Uncertainties In Boundary Conditions Research Articles

Stochastic heat transfer simulation of the cure of advanced composites

A stochastic cure simulation approach is developed to investigate the variability of the cure process during resin infusion related to thermal effects. Boundary condition uncertainty is quantified experimentally and appropriate stochastic processes are developed to represent the variability in tool/air temperature and surface heat transfer coefficient. The heat transfer coefficient presents a variation across different experiments of 12.3%, whilst the tool/air temperatures present a standard deviation over 1℃. The boundary condition variability is combined with an existing model of cure kinetics uncertainty and the full stochastic problem is addressed by coupling a cure model with Monte Carlo and the Probabilistic Collocation Method and applied to the case of thin carbon epoxy laminates. The overall variability in cure time reaches a coefficient of variation of about 22%, which is dominated by uncertainty in surface heat transfer and tool temperature; with ambient temperature and kinetics contributing variability in the order of 1%.

Evaluating SoilGen2 as a tool for projecting soil evolution induced by global change

To protect soils against threats, it is necessary to predict the consequences of human activities and global change on their evolution on a ten to hundred year time scale. Mechanistic modelling of soil evolution is then a useful tool. We analysed the ability of the SoilGen model to be used for projections of soil characteristics associated to various soil threats: vertical distributions of <2μm fraction, organic carbon content (OC), bulk density and pH. This analysis took the form of a functional sensitivity analysis in which we varied the initial conditions (parent material properties) and boundary conditions (co-evolution of precipitation and temperature; type and amount of fertilization and tillage as well as duration of agriculture). The simulated scenario variants comprised anthroposequences in Luvisols at two sites with one default scenario, six variants for initial conditions and 12 variants for boundary conditions. The variants reflect the uncertainties to our knowledge of parent material properties or reconstructed boundary conditions.We demonstrated a sensitivity of the model to climate and agricultural practices for all properties. We also conclude that final model results are not significantly affected by the uncertainties of boundary conditions for long simulations runs, although influenced by uncertainties on initial conditions.The best results were for organic carbon, although improvements can be reached through calibration or by incorporating a dynamic vegetation growth module in SoilGen. Results were poor for bulk density due to a fixed-volume assumption in the model, which is not easily modified. The <2μm fraction depth patterns are reasonable but the process of clay new formation needs to be added to obtain the belly shape of the Bt horizon.After calibration for organic carbon under agriculture, the model is suitable for producing soil projections due to global change.

Testing new sources of topographic data for flood propagation modelling under structural, parameter and observation uncertainty

ABSTRACTThis study assessed the utility of EUDEM, a recently released digital elevation model, to support flood inundation modelling. To this end, a comparison with other topographic data sources was performed (i.e. LIDAR, light detection and ranging; SRTM, Shuttle Radar Topographic Mission) on a 98-km reach of the River Po, between Cremona and Borgoforte (Italy). This comparison was implemented using different model structures while explicitly accounting for uncertainty in model parameters and upstream boundary conditions. This approach facilitated a comprehensive assessment of the uncertainty associated with hydraulic modelling of floods. For this test site, our results showed that the flood inundation models built on coarse resolutions data (EUDEM and SRTM) and simple one-dimensional model structure performed well during model evaluation.Editor Z.W. Kundzewicz; Associate editor S. Weijs

Stabilized FE simulation of prototype thermal-hydraulics problems with integrated adjoint-based capabilities

A critical aspect of applying modern computational solution methods to complex multiphysics systems of relevance to nuclear reactor modeling, is the assessment of the predictive capability of specific proposed mathematical models. In this respect the understanding of numerical error, the sensitivity of the solution to parameters associated with input data, boundary condition uncertainty, and mathematical models is critical. Additionally, the ability to evaluate and or approximate the model efficiently, to allow development of a reasonable level of statistical diagnostics of the mathematical model and the physical system, is of central importance. In this study we report on initial efforts to apply integrated adjoint-based computational analysis and automatic differentiation tools to begin to address these issues. The study is carried out in the context of a Reynolds averaged Navier–Stokes approximation to turbulent fluid flow and heat transfer using a particular spatial discretization based on implicit fully-coupled stabilized FE methods. Initial results are presented that show the promise of these computational techniques in the context of nuclear reactor relevant prototype thermal-hydraulics problems.

