Computer Simulation Research Articles

Tensorized Pauli decomposition algorithm

Abstract This paper introduces a novel general-purpose algorithm for Pauli decomposition that employs matrix slicing and addition rather than expensive matrix multiplication, significantly accelerating the decomposition of multi-qubit matrices. In a detailed complexity analysis, we show that the algorithm admits the best known worst-case scaling and more favorable runtimes for many practical examples. Numerical experiments are provided to validate the asymptotic speed-up already for small instance sizes, underscoring the algorithm's potential significance in the realm of quantum computing and quantum chemistry simulations.

Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions.

The success of machine learning in predicting material properties is largely dependent on the design of the model. However, the current designs of deep learning models in materials science have the following prominent problems. First, the model design lacks a rational guidance strategy and heavily relies on a large amount of trial and error. Second, numerous deep learning models are utilized across various fields, each with its own advantages and disadvantages. Therefore, it is important to incorporate a fusion strategy to fully leverage them and further expand the design strategies of the models. To address these problems, we analyze that the main reason is the lack of a new feedback method rich in physical insights. In this study, we developed a feedback method called the Chemical Environment Clustering Vector (CECV) of compounds at different thresholds, which is rich in physical insights. Based on CECV, we rationally designed the Long Short-Term Memory and Gated Recurrent Unit fused with Deep Convolutional Neural Network (L-G-DCNN) to explore the field of structure-agnostic material property predictions. L-G-DCNN accurately captures the interactions between elements in compounds, enabling more accurate and efficient predictions of the material properties. Our results demonstrate that the performance of the L-G-DCNN surpasses the current state-of-the-art structure-agnostic models across 28 benchmark data sets, exhibiting superior sample efficiency and faster convergence speed. By employing different visualization methods, we demonstrate that the fusion strategy based on CECV significantly enhances the comprehension of the L-G-DCNN model design and provides a fresh perspective for researchers in the field of materials informatics.

Dataset of Post-Event Survey of the 2024 Noto Peninsula Earthquake Tsunami in Japan

An earthquake with a moment magnitude of 7.5 (Mw) struck the northern Noto Peninsula, Ishikawa Prefecture, Japan, at 16:10 local time on January 1, 2024. This earthquake triggered a tsunami that propagated along the coastline of Ishikawa, Toyama, and Niigata Prefectures facing the Sea of Japan and significantly damaged coastal communities and infrastructure. Approximately 70 researchers from 23 universities or other institutes throughout Japan formed a joint research group to conduct a post-tsunami survey along a 340 km stretch of the coast. Based on the watermarks and traces of the tsunami, the inundation and run-up heights were surveyed using total stations, automatic optical levels, laser range finders, and a real-time kinematic (RTK) Global Navigation Satellite System (GNSS). The tidal correction was adjusted using astronomical tidal tables. In total, 303 survey records have been compiled, generating the NP2024TS (Noto Peninsula 2024 Tsunami Survey) dataset. This dataset provides comprehensive information on the inundation and run-up heights of the tsunami, which is useful for understanding tsunami characteristics and validating numerical tsunami models.

Systematic Evaluation of Hematoma Expansion Models in Spontaneous Intracerebral Hemorrhage: A Meta-Analysis and Meta-Regression Approach.

Accurate prediction of hematoma expansion (HE) in spontaneous intracerebral hemorrhage (sICH) is crucial for tailoring patient-specific treatments and improving outcomes. Recent advancements have yielded numerous HE risk factors and predictive models. This study aims to evaluate the characteristics and efficacy of existing HE prediction models, offering insights for performance enhancement. A comprehensive search was conducted in PubMed for observational studies and randomized controlled trials focusing on HE prediction, written in English. The prediction models were categorized based on their incorporated features and modeling methodology. Rigorous quality and bias assessments were performed. A meta-analysis of studies reporting C-statistics was executed to assess and compare the performance of current HE prediction models. Meta-regression was utilized to explore heterogeneity sources. From 358 initial records, 22 studies were deemed eligible, encompassing traditional models, hematoma imaging feature models, and models based on artificial intelligence (AI) or radiomics. Meta-analysis of 11 studies, involving 12,087 sICH patients, revealed an aggregated C-statistic of 0.74 (95% CI: 0.69 - 0.78) across seven HE prediction models. Eight characteristics related to development cohorts were identified as key factors contributing to performance variability among these models. The findings indicate that the current predictive capacity for HE risk remains suboptimal. Enhanced accuracy in HE prediction is vital for effectively targeting patient populations most likely to benefit from tailored treatment strategies.

