Initial Model Research Articles

Topological optimization design for support structure under multiple loading conditions

In view of the lightweight and high stiffness requirements of a certain type of spacecraft support, combined with the special performance and environment of the support, topology optimization technology is introduced into the optimal design of the support structure in the space field. The topology optimization and design of the support are carried out with the stiffness as the objective function and the quality as the constraint condition, and the design of "space hugging configuration" is proposed, and the simulation verification and experimental verification are carried out. Firstly, different load conditions of the support were analyzed and studied. Secondly, the initial model was established based on the relative position relationship between the support and the mounting surface of the loaded equipment. Finally, a design method of the support structure was established based on the combination of topology optimization under multi-load conditions and size optimization considering the influence of strength. The stiffness and strength of the optimized structure were checked under multiple load conditions, and the fundamental frequency was 2.05 times of the index requirement. The maximum response stress is lower than the yield limit of the material used, which meets the strength requirement. Finally, the vibration test is carried out to verify the support. After the test is completed, the position accuracy of the support interface meets the requirements of the index. The obtained bracket has high structural stability, meets the high performance requirements of the load equipment, verifies the feasibility of the topology optimization method, and provides an effective method for the design of lightweight high-stiffness structures.

Limits of inter-American cooperation: large dams and urban planning in Latin America after 1945

ABSTRACT Large dams are crucial sites for urban planning because they normally require the construction of relocated settlements, industrial towns, and workers’ camps. After 1945, Latin America emerged as one of the important centres for dam-building. The planning of urban structures built in this context represents a basic tension of inter-American cooperation. Whereas the initial model of such settlements was the U.S. company town and some countries like Venezuela even contracted urban planners from the United States, Latin American countries used urban planning to prove their planning capabilities. Even though they adapted Western lifestyle and housing, they were eager to keep out direct influence from abroad. On the other hand, dam towns were manifestations of the pitfalls of modern urban planning since they produced socially segregated spaces and strengthened social inequality. Engineers and middle-class inhabitants enjoyed a certain comfort, but resettled people and workers often faced difficult living conditions and protested against the urban planning policy.

Global and local magnitude and spatial pattern of uncertainty from geographically adaptive empirical and machine learning satellite-derived bathymetry models

ABSTRACT The spatial structure of local uncertainty of shallow-water satellite-derived bathymetry (SDB) relative to model type, imagery, and geographical adaptability was examined for an area near Key West, Florida (United States). The model types examined were a commonly used quasi-empirical linear regression model and a decision tree-based Categorical Boosting (CatBoost) machine learning (ML) model. Image types examined were (four) cloud-free Sentinel-2 images and a maximum blue band (Band 2) value image composite of the four Sentinel-2 images. Initial models fitted were based on band reflectances alone. Geographical adaptivity was added by including UTM coordinates and refitting the models. Major findings were: 1) The ML/CatBoost models provided substantially better depth estimates than the quasi-empirical models. 2) The geographically adaptive models outperformed the non-geographically adaptive models. 3) The ML/CatBoost models that included non-visible spectral bands including infra-red improved SDB accuracy compared to ML/CatBoost and quasi-empirical models based only on visible spectral bands. 4) Accuracies from ML/CatBoost models were comparable across all individual images and the composite suggesting that CatBoost models eliminate or at least minimize the need to find “the best” cloud-free image nor is it necessary to create a composite image. 5) Localized SDB inaccuracy was spatially random. 6) Significant spatial hotspots where SDB accuracy was consistently higher or lower across all images and models were present. Results suggest that image selection is less important for global and local SDB accuracy than using ML models that detect hidden interactions and non-linear relationships among pixel reflectance and geographic location. The spatially random local deviation from global accuracy suggests a weak ability to infer local accuracy from neighboring accuracies. This lack of spatial autocorrelation among errors is potentially problematic for the use of SDB maps for navigation since error at any location is generally inferred from known uncertainties at neighboring locations. Rigorous and robust uncertainty analysis is necessary in any effort to improve SDB, and the uncertainty analysis techniques employed that characterize SDB uncertainty in both statistical and geographical space could be an important part of quality assurance and continuous improvement.

