Load State Research Articles

Approaches and Challenges in FEED and Detailed Design Process of Floating Substructures

Abstract In the FEED or detailed design phases, the floating foundation designers have to deal with complex structural design aspects and meet certification or class requirements for structural verifications, such as yield and buckling checks as well as fatigue checks for steel structures. The designer faces several challenges during the structural verification process, starting with the accurate definition of the complex external load state with the simultaneous action of aerodynamic, hydrostatic and hydrodynamic loads, inertia and mooring loads, followed by the processing and handling of very large load case tables required for the analysis of floating wind turbines, down to time efficient post-processing of the analysis results compatible with typical commercial project schedules. The paper discusses some of the currently existing and applied methodologies on how the structural checks are typically performed in these circumstances, and addresses their advantages and shortcomings. In addition, a new Global Influence Superposition (GIS) methodology that has been developed by Ramboll is presented, which is able to address many of the existing challenges in an efficient manner.

Application of SWAT to assess peatland forestry impacts on water quality

ABSTRACT Peatland drainage can affect the natural state of hydrological conditions and nutrient loading but is rarely included in catchment-scale models. To understand the gap, we aimed to use the soil and water assessment tool (SWAT) model to observe drained peatlands and their properties to predict nutrients and suspended solids (SS) in the peatland-dominated Simojoki catchment. We integrated drainage networks in SWAT to (i) identify the parameters for the drainage; (ii) determine annual loads and mean concentrations of SS, organic phosphorus (Org-P), total phosphorus (TP), organic nitrogen (Org-N), and total nitrogen (TN); (iii) understand spatial variation of nutrients and SS; and (iv) investigate the uncertainty ranges for the estimates. The calibrated SWAT model showed a 9.6% PBIAS between the simulated flow and the observations with low to medium loading variations for the water quality parameters. For Org-N and TN, the highest loading per year was at the downstream outlet, whereas for SS, Org-P, and TP, it was higher at the upstream outlet of the catchment. This approach of representing the drained peatland in SWAT indicated a maximum spatial distributed load in the peat soil in the clear-cut area and it can be beneficial in future hydrological modelling efforts in identifying the status of nutrients or SS.

Low and intermediate temperature fracture performance of hot mix asphalt (HMA) reinforced with nano-reduced graphene oxide (NRGO) under freeze-thaw cycles and aging conditions

ABSTRACT This study evaluates the effect of nano-reduced graphene oxide (NRGO) and conditions of short- and long-term (three freeze-thaw cycles (3 FTC)) failure and six days of ageing (6 AM) on bituminous mixtures from the viewpoint of fracture toughness (KIIC and KIIIC) and fracture energy (FEII and FEIII) at ±15 °C. The results showed the fracture stiffness was improved at ±15 °C (under three conditioning states). However, the fracture flexibility was decreased and increased at low and medium temperatures, respectively. At low temperatures, the results indicated that FEIII and KIIC had the lowest resistance under unconditioned and AM conditions, while FEII and KIIC had the lowest resistance under FTC. At intermediate temperatures, FEIII and KIIC under non-conditioned and FTC conditions and FEII and KIIC under AM conditions obtained the lowest resistance. Therefore, it was recommended to check both loading states in determining the shear performance (in-plane and out-of-plane) of asphalt mixtures. Finally, by providing the minimum short- and long-term properties required to control shear and tear cracks, reinforced mixtures lead to increased service life, reduced maintenance costs and improved road safety in cold and tropical regions.

