Modeling Strategy Research Articles

An adaptive learning strategy for surrogate modeling of high-dimensional functions - Application to unsteady hypersonic flows in chemical nonequilibrium

Many engineering applications rely on the evaluation of expensive, non-linear high-dimensional functions. In this paper, we propose the RONAALP algorithm (Reduced Order Nonlinear Approximation with Adaptive Learning Procedure) to incrementally learn, on-the-fly as the application progresses, a fast and accurate reduced-order surrogate model of a target function. First, a combination of nonlinear auto-encoder, community clustering, and radial basis function networks allows us to learn an efficient and compact surrogate model with limited training data. Secondly, an active learning procedure overcomes any extrapolation issues during the online stage by adapting the surrogate model with high-fidelity evaluations that fall outside its current validity range. This approach results in generalizable, fast, and accurate reduced-order models of high-dimensional functions. The method is demonstrated on three direct numerical simulations of hypersonic flows in chemical nonequilibrium. Accurate simulations of these flows rely on detailed thermochemical gas models that dramatically increase the cost of such calculations. Using RONAALP to learn a reduced-order thermodynamic model surrogate on-the-fly, the cost of such simulations was reduced by up to 75% while maintaining an error of less than 10% on relevant quantities of interest.

Generalized field inversion strategies for data-driven turbulence closure modeling

Most data-driven turbulence closures are based on the general structure of nonlinear eddy viscosity models. Although this structure can be embedded into the machine learning algorithm and the Reynolds stress tensor itself can be fit as a function of scalar- and tensor-valued inputs, there exists an alternative two-step approach. First, the spatial distributions of the optimal closure coefficients are computed by solving an inverse problem. Subsequently, these are expressed as functions of solely scalar-valued invariants of the flow field by virtue of an arbitrary regression algorithm. In this paper, we present two general inversion strategies that overcome the limitation of being applicable only when all closure tensors are linearly independent. We propose to either cast the inversion into a constrained and regularized optimization problem or project the anisotropy tensor onto a set of previously orthogonalized closure tensors. Using the two-step approach together with either of these strategies then enables us to quantify the model-form error associated with the closure structure independent of a particular regression algorithm. Eventually, this allows for the selection of the a priori optimal set of closure tensors for a given, arbitrary complex test case.

Cell-centered Lagrange+Remap numerical strategy for a multi-material multi-velocity model

The present work is devoted to the numerical approximation of multi-material flows. The so-called 6-equation system is considered where each material has its own velocity but shares the same pressure with the rest of the mixture. This model is a necessary building block as dissipation is completely removed outside of shocks. A numerical scheme is then presented and extends the classical “Lagrange+Remap” strategy to a multi-velocity setting. Entropy considerations are the focal point of its derivation as a mean of stabilizing results, ensuring thermodynamical consistency and selecting shocks of interest. Finally the scheme’s robustness and ability to deal with the inner stiffness of contrasted mixtures are evaluated on several test cases.

Distributed dynamic economic dispatch of biogas-wind-solar-hydrogen multi-microgrid system considering individual selfishness

This paper proposes a biogas-wind-solar-hydrogen multi-microgrid system to address the issues of poor economy and reliability, as well as the waste of wind and solar energy, in single energy-based isolated microgrid systems. The study considers the coupling constraints of multiple time scales and establishes a dynamic economic dispatch model. Furthermore, a consensus-based distributed dynamic economic dispatch strategy is proposed. To tackle the challenge of unified economic dispatch caused by the interaction among multiple microgrids in the joint operation of the biogas-wind-solar-hydrogen multi-microgrid system, a microgrid selfishness impact model and elimination strategy are developed. Simulation results demonstrate the effectiveness and superiority of the proposed distributed dynamic economic dispatch strategy considering individual selfishness in the biogas-wind-solar-hydrogen multi-microgrid system.