Adaptation of Belk’s materialism scale to the Polish population and assessment of relationships between materialism, values, religiosity, and pro-environmental beliefs

ABSTRACT According to Russell Belk’s conceptualisation, materialism is a personality trait reflected in possessiveness, envy, and non-generosity. The aim of the study was to adapt Belk’s Materialism Scale (BMS) to the Polish population, specifically, to evaluate its psychometric properties (factor structure and reliability) and convergent and divergent validity. Also, we aimed to examine relationships between materialism and values (according to Schwartz’s theory), religiosity, and pro-environmental beliefs (within the New Ecological Paradigm). We conducted two studies: the adaptation study I (N = 770) and the hypotheses-testing study II (N = 568). In Study I, we performed confirmation and exploration factor analyses (CFA and EFA) and established a convergent validity (with the Richins & Dawson’s Materialism Value Scale) and a discriminant validity (with the Jaworska-Gruszczyńska’s Time Organization Scale). After modifying the initial model of the materialism scale, based on error covariance and changes in the number of items, we developed the Polish version of the BMS, which proved its applicability in the Polish population. The Study II results showed that materialism positively related to self-enhancement value, while it negatively related to openness to change and self-transcendence values, and religiosity.

Second-order cone programming for frictional contact mechanics using interior point algorithm

We report experiments of an implementation of a primal–dual interior point algorithm for solving mechanical models of one-sided contact problems with Coulomb friction. The objective is to recover an optimal solution with high accuracy and as quickly as possible. These developments are part of the design of Siconos, an open-source software for modelling and simulating non-smooth dynamical systems. Currently, Siconos uses mainly first-order methods for the numerical solution of these systems. These methods are very robust, but suffer from a linear rate of convergence and are therefore too slow to find accurate solutions in a reasonable time. Our main objective is to apply second-order methods to speed up convergence. In this paper, we focus on solving a relaxed formulation of the initial mechanical model, corresponding to the optimality conditions of a convex quadratic minimization problem with second-order cone constraints. We will present in detail a primal–dual interior point algorithm for solving this type of problem. The main difficulty in implementing this algorithm arises from the fact that at each iteration of the algorithm, a change of variable, called a scaling, has to be performed in order to guarantee the non-singularity of the linear system to be solved, as well as to recover a symmetric system. Although this scaling strategy is very nice from a theoretical point of view, it leads to an enormous deterioration of the conditioning of the linear system when approaching the optimal solution and therefore to all the numerical difficulties that result from it. We will describe in detail the numerical algebra that we have developed in our implementation, in order to overcome these problems of numerical instability. We will also present the solution of the models resulting from the problems of rolling friction, for which the cone of constraints is no longer self-dual like the Lorentz cone.

Utilizing productivity and health breeding-to-market information along with disease diagnostic data to identify pig mortality risk factors in a U.S. swine production system.

Aggregated diagnostic data collected over time from swine production systems is an important data source to investigate swine productivity and health, especially when combined with records concerning the pre-weaning and post-weaning phases of production. The combination of multiple data streams collected over the lifetime of the pigs is the essence of the whole-herd epidemiological investigation. This approach is particularly valuable for investigating the multifaceted and ever-changing factors contributing to wean-to-finish (W2F) swine mortality. The objective of this study was to use a retrospective dataset ("master table") containing information on 1,742 groups of pigs marketed over time to identify the major risk factors associated with W2F mortality. The master table was built by combining historical breed-to-market performance and health data with disease diagnostic records (Dx Codes) from marketed groups of growing pigs. After building the master table, univariate analyses were conducted to screen for risk factors to be included in the initial multivariable model. After a stepwise backward model selection approach, 5 variables and 2 interactions remained in the final model. Notably, the diagnosis variable significantly associated with W2F mortality was porcine reproductive and respiratory syndrome virus (PRRSV). Closeouts with clinical signs suggestive of Salmonella spp. or Escherichia coli infection were also associated with higher W2F mortality. Source sow farm factors that remained significantly associated with W2F mortality were the sow farm PRRS status, average weaning age, and the average pre-weaning mortality. After testing for the possible interactions in the final model, two interactions were significantly associated with wean-to-finish pig mortality: (1) sow farm PRRS status and a laboratory diagnosis of PRRSV and (2) average weaning age and a laboratory diagnosis of PRRS. Closeouts originating from PRRS epidemic or PRRS negative sow farms, when diagnosed with PRRS in the growing phase, had the highest W2F mortality rates. Likewise, PRRS diagnosis in the growing phase was an important factor in mortality, regardless of the average weaning age of the closeouts. Overall, this study demonstrated the utility of a whole-herd approach when analyzing diagnostic information along with breeding-to-market productivity and health information, to measure the major risk factors associated with W2F mortality in specified time frames and pig populations.

