Modeling Of Flexible Structures Research Articles

Mayfly Algorithm for Modelling a Horizontal Flexible Plate Structure

Flexible plates are widely used in engineering and the industry, primarily due to the lightweight nature compared to rigid counterparts. These structures offer benefits such as cost savings, lower energy consumptions and improved operational safety. However, a notable drawback is that flexible structures are vulnerable to unwanted vibrations, which can cause structural damages. Hence, the development of specialized models are essential to effectively addressing this challenge. Researchers have devised various approaches to suppress unwanted vibrations, with contemporary studies often employing system identification techniques utilizing swarm intelligence algorithms to construct dynamic models of flexible structures. Therefore, this research employs the potent mayfly algorithm (MA), known for its effectiveness in optimization tasks. The developed models using MA were then compared with traditional approach known as recursive least square (RLS) through a comparative analysis. The outcome reveals that RLS exhibited the lowest mean square error (MSE) at , while MA had an MSE of Yet, MA adeptly depicted the characteristics of the system, outperforming the RLS in these validation by indicating a 95% confidence level in the correlation test and exhibiting robust stability in the pole-zero diagram. Consequently, MA serves as a fitting algorithm to accurately depict the real behaviour of the flexible plate structure.

Underwater dynamic modeling and experiments of flexible structure with harmonic actuation of macro fiber composites

Flexible structures driven by smart actuators are promising alternatives for aquatic bionic propulsion vehicles. However, the hydrodynamic effects induced by viscous fluids on the dynamic response of flexible structures remain an ongoing challenge. Thus, this article presents a fluid-structure interaction dynamic model of a flexible underwater structure actuated by macro fiber composite actuators, and the underwater multimodal vibration characteristics are studied. The model is based on the extended Hamilton’s principle, which considers the effects of hydrodynamic forces. The segmented mode shape functions of the flexible structure are derived using the assumed mode method. The study shows that the first two mode shapes of the beam predicted by the model agrees with the experimental results. The underwater dynamic responses of the flexible structure in a board band covering the first two resonance frequencies are also investigated at different actuation levels. The underwater results indicate that the first two resonance frequencies are 1.05 and 6.1 Hz respectively, basically consistent with the simulation ones (1.07 and 6.61 Hz). Correspondingly, the maximum transverse deflections at the end of the beam are 4.81 and 1.31 mm respectively, which are slightly lower than the predicted values of 5.75 and 1.62 mm. Therefore, the validity of the proposed coupled dynamic model is verified, which can be utilized to predict the multimodal dynamic responses of underwater flexible structures actuated by MFC or other smart actuators.

Joint Learning of Failure Mode Recognition and Prognostics for Degradation Processes.

The paper aims to develop a joint learning method for failure mode recognition and RUL prediction of operating units. Specifically, the developed method addresses a challenging issue in practice, i.e., how to effectively conduct failure mode recognition and RUL prediction as a joint task based on interpretable extracted degradation features from multiple sensor signals. To implement this method in practice, four steps are included as follows: First, collect multiple sensor signals, failure time, and failure modes of historical units. Second, construct the joint learning model based on features extracted from sensor signals by considering the degradation mechanism. Third, estimate model parameters using the data of historical units. Fourth, recognize the failure mode and predict the RUL of an in-service unit. Since the proposed method is a data-driven neural network with flexible model structure that considers complex data relationships, it is expected to be applicable to many practical situations and use cases, especially for manufacturing systems with complex structures and unknown failure thresholds.