Family‐centered creative arts therapies for children with autism: A configurative systematic review

AbstractObjectiveThis configurative systematic review analyzed family‐centered creative arts therapies for children with autism to understand why and how they are done.BackgroundCreative arts therapies and family‐centered practice are both beneficial for children with autism. Research combining both approaches—that is, family‐centered creative arts therapies—is emerging, and reported findings are promising. A deeper understanding of the theoretical influences and mechanisms of these therapies may generate new ways of thinking and working with families with children with autism.MethodsFollowing registration with PROSPERO and in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) guidelines, a literature search was performed, resulting in 17 studies. Thematic analysis considered the studies in terms of theoretical influences, creative arts therapy components, and family‐centered components.ResultsThe development‐in‐context perspective was a key theoretical influence. In addition to music and singing, body movement/physical activity was a prominent component of creative arts therapies. Verbal support as well as modeling behaviors and strategies to parents were salient family‐centered components.ConclusionThis systematic review reveals common principles, practices, and gaps in the existing literature and their relevance for families with children with autism.ImplicationsThese findings bear implications for families, therapists, Certified Family Life Educators, and other professionals working with families with children with autism.

MPC Optimization Algorithm and Strategy for HVAC System under Smart City Construction

As a giant in energy consumption, buildings urgently need to optimize control strategies for the main energy consuming equipment inside buildings. It is of great significance to design advanced control algorithms to improve the efficiency of the main energy consuming equipment in buildings, namely air conditioning, in the current energy shortage. The model predictive control algorithms and strategies were used in this study to control the HVAC system to improve the energy utilization of the city. Then linear matrix inequality with robust model predictive feedback controller was used to optimize and get the model predictive control optimization algorithm. The research results showed that, under the influence of different factors, the three regions controlled by the model predictive control optimization algorithm showed a little overshooting in the initial state. But it was quickly corrected after adjustment. Meanwhile, the average tracking error of temperature and humidity in each region was 0.139◦ C and 0.13g/kg dry air, respectively. The average predicted mean vote was 0.32. In actual office buildings, the proposed algorithm controlled the temperature within the reference value range of 0.1◦ C throughout the entire process. The total electricity consumption and electricity price costs were reduced by 12.11% and 22.54%, respectively. In summary, the proposed method has good performance for HVAC system application, which can effectively realize energy saving and emission reduction and improve human comfort. This method makes important contributions to promote the construction of smart cities and the development of green buildings.

Cardiac and skeletal muscle manifestations in the G608G mouse model of Hutchinson-Gilford progeria syndrome.

Hutchinson-Gilford progeria syndrome (HGPS) is a rare premature aging disorder resulting from de novo mutations in the lamin A gene. Children with HGPS typically pass away in their teenage years due to cardiovascular diseases such as atherosclerosis, myocardial infarction, heart failure, and stroke. In this study, we characterized the G608G HGPS mouse model and explored cardiac and skeletal muscle function, along with senescence-associated phenotypes in fibroblasts. Homozygous G608G HGPS mice exhibited cardiac dysfunction, including decreased cardiac output and stroke volume, and impaired left ventricle relaxation. Additionally, skeletal muscle exhibited decreased isometric tetanic torque, muscle atrophy, and increased fibrosis. HGPS fibroblasts showed nuclear abnormalities, decreased proliferation, and increased expression of senescence markers. These findings provide insights into the pathophysiology of the G608G HGPS mouse model and inform potential therapeutic strategies for HGPS.

A PICTURE WORD INDUCTIVE MODEL TO ENHANCING VOCABULARY ACQUISITION AT THE PRIMARY LEVEL

The current research investigates the effect of using the Picture Word Inductive Model (PWIM) Strategy on Vocabulary Acquisition at the Primary Level. The research's null hypotheses have been formulated to be tested in light of the aim. The design of two equivalent groups, namely the experimental and control groups with pre and post-tests, has been adopted. The research sample included 60 female pupils from two primary schools intentionally selected from the center of Nineveh Governorate. They were divided into two groups: the experimental group included 30 female pupils taught according to the PWIM strategy at Al Talai Primary School. The control group included (30) female pupils taught according to the Subscribed method at Al Shaqa'ik Primary School. Both groups were equalized according to a set of variables: pupil's date of birth, pupil's general average of the preceding academic year, pupil's scores in English of the preceding academic year, and parents'. Moreover, the main requirements of this research were prepared, such as determining the teaching materials and preparing the teaching plans as well as; the researcher had prepared an instrument to collect the data based on the dependent variables of this research, namely the vocabulary Acquisition test of a passage followed by five questions. The result reveals a statistically significant difference (0.05) between the experimental and control groups in the Post-Test of Vocabulary Acquisition in favor of The Experimental group. Finally, recommendations and suggestions for further research are presented concerning the necessity of applying the (PWIM) in teaching the English language.