Mechanical‐performance deterioration of RC segment of shield tunnel under high corrosion degree and the transformation of eccentric‐failure mode

AbstractReinforcement corrosion in shield tunnel segments has an important impact on the structure's bearing capacity and failure mode. Generally, a shield tunnel structure is under small eccentric compression, but if the reinforcement corrosion develops sufficiently, then a transition occurs from small to large eccentric load state, thereby lowering the structural bearing capacity and endangering the safety of tunnel operation. In the study reported here, based on the compressive bending load characteristics of the shield lining structure, corrosion test columns with small eccentric loading were designed to simulate the actual force state of the tunnel lining, and the whole evolution process of their mid‐span strain, ultimate bearing capacity, and cracks under different corrosion degrees was analyzed. With increasing corrosion, the bearing capacity, concrete strain, and depth of the compressive zone of a test column decrease more and more rapidly, and at the maximum corrosion degree of 68.32%, the maximum reduction of bearing capacity is 53.23%. The tests show that the failure mode of a test column with high corrosion degree (≥46.25%) is transformed from small to large eccentric failure. Based on theoretical analysis of the normal section bearing capacity of reinforced concrete flexural members, a theoretical method is proposed for calculating the critical corrosion degree for the tunnel lining structure to transform from small to large eccentric failure. The theoretical calculation method is verified against finite‐element calculations, and the present research results offer theoretical support for the durability design and safety evaluation of shield tunnel lining structures.

Effect of length-to-height ratio on fracture properties of asymmetrical single-edge notched beam (ASENB) specimen made of ceramic under full range mixed mode I/II loading state

Asymmetrical single-edge notched beam (ASENB) specimen is among the suitable specimens for measuring full mode I/II fracture parameters. However, in lack of a standard or common method, researchers used this specimen with different geometers, which have proven can affect the results. This research evaluated the effect of ASENB’s geometry on fracture parameters numerically and experimentally. First, the finite element method determined the geometry factors (YI and YII) and non-singular (T-stress) fracture parameters. Then, the experimental fracture tests were conducted using ceramic material. Results show it is more reasonable to express the YI and YII based on S1/L and S2/H, instant S1/L, and S2/L. In other words, the geometry factors can be expressed better based on the height of the specimen and not length. So, for ASENB specimens tested in conventional S1/L of 0.7 to 0.9, the pure mode-II condition was generated in 0.05 < S2/H < 0.14. The modeling showed that the non-singular term of fracture (T-stress) was significant compared to fracture toughness, so the Biaxiality was measured as 0.5 to −2.5, more significant for pure mode-II and almost regardless of the a/H ratio. As experimental tests show, the relative length of the ASENB specimen has an insignificant effect on measured fracture toughness, so a more compact specimen with L/H of about 2 to 4 can suggested for tests.

Cavitation damage in rubber-like silicone adhesives

In this work, the failure of rubber-like materials by cavitation is investigated. Under local triaxial loading states, as they can occur in confined geometries of adhesive bonds, the instantaneous formation of voids can be observed. Analytical results of cavitation in a commercial silicone adhesive are provided. The results show that cavitation voids can be distinguished from manufacturing voids by the inner surface of the void. The surface of the cavitation void is rugged and shows a complex fracture pattern. Pancake experiments have been conducted for different ratios of radius and thickness. To assess the fracture process of cavitation onset one macroscopic and two asymptotic solutions of the energy release rate of cavitation voids are proposed. The solutions are compared and their limitations for the analysis of cavitation onset are discussed.

Extrapolation Framework and Characteristic Analysis of Load Spectrum for Agriculture General Power Machinery

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements.

Crack Growth Patterns of Aluminum Tubular Specimens Subjected to Cyclic Tensile Loads

This study presents a detailed analysis of a fatigue test campaign in order to identify different crack patterns. It was conducted on 6061-T6 aluminum tubular specimens featuring an internal diameter of 10 mm and different thicknesses (2, 3 and 4 mm). These specimens were subjected to cyclic tensile loads with a load ratio of R = 0.1, utilizing a sinusoidal load function at a frequency of 3 Hz. The investigation examines the crack growth rates, the stress intensity factor, and the final and intermediate fracture zones by applying overloads in some cases. The differences with two-dimensional specimens and the importance of this study for the interpretation of results with biaxial loading states are highlighted. The different states of crack growth detected are analyzed using artificial vision techniques. The differences between the exterior and interior faces of the specimen are revealed, and a series of states prior to the formation of the radial crack front expected in these specimens are identified.