The Pliocene Model Intercomparison Project (PlioMIP) Phase 2: scientific objectives and experimental design

Abstract. The Pliocene Model Intercomparison Project (PlioMIP) is a co-ordinated international climate modelling initiative to study and understand climate and environments of the Late Pliocene, as well as their potential relevance in the context of future climate change. PlioMIP examines the consistency of model predictions in simulating Pliocene climate and their ability to reproduce climate signals preserved by geological climate archives. Here we provide a description of the aim and objectives of the next phase of the model intercomparison project (PlioMIP Phase 2), and we present the experimental design and boundary conditions that will be utilized for climate model experiments in Phase 2. Following on from PlioMIP Phase 1, Phase 2 will continue to be a mechanism for sampling structural uncertainty within climate models. However, Phase 1 demonstrated the requirement to better understand boundary condition uncertainties as well as uncertainty in the methodologies used for data–model comparison. Therefore, our strategy for Phase 2 is to utilize state-of-the-art boundary conditions that have emerged over the last 5 years. These include a new palaeogeographic reconstruction, detailing ocean bathymetry and land–ice surface topography. The ice surface topography is built upon the lessons learned from offline ice sheet modelling studies. Land surface cover has been enhanced by recent additions of Pliocene soils and lakes. Atmospheric reconstructions of palaeo-CO2 are emerging on orbital timescales, and these are also incorporated into PlioMIP Phase 2. New records of surface and sea surface temperature change are being produced that will be more temporally consistent with the boundary conditions and forcings used within models. Finally we have designed a suite of prioritized experiments that tackle issues surrounding the basic understanding of the Pliocene and its relevance in the context of future climate change in a discrete way.

Impact of uncertainties in atmospheric boundary conditions on ocean model solutions

We quantify differences in ocean model simulations derived solely from atmospheric uncertainties and investigate how they relate to overall model errors as inferred from comparisons with data. For this purpose, we use a global configuration of the MITgcm to simulate 4 ocean solutions for 2000–2009 using 4 reanalysis products (JRA-25, MERRA, CFSR and ERA-Interim) as atmospheric forcing. The simulations are compared against observations and against each other for selected variables (temperature, sea-level, sea-ice, streamfunctions, meridional heat and freshwater transports). Forcing-induced differences are comparable in magnitude to model-observation misfits for most near-surface variables in the tropics and sub-tropics, but typically smaller at higher latitudes and polar regions. Forcing-derived differences are expectedly largest near the surface and mostly limited to the upper 1000m but can also be seen as deep as 4000m, especially in regions of deep water formation. Errors are not necessarily local in nature and can be advected to different basins. Results indicate that while forcing adjustments might suffice in optimization procedures of near-surface fields and at low-to-mid latitudes, other control parameters are likely needed elsewhere. Forcing-induced differences can be dominated by large spatial scales and specific time scales (e.g. annual), and thus appropriate error covariances in space and time need to be considered in optimization methodologies.

When does spatial resolution become spurious in probabilistic flood inundation predictions?

AbstractAdvances in remote sensing have enabled hydraulic models to run at fine scale resolutions, producing precise flood inundation predictions. However, running models at finer resolutions increase their computational expense, reducing the feasibility of running the multiple model realizations required to undertake uncertainty analysis. Furthermore, it is possible that precision gained by running fine scale models is smoothed out when treating models probabilistically. The aim of this paper is to determine the level of spatial complexity that is required when making probabilistic flood inundation predictions. The Imera basin, Sicily is used as a case study to assess how changing the spatial resolution of the hydraulic model LISFLOOD‐FP impacts on the skill of conditional probabilistic flood inundation maps given model parameter and boundary condition uncertainties. We find that model performance deteriorates at resolutions coarser than 50 m. This is predominantly caused by changes in flow pathways at coarser resolutions which lead to non‐stationarity in the optimum model parameters at different spatial resolutions. However, although it is still possible to produce probabilistic flood maps that contain a coherent outline of the flood extent at coarser resolutions, the reliability of these maps deteriorates at resolutions coarser than 100 m. Additionally, although the rejection of non‐behavioural models reduces the uncertainty in probabilistic flood maps the reliability of these maps is also reduced. Models with resolutions finer than 50 m offer little gain in performance yet are more than an order of magnitude computationally expensive which can become infeasible when undertaking probabilistic analysis. Furthermore, we show that using deterministic, high‐resolution flood maps can lead to a spurious precision that would be misleading and not representative of the overall uncertainties that are inherent in making inundation predictions. Copyright © 2015 The Authors Hydrological Processes Published by John Wiley &amp; Sons Ltd.