Modeling and simulation-assisted strategies for effective membrane-fouling mitigation during membrane bioreactor operation

This research principally aimed to present a suitable strategy for membrane-fouling mitigation in membrane-bioreactors (MBRs). The current strategies for membrane-fouling mitigation before initiating the process in many cases, are unmodifiable for a specific MBR system along the operations. Thus, membrane-fouling strategies during filtration should be applied. To select the best and most economical method for controlling fouling during the operations, the quality (site and mechanism) as well as quantity (thickness, mass, and porosity of the cake layer, and pore resistances) of fouling should be predicted. Accordingly, in this research, two powerful tools, i.e. modeling and simulation, have been used for predicting the quality and quantity of fouling, respectively. Through modeling, the best model describing the site and mechanism of fouling was chosen. Through simulation, the thickness, mass and porosity of the cake layer, along with resistance of cake and pores were calculated. In addition, the match between the results of modeling, simulation, and experimental results confirmed the accuracy of the performed predictions. Ultimately, to achieve the minimum membrane-fouling during filtration, based on the modeling results, the general solution of washing (physical or chemical), and based on the simulation results, its intensity (low, medium, and high) were proposed.

Prospects for a survey of the galactic plane with the Cherenkov Telescope Array

Approximately one hundred sources of very-high-energy (VHE) gamma rays are known in the Milky Way, detected with a combination of targeted observations and surveys. A survey of the entire Galactic Plane in the energy range from a few tens of GeV to a few hundred TeV has been proposed as a Key Science Project for the upcoming Cherenkov Telescope Array Observatory (CTAO). This article presents the status of the studies towards the Galactic Plane Survey (GPS). We build and make publicly available a sky model that combines data from recent observations of known gamma-ray emitters with state-of-the-art physically-driven models of synthetic populations of the three main classes of established Galactic VHE sources (pulsar wind nebulae, young and interacting supernova remnants, and compact binary systems), as well as of interstellar emission from cosmic-ray interactions in the Milky Way. We also perform an optimisation of the observation strategy (pointing pattern and scheduling) based on recent estimations of the instrument performance. We use the improved sky model and observation strategy to simulate GPS data corresponding to a total observation time of 1620 hours spread over ten years. Data are then analysed using the methods and software tools under development for real data. Under our model assumptions and for the realisation considered, we show that the GPS has the potential to increase the number of known Galactic VHE emitters by almost a factor of five. This corresponds to the detection of more than two hundred pulsar wind nebulae and a few tens of supernova remnants at average integral fluxes one order of magnitude lower than in the existing sample above 1 TeV, therefore opening the possibility to perform unprecedented population studies. The GPS also has the potential to provide new VHE detections of binary systems and pulsars, to confirm the existence of a hypothetical population of gamma-ray pulsars with an additional TeV emission component, and to detect bright sources capable of accelerating particles to PeV energies (PeVatrons). Furthermore, the GPS will constitute a pathfinder for deeper follow-up observations of these source classes. Finally, we show that we can extract from GPS data an estimate of the contribution to diffuse emission from unresolved sources, and that there are good prospects of detecting interstellar emission and statistically distinguishing different scenarios.Thus, a survey of the entire Galactic plane carried out from both hemispheres with CTAO will ensure a transformational advance in our knowledge of Galactic VHE source populations and interstellar emission.