Effect of iron oxidation state on the catalytic performance of Fe/C in liquid phase flow hydrogenation of 2-butyne-1,4-diol

Cis-2-butene-1,4-diol and 1,4-butanediol are crucial precursors in producing bioplastics and biodegradable plastics. Moreover, cis-2-butene-1,4-diol is used in the production of vitamins A and B6, fungicides and insecticides. On an industrial scale, they are produced by hydrogenation of 2-butyne-1,4-diol with Ni Raney as catalyst at > 160 °C and > 15 MPa. Herein, the effect of mesoporous carbon, iron loading (2, 6, 10 and 14 wt%), and metal oxidation state on the activity and selectivity of iron catalysts in the liquid phase continuous flow 2-butyne-1,4-diol hydrogenation at mild conditions (at temperature range 10–65 °C and pressure range 1–20 bar) was performed. Catalytic investigations supported by statistical modeling and physicochemical characterization (TPR, H2-TPD, physisorption, TEM, XPS, and Mössbauer spectroscopy), showed that the activity of the Fe/C can be determined by the presence of functional groups in mesoporous support which are responsible for high metal dispersion and strong metal-support interaction, which boosts the catalytic activity of iron catalysts. Additionally, the formation of metastable iron carbides which are active in hydrogenation cannot be excluded. All of the catalysts showed 100% selectivity toward cis-2-butene-1,4-diol at the lowest temperature. However, the best compromise between selectivity and activity (TOF=336 h−1) was achieved with 2 wt% Fe at 1 bar and 25 °C.

Prediction Accuracy of Hyperelastic Material Models for Rubber Bumper under Compressive Load.

Different hyperelastic material models (Mooney-Rivlin, Yeoh, Gent, Arruda-Boyce and Ogden) are able to estimate Treloar's test data series containing uniaxial and biaxial tension and pure shear stress-strain characteristics of rubber. If the rubber behaviour is only determined for the specific load of the product, which, in the case of rubber bumpers, is the compression, the time needed for the laboratory test can be significantly decreased. The stress-strain characteristics of the uniaxial compression test of rubber samples were used to fit hyperelastic material models. Laboratory and numerical tests of a rubber bumper with a given compound and complex geometry were used to determine the accuracy of the material models. Designing rubber products requires special consideration of the numerical discretization process due to the nonlinear behaviours (material nonlinearity, large deformation, connections, etc.). Modelling considerations were presented for the finite element analysis of the rubber bumper. The results showed that if only uniaxial compression test data are available for the curve fitting of the material model, the Yeoh model performs the best in predicting the rubber product material response under compressive load and complex strain state.

Extended B‐spline‐based implicit material point method for saturated porous media

AbstractThe large deformation and fluidization process of a solid–fluid mixture includes significant changes to the temporal scale of the phenomena and the shape and properties of the mixed material. This paper presents an extended B‐spline (EBS)‐based implicit material point method (EBS‐MPM) for the coupled hydromechanical analysis of saturated porous media to enhance the overall versatility of MPM in addressing such diverse phenomena. The proposed method accurately represents phenomena such as high‐speed motion in both the quasi‐static and dynamic states by employing a full formulation of coupled hydromechanical modeling. The weak imposition of boundary conditions based on Nitsche's method allows representing the boundary conditions independent of the relative position of the particles and computational grid. In addition, it enables dynamic changes in the boundary domain based on the deformation. The robustness of this boundary representation is reinforced using EBS basis functions, which prevent the degradation of the condition number of the system matrices regardless of the position of the boundary domain with respect to the computational grid. Furthermore, a stabilization method based on a variational multiscale method (VMS) approach is employed to provide the flexibility in choosing arbitrary basis functions for spatial discretization, facilitating the effective construction of EBS. Numerical examples including comparisons between a full formulation and a simplified formulation are presented to demonstrate the performance of the developed method under various boundary conditions and loading states across different time scales.

