Uncertainty In Input Parameters Research Articles

Single Intravenous Dose Dalbavancin Pathway for the Treatment of Acute Bacterial Skin and Skin Structure Infections: Considerations for Emergency Department Implementation and Cost Savings

BackgroundA pathway for the treatment of acute bacterial skin and skin structure infections (ABSSSI) with a single intravenous (IV) dose of dalbavancin was previously shown to reduce hospital admissions and shorten inpatient length of stay (LOS). ObjectivesTo describe pathway implementation at the emergency department (ED) and evaluate cost-effectiveness of a single-dose dalbavancin administered to ED patients who would otherwise be hospitalized to receive usual care with multidose IV antibiotics. MethodsThe dalbavancin pathway was previously implemented at 11 U.S. EDs (doi:10.1111/acem.14258). Patients with ABSSSI, without an unstable comorbidity or infection complication requiring complex management, were treated with a single dose of dalbavancin. At the emergency physicians’ discretion, patients were either discharged and received outpatient follow-up or were hospitalized for continued management. A decision analytic cost-effectiveness model was developed from the U.S. healthcare's perspective to evaluate costs associated with the dalbavancin pathway compared with inpatient usual care. Costs (2021 USD) were modeled over a 14-day horizon and included ED visits, drug costs, inpatient stay, and physician visits. One-way and probabilistic sensitivity analyses examined input parameter uncertainty. ResultsDriven largely by the per diem inpatient cost and LOS for usual care, the dalbavancin pathway was associated with savings of $5133.20 per patient and $1211.57 per hospitalization day avoided, compared with inpatient usual care. The results remained robust in sensitivity and scenario analyses. ConclusionThe new single-dose dalbavancin ED pathway for ABSSSI treatment, which was previously implemented at 11 U.S. EDs, offers robust cost savings compared to inpatient usual care.

A probabilistic design approach to the reinforced fill over a void problem

Current design methods for the problem of a thin reinforced fill over a void are based on analytical solutions which are most often solved using an allowable (working) stress deterministic (factor of safety) approach. However, in practice there are uncertainties in the estimate of the input parameter values that appear in the analytical equations. The paper shows how margins of safety for the limit states that appear in the wellknown BS8006 method can be computed using Monte Carlo simulation or an equivalent closed-form solution for reliability index. Reinforcement strain, strength and stiffness limit states are formulated to include input parameter uncertainty and model accuracy. The probabilistic limit state solutions are expressed by simple equations that are easily implemented in an Excel spreadsheet. The paper shows that using a probabilistic approach provides a more nuanced appreciation of the margins of safety for these limit states than deterministic factor of safety approaches. The probabilistic approach demonstrated in this paper is also in alignment with the movement toward reliability theory performance-based design of reinforced soil structures.

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.

Simple model combined with data assimilation to forecast buoyancy-driven ceiling jet propagation in tunnel fires

Faster-than-real-time forecast for the ceiling jet head propagation is crucial for evacuation guidance and rescue operation in the early stages of tunnel fires. The ceiling jet head propagation is driven by buoyancy and thus significantly dominated by the heat transfer process in the head region. Simplified models have been established but proved challenging in forecasting complex dynamic processes. The cumulative deviations induced by the model limitations and the input parameter uncertainties lead to distorted forecast during the ceiling jet dynamic propagation process, thereby constraining the applicability of such models in practical scenarios. In this study, an assimilation method, the ensemble Kalman filter (EnKF), was combined with a simple theoretical model to forecast the ceiling jet propagation. Using the EnKF to assimilate the monitored local data, the key input parameters such as mass flow rate m˙ and the temperature of the ceiling jet head Ts are corrected in real time, thus alleviating model prediction deviation. Meanwhile, a heat loss rate correction coefficient λα, introduced to correct the heat loss rate during the ceiling jet propagation process, is dynamically estimated as well. This enables the model to characterize the heat transfer process more accurately, consequently ensuring faster-than-real-time forecasts of the subsequent propagation stage. Finally, the forecasting effectiveness of the proposed combined method was examined in a 1:6 reduced-size experimental tunnel fire scenario and full-scale simulated tunnel fire scenarios with various fire source locations, tunnel geometries, and suddenly changing HRR. In response to the demand of minimizing both the type and quantity of sensors in practical applications, a factor analysis was also performed to examine the robustness of this method. The results exhibited good adaptability to different fire scenarios with very limited monitored data.