Influence of Rarefaction Degree and Aft-Body Geometry on Supersonic Flows

During atmospheric entry, super-/hypersonic vehicles cross distinct atmospheric layers characterized by large density variations and thus experience different flow regimes ranging from free molecular, transition, slip, to continuous regimes. Due to the distinct modeling strategy between these regimes and complex physical phenomena appearing near the vehicles (boundary-layer/shock interaction, base-flow recirculation, etc.), assessing their aerodynamic properties may be difficult. The present work focuses on supersonic flows around sharp-base geometries in both continuous and slip-flow regimes and aims at highlighting the influence of both rarefaction degree and base geometry on the vehicles’ aerodynamic features. For this purpose, three axisymmetric cone-cylinder geometries with right-angled, rounded, or flared rear parts are considered. Flow visualization, pressure, and drag measurements are carried out at Mach number Ma=4 and Knudsen numbers ranging from Kn=4×10−4 to 1×10−2 in the supersonic rarefied MARHy wind tunnel. The experimental data are compared with numerical results of simulations performed with a continuous-flow Navier–Stokes (NS) solver and two rarefied flows codes: a discrete-ordinate Bhatnagar–Gross–Krook (K) solver and a direct simulation Monte Carlo (SPARTA) solver. While the NS solver overestimates frictional drag as Kn rises, the rarefied K and SPARTA results show satisfactory agreement with experimental data. The latter numerical results highlight the main effects of rarefaction: as Kn increases, shocks become more diffuse, skin friction strengthens (leading to a significant increase in drag coefficients), and the extent of the base-recirculation decreases. Regarding the aft-body geometry, its influence on the base recirculation vanishes with increasing Kn.

Temperature, Crime, and Violence: A Systematic Review and Meta-Analysis.

Heat is known to affect many health outcomes, but more evidence is needed on the impact of rising temperatures on crime and/or violence. We conducted a systematic review with meta-analysis regarding the influence of hot temperatures on crime and/or violence. In this systematic review and meta-analysis, we evaluated the relationship between increase in temperature and crime and/or violence for studies across the world and generated overall estimates. We searched MEDLINE and Web of Science for articles from the available database start year (1946 and 1891, respectively) to 6 November 2023 and manually reviewed reference lists of identified articles. Two investigators independently reviewed the abstracts and full-text articles to identify and summarize studies that analyzed the relationship between increasing temperature and crime, violence, or both and met a priori eligibility criteria. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used to extract information from included articles. Some study results were combined using a profile likelihood random-effects model for meta-analysis for a subset of outcomes: violent crime (assault, homicide), property crime (theft, burglary), and sexual crime (sexual assault, rape). This review is registered at PROSPERO, CRD42023417295. We screened 16,634 studies with 83 meeting the inclusion criteria. Higher temperatures were significantly associated with crime, violence, or both. A 10°C (18°F) increase in short-term mean temperature exposure was associated with a 9% [95% confidence interval (CI): 7%, 12%] increase in the risk of violent crime (; eight studies). Studies had differing definitions of crime and/or violence, exposure assessment methods, and confounder assessments. Our findings summarize the evidence supporting the association between elevated temperatures, crime, and violence, particularly for violent crimes. Associations for some categories of crime and/or violence, such as property crimes, were inconsistent. Future research should employ larger spatial/temporal scales, consistent crime and violence definitions, advanced modeling strategies, and different populations and locations. https://doi.org/10.1289/EHP14300.

THE INFLUENCE OF DATA SAMPLING ON SOLVING THE PROBLEM OF PATTERN RECOGNITION FOR DIAGNOSTICS OF INDUSTRIAL EQUIPMENT

With the sophisticated technology that modern industrial organizations are equipped with, state prediction and diagnostics are essential duties. The current research aims to develop a more accurate modified artificial intelligence system for industrial equipment diagnostics in the oil and gas industry. Researching faulty signals and processing methods utilized by equipment in the oil and gas industry, as well as assessing the advantages and disadvantages of different signal extraction strategies, are the first steps in the process. The second is the application of artificial intelligence to decision-making and equipment defect detection. This method widely used by the oil and gas sectors to lower equipment failure rates. The recommended diagnostic system helps organizations reduce the financial risks associated with equipment defects by increasing production dependability, enabling for maintenance planning, predicting probable failures, and expediting equipment repairs. The article is devoted to the study of the data sampling influence on the classifier’s predictive ability in diagnosing of the industrial equipment. Various types of data samples were considered, such as: simple random sample, cluster sample, systematic sample. According to the results of listed data samples were built classifiers based on particle swarm optimization and ensemble models (bagging and voting type). The best results were achieved using the systematic sampled dataset and an ensemble modeling strategy with voting, which combines forecasting based on a neural net, gradient boosted trees and naive Bayes models: accuracy 93.6%; classification error 8%; recall 94.32%; precision 93.87%. The resulting best strategy for diagnosing equipment based on data sampling and an ensemble model was used for implementation in FMEA (Failure Mode and Effects Analysis) technology in order to obtain an improved version, which is adapted for working with big data.