Numerical investigation of the multiphase flow and loading state of underwater semiclosed initial interrupted bubbles

The pulsation of bubbles and the impact load from reverse flow, generated by the evolution of semiclosed initial intermittent bubble multiphase flow during underwater launches, are crucial factors affecting launch safety. This paper employs the mixture multiphase flow model and the interphase heat and mass transfer model to simulate the interaction between the gas inside a partially enclosed cylinder and the water medium outside the cylinder, combined with simulation and piggybacking experiments, to analyze the flow process and load state. The numerical model is further utilized to study the evolution of the multiphase flow field of the semienclosed initial intermittent bubble, the pulsating load of the bubble, the impact load of the inverted water flow, and the influence of structural dimensions on the load. The results show that the initial intermittent bubble in the mouth of the cylinder experiences an expansion–contraction–expansion pulsation process, and as the migration of the interface between the phases results in significant pressure pulsation, the peak pulsation can exceed twice the pressure difference between the initial gas pressure inside the cylinder and the hydrostatic pressure at the mouth of the cylinder. At the late stage of bubble pulsation, a large amount of water with pulsating bubbles flows into the semiclosed cylinder, and the pulsation-induced velocity and gravity are used to form a high-speed inverted water flow. The interaction between the inverted water and the gas inside the cylinder generates an oscillating shock load where the maximum shock load is significantly greater than the ambient pressure load. Additionally, the effect of structural dimensions on the load state under the same intermittent conditions is examined.

Research on Characteristics of Cab Interior noise under Different Conditions by Neural Network Algorithm

This study aimed at establishing models to predict commercial vehicle's running conditions by neural network algorithms. Initially, experiments were carried out to collect cab interior noise data and a total of 420 samples were obtained after repeated tests. Then, each sample was intercepted and converted into six psychological acoustic metrics, which were sound pressure level (SPL), loudness, sharpness, roughness, articulation index and fluctuation strength. Finally, neural network algorithms were used to establish models between the running condition and the acoustic metrics. Through iterations, AC state, loading state, speed level, and road surfaces prediction models were figured out with accuracies of 0.903, 0.753, 0.978, 0.946, respectively. The results indicate that noise induced by speed variances greatly affects SPL, whereas road surfaces and AC states greatly influence sharpness and articulation index, separately. However, more evidence should be offered to verify the loading states have significant effects on interior noise.

Advanced sampling discovers apparently similar ankle models with distinct internal load states under minimal parameter modification

Creating valid and trustworthy models is a key issue in biomedical engineering that affects the quality of life of both patients and healthy individuals in various scientific and industrial domains. This however is a difficult task due to the complex nature of biomechanical joints. In this study, a sampling strategy combining Genetic Algorithm and clustering is proposed to investigate biomechanical joints. A computational model of a human ankle joint with 43 input parameters serves as an illustrative case for the procedure. The Genetic Algorithm is used to efficiently search for distinct variants of the model with similar output, while clustering helps to quantify the obtained results. The search is performed in a close vicinity to the original model, mimicking subjective decisions in parameter acquisition. The method reveals twelve distinct clusters in the model parameter set, all resulting in the same angular displacements. These clusters correspond to three unique internal load states for the model, confirming the complex nature of the ankle. The proposed approach is general and could be applied to study other models in mechanical engineering and robotics.

Comparison of data-driven thresholding methods using directed functional brain networks.