Uncertainty Quantification of Large-Eddy Spray Simulations

Two uncertainty quantification (UQ) techniques, latin-hypercube sampling (LHS) and polynomial chaos expansion (PCE), have been used in an initial UQ study to calculate the effect of boundary condition uncertainty on Large-eddy spray simulations. Liquid and vapor penetration as well as multidimensional liquid and vapor data were used as response variables. The Morris one-at-a-time (MOAT) screening method was used to identify the most important boundary conditions. The LHS and PCE methods both predict the same level of variability in the response variables, which was much larger than the corresponding experimental uncertainty. Nested grids were used in conjunction with the PCE method to examine the effects of subsets of boundary condition variables. Numerical modeling parameters had a much larger effect on the resulting spray predictions; the uncertainty in spray penetration or multidimensional spray contours from physically derived boundary conditions was close to the uncertainty of the measurements.

Hygrothermal performance of cross-laminated timber wall assemblies: A stochastic approach

Cross-laminated timber (CLT) panels have the potential market in North America for building mid-rise or even taller structures due to their good structural and fire safety performance, carbon storage capacity, light weight, and prefabricated nature. However, prolonged exposure to moisture during construction and in service is a durability concern for most wood products including CLT. This paper presents the evaluation of the hygrothermal performance of CLT wall assemblies through simulations using a stochastic approach to account for the uncertainty of material properties, boundary conditions, and environmental loads. The moisture content of the CLT panel is used as the performance indicator. The influential factors are categorized into continuous and discrete random variables. The influence of the continuous random variables including material properties, boundary conditions and cladding ventilation rate is investigated under conditions represented by different combinations of the discrete random variables including wall configurations, orientation and rain leakage rate. The critical factors that lead to high moisture content are identified under each condition and the interaction between the discrete and continuous random variables is analyzed. The stochastic results show that the CLT wall assembly with low vapor permeance material placed outboard the CLT panel has a higher risk of moisture problem than CLT wall assembly with high vapor permeance material placed outboard the CLT panel. The sensitivity analysis indicates that the significance of the stochastic parameters on the hygrothermal performance of CLT panel depends on environmental loads, especially the rain load, and wall configurations.

A new application of conditional nonlinear optimal perturbation approach to boundary condition uncertainty

AbstractThe conditional nonlinear optimal perturbation (CNOP) approach was mainly used to investigate the effects of the uncertainties in initial condition and model parameters on model results. This study presents a new application of the CNOP approach to the investigation of the effects of boundary condition uncertainty. Specifically, we first give the general formulation of the CNOP approach for uncertainties of initial and boundary conditions and model parameters. The method is then applied to analyze the effects of nutrient perturbations at the bottom boundary of the water column on the modeled deep chlorophyll maximum (DCM) using an ocean ecosystem model. The results show that nutrient perturbations at the bottom boundary have significant impacts on the modeled DCM. Interestingly, the approach also reveals that nonlinear processes play important roles in the evolution of phytoplankton perturbations caused by nutrient perturbations at the bottom boundary. This implies that the CNOP approach is useful for investigating the effects of boundary condition uncertainty.

Fuzzy stochastic finite element method for the hybrid uncertain temperature field prediction

For the hybrid uncertain temperature field prediction involving both random and fuzzy uncertainties in material properties, external loads and boundary conditions, this paper proposes a new numerical technique named fuzzy stochastic finite element method (FSFEM) by a combination of perturbation theory and moment method. Random variables are adopted to quantify the stochastic uncertainty with sufficient experiment data; whereas, fuzzy variables are used to represent the non-probabilistic parameters associated with expert opinions. By using the level-cut method, the fuzzy parameters are equivalently decomposed into interval variables. Based on the first-order Neumann series and random interval moment method, the interval bounds of the probabilistic characteristics of the uncertain temperature field are calculated effectively. Their membership functions are eventually reconstructed from the fuzzy decomposition theorem. By comparing the results with traditional Monte Carlo simulation, the numerical example demonstrates the feasibility and effectiveness of the proposed method for solving hybrid uncertain heat conduction problems in engineering.

Interval analysis of steady-state heat convection–diffusion problem with uncertain-but-bounded parameters

In this paper, a sensitivity-based interval analysis method (SIAM) and an interval parameter perturbation method (IPPM) are proposed to estimate temperature intervals for the steady-state heat convection–diffusion problem with uncertainties in material properties, external loads and boundary conditions. Interval variables are used to characterize the uncertain parameters in the face of limited information. The first-order Taylor expansions are employed in SIAM, while IPPM introduces the surface rail generation method to approximate interval matrices and vectors. Some high-order terms of the Neumann series are retained to calculate the interval matrix inverse in IPPM. By comparing the results with traditional Monte Carlo simulations, two numerical examples are given to demonstrate the feasibility and effectiveness of the proposed approaches at predicting the uncertain temperature field.

