Global Structural Analysis Research Articles

Global buckling response of sandwich panels with additively manufactured lattice cores

This paper deals with the analytical and numerical global buckling analysis of rectangular sandwich plates utilized AlSi10 Mg in both facesheets and lattice cores. In this study, six different strut-based lattice core models are designed. Additionally, the buckling resistance of lattice panels is compared with a typical honeycomb panel. Analytical studies were carried out using Kirchoff plate theory (CLPT), first-order shear deformation theory (FSDT) coded on MATLAB and finite element (FE) analyses in Abaqus. In the theoretical approaches, the Navier solution is derived for sandwich plates with simply supported boundary conditions at all edges. In the FE analyses, validated homogenized lattice structures models were used to avoid excessive numbers of elements and to save computational time. The parametric effects of side-to-thickness ratio, and different designed core cells on global buckling responses are investigated. As a result of comparing the analytical results with the FE model, a good agreement is obtained, and it is found that analytical buckling analyses FSDT can be used within certain size limits for the global buckling analysis of lattice core sandwich structures.

Time-domain fatigue reliability analysis for floating offshore wind turbine substructures using coupled nonlinear aero-hydro-servo-elastic simulations

The present study aims to develop a novel methodology for the fatigue reliability analysis and design for a floating offshore wind turbine substructure using its fully coupled nonlinear dynamic responses in the time domain. The developed methodology offers a computationally efficient yet robust assessment for designing fatigue-critical welded joints on floating substructures. The methodology starts with the sea state selection based on the fatigue damage contribution of all the sea states in the scatter diagram. To this end, the dynamic response is analysed by fully coupled aero-hydro-servo-elastic simulations using OpenFAST. The resulting short-term fatigue loading is then used to estimate the hotspot stress history using the analytical solution for global structural analysis and the empirical solution for the stress concentration factor. The long-term fatigue damage is calculated as the joint probability-weighted sum of the short-term fatigue damage using Palmgren-Miner's linear damage rule. Afterwards, the selected sea states are used to perform dynamic response simulations with multiple random seeds, ensuring no significant accuracy loss. To obtain the probabilistic fatigue life prediction, a novel time-domain fatigue reliability analysis algorithm is developed based on the bootstrapping method, which creates a pool of short-term fatigue responses with different random seeds and combines them within a Monte Carlo Simulation. Finally, the fatigue reliability analysis considering the S-N and damage-tolerant approaches for design is carried out.

Comprehensive accuracy analysis for large closed-loop deployable structures based on matrix structure analysis and linear-complementarity-based contact analysis of spatial joint clearances

The surface accuracy of the deployable structure is crucial in determining the electromagnetic performance of the large satellite antenna. This paper proposes a comprehensive accuracy analysis framework for deployable structures considering clearances of spatial joints, geometric deviations, elastic deformations, external loads, and preloads, all of which can affect surface accuracy during the assembly process. Surface accuracy is calculated by combining the global equilibrium analysis of the deployable structure with the local equilibrium analysis of clearance-affected joints. First, a global elastostatic equilibrium analysis of the deployable structure is performed based on a unified mathematical formulation that describes the elasticity of beams and joints. Then, the local equilibrium analysis for the clearance-affected joints is transformed into a quadratic optimization problem through a linear-complementarity-based method. This method avoids the necessity for a combinatorial search for several traditional discontinuous contact configurations. Considering both global and local equilibrium in iterative analysis, the surface accuracy of the antenna is calculated. This integration avoids prior artificial assumptions about contact configurations of the joints. Finally, the proposed method is validated by comparing it with simulations and experimental results.