Beneficial normalization of cardiac repolarization by carnitine in transgenic SQT1 rabbit models.

Short-QT-syndrome type 1 (SQT1) is a genetic channelopathy caused by gain-of-function variants in HERG underlying the rapid delayed-rectifier K+ current (IKr), leading to QT-shortening, ventricular arrhythmias, and sudden cardiac death. Data on efficient pharmaco-therapy for SQT1 are scarce. In patients with primary carnitine-deficiency, acquired-SQTS has been observed and rescued by carnitine-supplementation. Here, we assessed whether carnitine exerts direct beneficial (prolonging) effects on cardiac repolarization in genetic SQTS. Adult wild-type (WT) and transgenic SQT1 rabbits (HERG-N588K, gain of IKr) were used. In vivo ECGs, ex vivo monophasic action potentials (APs) in Langendorff-perfused hearts, and cellular ventricular APs and ion currents were assessed at baseline and during L-Carnitine/C16-Carnitine-perfusion. 2D computer simulations were performed to assess reentry-based VT-inducibility.L-Carnitine/C16-Carnitine prolonged QT intervals in WT and SQT1, leading to QT-normalization in SQT1. Similarly, monophasic and cellular AP duration (APD) was prolonged by L-Carnitine/C16-Carnitine in WT and SQT1. As underlying mechanisms, we identified acute effects on the main repolarizing ion currents: IKr-steady, which is pathologically increased in SQT1, was reduced by L-Carnitine/C16-Carnitine and deactivation kinetics were accelerated. Moreover, L-Carnitine/C16-Carnitine decreased IKs-steady and IK1. In silico modelling identified IKr-changes as main factor for L-Carnitine/C16-Carnitine-induced APD-prolongation. 2D-simulations revealed increased sustained reentry-based arrhythmia formation in SQT1 compared to WT, which was decreased to the WT-level when adding carnitine-induced ion current changes. L-Carnitine/C16-Carnitine prolong/normalize QT and whole heart/cellular APD in SQT1 rabbits. These beneficial effects are mediated by acute effects on IKr. L-Carnitine may serve as potential future QT-normalizing, anti-arrhythmic therapy in SQT1.

Evaluation of ride quality in a passenger car with non-uniform tyre radial stiffness

This study is dedicated to examining the influence of varying radial stiffness along the circumference of tyres on a road vehicle ride quality and with a specific focus on aspects relevant to maintenance. Computer simulations employing a three-dimensional road-vehicle model, developed within commercial multi-body software, were conducted to explore the multifaceted factors influencing vehicle behaviour. These factors encompass road unevenness, driving speed, and the range of radial force variation resulting from harmonic changes in tire radial stiffness. Furthermore, simulation tests were carried out using a model of a diagnostic wheel balancing machine, which holds significance in vehicle maintenance practices. Measurements obtained from a passenger car equipped with standard tires with minimal non-uniformity, served as reference values for assessing the car's vibration levels. Additionally, selected scenarios were examined, involving vehicle with multiple wheels exhibiting non-uniform radial stiffness. The findings underscore that non-uniform radial stiffness can magnify the detrimental effects of road irregularities, potentially leading to amplified vibration levels and a heightened need for maintenance considerations.