One-Bit Supervision for Image Classification: Problem, Solution, and Beyond

This article presents one-bit supervision, a novel setting of learning with fewer labels, for image classification. Instead of the training model using the accurate label of each sample, our setting requires the model to interact with the system by predicting the class label of each sample and learn from the answer whether the guess is correct, which provides one bit (yes or no) of information. An intriguing property of the setting is that the burden of annotation largely is alleviated in comparison to offering the accurate label. There are two keys to one-bit supervision: (i) improving the guess accuracy and (ii) making good use of the incorrect guesses. To achieve these goals, we propose a multi-stage training paradigm and incorporate negative label suppression into an off-the-shelf semi-supervised learning algorithm. Theoretical analysis shows that one-bit annotation is more efficient than full-bit annotation in most cases and gives the conditions of combining our approach with active learning. Inspired by this, we further integrate the one-bit supervision framework into the self-supervised learning algorithm, which yields an even more efficient training schedule. Different from training from scratch, when self-supervised learning is used for initialization, both hard example mining and class balance are verified to be effective in boosting the learning performance. However, these two frameworks still need full-bit labels in the initial stage. To cast off this burden, we utilize unsupervised domain adaptation to train the initial model and conduct pure one-bit annotations on the target dataset. In multiple benchmarks, the learning efficiency of the proposed approach surpasses that using full-bit, semi-supervised supervision.

MRLM: A meta-reinforcement learning-based metaheuristic for hybrid flow-shop scheduling problem with learning and forgetting effects

In the real-world production environment, the employee skills affect production efficiency. Especially, the learning and forgetting effects largely influence the processing time. This paper investigates a hybrid flow-shop scheduling problem with learning and forgetting effects (HFSP-LF). Two learning and forgetting effects models are constructed. The sequence-dependent setup time (SDST) is also considered. The objective is to minimize the makespan of all jobs. To solve such problem, a mixed integer linear programming (MILP) model is built to formulate HFSP-LF. Then, a meta-reinforcement learning-based metaheuristic (MRLM) is proposed. In MRLM, a constructive heuristic is employed to generate the initial solution. Several problem-specific search operators are developed to explore and exploit the solution space. The search framework of MRLM comprises a meta-training phase and a Q-learning-driven search phase. In the meta-training phase, the search operators are trained to obtain prior knowledge of their selection and an initial learning model is constructed. In the Q-learning-driven search phase, Q-learning is employed to implement automatic selection of search operators through continuously perfecting the learning model and absorbing the feedback information of searching. Finally, we conduct a comprehensive experiment. The experimental results demonstrate that the designs of MRLM are effective, and MRLM significantly outperforms several well-performing methods on solving HFSP-LF.

On developing structural and technical solutions to ensure the vibratory displacement of the working medium

A research study into using vibration technologies for the transport of granular working medium was carried out. A vibration testing machine, whose calculation model includes a mechanical vibration system having two degrees of freedom with a rigid body on elastic supports, was selected as an object of research. The study involved analysing the variation in the system vibrations by using the structural theory for vibration isolation systems, where a dynamic equivalent represented by a structural diagram of an automatic control system is compared to the initial calculation model. The structural diagram of the system is based on the motion equations in operator form obtained using Lagrange differential equations of the second kind. The Laplace transform was used to transform the initial data for the system of differential motion equations. The paper addresses the characteristics of a new structural and technical solution in the field of vibratory displacement of a granular working medium using the working body of a vibration technological machine, which involves introducing a number of additional weights, levers, springs, and hinges. Here, springs comprise generalised structures containing both elastic elements and vibration dampers. To connect the coordinates of the endpoints in the working body of the vibration technological machine, analytical relations were obtained. It was established that varying the parameters of the elements within the elastic-lever blocks allows the dynamic state of the vibration technological machine to be controlled. In addition, it was shown that the obtained structural diagram helps to derive mathematical expressions for transfer functions, comprising the ratio between the motion coordinates of a technical object and an external force disturbance. On the basis of these expressions, the transfer function for the ratio of the motion coordinates of the vibration technological machine was formulated. A mathematical model of a vibration technological machine was obtained in the form of a transfer function, including a large number of additional elastic and massinertial elements, where the parameters of vibration displacement can be adjusted automatically. The research results will allow the existing technical solutions in the field of technological engineering to be modernised.