Application of WRF/Chem over East Asia: Part I. Model evaluation and intercomparison with MM5/CMAQ

Abstract In this work, the application of the online-coupled Weather Research and Forecasting model with chemistry (WRF/Chem) version 3.3.1 is evaluated over East Asia for January, April, July, and October 2005 and compared with results from a previous application of an offline model system, i.e., the Mesoscale Model and Community Multiple Air Quality modeling system (MM5/CMAQ). The evaluation of WRF/Chem is performed using multiple observational datasets from satellites and surface networks in mainland China, Hong Kong, Taiwan, and Japan. WRF/Chem simulates well specific humidity (Q2) and downward longwave and shortwave radiation (GLW and GSW) with normalized mean biases (NMBs) within 24%, but shows moderate to large biases for temperature at 2-m (T2) (NMBs of −9.8% to 75.6%) and precipitation (NMBs of 11.4–92.7%) for some months, and wind speed at 10-m (WS10) (NMBs of 66.5–101%), for all months, indicating some limitations in the YSU planetary boundary layer scheme, the Purdue Lin cloud microphysics, and the Grell–Devenyi ensemble scheme. WRF/Chem can simulate the column abundances of gases reasonably well with NMBs within 30% for most months but moderately to significantly underpredicts the surface concentrations of major species at all sites in nearly all months with NMBs of −72% to −53.8% for CO, −99.4% to −61.7% for NOx, −84.2% to −44.5% for SO2, −63.9% to −25.2% for PM2.5, and −68.9% to 33.3% for PM10, and aerosol optical depth in all months except for October with NMBs of −38.7% to −16.2%. The model significantly overpredicts surface concentrations of O3 at most sites in nearly all months with NMBs of up to 160.3% and NO 3 - at the Tsinghua site in all months. Possible reasons for large underpredictions include underestimations in the anthropogenic emissions of CO, SO2, and primary aerosol, inappropriate vertical distributions of emissions of SO2 and NO2, uncertainties in upper boundary conditions (e.g., for O3 and CO), missing or inaccurate model representations (e.g., secondary organic aerosol formation, gas/particle partitioning, dust emissions, dry and wet deposition), and inaccurate meteorological fields (e.g., overpredictions in WS10 and precipitation, but underpredictions in T2), as well as the large uncertainties in satellite retrievals (e.g., for column SO2). Comparing to MM5, WRF generally gives worse performance in meteorological predictions, in particular, T2, WS10, GSW, GLW, and cloud fraction in all months, as well as Q2 and precipitation in January and October, due to limitations in the above physics schemes or parameterizations. Comparing to CMAQ, WRF/Chem performs better for surface CO, O3, and PM10 concentrations at most sites in most months, column CO and SO2 abundances, and AOD. It, however, gives poorer performance for surface NOx concentrations at most sites in most months, surface SO2 concentrations at all sites in all months, and column NO2 abundances in January and April. WRF/Chem also gives lower concentrations of most secondary PM and black carbon. Those differences in results are attributed to differences in simulated meteorology, gas-phase chemistry, aerosol thermodynamic and dynamic treatments, dust and sea salt emissions, and wet and dry deposition treatments in both models.

An assessment of intra-patient variability on observed relationships between wall shear stress and plaque progression in coronary arteries.

BackgroundWall shear stress (WSS) has been associated with sites of plaque localization and with changes in plaque composition in human coronary arteries. Different values have been suggested for categorizing WSS as low, physiologic or high; however, uncertainties in flow rates, both across subjects and within a given individual, can affect the classification of WSS and thus influence the observed relationships between local hemodynamics and plaque changes over time. This study examines the effects of uncertainties in flow rate boundary conditions upon WSS values and investigates the influence of this variability on the observed associations of WSS with changes in VH-IVUS derived plaque components.MethodsThree patients with coronary artery disease underwent baseline and 12 month follow-up angiography and virtual histology-intravascular ultrasound (VH-IVUS) measurements. Coronary artery models were reconstructed from the data and models with and without side-branches were created. Patient-specific Doppler ultrasound (DUS) data were employed as inflow boundary conditions and computational fluid dynamics was used to calculate the WSS in each model. Further, the influence of representative coronary artery flow waveforms upon WSS values was investigated and the concept of treating WSS using relative, rather than actual, values was explored.ResultsModels that included side-branch outflows and subject-specific DUS velocities were considered to be the reference cases. Hemodynamic differences were caused by the exclusion of side-branches and by imposing alternative velocity waveforms. One patient with fewer side-branches and a scaled generic waveform had little deviation from the reference case, while another patient with several side-branches excluded showed much larger departures from the reference situation. Differences between models and the respective reference cases were reduced when data were analyzed using relative, rather than actual, WSS.ConclusionsWhen considering individual subjects, large variations in patient-specific flow rates and exclusion of multiple side-branches in computational models can cause significant differences in observed associations between plaque evolution and ranges of computed WSS. These differences may contribute to the large variability typically found among subjects in pooled populations. Relative WSS may be more useful than actual WSS as a correlative variable when there is a large degree of uncertainty in flow rate data.