Stratigraphic architecture of fluvial fans shaped by downstream changes in avulsion style

ABSTRACTNatural river diversion, or avulsion, controls the distribution of channels on a floodplain and channel sandstone bodies within fluvial stratigraphic architecture. Avulsions establish new flow paths and create channels through several recognized processes, or styles. These include reoccupying existing channels, or annexation, downcutting into the floodplain, or incision, and constructing new channels from crevasse‐splay distributary networks, or progradation. Recent remote sensing observations show that avulsion style changes systematically moving downstream along modern fluvial fans but, to date, no studies have assessed the significance of these trends on fluvial fan stratigraphy. Here, spatiotemporal changes in avulsion stratigraphy are investigated within the Salt Wash Member of the Morrison Formation, deposited in the Cordilleran foreland basin during the Late Jurassic epoch. Measured sections and photographic panels were analysed from 23 locations across the Salt Wash extent. Avulsion style was identified in the stratigraphic record by the basal contact of a channel storey with underlying strata: channel–channel contacts indicate annexation, channel–floodplain contacts indicate incision and channel–heterolithic contacts indicate progradation. Contact types change downstream, such that channel–channel and channel–floodplain contacts dominate proximal locations, while channel–heterolithic contacts become increasingly prevalent downstream. Outcrop results were compared to a numerical model of fluvial fan formation and remote‐sensing analysis of avulsions on modern fans. In both additional datasets, channels in proximal fan positions tend to avulse via annexation, reoccupying abandoned channels, while channels in more distal positions tend to avulse via increasingly significant progradation. These findings suggest a relationship between newly recognized downstream changes in avulsion style and well‐established downstream changes in fluvial fan architecture. Furthermore, this suggests that fan architecture can inform interpretations of ancient fluvial dynamics, including avulsion behaviour, and that avulsions can cause stratigraphically significant and measurable changes to fan architecture.

Numerical simulation of endovascular treatment options for cerebral aneurysms

AbstractPredicting the long‐term success of endovascular interventions in the clinical management of cerebral aneurysms requires detailed insight into the patient‐specific physiological conditions. In this work, we not only propose numerical representations of endovascular medical devices such as coils, flow diverters or Woven EndoBridge but also outline numerical models for the prediction of blood flow patterns in the aneurysm cavity right after a surgical intervention. Detailed knowledge about the postsurgical state then lays the basis to assess the chances of a stable occlusion of the aneurysm required for a long‐term treatment success. To this end, we propose mathematical and mechanical models of endovascular medical devices made out of thin metal wires. These can then be used for fully resolved flow simulations of the postsurgical blood flow, which in this work will be performed by means of a Lattice Boltzmann method applied to the incompressible Navier–Stokes equations and patient‐specific geometries. To probe the suitability of homogenized models, we also investigate poro‐elastic models to represent such medical devices. In particular, we examine the validity of this modeling approach for flow diverter placement across the opening of the aneurysm cavity. For both approaches, physiologically meaningful boundary conditions are provided from reduced‐order models of the vascular system. The present study demonstrates our capabilities to predict the postsurgical state and lays a solid foundation to tackle the prediction of thrombus formation and, thus, the aneurysm occlusion in a next step.

Experimental investigations and material modeling of an elastomer jaw coupling

Abstract This work investigates the hyper-viscoelastic behavior of a thermoplastic polyurethane IROGRAN A 92 E 5670 FCM used in the gear rim of a jaw coupling. The aim is to provide the material parameters for the modeling of jaw couplings in engineering tasks. Uniaxial compression tests were conducted at various temperatures and loading speeds to fit a hyper-viscoelastic material model. The material model uses the Yeoh free energy and the power law model with one Maxwell element. The parametrized material model showed very good results in comparison to experimental data. In addition, the influence of friction on the experiments was modeled, investigated, and discussed. Further component tests for a jaw coupling of size 28 were conducted with two different specimen types. The original gear rim and a modified gear rim with central boreholes for integrated sensors were used in the test. The influence of such a modification was investigated with four different loading cases, focusing on the torsion-torque characteristic and the relative damping. These component tests enable the validation of numerical models of jaw couplings.