A Unified Fracture Model for X65 Pipeline Material Under Various Constraints Using the Extended Finite Element Method (XFEM)

Abstract It is generally accepted that the fracture strain is dependent on geometry/constraints in metals. However, the current available implementation of XFEM assumes a fixed fracture strain criterion independent of the constraints. The objective of this paper is to develop and implement a variable fracture strain criterion in XFEM that is capable of predicting a wide range of fracture conditions in X65 pipeline steels with various crack tip constraints. Various small-scale tests with different out of plane constraints obtained from the literature were simulated using the XFEM in ABAQUS software. For each test, the value of the maximum principal strain (Maxpe) as a fracture initiation criterion in the cohesive zone model (CZM) in XFEM framework was varied while keeping the fracture energy constant until the model was able to accurately replicate the reported experimental results. For each test, the crack tip constraints were characterized and the stress triaxialities and Lode angle parameters at the onset of fracture initiation were calculated from the models. The results allowed expressing the fracture strain as an explicit function of stress triaxiality which was then implemented in ABAQUS XFEM using UDMGINI subroutine. For the sake of comparison, the tests were simulated in finite element method (FEM) using a similar damage initiation model namely, the Johnson-Cook (J-C) model. A single model in XFEM was able to accurately replicate the experimental observations for all specimens and compared well to experiments in the way simulated by FEM damage model.

Adaptive rock-paper-scissors game enhances eco-evolutionary performance at cost of dynamic stability

The rock-paper-scissors (RPS) game is a classic model for exploring the performance of how multiple strategies interact and evolve over time. The classic RPS game assumes a fixed benefit and cost for each strategy against another one when two players meet, while its evolutionary game considers the frequency dynamics of the three strategies with each's fitness influenced by its net payoff. This may not reflect the complexity of real-world scenarios as strategies can co-adapt with each other. We introduce an adaptive RPS game that captures the dynamics of strategy densities with trait-mediated payoffs, and the adaptive dynamics of coevolving traits via incremental mutations leading to adaptively evolving payoffs. Results show that the adaptive RPS game approaches a steady state of strategy density, if any, faster than the evolutionary RPS game. The stable coexistence of all strategies in the evolutionary game can be easily destabilized in the adaptive game. Strategies that are allowed to adaptively change their traits also performed better and achieved greater strategy densities than those fixed strategies in the adaptive game. The coevolving strategies in the adaptive RPS game exhibit complex and diverse attractors in the trait space, sensitive to both initial conditions and model parameters, but exhibiting positive payoffs with greater benefits than costs. These findings highlight how adaptive games enhance strategy performance by sacrificing eco-evolutionary dynamic stability.

Monte Carlo Full-Waveform Inversion of Cross-Hole Ground-Penetrating Radar Data Based on Improved Residual Network

A full-waveform inversion (FWI) of ground-penetrating radar (GPR) data can be used to effectively obtain the parameters of a shallow subsurface. Introducing the Markov chain Monte Carlo (MCMC) algorithm into the FWI can reduce the dependence on the initial model and obtain the global optimal solution, but it requires a large number of computations. In order to better detect underground targets based on ground-penetrating radar data, this paper proposes a joint scheme of an improved ResNet and an MCMC full-waveform inversion. This scheme combines the Bayesian MCMC algorithm and an improved ResNet to accurately invert the target dielectric permittivity. The introduction of deep learning networks into the forward calculation part of the MCMC inversion algorithm replaced the complex forward simulation process, greatly improving the inversion speed. It is worth noting that the neural network model was an approximation of complex forward modeling, and, therefore, it contained modeling errors. The MCMC method could quantify and explain modeling errors during the inversion process, reducing the impact of modeling errors on the inversion results. Finally, a simulation dataset was constructed for training and testing, and the errors were statistically analyzed. The results showed that this method can accurately reconstruct underground medium targets, and it has strong robustness and efficiency.

Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks

AbstractAutism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre‐trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state‐of‐the‐art models in terms of accuracy and computational cost.