Exposure to residential traffic and trajectories of unhealthy ageing: results from a nationally-representative cohort of older adults

BackgroundTraffic exposure has been associated with biomarkers of increased biological ageing, age-related chronic morbidities, and increased respiratory, cardiovascular, and all-cause mortality. Whether it is associated with functional impairments and unhealthy ageing trajectories is unknown.MethodsNationally representative population-based cohort with 3,126 community-dwelling individuals aged ≥60 years who contributed 8,291 biannual visits over a 10 year period. Unhealthy ageing was estimated with a deficit accumulation index (DAI) based on the number and severity of 52 health deficits, including 22 objectively-measured impairments in physical and cognitive functioning. Differences in DAI at each follow-up across quintiles of residential traffic density (RTD) at 50 and 100 meters, and closest distance to a petrol station, were estimated using flexible marginal structural models with inverse probability of censoring weights. Models were adjusted for sociodemographic and time-varying lifestyle factors, social deprivation index at the census tract and residential exposure to natural spaces.ResultsAt baseline, the mean (SD) age and DAI score of the participants were 69.0 (6.6) years and 17.02 (11.0) %, and 54.0% were women. The median (IQR) RTD at 50 and 100 meters were 77 (31-467) and 509 (182-1802) vehicles/day, and the mean (SD) distance to the nearest petrol station of 962 (1317) meters. The average increase in DAI (95%CI) for participants in quintiles Q2-Q5 (vs Q1) of RTD at 50 meters was of 1.51 (0.50, 2.53), 0.98 (-0.05, 2.01), 2.20 (1.18, 3.21) and 1.98 (0.90, 3.05), respectively. Consistent findings were observed at 100 meters. By domains, most of the deficits accumulated with increased RTD were of a functional nature, although RTD at 50 meters was also associated with worse self-reported health, increased vitality problems and higher incidence of chronic morbidities. Living closer to a petrol station was associated with a higher incidence of functional impairments and chronic morbidities.ConclusionsExposure to nearby residential traffic is associated with accelerated trajectories of unhealthy ageing. Diminishing traffic pollution should become a priority intervention for adding healthy years to life in the old age.

Dynamics modeling and attitude-vibration hybrid control of a large flexible space structure

The challenges in the dynamic modeling and control of large flexible space structures (LFSSs) include the high number of degrees of freedom in the model, the geometric nonlinearities in the flexible components, and the nonlinear coupling effects between overall motions and vibrations of the structure. In this study, we propose a systematic method to solve the mentioned problems in the research of LFSSs. We use the referenced nodal coordinate formulation (RNCF) to build the dynamic model. The modeling method is general and the model can better describe the large deformations of flexible structures compared with the modal-based dynamic model. The key feature of our work is that we use a hybrid control strategy for the attitude and vibration control directly performing on the full-scale dynamic model. Specifically, the control strategy combines a proportional and derivative (PD) control algorithm based on SO(3) for the attitude control and an analytical linear-quadratic (LQ) vibration control method for the vibration control. The simulation results are presented to demonstrate the effectiveness of the proposed hybrid control strategy both in the three-axis attitude maneuver task and on-orbit pointing scenario.

An interface-enhanced discrete element model (I-DEM) of bio-inspired flexible protective structures

Natural organisms have evolved protective structures that help them survive environmental threats without sacrificing flexibility. While the discrete element method (DEM) has been used to model bio-inspired flexible protective structures, existing DEM models struggle with modeling interactions between elements of complex geometries and finite deformation, making them unsuitable for describing interfacial interlocking and associated failure mechanisms observed in experiments. Here, we propose an interface-enhanced DEM (I-DEM) model that brings about a number of advantages over the existing methods. First, contact-induced geometric nonlinearity is incorporated via a nonlinear interaction function to model interfacial interlocking. Second, finite deformation of interfacial material is taken into account in modeling interfacial failure. Furthermore, contact detection and processing algorithms are utilized to model collision and post-contact behaviors. The utility of I-DEM is demonstrated in modeling bio-inspired flexible protective structures across three distinct interfaces, evidencing its broad applicability to varied interface geometries. FEM simulations of scaled armor structures with segmented helmet and space shield are performed to demonstrate an increase in computational efficiency of the proposed I-DEM model by about four orders of magnitude for static analysis and by two orders of magnitude for dynamic analysis, with numerical discrepancies within about 10%, compared to FEM.