A comprehensive review and sensitivity analysis of the factors affecting the performance of buildings equipped with Variable Refrigerant Flow system in Middle East climates

Variable Refrigerant Flow (VRF) systems are becoming increasingly popular in commercial and residential buildings due to their flexibility and efficiency. Building design and operational parameters play an important role in the process of predicting cooling loads, and these variables contain a variety of uncertainties that must be taken into account when seeking to obtain a reliable prediction. It is common practice to oversize the Heating, Ventilation, and Air Conditioning (HVAC) system installed in a building in order to reduce uncertainties in its performance. However, oversizing is undesirable due to additional capital and operating costs, inefficient space utilization, and excessive emissions of greenhouse gases. This study provided a comprehensive literature review on previous uncertainly analysis studies from six perspectives: classification of uncertainty factors, sensitivity analysis methods, building energy model for uncertainty analysis, sampling method, the most effective parameter, and distribution of uncertainty parameters., and then developed a framework to investigate the effects of uncertainty of operational and design parameters on annual cooling load, total energy consumption, HVAC electricity use per conditioned floor area, VRF system cost, and the cooling-to-total electricity consumption ratio (R) in residential buildings equipped with VRF systems located in the Middle East (ME) region (Kuwait City, Tehran, and Istanbul). Artificial Neural Network (ANN) model was also developed to predict output parameters in residential building. Latin Hypercube Sampling (LHS) simulation was also applied to generate near-random samples of uncertainty parameter values from a multidimensional distribution. It should be mentioned that a correlation was derived in this study to predict VRF cost. The study evaluated thirteen uncertainty parameters related to building characteristics, occupants, building energy systems, VRF variables, and lighting conditions. Several methods were used to evaluate the effects of input uncertainty parameters on the output variables, namely, the Standardized Regression Coefficient (SRC), Partial Correlation Coefficient (PCC), and Spearman Rank Correlation Coefficient (SRCC) or Pearson Product-Moment Correlation Coefficient (PPMCC). The results indicated that VRF cost and HVAC electricity use per area have a greater coefficient of variation compared with other output parameters. The density distribution of output parameters indicated that uncertainty parameters had the greatest impact on output parameters in Kuwait (hyper arid climate), whereas in Istanbul (humid subtropical climate), they were the least. Among the variables examined in the Sensitivity Analysis (SA), cooling setpoint had the greatest impact on residential building energy consumption in ME climates. Finally, this paper highlights emerging trends and offers recommendations for advancing future research in the realm of building energy uncertainty analysis.

Designing a resilient medical laboratory service network under disruption and uncertainty: A case study

Health service networks play a key role in ensuring the health of society. Disruptive and operational risks are constantly affecting the goals of healthcare supply chain and service networks. Therefore, adopting a suitable approach to deal with risks is critical in the mentioned networks. This study proposes a mathematical model for designing an integrated resilient medical laboratory service network and the supply chain of laboratory consumables under multiple facility disruptions to minimize the total network design costs. A robust possibilistic optimization approach is employed to deal with the uncertainty of input parameters. Then, a case study is presented to extract the results and evaluate the proposed model. Regarding the results, the integration of the laboratory service network and laboratory consumables supply chain significantly reduces costs and improves the service process in the long run. Also, adopting more efficient resiliency strategies will increase the cost savings of the entire network.

The inclusion of uncertainty in circularity transition modeling: A case study on wind turbine blade end-of-life management

The transition to a more circular economy (CE) is complex and hard to predict, including in sustainable energy technologies. Many sources of uncertainty make it challenging to model CE scenarios and their potential benefits. As an example, the high variability in costs and revenues of different recycling options makes future wind turbine blade recycling highly uncertain. To better understand this challenge, the circular economy, life cycle assessment and visualization (CELAVI) framework – a discrete event simulation and life cycle assessment framework – is modified to incorporate uncertainty analysis capabilities. Moreover, a 3-step procedure that covers different aspects of uncertainty in CE studies and includes a Monte-Carlo analysis is proposed. The procedure is tested in a case study on wind turbine blade recycling using CELAVI. Results highlight that grinding and landfilling costs are the most influential parameters for wind power circularity. The model’s output coefficients of variation (when input parameter uncertainties are propagated) are between 92% and 384% depending on the indicator. The approach developed in this study may help researchers and decision-makers who study circularity prioritize their data collection effort. Finally, our method contributes to a mounting yet critical body of research: the measurement of uncertainty in circularity transitions.

