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1. Global Sensitivity Analysis for Patient-Specific Aortic Simulations: The Role of Geometry, Boundary Condition and Large Eddy Simulation Modeling Parameters

   https://doi.org/10.1115/1.4048336  

   Xu, Huijuan ; Baroli, Davide ; Veneziani, Alessandro ( February 2021 , Journal of Biomechanical Engineering)

   null (Ed.)

   Abstract Numerical simulations for computational hemodynamics in clinical settings require a combination of many ingredients, mathematical models, solvers and patient-specific data. The sensitivity of the solutions to these factors may be critical, particularly when we have a partial or noisy knowledge of data. Uncertainty quantification is crucial to assess the reliability of the results. We present here an extensive sensitivity analysis in aortic flow simulations, to quantify the dependence of clinically relevant quantities to the patient-specific geometry and the inflow boundary conditions. Geometry and inflow conditions are generally believed to have a major impact on numerical simulations. We resort to a global sensitivity analysis, (i.e., not restricted to a linearization around a working point), based on polynomial chaos expansion (PCE) and the associated Sobol' indices. We regard the geometry and the inflow conditions as the realization of a parametric stochastic process. To construct a physically consistent stochastic process for the geometry, we use a set of longitudinal-in-time images of a patient with an abdominal aortic aneurysm (AAA) to parametrize geometrical variations. Aortic flow is highly disturbed during systole. This leads to high computational costs, even amplified in a sensitivity analysis -when many simulations are needed. To mitigate this, we consider here a large Eddy simulation (LES) model. Our model depends in particular on a user-defined parameter called filter radius. We borrowed the tools of the global sensitivity analysis to assess the sensitivity of the solution to this parameter too. The targeted quantities of interest (QoI) include: the total kinetic energy (TKE), the time-average wall shear stress (TAWSS), and the oscillatory shear index (OSI). The results show that these indexes are mostly sensitive to the geometry. Also, we find that the sensitivity may be different during different instants of the heartbeat and in different regions of the domain of interest. This analysis helps to assess the reliability of in silico tools for clinical applications. 

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2. Uncertainty quantification reveals the physical constraints on pumping by peristaltic hearts

   https://doi.org/10.1098/rsif.2020.0232  

   Waldrop, Lindsay D. ; He, Yanyan ; Battista, Nicholas A. ; Neary Peterman, Tess ; Miller, Laura A. ( September 2020 , Journal of The Royal Society Interface)

   null (Ed.)

   Most biological functional systems are complex, and this complexity is a fundamental driver of diversity. Because input parameters interact in complex ways, a holistic understanding of functional systems is key to understanding how natural selection produces diversity. We present uncertainty quantification (UQ) as a quantitative analysis tool on computational models to study the interplay of complex systems and diversity. We investigate peristaltic pumping in a racetrack circulatory system using a computational model and analyse the impact of three input parameters (Womersley number, compression frequency, compression ratio) on flow and the energetic costs of circulation. We employed two models of peristalsis (one that allows elastic interactions between the heart tube and fluid and one that does not), to investigate the role of elastic interactions on model output. A computationally cheaper surrogate of the input parameter space was created with generalized polynomial chaos expansion to save computational resources. Sobol indices were then calculated based on the generalized polynomial chaos expansion and model output. We found that all flow metrics were highly sensitive to changes in compression ratio and insensitive to Womersley number and compression frequency, consistent across models of peristalsis. Elastic interactions changed the patterns of parameter sensitivity for energetic costs between the two models, revealing that elastic interactions are probably a key physical metric of peristalsis. The UQ analysis created two hypotheses regarding diversity: favouring high flow rates (where compression ratio is large and highly conserved) and minimizing energetic costs (which avoids combinations of high compression ratios, high frequencies and low Womersley numbers). 

