Nonlinear Model Research Articles

Optimization of MTI structure based on material characteristics under extreme aerodynamic heating environment

Thermal insulation structure is a key factor to protect a high-speed aircraft to work safely and complete various tasks. The multilayer thermal insulation (MTI) structure has attracted much attention because of its light weight and good thermal insulation effect. The performance of an MTI structure is not only related to its physical sizes, but also closely related to the material optimization and temperature influences. A transient nonlinear thermal transfer model of the MTI structure is established, and a numerical solution strategy of the nonlinear thermal transfer model is proposed. Considering material selection, introducing the temperature influence of materials, and taking the geometric dimensions and equivalent mechanical properties of the MTI structures as constraints, an optimization frame of the MTI structures is established. Then two intelligent methods are adopted to solve the problem. Using the aerodynamic heating environment of X-43 flight trajectory, the results of a typical high-speed aircraft show that the proposed optimization framework can reduce the structural mass density from 13.95 kg‧m−2 to 9.10 kg‧m−2 by about 34.8 %. The temperature effect analysis shows that the temperature variation has an important influence on the optimization results. If the temperature effect on material properties is not considered, the optimized design scheme may not meet the temperature design constraints (The maximum temperature of the inner surface is 410 K, far exceeding the maximum allowable temperature of 320 K) which further leads to structural failures. Finally, the reliability and rationality of the optimization scheme are verified by a finite element model (FEM). The equivalent modulus of the optimized MTI structure is 146 GPa and the mass area density of the MTI structure is 7.7 kg‧m−2. The optimization design method of an MTI structure proposed in the current paper provides a technical support and lays a foundation for the overall design of a high-speed aircraft.

Textural indices of aerial images reveal urban landscapes temporal dynamics and their effects on plants and birds

Landscape temporal dynamics are a crucial concern in ecology to understand current biodiversity. But historical environmental data are often not easily available. This study assesses for the first time the potential of historical orthophotographs to describe past landscape conditions and determine the temporal lag between landscape changes and biotic communities responses.This method utilizes Gray level Co-occurrence Matrix (GLCM) texture indices computed from black and white orthophotographs to construct continuous metrics of landscape composition. Metrics were first developed using present-day data, i.e. 2015, and calibrated with categorical land cover maps of the Strasbourg Eurometropolis, France. Subsequently, these metrics were applied to historical orthophotographs from 1966, 1976, 1986, and 2000. Plant and bird data from research and citizen science programs were used to estimate the time delay in which these communities respond to evolutions in built areas and high vegetation.Obtained texture-based models reveal that built areas exhibit high contrast and homogeneity, depicted through a linear relation, and that high vegetation display low pixel brightness and high brightness diversity, better described via a nonlinear model. We successfully applied those findings to historical orthophotographs, and revealed dependencies on landscape composition up to 50 years ago for plants and up to 30 years ago for birds, with the time lag and the influence of built and high vegetation areas depending on the selected biodiversity indices.These results demonstrate the utility of archive black and white orthophotographs' texture indices for describing urban landscapes over the past five decades, making them valuable tools for ecological research. These indices are more accessible than categorical data like land cover maps for past years. They have the potential to greatly benefit future studies investigating time lags in landscape ecology, simplifying access to historical landscape features and contributing to sustainable urban planning efforts.

Characterization and Determinants of Long-Term Immune Recovery Under Suppressive Antiretroviral Therapy.