Study on material behaviours of additively manufactured high-impact polystyrene using artificial neural networks

AbstractFused Filament Fabrication (FFF), a process parameters-dependent manufacturing method, currently dominates the additive manufacturing (AM) sector because of its prominent ability to produce parts with intricate profiles, customise products, and minimise waste. Though the effects of FFF process parameters were investigated experimentally, recent research highlighted the importance of developing numerical modelling and computational methods on optimising the FFF printing process and FFF-printed materials. This study aims to investigate the tensile strength (TS) of FFF-printed high-impact polystyrene (HIPS) via devising a systematic testing and analysis framework, which combines experimental testing, representative volume element (RVE)-finite element method (FEM), rule of mixture (ROM), and artificial neural networks (ANN). HIPS samples are fabricated using FFF considering the variations of infill density, layer thickness, nozzle temperature, raster angle, and build orientation, and tested with standard tensile testing. The rule of mixtures (ROM) and its modified version (MROM) are employed to calculate the TS of longitudinally and transversely built samples at various infill densities, respectively, while an ANN model is constructed to investigate the effect of material anisotropy precisely. The optimal ANN architecture is built with five hidden layers with the number of neurons in each layer as 44, 82, 169, 362, and 50. Although both MROM and ANN perform well on the validation set, ANN exhibits superior accuracy with only a maximum error of 0.13% for training set and 11% for validation set. The combination of the RVE-FEM, MROM, and ANN approaches can significantly improve the FFF printing process of polymers for optimisation.

2D and 3D numerical modelling for preliminary assessment of long-term deterioration in Irish glacial till geotechnical slopes

ABSTRACT Long-term geotechnical slope deterioration, influenced by weathering and meteorological factors, presents stability challenges to infrastructure. Wetting and drying cycles lead to pore water pressure variations, causing deformations and slope failure. Studies on glacial tills which investigate deterioration in geotechnical slopes focus on variables like pore water pressure, soil water retention, compaction, freeze–thaw cycles and cracking. This research conducts a preliminary assessment of an Irish glacial till-cut slope, establishing a data-driven foundation for long-term slope behaviour studies. Data analysis, geospatial modelling and numerical simulations were performed on a cut slope in Castleblayney (Ireland), considering short-term/undrained and long-term/drained conditions. FLAC/Slope and Scoops3D were used for 2D and 3D slope stability analyses, applying the First-Order Second Moment (FOSM) probabilistic approach to assess how minor soil property changes affect slope stability, including long-term deterioration scenarios. The study underscores the importance of precise instrument placement within Irish glacial till geotechnical cut slopes, particularly at the uppermost part where shallow and deep failures coincide under long-term and short-term scenarios. This informs strategic instrument positioning for accurate slope deterioration investigations. This research lays the groundwork for understanding mechanisms driving geotechnical slope deterioration and provides insights for future studies on geotechnical asset deterioration models in Irish glacial tills.

Numerical investigations of GRS wall performance with tiered configurations

Geosynthetic Reinforced Soil (GRS) walls have become increasingly popular as a result of their numerous advantages. In some cases these structures are constructed in a multi-tiered configuration, which makes their behavior more complicated. Nevertheless, the response of the multi-tiered walls is insufficiently explained by the existing design manuals and literature studies. This paper investigated the performance of GRS walls with multi-tiered configurations using two-dimensional (2D) finite element numerical models. This study compares the performance of two-tiered GRS walls with simple GRS walls (single-tiered). It also examines the impact of offset distance, backfill strength properties, and reinforcement parameters (vertical spacing and reinforcement length) on horizontal deformations and reinforcement tensile loads in two-tiered GRS walls. Adopting a multi-tiered configuration for GRS walls can significantly reduce both the horizontal wall deformation and the maximum reinforcement tensile loads. Additionally, the critical offset distance found in this study is significantly smaller than that recommended by the Federal Highway Administration (FHWA) guidelines. The finite element results also demonstrate that using high-quality backfill soil can minimize interaction between lower and upper tiers. Using a uniform reinforcement length of 0.6H for both tiers significantly reduces horizontal deformation compared to the FHWA recommendation of 0.6H and 0.35H for the lower and the upper tier respectively.