Mathematical Modeling Approach to the Optimization of Biomass Storage Park Management

This paper addresses the critical issue of managing biomass parks, a key component in the shift towards sustainable energy sources. The research problem centers on optimizing the management of these parks to enhance production and economic viability. Our aim was to bridge the gap in current research by developing and applying mathematical models tailored for biomass park management. The study commenced by constructing a basic model based on assumptions such as uniform biomass and steady input rates. Progressing from this initial model, we explored sophisticated control strategies, including Pontryagin’s maximum principle and dynamic programming, and employed numerical methods to tackle the nonlinearities and complexities inherent in biomass management. Our approach’s scope extended to predicting and managing biomass flow, highlighting each method’s distinct advantages. The simple model laid the groundwork for understanding, while optimal control techniques revealed the system’s intricate dynamics. The numerical methods provided practical solutions to complex equations. We found that while each method is beneficial on its own, their combined use can significantly improve decision-making in biomass park management. This research emphasizes the importance of aligning the chosen method with specific operational challenges and desired outcomes for optimal efficacy, offering both theoretical insights and practical applications in the field of renewable energy management.

Construction and Validation of a Scale Regarding the Attitudes of Parents of Young Children towards Animals

Τhis study aims to discuss and analyze the reliability as well as construct the validity of the Attitudes of Young Children Parents Towards Animals Scale (PATAS) to be used in parents of preschool children. Data were obtained from eight hundred eighty-one parents of preschool children who participated in an educational program regarding pets. The initial model comprised four factors: Learning for and from animals (F1), Cruelty and stereotypes against animals (F2), Fears for children (F3), and Delinquency (F4). In the brief version of the scale, the factors proposed were Learning for and from animals (F1) and Cruelty and stereotypes against animals (F2). The PATAS presented satisfactory validity and reliability (internal consistency and reproducibility), allowing its use in parents. Results are discussed in terms of helping parents to promote positive changes in their approaches towards animals and influencing children to cultivate positive attitudes towards animals at the early stage of their development.

A broad learning model guided by global and local receptive causal features for online incremental machinery fault diagnosis

Intelligent fault diagnosis (IFD) plays a significant function in ensuring the reliable operation of mechanical equipment. However, most existing IFD methods are trained in batch learning way and the feature extraction process is unexplainable and it is challenging to satisfy the complex diagnosis requirements. Thus, this paper proposes a causal broad learning model (CBLM) guided by global and multi-scale local causal features for incremental machinery fault diagnosis. Firstly, rich global and multi-scale local causal features are extracted for training CBLM. Then, incremental learning capability is developed to update model by expanding or modifying the original weights, considerably reducing the time consumption and greatly improving the computational efficiency. When the initial model has inadequate nodes for feature learning, CBLM performs structural incremental learning by adding extra nodes to improve the diagnostic performance. As new samples with various fault degrees are entered, sample incremental learning is built to rapidly update based on previous model without retraining. Finally, two experimental results on bearings indicate that CBLM improves the testing accuracy of the initial model by 0.13% to 52.22% and 1.95% to 57.64%, respectively, and remains above 97.22% and 99.77% for the updated models. The training time consumption is reduced by 25.06 s to 133.0 s, 20.78 s and 141.9 s, respectively, and the subsequent models update time are only about 12 s and 10 s. Consequently, the proposed CBLM is an effective online IFD method because it is obviously superior to the existing IFD methods in regards to time consumption and diagnosis accuracy.

Bionic topology optimization design and multi-objective optimization of guide arm

Lightweight design is universally recognized as a critical criterion for many engineering problems. In addition to the well-developed topology optimization (TO) method, structural bionics is also considered an effective approach to developing innovative structure designs with lightweight. In the process of natural evolution, bamboo has developed a unique hollow structure with ingenious mechanical properties. Inspired by these characteristics, this paper selected bamboo as a bionic prototype to carry out bionic structure optimization of guide arm. First, the initial guide arm was modeled and simulated for its mechanical behavior. Secondly, similarity analysis between bamboo and guide arm was performed from three aspects, and the parametric bionic guide arm model was established step by step. Then, the key parameters affecting the performance of the bionic guide arm were verified by the parameter sensitivity analysis method. The multi-objective optimization was carried out with the minimum of the total mass and the maximum deformation of guide arm as the optimization objectives. The optimal solution of Pareto solution set was determined by multi-objective decision methods of the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Gray Relational Analysis (GRA). Finally, finite element (FE) method was used to make a comparison between the initial model and the optimal bionic model in terms of mechanical performance. According to the results, under the premise of the mass of the optimized bionic model decreased by 17.44%, the maximum deformation was decreased by 9.24%, the equivalent stress was decreased by 17.33%, and the first-order frequency was increased by 22.92%. Comparison results showed that the proposed bionic model provided the best lightweight solution for guide arm. This study reveals that structural bionics provides a new solution for the lightweight design of guide arm and similar beam structural components.