We developed a robust characterization of immune recovery trajectories in people living with HIV on antiretroviral treatment (ART) and relate our findings to epidemiological risk factors and bacterial pneumonia. Using data from the Swiss HIV Cohort Study and the Zurich Primary HIV Infection Cohort Study (n = 5907), we analyzed the long-term trajectories of CD4 cell and CD8 cell counts and their ratio in people living with HIV on ART for at least 8 years by fitting nonlinear mixed-effects models. The determinants of long-term immune recovery were investigated using generalized additive models. In addition, prediction accuracy of the modeled trajectories and their impact on the fit of a model for bacterial pneumonia was assessed. Overall, our population showed good immune recovery (median plateau [interquartile range]-CD4: 718 [555-900] cells/μL, CD8: 709 [547-893] cells/μL, CD4/CD8: 1.01 [0.76-1.37]). The following factors were predictive of recovery: age, sex, nadir/zenith value, pre-ART HIV-1 viral load, hepatitis C, ethnicity, acquisition risk, and timing of ART initiation. The fitted models proved to be an accurate and efficient way of predicting future CD4 and CD8 cell recovery dynamics: Compared with carrying forward the last observation, mean squared errors of the fitted values were lower by 1.3%-18.3% across outcomes. When modeling future episodes of bacterial pneumonia, using predictors derived from the recovery dynamics improved most model fits. We described and validated a method to characterize individual immune recovery trajectories of people living with HIV on suppressive ART. These trajectories accurately predict long-term immune recovery and the occurrence of bacterial pneumonia.

Anomaly detection using large-scale multimode industrial data: An integration method of nonstationary kernel and autoencoder

Kernel methods and neural networks (NNs) are two mainstream nonlinear data modeling methods and have been widely applied to industrial process monitoring. However, they both present imperfect properties, so the relevant applications are limited. On the one hand, kernels are not so reconstructable, scalable, and robust to hyperparameters that they suffer performance degradation for large-scale data modeling and monitoring. On the other hand, the high-dimensional parameter space of NNs that is sorted to parameter initialization presents severe anomaly detection performance inconsistency, which makes the industry cautious about using NNs. Motivated by these facts, we propose to integrate kernels and NNs, forming a new model structure that is scalable, reconstructable, and performance-consistent. Specifically, a novel autoencoder-based nonstationary pattern selection kernel (AE-NPSK) is proposed by (1) selecting from the training set the critical edges and interior data as the centers of the radial basis functions in the hidden layers and (2) adaptively adjusting the kernel width in the training procedure. Also, the new NN has strong performance consistency, which facilitates the search for optimal parameters. Finally, we test the performance of the proposed method on the challenging multimode processes. The results validate the efficacy of the proposed method.

Nonlinear Fast Modeling Method of Flux Linkage and Torque for a 12/8 Switched Reluctance Motors

Nonlinear Fast Modeling Method of Flux Linkage and Torque for a 12/8 Switched Reluctance Motors

A survey on cryptocurrency price prediction

A type of digital currency known as a cryptocurrency allows all transactions to be completed online. There is no hard cash version of this soft currency. We highlight that a decentralized currency differs from a centralized currency in the any user of a virtual currency can purchase services without the need for third parties to get involved. Due to its extreme price volatility, using these cryptocurrencies has an impact on trade and international relations. Moreover, the constantly fluctuating oscillations indicate the urgent need for a more precise method of predicting this price. Deep learning techniques that use effective learning models for training data, including the LSTM, GRU, and Feedback Neural Network, can be used to do this. Benchmark datasets are used to test the suggested strategy. That brings us to the neural network, one of the clever data mining technologies that researchers in many domains have been using for the past ten years. In the current economy, stock market data is essential. There are two types of forecasting methodologies: nonlinear models (ARCH, GARCH, Neural Network) and linear models (AR, MA, ARIMA, ARMA). To forecast a company's stock price based on past prices, we employed the Box Jenkins Model also known as ARIMA, and Long Short-Term Memory (LSTM), and Feedback Neural Network also known as RNN.

Nonlinear fatigue life prediction model based on isodamage curve considering two‐block loading

AbstractA non‐linear life prediction model considering the loading history is proposed based on isodamage curve. The stress‐controlled low‐high block loading of low cycle fatigue (LCF) at high temperature, with various previous cycles, was performed on a nickel‐based superalloy. The damage evolvement of the prior loading was revealed using scanning electron microscopy under the low‐high block loading, especially the failure mechanism of coaxing effect. In addition, load history correlation factor was introduced to describe the influence of prior cycle. Based on the Ni‐based superalloy, the life prediction model of two‐step loading, including the low‐high block (LH) and high‐low block loading (HL), was proposed, agreeing well with the experimental results for different metals. Compared with other life prediction models, the proposed model demonstrated the higher prediction accuracy and wider applicability.

