Efficient global sensitivity analysis framework and approach for structures with hybrid uncertainties

Non-Linear Partial-Least-Squares-based Polynomial Chaos Expansion (NLPLS-based PCE) approach for global sensitivity analysis of a High-Q Partially Air-Filled Pedestal Resonator Integrated in a Printed Circuit Board (PCB)

Global Sensitivity Analysis of Arrhythmic Risk Biomarkers in Cardiac Single Cell Models

Unraveling the complex dynamics of temporal moments for multispecies contaminant transport in saturated porous media: A global sensitivity analysis approach

<strong class="journal-contentHeaderColor">Abstract.</strong> Efforts to develop effective climate mitigation strategies for agriculture require methods to estimate nitrous oxide (N₂O) emissions from soil. Process-based biogeochemical models have been used for such estimations but were mainly tested with field-scale measurements. In this study, results from a short-term (43-day) factorial incubation experiment were used to investigate the ability of a process-oriented model (CoupModel) to estimate N₂O and carbon fluxes, and soil mineral nitrogen (N) dynamics. This study identified the sensitivities of model parameters when estimating three output variables using a global sensitivity analysis approach. Our results suggested that important parameters regarding N₂O flux estimates were linked to the decomposability of soil organic matter (e.g. organic C pool sizes) and the denitrification process (e.g. Michaelis constant and denitrifier respiratory rates). The model was able to simulate low-magnitude daily and cumulative N₂O fluxes with model errors (MEs) close to zero, but tended to underestimate N₂O fluxes as observed daily values increased over 0.1 g N m^-2 day^-1. Besides, the response of N₂O emissions to soil moisture was not well reflected in the model, probably related to the indirect involvement of soil moisture response function in the denitrification process. We also evaluated ancillary variables regarding N cycling, which indicates that more frequent measurements and additional types of observed data such as soil oxygen content and the microbial sources of emitted N₂O are required to further evaluate model performance and biases. The current description of the N cycling process in the model may not consistently represent the temporal scale of nitrification and denitrification processes behind N₂O emissions. The major challenges for calibration are associated with high sensitivities of denitrification parameters to initial soil moisture abiotic conditions and residue amendment. For the development of process-based models, we suggest there is a need to address soil heterogeneity, and to revisit current subroutines of moisture response functions.

<strong class="journal-contentHeaderColor">Abstract.</strong> Efforts to develop effective climate mitigation strategies for agriculture require methods to estimate nitrous oxide (N₂O) emissions from soil. Process-based biogeochemical models have been used for such estimations but were mainly tested with field-scale measurements. In this study, results from a short-term (43-day) factorial incubation experiment were used to investigate the ability of a process-oriented model (CoupModel) to estimate N₂O and carbon fluxes, and soil mineral nitrogen (N) dynamics. This study identified the sensitivities of model parameters when estimating three output variables using a global sensitivity analysis approach. Our results suggested that important parameters regarding N₂O flux estimates were linked to the decomposability of soil organic matter (e.g. organic C pool sizes) and the denitrification process (e.g. Michaelis constant and denitrifier respiratory rates). The model was able to simulate low-magnitude daily and cumulative N₂O fluxes with model errors (MEs) close to zero, but tended to underestimate N₂O fluxes as observed daily values increased over 0.1 g N m^-2 day^-1. Besides, the response of N₂O emissions to soil moisture was not well reflected in the model, probably related to the indirect involvement of soil moisture response function in the denitrification process. We also evaluated ancillary variables regarding N cycling, which indicates that more frequent measurements and additional types of observed data such as soil oxygen content and the microbial sources of emitted N₂O are required to further evaluate model performance and biases. The current description of the N cycling process in the model may not consistently represent the temporal scale of nitrification and denitrification processes behind N₂O emissions. The major challenges for calibration are associated with high sensitivities of denitrification parameters to initial soil moisture abiotic conditions and residue amendment. For the development of process-based models, we suggest there is a need to address soil heterogeneity, and to revisit current subroutines of moisture response functions.