Over the past two centuries, intensive empirical research has been conducted on the human brain. As an electroencephalogram (EEG) records millisecond-to-millisecond changes in the electrical potentials of the brain, it has enormous potential for identifying useful information about neuronal transactions. The EEG data can be modelled as graphs by considering the electrode sites as nodes andthe linear and nonlinear statistical dependencies among them as edges (with weights). The graph theoretical modelling of EEG data results in functional brain networks (FBNs), which are fully connected (complete) weighted undirected/directed networks. Since various brain regions are interconnected via sparse anatomical connections, the weak links can be filtered out from the fullyconnected networks using a process called thresholding. Multiple researchers in the past decades proposed many thresholding methods to gather more insights about the influential neuronal connections of FBNs. This paper reviews various thresholding methods used in the literature for FBN analysis. The analysis showed that data-driven methods are unbiased since no arbitrary user-specified threshold is required. The efficacy of four data-driven thresholding methods, namely minimum spanning tree (MST), minimum connected component (MCC), union of shortest path trees (USPT), and orthogonal minimum spanning tree (OMST), in characterizing cognitive behavior of the normal human brain is analysed using directed FBNs constructed from EEG data of different cognitive load states. The experimental results indicate that both MCC and OMST thresholding methods can detect cognitive load-induced changes in the directed functional brain networks.

Coupled field modeling of thermoresponsive hydrogels with upper/lower critical solution temperature

An inf–sup stable FE formulation for the thermo-chemo-mechanical simulation of thermoresponsive hydrogels is herein proposed by approximating the displacement field via quadratic shape functions and both the chemical potential (fluid pressure) and the temperature fields by linear functions. The formulation is implemented into a stable thermo-chemo-mechanical user-element subroutine (UEL) in Abaqus, denoted as Q2Q1Q1. The proposed formulation has been validated in relation to thermoresponsive hydrogels to interpret several examples of transient diffusion-driven swelling deformations. First, the upper/lower critical solution temperature behaviors of thermoresponsive hydrogels has been captured, studying several peculiarities comprising the diffusion length influence at the instantaneous loading state and the overlooked influence of the mass flux and the hyperelastic stretching on the temperature field. Subsequently, numerical analysis have been conducted in order to investigate the impact of temperature-dependent swelling ratio on the mechanical behavior of spheres undergoing compression. The accuracy of the proposed formulation has been assessed by numerically replicating the seminal experiments that explore the influence of crosslinking density on the thermally driven swelling of PNIPAAm hydrogels.

Taste or health: The impact of packaging cues on consumer decision-making in healthy foods

According to the theory of dietary regulation, consumers frequently encounter conflicts between healthiness and tastiness when selecting healthy foods. This study explores how packaging cue that highlight “tasty” versus “healthy” affect consumers’ intentions to purchase healthy food. After an Implicit Association Test (IAT) confirmed a perceived lack of tastiness in health foods in the Preliminary study, Study 1 analyzed pricing and packaging details of the top 200 most-popular items in each of the ten healthy food categories on a major online shopping platform. Results showed that products with taste-focused cues commanded higher prices, indicating stronger consumer acceptance of healthy foods marketed as delicious. To address the causality limitations of observational studies, Study 2 used an experimental design to directly measure the impact of these cues on purchase intentions and perceptions of energy, healthiness, and tastiness. Findings revealed that taste-focused cues significantly boosted purchase intentions compared to health-focused cues, although they also diminished the perceived healthiness of the products. Moreover, in the control group exposed to unhealthy food options, health-emphasized packaging also increased purchase intentions, indicating that consumers seek a balance between healthiness and tastiness, rather than prioritizing health alone. Study 3 further explored the impact of cognitive load over these cue influences, revealing a heightened inclination among consumers to purchase healthy products with taste-focused cue under high cognitive load state. These insights have direct implications for food packaging design, suggesting that emphasizing a balance of taste and health benefits can effectively enhance consumer engagement. The study, which conducted in China, also opens avenues for future research to explore similar effects, maybe in different cultural contexts, different consumer groups, and under varied cognitive conditions.

Rag-Ragulator is the central organizer of the physical architecture of the mTORC1 nutrient-sensing pathway.