Estimating the effectiveness of electric vehicles braking when determining the circumstances of a traffic accident

In the vast majority of cases, the braking process is used to prevent traffic accidents. The effectiveness of this process depends on the design and functionality of vehicle braking systems (presence of anti-lock braking system, emergency braking system, preventive safety systems, etc.) and is limited by the amount of frictional forces in contact of tires with the road. The improvement of methodical approaches to evaluating the effectiveness of braking of cars contributes to increasing the accuracy and objectivity of establishing the circumstances of the occurrence of emergency situations. The paper analyses existing methods of evaluating the braking parameters of vehicles (including those with an electric drive) and modern methods of evaluating electric vehicle braking parameters and conducting auto-technical investigations of traffic accidents, which relate to using different methodological approaches and digital technologies at all stages of expert research. In contrast to existing models, the proposed mathematical model for estimating the trajectory of two-axle cars during braking allows for considering various types of input parameter uncertainty, reducing the range of possible modeling errors by 39%. Comparing simulation results and experimental data showed that the average relative error is 4.58%, and the maximum error did not exceed 7.82%. The performed study of the stability of the electric vehicles' movement during emergency braking with the help of developed mathematical models in the Mathcad software environment reveals the content of the algorithm of a similar calculation in specialized computer programs of auto technical examination. Conducting such calculations is relevant in the analysis of real accident situations, where specific circumstances and features that cannot be considered during modeling in specialized software must be taken into account. Simultaneously, the probability of type I errors is reduced by 2–19%, and type II errors are reduced by 43–68%.

Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations

An understanding of how input parameter uncertainty in the numerical simulation of physical models leads to simulation output uncertainty is a challenging task. Common methods for quantifying output uncertainty, such as performing a grid or random search over the model input space, are computationally intractable for a large number of input parameters represented by a high-dimensional input space. It is, therefore, generally unclear as to whether a numerical simulation can reproduce a particular outcome (e.g., a set of experimental results) with a plausible set of model input parameters. Here, we present a method for efficiently searching the input space using Bayesian optimization to minimize the difference between the simulation output and a set of experimental results. Our method allows explicit evaluation of the probability that the simulation can reproduce the measured experimental results in the region of input space defined by the uncertainty in each input parameter. We apply this method to the simulation of charge-carrier dynamics in the perovskite semiconductor methyl-ammonium lead iodide (MAPbI3), which has attracted attention as a light harvesting material in solar cells. From our analysis, we conclude that the formation of large polarons, quasiparticles created by the coupling of excess electrons or holes with ionic vibrations, cannot explain the experimentally observed temperature dependence of electron mobility.

Integrated supply chain network design and superstructure optimization problem: A case study of microalgae biofuel supply chain

In recent years, several strategies have been developed and adopted in a bid to manage the biofuel supply chain. In this paper, a two-stage optimization model is proposed for integrated microalgae biofuel supply chain network design and superstructure optimization problems. In the first stage, the design of the carbon capture, utilization, and storage (CCUS) network is taken into account. A robust mixed integer linear programming (RMILP) model is proposed to optimize the strategic CCUS decisions, including the simultaneous selection of emission sources, capture facilitates, Carbon Dioxide (CO2) pipelines, intermediate transportation sites and storage sites, or microalgae cultivation sites. The second stage is dedicated to biorefinery superstructure optimization in order to determine the optimal/promising biorefinery configurations. The presented model is able to handle the inherent uncertainty of critical input parameters. Moreover, the results show that biodiesel production cost cannot compete with current diesel price, but it can be reduced significantly by improving biomass productivity.

Cost-effectiveness analysis of leadless cardiac resynchronization therapy.