Evaluating the effectiveness of the modified multi-scale multi-physics coupled model for solid oxide fuel cells: A comparative analysis

A multi-scale multi-physics coupling numerical model serves as an effective tool to study the performance and improve the efficiency of solid oxide fuel cells (SOFCs) and provides the theory and data support for optimizing SOFC structure and operational strategy. To improve the accuracy and reliability of the model, this work established a modified simulation model that considers the effect of operational, structural, and physical parameters of SOFCs. First, the electrochemical kinetics equation, multi-component diffusion lattice Boltzmann (LB) model, and heat transfer LB model are modified by incorporating the reaction gas molar fraction, gas mixture concentration, temperature, and other parameters. Second, a regional hierarchical modeling strategy is proposed, and the multi-scale coupling model is modified by coupling mass and heat transfer. Third, considering the effect of carbon monoxide molar fraction, temperature difference, concentration change and porosity on electrochemical, mass, and heat transfer processes, the effectiveness of modification model is analyzed and validated. Results demonstrate that the modified model considerably improves the numerical simulation to handle the complex operational conditions of SOFCs. The modified model's predictions show better accuracy compared with experimental data, while maintaining consistency with the trends predicted by the original numerical models.

A feedforward kinematic error controller with an angular positioning deviations model for backlash compensation of industrial robots

The accuracy of industrial robots, typically on the order of several millimeters, has inhibited their adoption in advanced aerospace manufacturing applications, such as robotic machining. For this reason, extensive investigations have been conducted to develop solutions to improve their accuracy. These solutions can be categorized into offline and online compensation strategies, both of which have advantages and limitations. Offline compensation has been shown to improve the accuracy of industrial robots by two to three orders of magnitude; however, the sensitivity of these solutions to environmental changes and external disturbances can degrade their performance. In contrast, online compensation, which utilizes real-time measurement and compensation, is robust to these changes; however, the performance of these solutions is limited by the causality of time, preventing them from compensating fast changing errors such as backlash. In this work, a novel kinematic modeling strategy, which models the robot’s angular positioning deviations to predict when backlash will occur, is integrated into a novel online kinematic error compensation framework to leverage the advantages of both solutions. By integrating the proposed kinematic model into the online compensation framework, backlash errors can be predicted and preemptively compensated to improve performance. The performance of the combined system was evaluated using ball bar measurements, where it was shown to improve the circularity of the ball bar motion by 25 %.

Enhancing soil nitrogen measurement via visible-near infrared spectroscopy: Integrating soil particle size distribution with long short-term memory models

Good quality of soil nitrogen data, which is essential for the advancement of both enhanced agricultural management and ecological environment, traditionally depends on labor intensive chemical procedures. Visible near-infrared (Vis-NIR) spectroscopy, acknowledged for its efficiency, environmental compatibility and rapidity, merges as a promising alternative. However, the effectiveness of Vis-NIR measurement models are significantly compromised by soil particle size distribution (PSD), presenting a substantial challenge in improving the measurement accuracy and reliability. Here an innovative deep learning methodology that integrates PSD with Vis-NIR spectroscopy was proposed for the measurement of nitrogen content in soil samples. By leveraging the LUCAS dataset, different strategies for integrating PSD with Vis-NIR spectral data in deep learning models were explored, revealing that our proposed InSGraL framework, which incorporated mixed features of PSD and spectra as LSTM inputs achieves superior performance. Compared to models utilizing solely Vis-NIR data, InSGraL exhibits a 39.47 % reduction in RMSE and a 42.55 % decrease in MAE, and demonstrates robust performance across various land cover types, achieving an R2 of 0.94 on grassland samples. Moreover, Shapley Additive exPlanations (SHAP) analysis revealed that incorporating PSD modifies the spectral input importance distribution, effectively mitigating spectral interference from particle size while highlighting critical wavelengths previously obscured. This study provides an innovative modeling strategy to mitigate the influence of PSD by integrating it within deep learning framework using Vis-NIR, contributing a deeper understanding of the relationship between PSD and Vis-NIR spectra for the measurement of nitrogen content and offering an effective means to attain soil nitrogen data.