Modeling and Analysis of Flexible Deployable Structure with Scissor-Like Elements Using a Novel Non-Locking Beam Element

The main goal of this paper is to develop a comprehensive dynamic modeling and analysis method based on the absolute node coordinate method to predict the impact of the member flexibility on the dynamic performance of scissor deployable structure under the framework of flexible multi-body dynamics. In the process, three locking problems in the classical plane shear beam element are reviewed and discussed. Then a novel non-locking planar beam element is proposed to solve the element locking problem, and the dynamic model of the scissor deployable structure is established. Next, the cantilever beam, simply supported beam and flexible simple pendulum are respectively used to verify the correctness of the proposed non-locking beam element from the static and dynamic perspectives. Finally, the simulation and experiment results show that the proposed new non-locking element and normal matrix modeling method can effectively evaluate the dynamic performance of deployable structure and improve the computational efficiency. This study not only provides theoretical guidance for the dynamic behavior analysis and prediction of scissor deployable structure, but also offers reference for the layout and design of same type mechanisms.

Identification of structured nonlinear state–space models for hysteretic systems using neural network hysteresis operators

Hysteretic system behavior is ubiquitous in science and engineering fields including measurement systems and applications. In this paper, we put forth a nonlinear state–space system identification method that combines the state–space equations to capture the system dynamics with a compact and exact artificial neural network (ANN) representation of the classical Prandtl–Ishlinskii (PI) hysteresis. These ANN representations called PI hysteresis operator neurons employ recurrent ANNs with classical activation functions, and thus can be trained with classical neural network learning algorithms. The structured nonlinear state–space model class proposed in this paper, for the first time, offers a flexible interconnection of PI hysteresis operators with a linear state–space model through a linear fractional representation. This results in a comprehensive and flexible model structure. The performance is validated both on numerical simulation and on measurement data.

Rotation tracking control strategy for space flexible structures based on neural network compensation

Flexible space structures play an invaluable role in space operation tasks as large attachments to spacecraft and artificial satellites. Due to the characteristics of flexibility and slenderness, flexible space structures will inevitably cause flexible deformation and vibrate during motion, which will affect the rotation control accuracy. To solve this problem, this paper proposes a rotation tracking control strategy based on sliding mode controller with neural network compensation. The control law is designed according to the dynamic model established by the new flexible deformation description method. Firstly, according to the new flexible deformation description method, the dynamic model of space flexible structures is established by the assumed modal method and Lagrange principle. Through a comparison of the accuracy of the dynamics model deformation, it is found that the new flexible deformation description method has high model accuracy. Based on the space flexible structures’ dynamics model, the sigmoid function is proposed as the convergence law for designing the sliding mode controller. Besides, neural networks are proposed to identify and compensate for the disturbing torque caused by ignoring nonlinear factors. Finally, the effectiveness of the sliding mode controller with neural network compensation is validated by simulation and ground control experiments.

Neuron-Compressed Deep Neural Network and Its Application in Industrial Anomaly Detection

Data modeling and online monitoring are two critical stages for data-driven anomaly detection. Regarding data modeling, deep neural networks (DNNs) can learn good decision boundaries to separate the anomaly and normal regions, due to its flexible model structures and excellent fitting ability. However, DNNs, using nonlinear activations with specific boundaries, may indirectly cause a limited anomaly detection margin, especially when there are samples far from centroids. Moreover, an anomaly detection model with a narrow detection margin is deemed insensitive to general faults.An anomaly detection model with a tight detection margin will suffer a severe performance degradation. To mitigate the intrinsic drawbacks of DNNs, we develop a new regularizer based on the maximum likelihood of complete data (i.e., observations and latent variables). The regularizer is neuron-wise and mathematically acts as compressing neurons, dragging the marginal points into the centroids. Combining the regularizer with the encoding-decoding structure networks, we perform an industrial case study to verify the superiority of the proposed method.

A high level estimation of the net economic benefits to small-scale livestock producers arising from animal health product distribution initiatives.