An assessment of fuel variability effect on biogas-hydrogen combustion using uncertainty quantification

Spatial Global Sensitivity Analysis of High Resolution classified topographic data use in 2D urban flood modelling

A novel magnet array system was constructed to use Larmor precession for boosting the signal intensity of rf-GD-MS. The enhancement mechanism with four magnet array devices of a single-block magnet and 2 × 2, 3 × 2, and 3 × 4 magnet arrays was simulated and studied by COMSOL Multiphysics Software 5.4.0 (COMSOL) to determine if the electrons in the discharge plasma could perform Larmor precession along the direction perpendicular to the magnetic field. Induced by Larmor precession, inelastic collisions between the primary electrons and the sample produced numerous secondary electrons and further improved the ionization efficiency. Moreover, the fuzzy synthetic evaluation result predicted that the device with a 3× 2 magnet array would display the greatest enhancement effect among the four devices. On the basis of these theoretical studies, a magnet array system with four magnet array devices was fabricated and utilized for studies of two scintillation crystals BGO and PWO. The observations indicated that the signal intensities obtained for 209Bi and 208Pb with the magnet array system were 630-3600 times of that obtained without a magnet and were enhanced by a factor of 1.5-2.8 compared with a previously reported stacked magnetic device. Two NIST samples were used to validate the method, and the results suggested that relative errors were less than 10%, and the lowest detection limit for the 3 × 2 magnet array could reach 0.0032 μg·g-1. Furthermore, the magnet array enhancement system with Larmor precession offers an efficient and sensitive approach for direct analysis of nonconducting materials.

Identifying and discriminating plausible treatment targets for remodeling related bone disorders is a difficult task often involving medical studies and clinical experiments. We propose to apply a global sensitivity analysis approach to a mathematical model describing the process of force-induced bone growth and adaptation. The considered sensitivity analysis approach finds an outer bound on the set of possible steady states for regions of parameters and inputs/stimuli. The outer bounding is achieved by a reformulation as a feasibility problem, which is convexified and solved via a semidefinite program. In this work, besides the application of this method to the bone growth and adaptation model, we improve the outer bounds by using a smarter multidimensional bisection algorithm. The results obtained allow for structure discrimination between different treatment therapies with a preferable counteractive effect in relation to the severity degree of the bone loss condition.

A special boundary integral formulation had been proposed to analyse many engineering problems of conduction heat transfer in complex three-dimensional geometries (closely spaced surface and circular hole in infinite domain or simple modification of it) by Rezayat and Burton. One example of such geometries is the mold sets in the injection molding process. In this paper, an efficient and accurate approach for the design sensitivity analysis (DSA) is presented for these kinds of problems in the similar complex geometries using the direct differentiation approach (DDA) based on the above special boundary integral formulation. The present approach utilizes the implicit differentiation of the boundary integral equations with respect to the design variables (radii and locations of circular holes) to yield the sensitivity equations. A sample problem (heat transfer of injection molding cooling system) is solved to demonstrate the accuracy of the present sensitivity analysis formulation. Although the techniques introduced here are applied to a particular problem in heat transfer of injection molding cooling system, their potential application is quite broad.

A formulation is developed for analyzing the dynamic response of multi-support structures with coupled non-classical damping using spectral moments instead of the frequently used cross-correlation coefficients. Its advantages are that the formulation considering the modal response variances and cross-covariances is more efficient in calculation than the conventional formulation, and the coupled damping of structure is taken into consideration. For the dynamic analysis of a structure equipped with coupled damper, the classical mode decomposition method is inapplicable and the velocity term of the external load in vibration equation cannot be neglected. The dynamic equation is firstly decoupled by complex mode decomposition method in this paper. Then, the complete SRSS rule (c-SRSS) addressed by Heredia-Zavoni [2011, 2015] is developed to consider the non-classically damping system under multi-support excitations. Moreover, spectral moments of orders higher than 3 are newly introduced into the dynamic analysis and the convergence difficulty for these spectral moments is overcome by employing the band-limited power spectra. A large-span structure equipped with a coupled damper is taken as an example to calculate its dynamic response using the conventional and proposed formulations, and numerical results show that the proposed formulation is accurate and efficient.