Influence of empirically derived filtering parameters, amplicon sequence variant, and operational taxonomic unit pipelines on assessing rumen microbial diversity

Microbes play an important role in human and animal health, as well as animal productivity. The host microbial interactions within ruminants play a critical role in animal health and productivity and provide up to 70% of the animal's energy needs in the form of fermentation products. As such, many studies have investigated microbial community composition to understand the microbial community changes and factors that affect microbial colonization and persistence. Advances in next-generation sequencing technologies and the low cost of sequencing have led many studies to use 16S rDNA-based analysis tools for interrogation of microbiomes at a much finer scale than traditional culturing. However, methods that rely on single base pair differences for bacterial taxa clustering may inflate or underestimate diversity, leading to inaccurate identification of bacterial diversity. Therefore, in this study, we sequenced mock communities of known membership and abundance to establish filtration parameters to reduce the inflation of microbial diversity due to PCR and sequencing errors. Additionally, we evaluated the effect of the resulting filtering parameters proposed using established bioinformatic pipelines on a study consisting of Holstein and Jersey cattle to identify breed and treatment effects on the bacterial community composition and the impact of filtering on global microbial community structure analysis and results. Filtration resulted in a sharp reduction in bacterial taxa identified, yet retain most sequencing data (retaining >79% of sequencing reads) when analyzed using 3 different microbial analysis pipelines (DADA2, Mothur, USEARCH). After filtration, conclusions from α-diversity and β-diversity tests showed very similar results across all analysis methods. The mock community-based filtering parameters proposed in this study help provide a more realistic estimation of bacterial diversity. Additionally, filtration reduced the variation between microbiome analysis methods and helped to identify microbial community differences that could have been missed due to the large animal-to-animal variation observed in the unfiltered data. As such, we believe the new filtering parameters described in this study will help to obtain diversity estimates that are closer to realistic values, improve the ability to detecting microbial community differences, and help to better understand microbial community changes in 16S rDNA-based studies.

Rail Transit Networks and Network Motifs: A Review and Research Agenda

The railway plays an essential role in urban and intercity transport of goods and people. Intercity and urban rail transit infrastructures contribute to the economic and environmental sustainability of global economies. Those infrastructures can be modeled as complex networks, so that we can evaluate system properties of the network structure. This stream of research has focused on the topological analysis of global network structure, but little research exists that examines how local network structures affect system properties. The local structure of complex networks can be examined with network motif analysis, as those network motifs are the building blocks of networked systems. Nevertheless, there has been scarce attention given to local network properties in rail transit networks. We contribute to covering this gap in the literature with a literature review of motif analysis research and its application to weighted and unweighted rail transit networks, also covering the current state-of-the-art of network motif decomposition and analysis. We demonstrate that network motif analysis is not only applicable, but also beneficial for the design and planning of rail transit networks, enhancing their sustainability by improving efficiency, reducing environmental impact, and optimizing resource allocation. Based on our findings, we propose future research directions that involve applying motif analysis to enhance the sustainability features of both unweighted and weighted rail transit networks.

Continental Residual Topography Extracted From Global Analysis of Crustal Structure

AbstractContinental topography is dominantly controlled by a combination of crustal thickness and density variations. Nevertheless, it is clear that some additional topographic component is supported by the buoyancy structure of the underlying lithospheric and convecting mantle. Isolating these secondary sources is not straightforward, but provides valuable information about mantle dynamics. Here, we estimate and correct for the component of topographic elevation that is crustally supported to obtain residual topographic anomalies for the major continents, excluding Antarctica. Crustal thickness variations are identified by assembling a global inventory of 26,725 continental crustal thickness estimates from local seismological data sets (e.g., wide‐angle/refraction surveys, calibrated reflection profiles, receiver functions). In order to convert crustal seismic velocity into density, we develop a parametrization that is based upon a database of 1,136 laboratory measurements of seismic velocity as a function of density and pressure. In this way, 4,120 new measurements of continental residual topography are obtained. Observed residual topography mostly varies between ±1 and 2 km on wavelengths of 1,000–5,000 km. Our results are generally consistent with the pattern of residual depth anomalies observed throughout the oceanic realm, with long‐wavelength free‐air gravity anomalies, and with the distribution of upper mantle seismic velocity anomalies. They are also corroborated by spot measurements of emergent marine strata and by the global distribution of intraplate magmatism that is younger than 10 Ma. We infer that a significant component of residual topography is generated and maintained by a combination of lithospheric thickness variation and sub‐plate mantle convection. Lithospheric composition could play an important secondary role, especially within cratonic regions.