A fundamental challenge for charities that facilitate distribution of animal health products to small-scale livestock producers (SSPs) in low and middle income countries (LMICs) is identifying the products and market mechanisms that provide the greatest positive impact for SSPs and estimating their associated impact. This paper describes a pragmatic approach to modeling the impact of market-led product distribution initiatives based on estimating the net economic benefit of administration of animal health products. The model estimates the economic impact of diseases at the individual animal level for poultry, small ruminants, and cattle. The economic impact of mortality and growth inhibition associated with disease are then estimated in conjunction with the losses averted or recovered by preventing or treating the disease. Economic benefit is estimated in 2014-2017 values and also adjusted to 2023 values. The flexible model structure allows for addition of new geographies, new products, and increased granularity of modeled production systems. Applied to the Global Alliance for Livestock Veterinary Medicines (GALVmed) product distribution initiatives conducted in Africa and South Asia (SA) between 2014 and 2017, the model estimates an adjusted total net economic benefit of 139.9 million USD from sales of vaccines and poultry anthelminthics in these initiatives. Within SSA, the greatest net economic benefit was realized from East Coast fever and Newcastle disease vaccines, while in SA, peste des petits ruminants and Newcastle disease vaccines had the greatest net economic benefits. This translated to an adjusted $37.97 of net economic benefit on average per SSP customer, many of whom were small poultry producers. While the model currently estimates impacts from mortality and growth inhibition in livestock, there is the potential to extend it to cover impacts of further initiatives, including interventions targeted at diseases that impact production of milk, eggs, and reproduction.

Dynamics of Medical Screening: A Simulation Model of PSA Screening for Early Detection of Prostate Cancer

In this study, we present a novel simulation model and case study to explore the long-term dynamics of early detection of disease, also known as routine population screening. We introduce a realistic and portable modeling framework that can be used for most cases of cancer, including a natural disease history and a realistic yet generic structure that allows keeping track of critical stocks that have been generally overlooked in previous modeling studies. Our model is specific to prostate-specific antigen (PSA) screening for prostate cancer (PCa), including the natural progression of the disease, respective changes in population size and composition, clinical detection, adoption of the PSA screening test by medical professionals, and the dissemination of the screening test. The key outcome measures for the model are selected to show the fundamental tradeoff between the main harms and benefits of screening, with the main harms including (i) overdiagnosis, (ii) unnecessary biopsies, and (iii) false positives. The focus of this study is on building the most reliable and flexible model structure for medical screening and keeping track of its main harms and benefits. We show the importance of some metrics which are not readily measured or considered by existing medical literature and modeling studies. While the model is not primarily designed for making inferences about optimal screening policies or scenarios, we aim to inform modelers and policymakers about potential levers in the system and provide a reliable model structure for medical screening that may complement other modeling studies designed for cancer interventions. Our simulation model can offer a formal means to improve the development and implementation of evidence-based screening, and its future iterations can be employed to design policy recommendations to address important policy areas, such as the increasing pool of cancer survivors or healthcare spending in the U.S.

On variable stiffness of flexible parallel electroadhesive structures

Electrostatic layer jamming represents a lightweight, low energy consumption, electrically tunable, and cost-effective variable stiffness structure. Flexible parallel electroadhesive structures are the simplest form of electrostatic layer jamming. There is a lack of comprehensive and experimentally validated theoretical variable stiffness models of flexible parallel electroadhesive structures. Here we present the first variable stiffness model of flexible parallel electroadhesive structures under three-point bending, cantilever beam bending subjected to tip concentrated forces, and cantilever beam bending subjected to uniformly distributed forces, using the Euler–Bernoulli beam theory and considering friction and slip between layers by integrating the Maxwell stress tensor into the model. We find that: (1) three-point bending and cantilever beam bending under tip concentrated forces only have pre-slip and full-slip, whereas cantilever beam bending under uniformly distributed forces has an additional partial-slip which can be used for stiffness modulation; (2) the stiffness during the pre-slip stage is four times larger than the stiffness in the full-slip stage; and (3) increasing the voltage, dielectric permittivity, and coefficient of friction can elongate the pre-slip stage, thus enhancing the structural load capability. A customized three-point bending and a cantilever beam bending experimental setup were developed and the experimental deflection–force curve agreed relatively well with the theoretical one. The model, which considered electrode thickness and Young’s modulus, and the results presented in this work are useful insights for understanding the variable stiffness mechanism of electroadhesive layer jamming and are helpful for their structural optimization towards practical applications.