Computational assessment of carbon fabric reinforced polymer made prosthetic knee: Mechanics, finite element simulations and experimental evaluation.

A prosthetic knee is designed to replace the functionality of an anatomical knee in transfemoral amputees. The purpose of a prosthetic knee is to restore mobility and compensate amputees for their impairment. In the present research numerical modelling and simulation of a carbon fabric reinforced polymer made polycentric prosthetic knee with four-bar mechanism was performed. Virtual prototyping with computer-aided design and computer-aided engineering software ensured geometric and structural stability of the knee design. The linkage mechanism, instantaneous centre's location and trajectory were investigated using multibody dynamics and analytical formulations. Computational simulations with a non-linear finite element model were employed with joints, contact formulations and an orthotropic material model to predict the displacement, stress formulated and life of the knee prosthesis under static and cyclic loading conditions. Finite element analysis assessed the strength and durability of knee in accordance to standards. Maximum Principal stress of 155 MPa and life expectancy of 3.1 × 106 cycles were determined for the composite knee through numerical simulations ensuring a safe design. Experimental testing was also conducted as per standards and the percentage error was estimated to be 2.52%, thereby establishing the validity of the finite element model deployed. This type of simulation-based approach can be implemented to efficiently and affordably design and prototype a prosthetic knee with desired functioning criteria.

Carbon Emission Forecast Based on Multilayer Perceptron Network and STIRPAT Model

INTRODUCTION: It is of great research significance to explore whether China can achieve the "two-carbon target" on time. The MLP model combines nonlinear modeling principles with other techniques, possessing powerful adaptive learning capabilities, and providing a viable solution for carbon emission prediction.
 OBJECTIVES: This study models and forecasts carbon emissions in Jiangsu Province, one of China's largest industrial provinces, aiming to forecast whether Jiangsu province will achieve the two-carbon target on time plan and provide feasible pathways and theoretical foundations for achieving dual carbon goals.
 METHODS: Based on the analysis of the contributions of relevant indicators using the Grey Relational Analysis method, a comprehensive approach integrating the STIRPAT model, Logistic model, and ARIMA model is adopted. Ultimately, an MLP prediction model for carbon emission variations is established. Using this model, simulations are conducted to analyze the carbon emission levels in Jiangsu Province under different scenarios from 2021 to 2060.
 RESULTS: The time to reach carbon peak and the likelihood of achieving carbon neutrality vary under three scenarios. Under the natural scenario of no human intervention, achieving carbon neutrality is not feasible. While under human-made intervention scenarios including baseline and intervention scenarios, Jiangsu Province is projected to achieve the carbon neutrality target as scheduled, attaining the peak carbon goal, however, proves challenging to realize by the year 2030.
 CONCLUSION: The MLP model exhibits high accuracy in predicting carbon emissions. To expedite the realization of dual carbon goals, proactive government intervention is necessary.

EDNA Adsorption onto Microplastics: Impacts of Water Chemistry and Polymer Physiochemical Properties.

Adsorption of biomacromolecules onto polymer surfaces, including microplastics (MPs), occurs in multiple environmental compartments, forming an ecocorona. Environmental DNA (eDNA), genetic material shed from organisms, can adsorb onto MPs which can potentially either (1) promote long-range transport of antibiotic resistant genes or (2) serve to gain insights into the transport pathways and origins of MPs by analyzing DNA sequences on MPs. However, little is known about the capacity of MPs to adsorb eDNA or the factors that influence sorption, such as polymer and water chemistries. Here we investigated the adsorption of extracellular linear DNA onto a variety of model MP fragments composed of three of the most environmentally prevalent polymers (polyethylene, polyethylene terephthalate, and polystyrene) in their pristine and photochemically weathered states. Batch adsorption experiments in a variety of water chemistries were complemented with nonlinear modeling to quantify the rate and extent of eDNA sorption. Ionic strength was shown to strongly impact DNA adsorption by reducing or inhibiting electrostatic repulsion. Polyethylene terephthalate exhibited the highest adsorption capacity when normalizing for MP specific surface area, likely due to the presence of ester groups. Kinetics experiments showed fast adsorption (majority adsorbed under 30 min) before eventually reaching equilibrium after 1-2 h. Overall, we demonstrated that DNA quickly binds to MPs, with pseudo-first- and -second-order models describing adsorption kinetics and the Freundlich model describing adsorption isotherms most accurately. These insights into DNA sorption onto MPs show that there is potential for MPs to act as vectors for genetic material of interest, especially considering that particle-bound DNA typically persists longer in the environment than dissolved DNA.