The Wireless Stimulation Endocardially for CRT (WiSE-CRT) system is a novel technology used to treat patients with dyssynchronous heart failure (HF) by providing leadless cardiac resynchronization therapy (CRT). Observational studies have demonstrated its safety and efficacy profile, however, the treatment cost-effectiveness has not previously been examined. A cost-effectiveness evaluation of the WiSE-CRT System was performed using a cohort-based economic model adopting a "proportion in state" structure. In addition to the primary analysis, scenario analyses and sensitivity analyses were performed to test for uncertainty in input parameters. Outcomes were quantified in terms of quality-adjusted life year (QALY) differences. The primary analysis demonstrated that treatment with the WiSE-CRT system is likely to be cost-effective over a lifetime horizon at a QALY reimbursement threshold of £20 000, with a net monetary benefit (NMB) of £3781 per QALY. Cost-effectiveness declines at time horizons shorter than 10 years. Sensitivity analyses demonstrated that average system battery life had the largest impact on potential cost-effectiveness. Within the model limitations, these findings support the use of WiSE-CRT in indicated patients from an economic standpoint. However, improving battery technology should be prioritized to maximize cost-effectiveness in times when health services are under significant financial pressures.

Modelling of spent fuel dissolution process for assessment of criticality safety margin

Criticality safety margin assessment is a challenging task for a large dissolution tank under uncertain process conditions. The Monte-Carlo based neutron transport code (MCkeff) was used to model geometry, material and process conditions of dissolution tank containing fissile material. This manuscript presents a sampling-based probabilistic technique for estimating single-sided tolerance limit on criticality safety parameter due to random variation of process parameters. Based on their effect on fissile mass, fissile concentration, neutron moderation and neutron reflection, potential uncertain parameters impacting the criticality safety margin were identified. By withdrawing random samples from uncertain domain of process parameter, a simulation test matrix was formed to study the variability of the neutron multiplication factor. The Wilk's formula, which corresponds to the 95th percentile and 95% confidence level, was used to estimate required sample size to minimise statistical error (i.e., Type-I error associated to the rejection of the genuine sample and Type-II error connected to the acceptance of the false sample). In order to cut down the computation costs associated with requirement of large computer runs, a meta-model for the criticality safety problem was also constructed using a linear regression model. Additionally, associated model uncertainty was quantified. The variation in neutron multiplication factor caused by changes in process variables like pellet diameter, linear pellet mass, temperature, etc. was also investigated as an input parameter uncertainty. It is found that model uncertainty plays significant role while assessing the criticality safety margin using meta-model.

Multi-objective joint optimization for concurrent execution of design-construction tasks in design-build mode

PERT (Program Evaluation and Review Technique) is suitable for evaluating schedule optimization of DBB (Design-Bid-Build) projects based on serial execution of design and construction. However, it is not suitable for evaluating the multi-objective joint optimization of DB (Design-Build) projects based on concurrent execution of design and construction. Therefore, focusing on the unique construction method and construction objective under DB mode, this study adopts the multi-parameter grey GERT (Graphical Evaluation and Review Technique) based on z-tag to construct a multi-objective joint optimization model of concurrent execution of design-construction tasks. The research methods adopted are as follows: Firstly, based on the modeling idea of PERT, a multi-parameter grey GERT was constructed, which was composed of sub-GERTs representing the execution flow of design-construction tasks. Second, based on discussing the functional relationship between time, cost and quality in normal execution and rework execution, a method of constructing a moment generating function for concurrent execution time, cost and quality to characterize the uncertainty of input and output parameters is proposed. Thirdly, on the basis of analyzing the solving methods of each parameter of the multi-parameter GERT, the solving methods for starting execution time, information transmission duration, and total execution duration of the critical time nodes are given. Finally, according to the requirements of DB projects, the multi-objective joint optimization models based on progress oriented and quality oriented are constructed respectively, and the gamultiobj algorithm is used to solve the models. The proposed approach provides a scientific quantitative analysis method for evaluating the progress, cost and quality objectives of the concurrent execution of design-construction tasks, and also lays theoretical guidance for further efficiency popularization of DB mode.

Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion

Abstract. Several metrics have been proposed and utilized to diagnose the performance of linear Bayesian and geostatistical atmospheric inverse problems. These metrics primarily assess the reductions in the prior uncertainties, compare modeled observations to true observations, and check distributional assumptions. Although important, these metrics should be augmented with a sensitivity analysis to obtain a comprehensive understanding of the atmospheric inversion performance and improve the quality and confidence in the inverse estimates. In this study, we derive closed-form expressions of local sensitivities for various input parameters, including measurements, covariance parameters, covariates, and a forward operator. To further enhance our understanding, we complement the local sensitivity analysis with a framework for a global sensitivity analysis that can apportion the uncertainty in input parameters to the uncertainty associated with inverse estimates. Additionally, we propose a mathematical framework to construct nonstationary correlation matrices from a precomputed forward operator, which is closely tied to the overall quality of inverse estimates. We demonstrate the application of our methodology in the context of an atmospheric inverse problem for estimating methane fluxes in Los Angeles, California.