Comparative interactome analysis of α-arrestin families in human and Drosophila

The α-arrestins form a large family of evolutionally conserved modulators that control diverse signaling pathways, including both G-protein-coupled receptor (GPCR)-mediated and non-GPCR-mediated pathways, across eukaryotes. However, unlike β-arrestins, only a few α-arrestin targets and functions have been characterized. Here, using affinity purification and mass spectrometry, we constructed interactomes for 6 human and 12 Drosophila α-arrestins. The resulting high-confidence interactomes comprised 307 and 467 prey proteins in human and Drosophila, respectively. A comparative analysis of these interactomes predicted not only conserved binding partners, such as motor proteins, proteases, ubiquitin ligases, RNA splicing factors, and GTPase-activating proteins, but also those specific to mammals, such as histone modifiers and the subunits of V-type ATPase. Given the manifestation of the interaction between the human α-arrestin, TXNIP, and the histone-modifying enzymes, including HDAC2, we undertook a global analysis of transcription signals and chromatin structures that were affected by TXNIP knockdown. We found that TXNIP activated targets by blocking HDAC2 recruitment to targets, a result that was validated by chromatin immunoprecipitation assays. Additionally, the interactome for an uncharacterized human α-arrestin ARRDC5 uncovered multiple components in the V-type ATPase, which plays a key role in bone resorption by osteoclasts. Our study presents conserved and species-specific protein–protein interaction maps for α-arrestins, which provide a valuable resource for interrogating their cellular functions for both basic and clinical research.

Comparative interactome analysis of α-arrestin families in human and Drosophila.

The α-arrestins form a large family of evolutionally conserved modulators that control diverse signaling pathways, including both G-protein-coupled receptor (GPCR)-mediated and non-GPCR-mediated pathways, across eukaryotes. However, unlike β-arrestins, only a few α-arrestin targets and functions have been characterized. Here, using affinity purification and mass spectrometry, we constructed interactomes for 6 human and 12 Drosophila α-arrestins. The resulting high-confidence interactomes comprised 307 and 467 prey proteins in human and Drosophila, respectively. A comparative analysis of these interactomes predicted not only conserved binding partners, such as motor proteins, proteases, ubiquitin ligases, RNA splicing factors, and GTPase-activating proteins, but also those specific to mammals, such as histone modifiers and the subunits of V-type ATPase. Given the manifestation of the interaction between the human α-arrestin, TXNIP, and the histone-modifying enzymes, including HDAC2, we undertook a global analysis of transcription signals and chromatin structures that were affected by TXNIP knockdown. We found that TXNIP activated targets by blocking HDAC2 recruitment to targets, a result that was validated by chromatin immunoprecipitation assays. Additionally, the interactome for an uncharacterized human α-arrestin ARRDC5 uncovered multiple components in the V-type ATPase, which plays a key role in bone resorption by osteoclasts. Our study presents conserved and species-specific protein-protein interaction maps for α-arrestins, which provide a valuable resource for interrogating their cellular functions for both basic and clinical research.

A surrogate model for uncertainty quantification and global sensitivity analysis of nonlinear large-scale dome structures

A surrogate model for uncertainty quantification and global sensitivity analysis of nonlinear large-scale dome structures

A Bayesian multi-model inference methodology for imprecise moment-independent global sensitivity analysis of rock structures

Traditional global sensitivity analysis (GSA) neglects the epistemic uncertainties associated with the probabilistic characteristics (i.e. type of distribution type and its parameters) of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response. This paper proposes an augmented Bayesian multi-model inference (BMMI) coupled with GSA methodology (BMMI-GSA) to address this issue by estimating the imprecision in the moment-independent sensitivity indices of rock structures arising from the small size of input data. The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties. The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo's indices by employing a reweighting approach on candidate probabilistic models. The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India. The proposed methodology was superior to the conventional GSA (neglects all epistemic uncertainties) and Bayesian coupled GSA (B-GSA) (neglects model uncertainty) due to its capability to incorporate the uncertainties in both model type and parameters of properties. Imprecise Borgonovo's indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates, which makes the user more informed in the data collection efforts. Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes. The accurate importance ranking of properties was only possible via samples of large sizes. Further, the impact of the prior knowledge in terms of prior ranges and distributions was significant; hence, any related assumption should be made carefully.