Bayesian analysis of the growth of pears in the cultivate Shinseiki using non-linear models

The pear of the Pyrus genus is the third most consumed fruit in Brazil, however, internal production is low and most demand is obtained through imports. Brazil has favorable conditions for its cultivation and one of its main economic sectors is agriculture, being responsible for generating a large number of jobs and income for the country. In this sense, studies related to this fruit can provide information and encourage its production. Using non-linear models, it is possible to identify growth patterns such as fruit length and diameter, which can help determine the ideal harvest point. Therefore, the goal of this article was to compare the Logistic and Gompertz non-linear models in describing the growth curves in diameter and length of the Asian pear tree using the Bayesian approach. The results indicated the Logistic model as the best to describe these two variables and provided asymptotic averages of 70.0607 and 79.706 for length and diameter, respectively.

A nonlinear elastic-strain hardening model for frozen improved sandy soil under uniaxial compression loading condition

Artificial freezing is one of the most effective methods in the excavation of water-rich soils. This work aims at investigating the influence of cement‑sodium silicate grout (C-S grout) and organic polymer stabilizer (OPS) on the uniaxial compressive strength (UCS), stress-strain curve, and unfrozen water content of frozen sandy soil. A series of uniaxial compression tests and nuclear magnetic resonance (NMR) tests were conducted on the saturated frozen sandy soils improved by C-S grout and OPS (“C-S grout-improved soil” and “OPS-improved soil”) under different negative temperatures (i.e., −5 °C, −10 °C, −15 °C, and −20 °C). Based upon the experiment results and existing stress-strain models, including improved Duncan-Chang model and elastic-strain hardening model, a nonlinear elastic-strain hardening constitutive model for improved soils was proposed, in which each parameter has well-defined physical meaning. The results showed that, as the temperature decreases, the strengths of frozen improved soils gradually increase. The strength of OPS-improved soil first increases and then decreases with the increase of OPS dosage. Contrary to the UCS, the unfrozen water content of two improved soils was observed to gradually decrease with the decrease of temperature. As the OPS dosage increases, the unfrozen water content of improved soils decreases first and then increases. When the strain is <0.2, the stress-strain curves of frozen C-S grout-improved soil exhibits a behavior of yielding first and then hardening after the nonlinear elastic stage, while OPS-improved soil exhibits continuous strain hardening behavior. With temperature and unfrozen water content given, the new nonlinear elastic-strain hardening model can accurately predict the stress-strain behavior of frozen improved soils. This study is helpful to the stability analysis of artificial frozen walls and pre-control of environmental deformation during the excavation of water-rich sandy soils.

Modeling Nonlinear Dynamics of Functionalization Layers: Enhancing Gas Sensor Sensitivity for Piezoelectrically Driven Microcantilever.