Non-Probabilistic Uncertainty and Correlation Propagation Analysis Methods Based on Multidimensional Parallelepiped Model

In engineering practice, the uncertainty and correlation may coexist in the input parameters, as well as in the output responses. To address such cases, several methods are developed for the non-probabilistic uncertainty and correlation propagation analysis in this study. In the proposed methods, the multidimensional parallelepiped model (MPM) is introduced to quantify the uncertainty and correlation of input parameters. In the uncertainty propagation analysis, three methods are presented to calculate the interval bounds of output responses. Among the methods, the Monte Carlo uncertainty analysis method (MCUAM) is firstly presented as a reference method, and then the first-order perturbation method (FOPM) is employed to promote the computational efficiency, and the sub-parallelepiped perturbation method (SPPM) is further developed to handle the correlated parameters with large uncertainty. In the correlation propagation analysis, the Monte Carlo correlation analysis method (MCCAM) is proposed based on the MPM and Monte Carlo simulation, which aims to compute the correlation among different output responses. The uncertainty domains between any two responses can also be constructed by the MCCAM. The effectiveness of the proposed methods on dealing with the uncertainty and correlation propagation problems is demonstrated by three numerical examples.

Integrated planning for a global pharmaceutical supply chain: an ambidexterity perspective

AbstractBased on the concept of ambidexterity, we develop a multi-objective, multi-product, and multi-period model to integrate planning for research and development (R&D) and production and distribution (P&D) in a global pharmaceutical supply chain (PSC) considering delays in the entire supply chain. We also propose robust possibilistic programming (RPP) approach to deal with the epistemic uncertainty of some critical input parameters. Applying the ambidexterity approach that emphasizes optimizing a balanced framework based on the R&D and P&D planning, our study reconciles the explorative and exploitative supply chain strategies in the context of global PSCs. The proposed integrated model can manage the inherent delays and uncertainties in the R&D processes and P&D systems via a novel, credibility-based, robust possibilistic programming model. We illustrate the application of our model using a real-world case study of one of the largest and most reputable pharmaceutical companies in Iran. The results affirm the credibility and feasibility of the proposed model when juxtaposed with a non-integrated model. Our study suggests the use of ambidexterity approach in resource allocation planning, risk management, and enhancement of performance in sophisticated settings such as global PSCs.

A Review of Conventional versus Additive Manufacturing for Metals: Life-Cycle Environmental and Economic Analysis

Additive manufacturing (AM) is a recent emerging technology that is being adopted in various industry sectors and supply chains. Life cycle assessment (LCA) and life cycle costing (LCC) are powerful methods that can be used for assessing the environmental and economic performance of emerging manufacturing technologies. This study aims to evaluate the life cycle environmental impacts and cost of computerized numerical control-based (CNC) manufacturing and direct metal laser sintering technology (DMLS) through a cradle-to-gate life cycle analysis. This research has four main novel elements: (i) conducting a recent comprehensive review of metal AM and conventional manufacturing (CM) processes using a systematic method and meta-analysis (ii) comparing the conventional process “CNC machining” and the additive technology “direct metal laser sintering” from the environmental (LCA) and economic (LCC) perspectives, (iii) investigating the influence of geometry complexity and shape size factors on the environmental and cost performance of both manufacturing methods, and (iv) conducting a Monte Carlo simulation-based sensitivity analysis to tackle uncertainty in LCC input parameters. The midpoints and endpoints impact for CNC and AM processes were estimated using the Ecoinvent v3.8 database and ReCiPe (E) impact assessment method. The review revealed that global warming potential is one of the most widely studied environmental indicators; however, only 6% of the studies have investigated the life cycle economic impacts of AM technologies using sensitivity and uncertainty analysis. The results have shown that in terms of ReCiPe endpoints, DMLS has the highest environmental impact on human health while CM has more impact on the eco-system quality. Electricity consumption is the main contributor to environmental impact categories in both manufacturing technologies. This is due to the high electricity utilization for casting and milling conventionally manufactured parts and printing the AM parts. LCC net present values revealed that manufacturing all parts with AM costs 91% more compared to CNC. The LCC analysis has shown that AM is more suitable and cost-effective for parts with highly complex geometries. Whereas CNC machining was found to be economically feasible for large-sized and low-complexity parts. The Monte Carlo sensitivity analysis demonstrated that for the case of AM, the most significant parameter is the processing cost followed by material cost, which highlighted the importance of energy-efficient AM and dematerialization through design for circularity.