With decades of electronic health records linked to genetic data, large biobanks provide unprecedented opportunities for systematically understanding the genetics of the natural history of complex diseases. Genome-wide survival association analysis can identify genetic variants associated with ages of onset, disease progression and lifespan. We propose an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness. Our method utilizes state-of-the-art optimization strategies to reduce the computational cost. The saddlepoint approximation is used to allow for analysis of heavily censored phenotypes (>90%) and low frequency variants (down to minor allele count 20). We demonstrate the performance of our method through extensive simulation studies and analysis of five TTE phenotypes, including lifespan, with heavy censoring rates (90.9% to 99.8%) on ~400,000 UK Biobank participants with white British ancestry and ~180,000 individuals in FinnGen. We further analyzed 871 TTE phenotypes in the UK Biobank and presented the genome-wide scale phenome-wide association results with the PheWeb browser.

Coupled ship simulation in hydrodynamics and structural dynamics provides a comprehensive approach to understanding the dynamic behavior of ships under wave-induced loads. Improvements in computer power have made it much easier to create coupled simulation methods that combine structural and hydrodynamics analyses. A literature review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 is used to look at future trends in this literature review. We have filtered 1440 articles in PRISMA 2020, including 93 articles for analysis. The bibliographic analysis reveals that China emerged as the first according to the first authors due to significant industrial and funding support. Based on 93 articles, computational methods can be grouped by the coupling method (one-way and two-way), the hydrodynamic analysis approach (potential flow and CFD), the structural analysis approach (FEM, TMM, and DMB), the hydrodynamics element type (2D and 3D), and the structural element type (1D and 3D). As an outcome of the review, it can be concluded that the most common approach is a two-way connection of the potential flow and FEM methods, which both use 3D elements for structural and hydrodynamic analyses. Future trends of this research should be explored based on the application of variables, reducing computational resources, and using artificial intelligence.

Sensitivity analyses are frequently used during the design of engineering systems to qualify and quantify the effect of parametric variation in the performance of a system. Two primary types of sensitivity analyses are generally used: local and global. Local analyses, generally involving derivative-based measures, have a significantly lower computational burden than global analyses but only provide measures of sensitivity around a nominal point. Global analyses, generally performed with a Monte Carlo sampling approach, and variation-based measures provide a complete description of sensitivity but incur a large computational burden and require information regarding the distributions of the design parameters in a concept. Local analyses are generally suited to the early stages of design when parametric information is limited, and a large number of concepts must be evaluated (necessitating a light computational burden). Global analyses are more suited to the later stages of design when more information about parametric distributions is available and fewer concepts are under consideration. Current derivative-based local approaches provide a different and incompatible set of measures than a global variation-based analysis. This makes a direct comparison of local to global measures ill posed. To reconcile local and global sensitivity analyses, a hybrid local variation-based sensitivity (HyVar) approach is presented. This approach has a similar computational burden to a local approach but produces measures or percentage contributions. The HyVar approach is directly comparable to global variation-based approaches. In this paper, the HyVar sensitivity analysis method is developed in the context of a functional based behavioral modeling framework. An example application of the method is presented along with a summary of results produced from a more comprehensive example.

In this paper, the principles of the developed structural analysis approach for reactive moulding processes are rationalised and described. So far in the industry, the prediction of reactive moulding process induced problems that are directly related to the structural integrity of a finished product have largely been disregarded in the simulations. With the addition of the structural analysis method for reactive moulding processes, the fully three-dimensional simulation approach has been extended to cover all the process stages from mould filling, curing and post-casting heat treatment to final cooling down. The relevant physical phenomena, such as thermal and chemical deformations, that are affecting the structural integrity of analysed castings are taken into account. The utilisation of the structural analysis approach to a medium voltage industrial application case along with selected results is presented later in the paper. The combined effects of numerous variables contributing to the casting process formulate the biggest challenge for reliable analysis results.