Spectral decomposition of atomic structures in heterogeneous cryo-EM

We consider the problem of recovering the three-dimensional atomic structure of a flexible macromolecule from a heterogeneous cryogenic electron microscopy (cryo-EM) dataset. The dataset contains noisy tomographic projections of the electrostatic potential of the macromolecule, taken from different viewing directions, and in the heterogeneous case, each cryo-EM image corresponds to a different conformation of the macromolecule. Under the assumption that the macromolecule can be modelled as a chain, or discrete curve (as it is for instance the case for a protein backbone with a single chain of amino-acids), we introduce a method to estimate the deformation of the atomic model with respect to a given conformation, which is assumed to be known a priori. Our method consists on estimating the torsion and bond angles of the atomic model in each conformation as a linear combination of the eigenfunctions of the Laplace operator in the manifold of conformations. These eigenfunctions can be approximated by means of a well-known technique in manifold learning, based on the construction of a graph Laplacian using the cryo-EM dataset. Finally, we test our approach with synthetic datasets, for which we recover the atomic model of two-dimensional and three-dimensional flexible structures from simulated cryo-EM images.

Does symmetry preclude the evolution of senescence? A comment on Pen and Flatt 2021

Does symmetry preclude the evolution of senescence? A comment on Pen and Flatt 2021

Computer-assisted modeling of Quorum sensing in bacterial population exposed to antibiotics

A mathematical model for bacterial growth and control by antibiotics treatment, including Quorum sensing as a special kind of communication, is introduced. We aim in setting up a flexible model structure allowing for fast simulations and overview about the general behavior. The deterministic approach can be used for in silico studies of bacterial cooperative behavior in the special case of Quorum sensing. Since antibiotic treatment is the basic and vital way to fight pathogenic bacteria, in the present study, we propose a modification of a reaction-diffusion model of communication processes in a bacterial population exposed to antibiotics. The dynamical biological system is formalized by a system of semilinear parabolic PDEs. The numerical solution of the 2D problem is based on a hybrid computing procedure, which includes a finite difference method combined with a Monte-Carlo simulation of population dynamics. Computational experiments are performed to describe space-time distributions of key chemical compounds characterizing Quorum sensing during the growth of a bacterial population and its decrease resulting from the predetermined strategy of antibiotic treatment.

Bayesian Workflow and Hidden Markov Energy-Signature Model for Measurement and Verification

A Bayesian data analysis workflow offers great advantages to the process of measurement and verification, including the estimation of savings uncertainty regardless of the chosen numerical model. However, it is still rarely used in practice, perhaps because practitioners are less familiar with the required tools. The present work documents a Bayesian methodology for the assessment of energy savings at the scale of a whole facility, following an energy-conservation measure. The first model, an energy signature commonly used in practice, demonstrates the steps of the Bayesian workflow and illustrates its advantages. The posterior distributions obtained by training this first model are used as prior distributions for a second, more complex model. This so-called “hidden Markov energy signature” model combines the energy signature with a hidden Markov model at an hourly resolution, and allows detection of occupancy. It has a large number of parameters and would likely not be identifiable without the Bayesian workflow. The results illustrate the advantages of the Bayesian methodology for measurement and verification: a probabilistic description of all variables, including predictions of energy use and savings; the applicability to any model structure; the ability to include prior knowledge to facilitate training complex models. Savings are estimated by the new hidden Markov energy-signature model with a much lower uncertainty than with a lower-resolution model. The highlights of the paper are twofold: it serves as a tutorial on Bayesian inference for measurement and verification; it also proposes a new flexible model structure for hourly prediction of energy use and occupancy detection.