Understanding the Influence of API Conformations on Amorphous Dispersion Formation Potential Predictions using the R3m Molecular Descriptor.

The R3m molecular descriptor (R-GETAWAY third-order autocorrelation index weighted by the atomic mass) has previously been shown to encode molecular attributes that appear to be physically and chemically relevant to grouping diverse active pharmaceutical ingredients (API) according to their potential to form persistent amorphous solid dispersions (ASDs) with polyvinylpyrrolidone-vinyl acetate copolymer (PVPVA). The initial R3m dispersibility model was built by using a single three-dimensional (3D) conformation for each drug molecule. Since molecules in the amorphous state will adopt a distribution of conformations, molecular dynamics simulations were performed to sample conformations that are probable in the amorphous form, which resulted in a distribution of R3m values for each API. Although different conformations displayed R3m values that differed by as much as 0.4, the median of each R3m distribution and the value predicted from the single 3D conformation were very similar for most structures studied. The variability in R3m resulting from the distribution of conformations was incorporated into a logistic regression model for the prediction of ASD formation in PVPVA, which resulted in a refinement of the classification boundary relative to the model that only incorporated a single conformation of each API.

A case study with extreme low-level jet in southern Brazil

This study aimed to analyze a period of intense Low-Level Jets (LLJ) in southern Brazil that occurred in a year of intense El Nino-South Oscillation signal. Observational data were collected by radiosonde data carried out at Salgado Filho airport, which measured values of the order of 30 ms-1 in intensity at 700/750 meters in height and a predominant direction of 270 degrees. The LLJ’s are horizontal vortices in the low-level turbulence process, optimizing the transport of energy and water vapor at the mesoscale; they are low-cost energy structures with maximum transport efficiency. In general, their modeling by numerical models at high resolution is difficult because they have a thin layer with a small thickness of the order of 50 meters and have a non-linear nature in the turbulence processes. However, global reanalysis models, in general, cannot represent the optimization effects in the turbulent process performed by LLJ. However, there is a tendency to represent the magnitude of the wind modulus, an approximation of this value by the current state of the art in global reanalysis modeling in models, in this case, the maximum wind. This implies that global models generally underestimate flows at low levels, in general, as they do not represent the optimization effects associated with LLJ. In this case study, we tried to use an extreme situation of occurrence of LLJ for analysis, therefore, the regional climate model RegCM-4.9 in high spatial resolution was used to simulate the period from May to June 1997. The analyses were concentrated in June 1997. The initialization and boundary conditions were used from the EC-EARTH historical of the Earth system model based on the operational seasonal forecast system of the European Center for Medium-Range Weather Forecasts (ECMWF). It was observed that the simulation result could significantly underestimate the wind intensity and the processes associated with the LLJ with the initialization model, with low information resolution, the global initialization models do not have adequate resolution for this type of simulation.

Application of model updating to a large‐scale hybrid simulation

AbstractModel updating can enhance hybrid simulation by utilizing the experimental data from the physically tested substructure to update the parameters of like‐components in the numerical substructure throughout the test, improving the overall accuracy and reducing the extent of the experimental setup. Identifying and updating parameters can be challenging, especially when coupling between degrees of freedom (DOF) must be considered or the specimen experiences loading scenarios which result in newly observed behavior. To explore the performance of model updating under these challenging conditions, a large‐scale hybrid simulation was conducted using a model of a major toll bridge with seismic isolation lead rubber bearings (LRB). One LRB is physically tested considering axial, shear, and rotational loading, while the remainder of the bearings are simulated and updated with a phenomenological model within the numerical substructure. A weighted adaptive constrained unscented Kalman filter is applied as the online model updating algorithm. The study explored the effect of learning over different loading patterns, the selection of initial model parameters, and the selection of the physically tested substructure. The improvement of numerical model hysteresis performance accuracy of the force prediction demonstrates the benefits of model updating in large‐scale hybrid simulation.