Updating the Upper Cretaceous (Campanian) Two Medicine Formation of Montana: Lithostratigraphic revisions, new CA-ID-TIMS U-Pb ages, and a calibrated framework for dinosaur occurrences

The Campanian Two Medicine Formation of northwestern Montana, USA, is richly fossiliferous, and discoveries made within the unit over the past century have greatly advanced our appreciation of dinosaur paleobiology and evolution. Previously undifferentiated from a lithostratigraphic perspective, the formation is now subdivided into four new members that include (from base to top) (1) the Rock City Member, (2) the Shields Crossing Member, (3) the Hagans Crossing Member, and (4) the Flag Butte Member. These new formal units and their associated fossil occurrences are also now included in an age model founded on eight high-resolution chemical abrasion−isotope dilution−thermal ionization mass spectrometry (CA-ID-TIMS) U-Pb ages. New age data confirm that the Two Medicine Formation accumulated during much of the Campanian, with deposition spanning ca. 82.4 Ma to 74.4 Ma. New age data further indicate that a major reorganization of depositional systems, marked by a shift from predominantly lacustrine to alluvial facies and accompanied by a dramatic increase in accommodation, transpired near the base of the new Flag Butte Member at ca. 76.3 Ma. This change in depositional regime correlates in age with the Judith River−Belly River discontinuity, which marks the contact between the McClelland Ferry and Coal Ridge Members in the Judith River Formation and coincides with the onset of the Bearpaw transgression in north-central Montana. The new lithostratigraphic and chronostratigraphic framework for the Two Medicine Formation serves to contextualize and calibrate the formation’s rich dinosaur fossil record, which can now be interrogated with increased clarity and precision. These results also provide ground truth for numerical models that explore the structure of the fossil record in relation to alluvial architecture and terrestrial sequence stratigraphy.

Convergence, sampling and total order estimator effects on parameter orthogonality in global sensitivity analysis.

Dynamical system models typically involve numerous input parameters whose "effects" and orthogonality need to be quantified through sensitivity analysis, to identify inputs contributing the greatest uncertainty. Whilst prior art has compared total-order estimators' role in recovering "true" effects, assessing their ability to recover robust parameter orthogonality for use in identifiability metrics has not been investigated. In this paper, we perform: (i) an assessment using a different class of numerical models representing the cardiovascular system, (ii) a wider evaluation of sampling methodologies and their interactions with estimators, (iii) an investigation of the consequences of permuting estimators and sampling methodologies on input parameter orthogonality, (iv) a study of sample convergence through resampling, and (v) an assessment of whether positive outcomes are sustained when model input dimensionality increases. Our results indicate that Jansen or Janon estimators display efficient convergence with minimum uncertainty when coupled with Sobol and the lattice rule sampling methods, making them prime choices for calculating parameter orthogonality and influence. This study reveals that global sensitivity analysis is convergence driven. Unconverged indices are subject to error and therefore the true influence or orthogonality of the input parameters are not recovered. This investigation importantly clarifies the interactions of the estimator and the sampling methodology by reducing the associated ambiguities, defining novel practices for modelling in the life sciences.

Quantitative stakeholder-driven assessment of radiation protection issues via a PIANOFORTE online survey.