Shear Force and Bending Moment Tuning Algorithm of Shuttle Tanker Model for Global Structural Analysis

Global ship analysis is conducted using a finite element model (FE model) for ship design and construction, which involves structural, motion, and vibration analyses. It is crucial to examine the structural safety of the hull and motion response. In the ship FE model used in global ship analysis, weight distribution is employed to adjust the light weight and center of gravity (COG), which are required to perform the analysis. Further, the FE model needs to satisfy the required longitudinal shear force (SF) and bending moment (BM) under the loading conditions of the ship. Moreover, the SF and BM in the ship Trim and Stability data are utilized to perform shear force tuning (SFT) and bending moment tuning (BMT) for the ship FE model. This ensures the ship model exhibits curves of the SF and BM that coincide with those of the ship. The SFT and BMT for the ship FE model are time-consuming and costly. Thus, to address these limitations, we propose an effective and accurate algorithm and program for SFT and BMT. Accordingly, we developed a C#-based algorithm to tune the weight, SF, BM, and COG of the ship FE model to the required target value. Finally, the accuracy of the newly developed algorithm was analyzed and compared by applying it to the shuttle tanker FE model under the ballast and full load conditions. Accuracy was within tolerance in both loading conditions. The average errors of SF and BM were smaller in the ballast condition than in the full load condition, and the errors were smaller at the bow than at the stern.

Global optimization and structural analysis of Coulomb and logarithmic potentials on the unit sphere using a population-based heuristic approach

Global optimization of high-dimensional non-convex functions arises in many important applications and researches. In this paper, we focus on the minimum energy configuration problem on the unit sphere, whose goal is to determine the minimum-energy configuration of N particles interacting via the standard Coulomb potential or the discrete logarithmic potential on the unit sphere. This is a classic global optimization problem with many important applications in mathematics, physics, biology and chemistry, and is one of the 18 great mathematical problems for the twenty-first century listed by the Fields Medal winner (Smale, 1998). To determine the minimum-energy configuration for these two potential functions, we propose a population-based global optimization algorithm that combines a two-phase local optimization method with early-stopping strategy, a random perturbation operator, a distance-based population update strategy using a novel distance function to measure the difference between solutions, and a population rebuilding method. Computational results show that the proposed algorithm is very competitive compared to the best known results in the literature. In particular, it improves the best-known result for one instance of the classic Thomson problem (i.e., N=360) widely studied in the literature. The configurations of instances with up to N=500 particles are systematically optimized for the first time by the proposed algorithm for the discrete logarithmic potential. The comparative study shows that the putative optimal configurations have a high similarity for the Coulomb and logarithmic potentials and that the defects play an important role in reducing the energy of configuration. The energies of particles on the putative optimal configurations are analyzed for the studied logarithmic potential.

Global shape of SvGT, a metal-dependent bacteriocin modifying S/O-HexNActransferase from actinobacteria: c -terminal dimerization modulates the function of this GT