Data-Driven Relationship for Reduction Factor of Ballasted Railway Bridge Deflections Due to Load Distribution Within Track

Increasing the operating speed of the trains on modern networks necessitates performing dynamic analyses to assess the performance of bridges under passage of trains. The detailed investigation of their responses requires constructing complex computational models capable to take the train–track–bridge interaction effects into account. Such models have successfully been developed; however, employing those elaborated models for practical engineering applications, or to perform studies that require a large number of analyses may become infeasible. Among such situations are conducting probabilistic investigations, screening of entire networks, or sensitivity analyses. These concerns have been addressed by employing simplified models mostly relying on moving load modeling strategy which disregards the train–track–bridge interaction effects. Those neglected contributions can be compensated by implementing additional correction factors. The distribution of loads within track is one of those disregarded effects where a reduction factor is recommended by design guidelines to take its contribution into account. It has been shown that the existing relationship for these reduction factors delivers an acceptable performance for vertical accelerations, while showing a less favorable performance for displacements. Then, a data-driven strategy is adopted in this study to propose easy-to-apply relationships for reduction factors of deflections, due to load distribution within the track. In this context, three different distributive lengths of triangular load footprints have been considered, namely 2.0, 2.5 and 3.0[Formula: see text]m. The procedure employed has trained and tested for more than 1[Formula: see text]200 train configurations, comprising conventional, articulated and regular vehicles, and including several tens of thousand data points for each distributive length. The performance observed in the new models revealed a considerable improvement with respect to the existing relationship.

From protein structure to an optimized chromatographic capture step using multiscale modeling.

Optimizing a biopharmaceutical chromatographic purification process is currently the greatest challenge during process development. A lack of process understanding calls for extensive experimental efforts in pursuit of an optimal process. In silico techniques, such as mechanistic or data driven modeling, enhance the understanding, allowing more cost-effective and time efficient process optimization. This work presents a modeling strategy integrating quantitative structure property relationship (QSPR) models and chromatographic mechanistic models (MM) to optimize a cation exchange (CEX) capture step, limiting experiments. In QSPR, structural characteristics obtained from the protein structure are used to describe physicochemical behavior. This QSPR information can be applied in MM to predict the chromatogram and optimize the entire process. To validate this approach, retention profiles of six proteins were determined experimentally from mixtures, at different pH (3.5, 4.3, 5.0, and 7.0). Four proteins at different pH's were used to train QSPR models predicting the retention volumes and characteristic charge, subsequently the equilibrium constant was determined. For an unseen protein knowing only the protein structure, the retention peak difference between the modeled and experimental peaks was 0.2% relative to the gradient length (60 column volume). Next, the CEX capture step was optimized, demonstrating a consistent result in both the experimental and QSPR-based methods. The impact of model parameter confidence on the final optimization revealed two viable process conditions, one of which is similar to the optimization achieved using experimentally obtained parameters. The multiscale modeling approach reduces the required experimental effort by identification of initial process conditions, which can be optimized.

A fast divide-and-conquer strategy for single-index model with massive data

A fast divide-and-conquer strategy for single-index model with massive data

Numerical modeling of the impact of a large scale waterfall on a solid plate

Recent hydroelectric power plant safety developments have necessitated detailed studies on dam spillway operations, especially during emergency water discharge scenarios such as overtopping. This paper presents a numerical investigation of a 10-meter high waterfall impinging on a solid plate using two-phase flow models. Our approach integrates a coupled Eulerian–Lagrangian Spray Atomization (ELSA) model with an Interface Capturing Method (ICM) to simulate the complex multiphase flow and atomization processes. Our model proficiently mapped the velocity and turbulence within the pre-impact region. To refine the accuracy of the impact pressures, we isolated this zone and implemented the Injection–Reinjection strategy, a novel numerical procedure for this type of configuration. The study reveals significant insights into the dynamic interactions of water packets with the dam structure, highlighting critical impact pressures that can influence dam stability and integrity. Our results, compared against experimental data, demonstrate the effectiveness of the modeling strategies in predicting the fluid behaviors and the subsequent pressures exerted on structural components.