To enhance stakeholder engagement and foster the inclusion of interests of citizens in radiation protection research, a comprehensive online survey was developed within the framework of the European Partnership PIANOFORTE. This survey was performed in 2022 and presented an opportunity for a wide range of stakeholders to voice their opinions on research priorities in radiation protection for the foreseeable future. Simultaneously, it delved into pertinent issues surrounding general radiation protection. The PIANOFORTE e-survey was conducted in the English language, accommodating a diverse range of participants. Overall, 440 respondents provided their insights and feedback, representing a broad geographical reach encompassing 29 European countries, as well as Canada, China, Colombia, India, and the United States. To assess the outcomes, the Positive Matrix Factorization numerical model was applied, in addition to qualitative and quantitative assessment of individual responses, enabling the discernment of four distinct stakeholder groups with varying attitudes. While the questionnaire may not fully represent all stakeholders due to the limited respondent pool, it is noteworthy that approximately 70% of the participants were newcomers to comparable surveys, demonstrating a proactive attitude, a strong willingness to collaborate and the necessity to continuously engage with stakeholder groups. Among the individual respondents, distinct opinions emerged particularly regarding health effects of radiation exposure, medical use of radiation, radiation protection of workers and the public, as well as emergency and recovery preparedness and response. In cluster analysis, none of the identified groups had clear preferences concerning the prioritization of future radiation protection research topics.

Optimising synthetic datasets for machine learning-based prediction of building damage due to tunnelling

Assessment of tunnelling-induced building damage is a complex Soil-Structure Interaction (SSI) probelm, influenced by numerous geometric and material parameters of both the soil and structures, and is characterised by strong non-linear behaviour. Currently, there is a trend towards developing data-driven models using Machine Learning (ML) to capture this complex behaviour. Given the scarcity of real data, which typically comes from specific case studies, many researchers have turned to creating extensive synthetic datasets via sophisticated and validated numerical models like Finite Element Method (FEM). However, the development of these datasets and the training of advanced ML algorithms present significant challenges. poses significant challenges. Reliance solely on parameter domains and ranges derived from case studies can lead to imbalanced data distributions and subsequently poor performance of models in less populated regions. In this paper, we introduce a strategy for designing optimal high-confidence datasets through an iterative procedure. This process begins with a systematic literature review to determine the importance of parameters, their ranges, and dependencies as they pertain to building damage induced by SSI. Starting with several hundred FEM simulations, we generate an initial dataset and assess its quality and impact through Sensitivity Analysis (SA) studies, statistical modelling, and re-sampling in statistically significant regions. This evaluation allows us to refine the model’s input space, seeking scenarios that mitigate output distribution imbalances. The procedure is repeated until the datasets achieve a satisfactory balance for training metamodels, minimising bias effectively. Our findings highlight the success of this approach in identifying an optimal and feasible input space that significantly reduces imbalanced distributions of output features. This approach not only proves effective in our study but also offers a versatile methodology that could be adapted to other disciplines aiming to generate high-quality synthetic datasets.

Nonlinear response of a multidirectional negative‐stiffness isolation system via semirecursive multibody dynamic approach

AbstractThis paper investigates an innovative negative‐stiffness device (NSD) that modifies the apparent stiffness of the supported structure for seismic isolation. The NSD comprises a lower base on the bottom and a cap on the top, together with a connecting rod, vertical movable wall, and compressed elastic spring, as well as circumferentially arranged, pretensioned external ropes, and inclined shape memory wires. This configuration can deliver negative stiffness and energy dissipation in any direction within the horizontal plane. A numerical model of the device is developed through a two‐step semirecursive method to obtain the force–displacement characteristic relationship. Such a model is first validated through comparison with the results obtained via the commercial software ADAMS. Finally, a large parametric study is performed to assess the role and the influence of each design variable on the overall response of the proposed device. Useful guidelines are drawn from this analysis to guide the system design and optimization.

