introduces significant new challenges since boiling and condensation processes can lead to dry-out conditions with subsequent re-wetting. The transition between two-phase and single-phase behavior can require changes to the primary dependent variables adding discontinuities as well as extending constitutive nonlinear relations to extreme physical conditions. Practical simulations of large-scale engineered domains lead to Jacobian systems with a very large number of unknowns that must be solved efficiently using iterative methods in parallel on high-performance computers. Performance assessment of potential nuclear repositories, carbon sequestration sites and geothermal reservoirs can require numerous Monte-Carlo simulations to explore uncertainty in material properties, boundary conditions, and failure scenarios. Due to the numerical challenges, standard NR iteration may not converge over the range of required simulations and require more sophisticated optimization method like trust-region.We use the open-source simulator PFLOTRAN for the important practical problem of the safety assessment of future nuclear waste repositories in the U.S. DOE geologic disposal safety assessment Framework. The simulator applies the PETSc parallel framework and a backward Euler, finite volume discretization. We demonstrate failure of the conventional NR method and the success of trust-region modifications to Newton’s method for a series of test problems of increasing complexity. Trust-region methods essentially modify the Newton step size and direction under some circumstances where the standard NR iteration can cause the solution to diverge or oscillate. We show how the Newton Trust-Region method can be adapted for Primary Variable Switching (PVS) when the multiphase state changes due to boiling or condensation. The simulations with high-temperature heat sources which led to extreme nonlinear processes with many state changes in the domain did not converge with NR, but they do complete successfully with the trust-region methods modified for PVS. This implementation effectively decreased weeks of simulation time needing manual adjustments to complete a simulation down to a day. Furthermore, we show the strong scalability of the methods on a single node and multiple nodes in an HPC cluster.

Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation

Ice sheet models are used to study the deglaciation of North America at the end of the last ice age (past 21,000 years), so that we might understand whether and how existing ice sheets may reduce or disappear under climate change. Though ice sheet models have a few parameters controlling physical behaviour of the ice mass, they also require boundary conditions for climate (spatio-temporal fields of temperature and precipitation, typically on regular grids and at monthly intervals). The behaviour of the ice sheet is highly sensitive to these fields, and there is relatively little data from geological records to constrain them as the land was covered with ice. We develop a methodology for generating a range of plausible boundary conditions, using a low-dimensional basis representation of the spatio-temporal input. We derive this basis by combining key patterns, extracted from a small ensemble of climate model simulations of the deglaciation, with sparse spatio-temporal observations. By jointly varying the ice sheet parameters and basis vector coefficients, we run ensembles of the Glimmer ice sheet model that simultaneously explore both climate and ice sheet model uncertainties. We use these to calibrate the ice sheet physics and boundary conditions for Glimmer, by ruling out regions of the joint coefficient and parameter space via history matching. We use binary ice/no ice observations from reconstructions of past ice sheet margin position to constrain this space by introducing a novel metric for history matching to binary data.