This article presents a parametrized response model that enhances the limit of detection (LOD) of piezoelectrically driven microcantilever (PD-MC) based gas sensors by accounting for the adsorption-induced variations in elastic properties of the functionalization layer (binder) and the nonlinear motional dynamics of the PD-MC. The developed model is demonstrated for quantifying cadaverine, a volatile biogenic diamine whose concentration is used to assess the freshness of meat. At low concentrations of cadaverine, an increase in the resonance frequency is observed, contrary to the expected reduction due to mass added by adsorption. The study explores the variations in the elastic modulus vis-à-vis the adsorbed mass of cadaverine and derives the resonance frequency to the adsorbed mass response function. We advance a blended technique involving the analysis of atomic force microscopy (AFM) force-distance (f-d) curves and fitting of the quartz crystal microbalance (QCM) impedance response spectrum to deduce the adsorption-induced changes in the viscoelastic properties of the functionalization layer. The findings obtained are subsequently employed in modeling the response function for a structurally nonhomogenous PD-MC, highlighting the significance of the functionalization layer to the global elastic properties. The structural composition of the PD-MC beam adopted herein features a trapezoidal base hosting the actuating piezoelectric stratum and a rectangular free end with a functionalization layer. The Euler-Bernoulli beam theory coupled with Hamilton's principle is used to develop the equation of motion, which is subsequently discretized into a set of nonlinear ordinary differential equations via Galerkin expansion, and the solutions to the first fundamental mode of vibration are determined using the method of multiple scales. The obtained solutions provide a basis for deducing the nonlinear response function model to the adsorbed mass. The derived model is validated by recorded resonance frequency changes resulting from exposure to known concentrations of cadaverine. We demonstrate that the increase in resonance frequency for low concentrations of cadaverine is due to the dominance of the variation of the elastic modulus of the functionalization layer originating from the initial binder-analyte interactions over damping due to added mass. It is concluded that the developed nonlinear response function model can reliably be used to quantify the cadaverine concentration at low concentrations with an elevated Limit of Detection.

Forecasting cauliflower prices in Nepal: a comparative analysis using seasonal time series and nonlinear models

Forecasting cauliflower prices in Nepal: a comparative analysis using seasonal time series and nonlinear models

Interval parity relations design for fault diagnosis

SummaryThe problem of interval parity relations design for systems described by linear and nonlinear models under the external disturbances is considered. The problem is solved based on the reduced‐order model of the original system. The relations allowing designing interval parity relations insensitive or having minimal sensitivity to the disturbances are obtained. The obtained interval parity relations are used to solve the problem of fault diagnosis. Theoretical results are illustrated by example.

Optimizing large-scale hydrogen storage: A novel hybrid genetic algorithm approach for efficient pipeline network design

As hydrogen production technologies continue to advance, efficient and high-capacity hydrogen storage solutions become increasingly crucial. To address the complex optimization challenges in the design of large-scale hydrogen storage pipeline networks, the study introduces a mixed integer nonlinear optimization model inspired by hydrogen pipeline design optimization theory. The model not only focuses on optimizing network design parameters but also aims to minimize investment costs. To achieve this, the study proposes an innovative hybrid genetic algorithm that combines the Modified Feasible Directions Method (MFDM) and Genetic Algorithm Theory (GAT), specifically targeting the optimization of pipeline diameter's discrete distribution challenges. Comparative analyses with SUMT and GA algorithms demonstrate the superiority of the approach. In continuous space, the algorithm achieves a lower convergence cost of 232.4437 × 104 Yuan, while in discrete space, the cost further decreases to 212.1636 × 104 Yuan. Additionally, the study delves into the sensitivity of the HGA-MFDM algorithm to ambient temperature and wellhead temperature in hydrogen storage facilities. The analysis reveals that wellhead temperature has a more significant impact on pipeline network investment, whereas ambient temperature exhibits a more pronounced effect on iteration curves. The findings provide valuable insights for the precise adjustment and optimization of hydrogen storage pipeline networks in practical applications.

Adaptive agents may be smarter than you think: unbiasedness in adaptive expectations

Abstract Experimental evidence shows that human subjects frequently rely on adaptive heuristics to form expectations but their forecasting performance in the lab is not as inadequate as assumed in macroeconomic theory. In this paper, we use an agent-based model (ABM) to show that the average forecasting error is indeed close to zero even in a complex environment if we assume that agents augment the canonical adaptive algorithm with a Belief Correction term which takes into account the previous trend of the variable of interest. We investigate the reasons for this result using a streamlined nonlinear macro-dynamic model that captures the essence of the ABM.