Isotopomer labeling and oxygen dependence of hybrid nitrous oxide production

Abstract. Nitrous oxide (N2O) is a potent greenhouse gas and ozone depletion agent, with a significant natural source from marine oxygen-deficient zones (ODZs). Open questions remain, however, about the microbial processes responsible for this N2O production, especially hybrid N2O production when ammonia-oxidizing archaea are present. Using 15N-labeled tracer incubations, we measured the rates of N2O production from ammonium (NH4+), nitrite (NO2-), and nitrate (NO3-) in the eastern tropical North Pacific ODZ and the isotopic labeling of the central (α) and terminal (β) nitrogen (N) atoms of the N2O molecule. We observed production of both doubly and singly labeled N2O from each tracer, with the highest rates of labeled N2O production at the same depths as the near-surface N2O concentration maximum. At most stations and depths, the production of 45N2Oα and 45N2Oβ were statistically indistinguishable, but at a few depths there were significant differences in the labeling of the two nitrogen atoms in the N2O molecule. Implementing the rates of labeled N2O production in a time-dependent numerical model, we found that N2O production from NO3- dominated at most stations and depths, with rates as high as 1600 ± 200 pM N2O d−1. Hybrid N2O production, one of the mechanisms by which ammonia-oxidizing archaea produce N2O, had rates as high as 230 ± 80 pM N2O d−1 that peaked in both the near-surface and deep N2O concentration maxima. Based on the equal production of 45N2Oα and 45N2Oβ in the majority of our experiments, we infer that hybrid N2O production likely has a consistent site preference, despite drawing from two distinct substrate pools. We also found that the rates and yields of hybrid N2O production were enhanced at low dissolved oxygen concentrations ([O2]), with hybrid N2O yields as high as 20 % at depths where [O2] was below detection (880 nM) but nitrification was still active. Finally, we identified a few incubations with [O2] up to 20 µM where N2O production from NO3- was still active. A relatively high O2 tolerance for N2O production via denitrification has implications for the feedbacks between marine deoxygenation and greenhouse gas cycling.

Verification and validation of a coupled CFD–FEM approach with overset structured grid through free decay and regular wave tests

This paper presents a comprehensive verification and validation study applied to the hydrodynamic analysis of a semi-submersible Floating Offshore Wind Turbine (FOWT) platform. The numerical simulation is conducted using a coupled Computational Fluid Dynamics-Finite Element Method (CFD–FEM) approach. The CFD program incorporates an overset grid interpolation method, facilitating a more systematic grid refinement in the estimation of discretization uncertainties. A mooring analysis program, based on the FEM scheme, is integrated with the CFD program to simulate the dynamic responses of mooring system. The necessity of the nonlinear mooring model is demonstrated by comparing solutions with a comparative test using a linear mooring model. The verification study, based on a least-squares-based extrapolation method, quantifies the spatial and temporal discretization errors of the numerical model. The numerical solutions based on the baseline model are further validated against the model test measurements. A pitch free decay model test and a regular wave model test conducted within the OC5 project are utilized for the verification and validation study. Results from both tests demonstrate the reliability and accuracy of the coupled method.

Analysis of current and heat transfer in location with reduced critical current in coated conductor tape

Abstract Variation of critical current along the conductor length is the feature commonly encountered in industrially produced REBCO tapes called coated conductors (CC). Reduction of critical current exceeding several percent in the portions few millimetres long can be observed in the data obtained by reel-to-reel characterisation provided by manufacturers. Metallic layers in the CC architecture can take over some current in such „weak spot”, and help to keep the local temperature stable, preventing its thermal runaway and conversion to a “hot spot”. Understanding the phenomenon is particularly crucial when the space and weight limitations do not allow to add metallic layer with the thickness, that would be sufficient to match the lost transport capability of superconductor. For this purpose, we studied a set of samples, representing both standard as well as infrequent profiles of weak spots identified in direct transport experiments. Analytical theory was then utilised as basic tool for recovering the properties serving as input for numerical modelling. Temperature profiles and current redistribution in the weak spot locations were found, and the effect of cooling conditions and metallic layer thickness on the weak spot resistance against thermal runaway analysed. Quantitative assessment of the possibility to improve the performance of CC tape by adding Cu stabilising layer or improving cooling settings could help to optimise the architecture of coated conductor intended for use in electric transport.