A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves

AbstractThere has been significant progress made towards understanding the process controls that govern the shapes of flow duration curves (FDC). However, the transferability of the knowledge gained to large spatial scales is still lacking, partly due to the fixed representation of the model structures representing the unobserved underground layer. In the context of improving large scale applicability/transferability of FDC studies: (1) we propose a novel approach to transform/restructure the representation of the subsurface component in hydrological models to be flexible and adaptable in any environment and (2) provide a thorough interpretation of the dominant processes that impact the shape of FDCs. The flexible structure is designed from a series of multiple connected linear reservoirs that are developed from a unique multiple hydrograph separation procedure, to estimate the number of distinct hydrological processes at play. The main results are: (1) wet catchments have more complex model structures (more reservoirs) than arid catchments; indicating that subsurface processes are potentially more present in wet catchments than in drier ones. Dry catchments have more near surface occurring processes, thus have less complex model structures. However, more complexity is potentially required within the internal reservoirs of dry catchments. (2) The strength of interference in the intermediate reservoirs in controlling the dominant processes towards the bottom determined the shape of FDCs, and this increased with aridity. The novel finding is that a model structure can be determined by the catchment's ability to generate flows as well as the strength of low flow persistence. Hence, we propose a conceptual framework for the development of flexible model structures based on the shapes of FDCs. The transformation of models to be flexible in this research, should contribute to studies that seek the transferability of models over large spatial scales.

Machine learning approach for spatial modeling of ridesourcing demand

Accurately forecasting ridesourcing demand is important for effective transportation planning and policy-making. With the rise of Artificial Intelligence (AI), researchers have started to utilize machine learning models to forecast travel demand, which, in many cases, can produce higher prediction accuracy than statistical models. However, most existing machine-learning studies used a global model to predict the demand and ignored the influence of spatial heterogeneity (i.e., the spatial variations in the impacts of explanatory variables). Spatial heterogeneity can drive the parameter estimations varying over space; failing to consider the spatial variations may limit the model's prediction performance. To account for spatial heterogeneity, this study proposes a Clustering-aided Ensemble Method (CEM) to forecast the zone-to-zone (census-tract-to-census-tract) travel demand for ridesourcing services. Specifically, we develop an interactive clustering approach (powered by human-in-the-loop AI) to split the origin-destination pairs into different clusters and ensemble the cluster-specific machine learning models for prediction. We implement and test the proposed methodology by using the ridesourcing-trip data in Chicago. The results show that, with a more transparent and flexible model structure, the CEM significantly improves the prediction accuracy than the benchmark models (i.e., global machine-learning and statistical models directly trained on all observations). This study offers transportation researchers and practitioners a new methodology of travel demand forecasting, especially for new travel modes like ridesourcing and micromobility.

Comparative Study of Lifting Surface and CFD Methods in the Simulation of Morphing Process of Folding Wing

A folding wing aircraft can autonomously change its configuration in flight to respond to different flying environments. Hinge moment during morphing process is an important basis for driving mechanism design and structural strength check, and its calculation depends on the simulation of the morphing process. Existing studies mostly use the simplified lifting surface method for aerodynamic modeling. In this paper, the CFD-based simulation method is studied, and the results are compared with those by the lifting surface method. First, the unsteady aerodynamic modeling method of the folding wing based on the CFD method is studied, an effective description method is given to define the wall mesh motion caused by wing folding and aileron deflection, and an unfolding–refolding strategy is proposed to improve the quality of the internal mesh in the case of large folding angles. Then, the CFD aerodynamic model is coupled through a developed coupling calculation program with the flexible multibody structure model for the simulation of a flight-morphing process. The comparison with the results of the lifting surface method shows that hinge moments obtained according to the two aerodynamic models are markedly different. Analysis of the difference shows that airfoil thickness considerably affects aerodynamic loading distributions and hinge moments of folding wing aircraft. The lifting surface method ignores airfoil thickness, which will cause large simulation errors in hinge moments.