A Dynamics-Weighted Principal Components Analysis of Dominant Atmospheric Drivers of Ocean Variability with an Application to the North Atlantic Subpolar Gyre

Abstract This paper describes a framework for identifying dominant atmospheric drivers of ocean variability. The method combines statistics of atmosphere–ocean fluxes with physics from an ocean general circulation model to derive atmospheric patterns optimized to excite variability in a specified ocean quantity of interest. We first derive the method as a weighted principal components analysis and illustrate its capabilities in a toy problem. Next, we apply our analysis to the problem of interannual upper ocean heat content (HC) variability in the North Atlantic Subpolar Gyre (SPG) using the adjoint of the MITgcm and atmosphere–ocean fluxes from the ECCOv4-r4 state estimate. An unweighted principal components analysis reveals that North Atlantic heat and momentum fluxes in ECCOv4-r4 have a range of spatiotemporal patterns. By contrast, dynamics-weighted principal components analysis collapses the space of these patterns onto a small subset—principally associated with the North Atlantic Oscillation—that dominates interannual SPG HC variance. By perturbing the ECCOv4-r4 state estimate, we illustrate the pathways along which variability propagates from the atmosphere to the ocean in a nonlinear ocean model. This technique is applicable across a range of problems across Earth system components, including in the absence of a model adjoint. Significance Statement While the oceans have absorbed 90% of the excess heat associated with human-forced climate change, the change in the ocean’s heat content is not steady, with peaks and troughs superimposed upon a general increase. These fluctuations come from chaotic changes in the atmosphere and ocean and can be hard to disentangle. We use this case of ocean heat content variability to introduce a new method for determining the patterns of weather and climate in the atmosphere that are most effective at generating fluctuations in the ocean. To do this, we combine the statistics of recent atmospheric activity with output from a state-of-the-art numerical ocean model that reveals physical processes driving changes in ocean quantities including ocean heat content. This approach suggests that the atmospheric patterns that stimulate the most energetic changes in ocean heat content in the northern North Atlantic are not necessarily the most energetic patterns present in the atmosphere. We test our findings by preventing these patterns from affecting the ocean in our numerical model and measure a strong reduction in ocean heat content fluctuations.

Unlocking the dynamic linkages between sustainable equity investment and economic policy uncertainty: An empirical analysis for G‐20 countries

AbstractThe study examines the dynamic relationships between sustainable equity investment and economic policy uncertainty of G‐20 countries using monthly environmental, social, and governance (ESG) equity and economic policy uncertainty (EPU) indices. Using the autoregressive distributed lag model and nonlinear autoregressive distributed lag model models, the study finds a negative long‐run relationship between sustainable equity investments and economic policy uncertainties in Australia, Canada, the USA, Brazil, Mexico, Germany, Italy, and Japan. Investors in these G20 countries may perceive that companies with higher ESG performance are more likely to face regulatory scrutiny, legal action, or reputational damage if they are associated with high levels of economic policy uncertainties. As a result, ESG market indices may underperform when EPU is high and vice versa. It supports prospect theory and suggests that individuals are more sensitive to potential losses than gains. On the contrary, the relationship is positive in the case of the USA, Brazil, China, and to some extent India. This might be because firms with high ESG performance could manage risks better and seize opportunities associated with EPU, which helps ESG market indices to outperform when EPU is high. It is supported by the legitimacy theory that says to maintain the legitimacy and credibility of the company, the investment must be invested in ESG initiatives, which can lead to improved long‐term financial performance and market value.

Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model using Probabilistic Learning with Partial Observability and Incomplete dataset

Abstract This paper introduces a methodology for updating the nonlinear stochastic dynamics of a nozzle with uncertain computational model. The approach focuses on a high-dimensional nonlinear computational model constrained by a small target dataset. Challenges include the large number of degrees-of-freedom, geometric nonlinearities, material uncertainties, stochastic external loads, under-observability, and high computational costs. A detailed dynamic analysis of the nozzle is presented. An updated statistical surrogate model relating the observations of interest to the control parameters is constructed. Despite small training and target datasets, and partial observability, the study successfully applies Probabilistic Learning on Manifolds (PLoM) to address these challenges. PLoM captures geometric nonlinear effects and uncertainty propagation, improving conditional mean statistics compared to training data. The conditional confidence region demonstrates the ability of the methodology to accurately represent both observed and unobserved output variables, contributing to advancements in modeling complex systems.