The mechanistic target of rapamycin complex 1 (mTORC1) pathway regulates cell growth and metabolism in response to many environmental cues, including nutrients. Amino acids signal to mTORC1 by modulating the guanine nucleotide loading states of the heterodimeric Rag GTPases, which bind and recruit mTORC1 to the lysosomal surface, its site of activation. The Rag GTPases are tethered to the lysosome by the Ragulator complex and regulated by the GATOR1, GATOR2, and KICSTOR multiprotein complexes that localize to the lysosomal surface through an unknown mechanism(s). Here, we show that mTORC1 is completely insensitive to amino acids in cells lacking the Rag GTPases or the Ragulator component p18. Moreover, not only are the Rag GTPases and Ragulator required for amino acids to regulate mTORC1, they are also essential for the lysosomal recruitment of the GATOR1, GATOR2, and KICSTOR complexes, which stably associate and traffic to the lysosome as the "GATOR" supercomplex. The nucleotide state of RagA/B controls the lysosomal association of GATOR, in a fashion competitively antagonized by the N terminus of the amino acid transporter SLC38A9. Targeting of Ragulator to the surface of mitochondria is sufficient to relocalize the Rags and GATOR to this organelle, but not to enable the nutrient-regulated recruitment of mTORC1 to mitochondria. Thus, our results reveal that the Rag-Ragulator complex is the central organizer of the physical architecture of the mTORC1 nutrient-sensing pathway and underscore that mTORC1 activation requires signal transduction on the lysosomal surface.

Deep reinforcement learning-based resource scheduling for energy optimization and load balancing in SDN-driven edge computing

Traditional techniques for edge computing resource scheduling may result in large amounts of wasted server resources and energy consumption; thus, exploring new approaches to achieve higher resource and energy efficiency is a new challenge. Deep reinforcement learning (DRL) offers a promising solution by balancing resource utilization, latency, and energy optimization. However, current methods often focus solely on energy optimization for offloading and computing tasks, neglecting the impact of server numbers and resource operation status on energy efficiency and load balancing. On the other hand, prioritizing latency optimization may result in resource imbalance and increased energy waste. To address these challenges, we propose a novel energy optimization method coupled with a load balancing strategy. Our approach aims to minimize overall energy consumption and achieve server load balancing under latency constraints. This is achieved by controlling the number of active servers and individual server load states through a two stage DRL-based energy and resource optimization algorithm. Experimental results demonstrate that our scheme can save an average of 19.84% energy compared to mainstream reinforcement learning methods and 49.60% and 45.33% compared to Round Robin (RR) and random scheduling, respectively. Additionally, our method is optimized for reward value, load balancing, runtime, and anti-interference capability.

Structure and dimerization properties of the plant-specific copper chaperone CCH

Copper chaperones of the ATX1 family are found in a wide range of organisms where these essential soluble carriers strictly control the transport of monovalent copper across the cytoplasm to various targets in diverse cellular compartments thereby preventing detrimental radical formation catalyzed by the free metal ion. Notably, the ATX1 family in plants contains two distinct forms of the cellular copper carrier. In addition to ATX1 having orthologs in other species, they also contain the copper chaperone CCH. The latter features an extra C-terminal extension whose function is still unknown. The secondary structure of this extension was predicted to be disordered in previous studies, although this has not been experimentally confirmed. Solution NMR studies on purified CCH presented in this study disclose that this region is intrinsically disordered regardless of the chaperone’s copper loading state. Further biophysical analyses of the purified metallochaperone provide evidence that the C-terminal extension stabilizes chaperone dimerization in the copper-free and copper-bound states. A variant of CCH lacking the C-terminal extension, termed CCHΔ, shows weaker dimerization but similar copper binding. Computational studies further corroborate the stabilizing role of the C-terminal extension in chaperone dimerization and identify key residues that are vital to maintaining dimer stability.