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3. Modeling uncertainty of specimens employing spines and force‐limiting connections tested at E‐defense shake table

   https://doi.org/10.1002/eqe.3976  

   Astudillo, Bryam ; Rivera, David ; Duke, Jessica ; Simpson, Barbara ; Fahnestock, Larry A. ; Sause, Richard ; Ricles, James ; Kurata, Masahiro ; Okazaki, Taichiro ; Kawamata, Yohsuke ; et al ( July 2023 , Earthquake Engineering & Structural Dynamics)

   In light of the significant damage observed after earthquakes in Japan and New Zealand, enhanced performing seismic force‐resisting systems and energy dissipation devices are increasingly being utilized in buildings. Numerical models are needed to estimate the seismic response of these systems for seismic design or assessment. While there have been studies on modeling uncertainty, selecting the model features most important to response can remain ambiguous, especially if the structure employs less well‐established lateral force‐resisting systems and components. Herein, a global sensitivity analysis was used to address modeling uncertainty in specimens with elastic spines and force‐limiting connections (FLCs) physically tested at full‐scale at the E‐Defense shake table in Japan. Modeling uncertainty was addressed for both model class and model parameter uncertainty by varying primary models to develop several secondary models according to pre‐established uncertainty groups. Numerical estimates of peak story drift ratio and floor acceleration were compared to the results from the experimental testing program using confidence intervals and root‐mean‐square error. Metrics such as the coefficient of variation, variance, linear Pearson correlation coefficient, and Sobol index were used to gain intuition about each model feature's contribution to the dispersion in estimates of the engineering demands. Peak floor acceleration was found to be more sensitive to modeling uncertainty compared to story drift ratio. Assumptions for the spine‐to‐frame connection significantly impacted estimates of peak floor accelerations, which could influence future design methods for spines and FLC in enhanced lateral‐force resisting systems.

    

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4. Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals

   https://doi.org/10.1038/s41524-024-01200-1  

   Mahata, A. ; Mukhopadhyay, T. ; Chakraborty, S. ; Asle Zaeem, M. ( January 2024 , npj Computational Materials)

   Solidification phenomenon has been an integral part of the manufacturing processes of metals, where the quantification of stochastic variations and manufacturing uncertainties is critically important. Accurate molecular dynamics (MD) simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary. The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization, causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo (MC) simulations. In this work, we present a machine learning (ML) approach, as a data-driven surrogate to MD simulations, which only needs a few MD simulations. This efficient yet high-fidelity ML approach enables MC simulations for full-scale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate. Unlike conventional ML models, the proposed hybrid polynomial correlated function expansion here, being a Bayesian ML approach, is data efficient. Further, it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations, which in principle addresses the issue of repeatability in stochastic simulations with low variance. Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus, yield strength and ultimate strength, illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency.

    

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5. Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind Using the Alfvén Wave Solar Atmosphere Model

   https://doi.org/10.1029/2022SW003262  

   Jivani, Aniket ; Sachdeva, Nishtha ; Huang, Zhenguang ; Chen, Yang ; van der Holst, Bart ; Manchester, Ward ; Iong, Daniel ; Chen, Hongfan ; Zou, Shasha ; Huan, Xun ; et al ( January 2023 , Space Weather)

   Modeling the impact of space weather events such as coronal mass ejections (CMEs) is crucial to protecting critical infrastructure. The Space Weather Modeling Framework is a state‐of‐the‐art framework that offers full Sun‐to‐Earth simulations by computing the background solar wind, CME propagation, and magnetospheric impact. However, reliable long‐term predictions of CME events require uncertainty quantification (UQ) and data assimilation. We take the first steps by performing global sensitivity analysis (GSA) and UQ for background solar wind simulations produced by the Alfvén Wave Solar atmosphere Model (AWSoM) for two Carrington rotations: CR2152 (solar maximum) and CR2208 (solar minimum). We conduct GSA by computing Sobol' indices that quantify contributions from model parameter uncertainty to the variance of solar wind speed and density at 1 au, both crucial quantities for CME propagation and strength. Sobol' indices also allow us to rank and retain only the most important parameters, which aids in the construction of smaller ensembles for the reduced‐dimension parameter space. We present an efficient procedure for computing the Sobol' indices using polynomial chaos expansion surrogates and space‐filling designs. The PCEs further enable inexpensive forward UQ. Overall, we identify three important model parameters: the multiplicative factor applied to the magnetogram, Poynting flux per magnetic field strength constant used at the inner boundary, and the coefficient of the perpendicular correlation length in the turbulent cascade model in AWSoM.

    

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