Here, we describe hitherto unknown shape-function of S/O-HexNActransferase SvGT (ORF AQF52_3101) instrumental in glycosylation of bacteriocin SvC (ORF AQF52_3099) in Streptomyces venezuelae ATCC 15439. Data from gel filtration, mass spectrometry, analytical ultracentrifugation, and Small Angle X-ray Scattering (SAXS), experiments confirmed elongated dimeric shape in solution for SvGT protein. Enzyme assays confirmed the dependence of SvGT on the availability of Mg2+ ions to be functionally activated. SAXS data analysis provided that apo and Mg2+-activated protein adopt a shape characterized by a radius of gyration and maximum linear dimension of 5.2 and 17.0 nm, and 5.3 and 17.8 nm, respectively. Alphafold2 server was used to model the monomeric chain of this protein which was docked on self to obtain different poses of the dimeric entity. Experimental SAXS data was used to select and refine the structure of SvGT dimer. Results showed that Mg2+ ions induce reorientation of the GT domain of one chain leading to a dimer with C2 symmetry, and the C-terminal portion entangles with each other in all states. Mutation-rendered alteration in activity profiles confirmed the role of conserved residues around catalytic motif. Global structure analysis puts forth the need to understand the role of constitutionally diverse C-terminal portion in regulating substrate selectivity. Communicated by Ramaswamy H. Sarma

Uncertainties in the structural design of the high‐bay cold storage warehouses

AbstractThe paper presents the structural analysis and critical evaluation of the modelling process of a high‐bay cold storage warehouse structure, which is a clad rack structure, composed of cold‐formed steel members, with different profile shapes and thicknesses. A major issue of the global structural analyses of these types of steel structures is the mixed application of design rules since the design is governed by racking structural rules, seismic design rules and cold‐formed steel design rules. To manage this problem, the structure's spatial model was divided into characteristic partial models, and spring supports were used to make the behavior of the partial models more realistic. The influence of the gravity loads (self‐weight, show), horizontal loads (wind and seismic action), geometrical imperfections, and loads induced by the temperature variation on the structure was investigated in the structural design. Even the wind actions resulted as dominant, some uncertainties related to the seismic analysis of these structures are presented in the paper.

Research on stress curve clustering algorithm of Fiber Bragg grating sensor

The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors.

Methodology for global structural load effect analysis of the semi-submersible hull of floating wind turbines under still water, wind, and wave loads

In designing the support structures of floating wind turbines (FWTs), a key challenge is to determine the load effects (at the cross-sectional load and stress level). This is because FWTs are subjected to complex global, local, static, and dynamic loads in stochastic environmental conditions. Up to now, most of the studies of FWTs have focused on the dynamic motion characteristics of FWTs, while minimal research has touched upon the internal load effects of the support structure. However, a good understanding of the structural load effects is essential since it is the basis for achieving a good design. Motivated by the situation, this study deals with the global load effect analysis for FWT support structures. A semi-submersible hull of a 10-MW FWT is used in the case study. A novel analysis method is employed to obtain the time-domain internal load effects of the floater, which account for the static and dynamic global loads under the still water, wind, and wave loads and associated motions. The investigation of the internal stresses resulting from various global loads under operational and parked conditions and the dynamic behavior of the structural load effects in various environmental conditions are made. The dominating load components for structural responses of the semi-submersible floater and the significant dynamic characteristics under different wind and wave conditions are identified. The dynamic load effects of the floating support structure are investigated by considering the influence of the second-order wave loads, viscous drag loads induced global motions, and wind and wave misalignments. The main results are discussed, and the main findings are summarized. The insights gained provide a basis for improving the design and analysis of FWT support structures.

Study of the thermo-mechanical performances of the EU-DEMO Water-Cooled Lead Lithium Left Outboard Blanket segment

The development of a sound conceptual design for the Water-Cooled Lead Lithium Breeding Blanket (WCLL BB) is pivotal to make a breakthrough towards the selection of the driver blanket concept for the EU-DEMO. To this goal, a research campaign has been launched over the last years at the University of Palermo, in close cooperation with ENEA Brasimone, under the umbrella of EUROfusion. In this frame, the analysis of the thermo-mechanical behaviour of the WCLL Left Outboard Blanket (LOB) segment is being performed. In a first phase, the assessment of the segment's overall structural performances was addressed, allowing the investigation of its global response under the selected loading scenarios. On this basis, the local structural analysis of the central region and of the upper and lower regions presenting geometric discontinuities (namely those regions where the stiffeners numbers changes) is presented in this paper, with the aim of assessing in detail their structural behaviour under the nominal BB operating conditions as well as steady-state accidental loading scenarios. Adopting the sub-modelling technique, the displacement field calculated in previous LOB global structural analysis can be mapped and applied at the boundaries of each local model. Moreover, it is possible to include there some structural details missing in the global analysis, like the Segment Box cooling channels. In this way, it is possible to study the thermo-mechanical behaviour of these regions in detail, assuming at the borders the mechanical action of the rest of the structure. The assessment has been performed in compliance with the RCC-MRx code, adopting the set of criteria on the basis of the nature of the considered loading scenario. The obtained results showed a promising structural behaviour of the segment and highlighted the necessity to revise the attachment system layout, which originates excessive deformation leading to the prediction of high stress.