Representing Forecast Error in a Convection-Permitting Ensemble System

Abstract Ensembles provide an opportunity to greatly improve short-term prediction of local weather hazards, yet generating reliable predictions remain a significant challenge. In particular, convection-permitting ensemble forecast systems (CPEFSs) have persistent problems with underdispersion. Representing initial and or lateral boundary condition uncertainty along with forecast model error provides a foundation for building a more dependable CPEFS, but the best practice for ensemble system design is not well established. Several configurations of CPEFSs are examined where ensemble forecasts are nested within a larger domain, drawing initial conditions from a downscaled, continuously cycled, ensemble data assimilation system that provides state-dependent initial condition uncertainty. The control ensemble forecast, with initial condition uncertainty only, is skillful but underdispersive. To improve the reliability of the ensemble forecasts, the control ensemble is supplemented with 1) perturbed lateral boundary conditions; or, model error representation using either 2) stochastic kinetic energy backscatter or 3) stochastically perturbed parameterization tendencies. Forecasts are evaluated against stage IV accumulated precipitation analyses and radiosonde observations. Perturbed ensemble forecasts are also compared to the control forecast to assess the relative impact from adding forecast perturbations. For precipitation forecasts, all perturbation approaches improve ensemble reliability relative to the control CPEFS. Deterministic ensemble member forecast skill, verified against radiosonde observations, decreases when forecast perturbations are added, while ensemble mean forecasts remain similarly skillful to the control.

Closing the sea level budget at the Last Glacial Maximum

Establishing the volume of excess ice contained in the global ice sheets during the Last Glacial Maximum (LGM; ∼26,000–19,000 y ago) remains a longstanding problem in Ice Age climate dynamics. Expressed as the equivalent lowering of global mean sea level (GMSL), estimates of this value have varied from 105 (1) to 163 m (2), with many estimates suggesting ∼120 m (3). This wide range introduces substantial uncertainties in LGM global boundary conditions (ice sheet height and extent and continental shelf exposure) that strongly influence climate through their impacts on atmospheric and ocean circulation and global temperature. More recently, this uncertainty has influenced our understanding of present sea level change, whereby the ongoing glacial isostatic adjustment (GIA) of the solid Earth to the redistribution of mass since the LGM must be accounted for in satellite measurements of sea level and of current mass changes from shrinkage of glaciers and ice sheets. In PNAS, Lambeck et al. (4) present a comprehensive analysis of nearly 1,000 paleo-sea level markers to reconstruct GMSL change over the last 35,000 y, with their estimate for the LGM GMSL of 134–140 m being significantly larger than most published estimates but similar to a recent reassessment of the Barbados sea level record (130 m) that had been the basis for many estimates of ∼120 m (5). If it stands, this reconstruction will reduce uncertainties in our understanding of Ice Age climate and modern sea level change. At the same time, however, it raises a significant challenge: is it possible to explain this total sea level change from our best estimates of the contributions from individual LGM glaciers and ice sheets?

Hybrid uncertain analysis for steady-state heat conduction with random and interval parameters

Abstract A new numerical technique named stochastic interval finite volume method is proposed for the steady-state temperature field prediction with hybrid uncertainties in material properties, external loads and boundary conditions. Random variables are used to treat the uncertain parameters with sufficient information; whereas, interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on the perturbation theory and random interval moment method, the expectation and variance of the interval bounds of the temperature field are calculated effectively. By comparing the results with traditional Monte Carlo simulation, three numerical examples are given to verify the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problems with hybrid uncertain parameters, pure random parameters and pure interval parameters, respectively.