De‐Tuning Albedo Parameters in a Coupled Climate Ice Sheet Model to Simulate the North American Ice Sheet at the Last Glacial Maximum

AbstractThe Last Glacial Maximum extent of the North American Ice Sheets is well constrained empirically but has proven to be challenging to simulate with coupled Climate‐Ice Sheet models. Coupled Climate‐Ice Sheet models are often too computationally expensive to sufficiently explore uncertainty in input parameters, and it is unlikely that values calibrated to reproduce modern ice sheets will reproduce the known extent of the ice at the Last Glacial Maximum. To address this, we run an ensemble with a coupled Climate‐Ice Sheet model (FAMOUS‐ice), simulating the final stages of growth of the last North American Ice Sheets' maximum extent. Using this large ensemble approach, we explore the influence of numerous uncertain ice sheet albedo, ice sheet dynamics, atmospheric, and oceanic parameters on the ice sheet extent. We find that ice sheet albedo parameters determine the majority of uncertainty when simulating the Last Glacial Maximum North American Ice Sheets. Importantly, different albedo parameters are needed to produce a good match to the Last Glacial Maximum North American Ice Sheets than have previously been used to model the contemporary Greenland Ice Sheet due to differences in cloud cover over ablation zones. Thus, calibrating coupled climate‐ice sheet models on one ice sheet may produce strong biases when the model is applied to a new domain.

Fatigue Life Uncertainty Quantification of Front Suspension Lower Control Arm Design

The purpose of this study is to investigate the uncertainty of the design variables of a front suspension lower control arm under fatigue-loading circumstances to estimate a reliable and robust product. This study offers a method for systematic uncertainty quantification (UQ), and the following steps were taken to achieve this: First, a finite element model was built to predict the fatigue life of the control arm under bump-loading conditions. Second, a sensitivity scheme, based on one of the global analyses, was developed to identify the model’s most and least significant design input variables. Third, physics-based and data-driven uncertainty quantification schemes were employed to quantify the model’s input parameter uncertainties via a Monte Carlo simulation. The simulations were conducted using 10,000 samples of material properties and geometrical uncertainty variables, with the coefficients of variation ranging from 1 to 3%. Finally, the confidence interval results show a deviation of about 21.74% from the mean (the baseline). As a result, by applying systematic UQ, a more reliable and robust automobile suspension control arm can be designed during the early stages of design to produce a more efficient and better approximation of fatigue life under uncertain conditions.

A surrogate-assisted uncertainty-aware Bayesian validation framework and its application to coupling free flow and porous-medium flow

Existing model validation studies in geoscience often disregard or partly account for uncertainties in observations, model choices, and input parameters. In this work, we develop a statistical framework that incorporates a probabilistic modeling technique using a fully Bayesian approach to perform a quantitative uncertainty-aware validation. A Bayesian perspective on a validation task yields an optimal bias-variance trade-off against the reference data. It provides an integrative metric for model validation that incorporates parameter and conceptual uncertainty. Additionally, a surrogate modeling technique, namely Bayesian Sparse Polynomial Chaos Expansion, is employed to accelerate the computationally demanding Bayesian calibration and validation. We apply this validation framework to perform a comparative evaluation of models for coupling a free flow with a porous-medium flow. The correct choice of interface conditions and proper model parameters for such coupled flow systems is crucial for physically consistent modeling and accurate numerical simulations of applications. We develop a benchmark scenario that uses the Stokes equations to describe the free flow and considers different models for the porous-medium compartment and the coupling at the fluid–porous interface. These models include a porous-medium model using Darcy’s law at the representative elementary volume scale with classical or generalized interface conditions and a pore-network model with its related coupling approach. We study the coupled flow problems’ behaviors considering a benchmark case, where a pore-scale resolved model provides the reference solution. With the suggested framework, we perform sensitivity analysis, quantify the parametric uncertainties, demonstrate each model’s predictive capabilities, and make a probabilistic model comparison.