An approach of calculating winding pack smeared properties for TF magnets using artificial neural network

Tokamak superconducting TF coils experience severe structural and thermal loads during operation. Hence optimizing the design of the conductor and the coil is crucial, making it inevitable to repeatedly try different conductor and WP configurations, and then perform structural analyses for the magnet. However, due to the fact that the magnet is huge in size, meanwhile the WP constituents are heterogeneous, complex and small in dimension, it is generally unrealistic to perform a global 3D structural analysis on a magnet model with detailed WPs. Smeared WPs with equivalent homogeneous properties are therefore commonly used to build the finite-element model. Usually, the smeared properties of WP are calculated by finite-element analyses, which are time costly and cumbersome. Here the ANN approach is proposed to calculate the WP smeared properties. First, the conductor and WP configurations are summarized, and a universal representative WP model is developed. Then, 4322 training cases are calculated by finite-element analyses with ANSYS. Next, the ANN model is built and the training cases are fed into it, after 298 iterations the ANN achieves convergence with excellent performance. Finally, the effectiveness of the trained ANN is demonstrated. Our results indicate that the ANN approach of calculating the WP smeared properties is reliable and can reduce the calculation time by 5 order of magnitude than the finite-element analysis.

An Efficient Reduced-Order Multi-Scale Simulation Approach for Nonlinear Analysis of Frame Structures with Nonlinear Localization

ABSTRACT This paper presents a reduced-order multi-scale simulation approach for nonlinear analysis of structures with local nonlinearities. Using it, the traditional global structural analysis can be changed to that of a reduced-order system where only nodal displacements of nonlinear elements are solved using the Newton–Raphson method in a single iterative procedure. Meanwhile, two nonlinear force/stiffness correctors are introduced based on the stress decomposition to describe material nonlinearities of frame members. In addition, two simulation ways are used to deal with the nonlinearities of the yielding frame members. Finally, the static and transient structural systems are simulated to verify the approach.

Improved dynamic kernelPCAbased on local preserving projections and its application for electric submersible pump fault diagnosis

AbstractDynamic kernel principal component analysis (DKPCA) has been frequently implemented for nonlinear and dynamic process monitoring of complex industrial processes. However, traditional DKPCA focuses only on the global structural analysis of data sets and strongly neglects the local information, which is equally essential for process detection and identification. In this paper, an improved DKPCA, referred to as the local DKPCA (LDKPCA), is proposed based on local preserving projections (LPP) for nonlinear dynamic process fault diagnosis. The method combines the advantages of LPP and DKPCA by utilizing the local structure feature to maintain the geometric structure of the data in a unified framework. To achieve a highly comprehensive feature extraction, the local characteristics are fused in DKPCA to produce an optimization objective. The neighbouring points of the new objective function projection in the feature space are still maintained in proximity, and the variance information is retained simultaneously. For the purpose of fault detection, two statistics, known as theT2and squared prediction error (SPE) statistics, are constructed, based on the LDKPCA model, and used to monitor the latent variable space and the residual space, respectively. In addition, the sensitivity analysis is brought in for fault identification of the two statistics. Based on the experimental analysis using the shaft breakage data of an offshore oilfield electric submersible pump (ESP), the proposed method outperforms the conventional DKPCA in terms of fault monitoring performance. The experimental results demonstrate the potential of the method in nonlinear dynamic process fault diagnosis.