Constructive epistemic modeling of groundwater flow with geological structure and boundary condition uncertainty under the Bayesian paradigm

• BMA trees of model weights, prediction and variance serve as a learning tool. • Candidate propositions are tested to learn about individual model components. • Posterior model probability is a degree of belief given our state of knowledge. • Geological structure uncertainty is larger than boundary condition uncertainty. • Model structure uncertainty is larger than parameter uncertainty. Constructive epistemic modeling is the idea that our understanding of a natural system through a scientific model is a mental construct that continually develops through learning about and from the model. Using hierarchical Bayesian model averaging (BMA), this study shows that segregating different uncertain model components through a BMA tree of posterior model probability, model prediction, within-model variance, between-model variance and total model variance serves as a learning tool. First, the BMA tree of posterior model probabilities permits the comparative evaluation of the candidate propositions of each uncertain model component. Second, systemic model dissection is imperative for understanding the individual contribution of each uncertain model component to the model prediction and variance. Third, the hierarchical representation of the between-model variance facilitates the prioritization of the contribution of each uncertain model component to the overall model uncertainty. We illustrate these concepts using the groundwater flow model of a siliciclastic aquifer-fault system. We consider four uncertain model components. With respect to geological structure uncertainty, we consider three methods for reconstructing the hydrofacies architecture of the aquifer-fault system, and two formation dips. We consider two uncertain boundary conditions, each having two candidate propositions. Through combinatorial design, these four uncertain model components with their candidate propositions result in 24 base models. The study shows that hierarchical BMA analysis helps in advancing knowledge about the model rather than forcing the model to fit a particularly understanding or merely averaging several candidate models.

An ensemble study of HyMeX IOP6 and IOP7a: sensitivity to physical and initial and boundary condition uncertainties

Abstract. The first Special Observation Period of the HyMeX campaign took place in the Mediterranean between September and November 2012 with the aim of better understanding the mechanisms which lead to heavy precipitation events (HPEs) in the region during the autumn months. Two such events, referred to as Intensive Observation Period 6 (IOP6) and Intensive Observation Period 7a (IOP7a), occurred respectively on 24 and 26 September over south-eastern France. IOP6 was characterised by moderate to weak low-level flow which led to heavy and concentrated convective rainfall over the plains near the coast, while IOP7a had strong low-level flow and consisted of a convective line over the mountainous regions further north and a band of stratiform rainfall further east. Firstly, an ensemble was constructed for each IOP using analyses from the AROME, AROME-WMED, ARPEGE and ECMWF operational models as initial (IC) and boundary (BC) conditions for the research model Meso-NH at a resolution of 2.5 km. A high level of model skill was seen for IOP7a, with a lower level of agreement with the observations for IOP6. Using the most accurate member of this ensemble as a CTRL simulation, three further ensembles were constructed in order to study uncertainties related to cloud physics and surface turbulence parameterisations. Perturbations were introduced by perturbing the time tendencies of the warm and cold microphysical and turbulence processes. An ensemble where all three sources of uncertainty were perturbed gave the greatest degree of dispersion in the surface rainfall for both IOPs. Comparing the level of dispersion to that of the ICBC ensemble demonstrated that when model skill is low (high) and low-level flow is weak to moderate (strong), the level of dispersion of the ICBC and physical perturbation ensembles is (is not) comparable. The level of sensitivity to these perturbations is thus concluded to be case dependent.

GCR environmental models II: Uncertainty propagation methods for GCR environments

In order to assess the astronaut exposure received within vehicles or habitats, accurate models of the ambient galactic cosmic ray (GCR) environment are required. Many models have been developed and compared to measurements, with uncertainty estimates often stated to be within 15%. However, intercode comparisons can lead to differences in effective dose exceeding 50%. This is the second of three papers focused on resolving this discrepancy. The first paper showed that GCR heavy ions with boundary energies below 500 MeV/n induce less than 5% of the total effective dose behind shielding. Yet, due to limitations on available data, model development and validation are heavily influenced by comparisons to measurements taken below 500 MeV/n. In the current work, the focus is on developing an efficient method for propagating uncertainties in the ambient GCR environment to effective dose values behind shielding. A simple approach utilizing sensitivity results from the first paper is described and shown to be equivalent to a computationally expensive Monte Carlo uncertainty propagation. The simple approach allows a full uncertainty propagation to be performed once GCR uncertainty distributions are established. This rapid analysis capability may be integrated into broader probabilistic radiation shielding analysis and also allows error bars (representing boundary condition uncertainty) to be placed around point estimates of effective dose.