Experiment Modeling Research Articles

Enhancing optimization and reducing machining time of freeform shapes through modeling, simulation, and Taguchi design of experiments with artificial neural networks

Enhancing optimization and reducing machining time of freeform shapes through modeling, simulation, and Taguchi design of experiments with artificial neural networks

A hybrid method of system dynamics and design of experiments for investigating the economic and environmental indicators of electricity industry

Electricity plays a pivotal role in the socio-economic development of nations. However, heavy reliance on fossil fuels for electricity generation, as observed in Iran, poses significant environmental challenges. This study proposes a novel hybrid methodology that combines system dynamics modeling and Design of Experiments (DOE) to examine economic and environmental indicators within Iran's electricity sector. The system dynamics model delineates four key subsystems: consumption, production, CO2 emissions, and power trade. By integrating DOE into this framework, various economic and environmental metrics are assessed for the year 2040. Through a comprehensive analysis of variable impacts on these indicators, optimal levels are identified to achieve favorable outcomes. Notably, variables such as the allocation coefficient of export income to capacity development and electricity export price emerge as critical determinants. Due to economic, environmental, and economic-environmental indicators, the most appropriate level of allocation of export income towards capacity development is estimated at 30, 10, and 20 percent, respectively. The study recommends allocating 80 % of the capacity development budget to renewable energy sources and 20 % to thermal power plants to optimize future conditions. In business as usual, the Export CO2 emission damage to export income index will be 0.19. In implementing the proposed scenario, according to the economic-environmental index, this value will decrease and reach 1.73E-06, which indicates the improvement of electricity export from the economic-environmental dimension. This research underscores the importance of balancing economic prosperity with environmental sustainability in electricity industry planning and policy formulation.

Optimization of the concentration of ozone generated by DBD using PSO algorithm for water treatment process

The water treatment process with ozone is influenced by various operating parameters and environmental factors that can impact its efficiency. In this study, experiments were conducted using a Venturi pumping frame to investigate the effects of three controllable variables: oxygen flow height, applied voltage level, and water flow rate. The tests aimed to develop a mathematical model that accurately represents the relationship between these input variables and the resulting ozone concentration in the treated water. The experimental data was analyzed using the MODDE 5.0 software, a specialized application for statistical modeling and design of experiments. By fitting the data to appropriate model equations, a mathematical model was obtained that quantifies the influence of each variable and their interactions on the ozone concentration response. To optimize the process performance, a particle swarm optimization (PSO) algorithm was employed to extract the best-fit parameters for the mathematical model. PSO is a computational technique inspired by the social behavior of bird flocks or fish schools, utilizing a population of candidate solutions that evolve iteratively to converge on the global optimum solution. In this case, PSO searched for the model parameter values that minimized the error between predicted and experimentally measured ozone concentrations, rapidly converging to an accurate solution. The optimized mathematical model enables predicting the ozone concentration under any combination of oxygen flow height, voltage, and water flow rate within the experimental range. This predictive capability facilitates identifying the optimum operating conditions to maximize ozone concentration, thereby enhancing the efficiency of the water treatment process. The model serves as a valuable tool for process control, monitoring, and optimization, ensuring consistent treatment quality while minimizing resource consumption and operational costs.

Effect of Stabilized nZVI Nanoparticles on the Reduction and Immobilization of Cr in Contaminated Soil: Column Experiment and Transport Modeling.

Batch and transport experiments were used to investigate the remediation of loamy sand soil contaminated with Cr(VI) using zero-valent iron nanoparticles (nZVI) stabilized by carboxymethylcellulose (CMC-nZVI). The effect of pH, ionic strength (IS), and flow rate on the removal efficiency of Cr(VI) were investigated under equilibrium (uniform transport) and non-equilibrium (two-site sorption) transport using the Hydrus-1D model. The overall removal efficiency ranged from 70 to over 90% based on the chemical characteristics of the CMC-nZVI suspension and the transport conditions. The concentration and pH of the CMC-nZVI suspension had the most significant effect on the removal efficiency and transport of Cr(VI) in the soil. The average removal efficiency of Cr(VI) was increased from 24.1 to 75.5% when the concentration of CMC-nZVI nanoparticles was increased from 10 to 250 mg L-1, mainly because of the increased total surface area at a larger particle concentration. Batch experiments showed that the removal efficiency of Cr(VI) was much larger under acidic conditions. The average removal efficiency of Cr(VI) reached 90.1 and 60.5% at pH 5 and 7, respectively. The two-site sorption model described (r2 = 0.96-0.98) the transport of Cr(VI) in soil quite well as compared to the uniform transport model (r2 = 0.81-0.98). The average retardation of Cr(VI) was 3.51 and 1.61 at pH 5 and 7, respectively, indicating earlier arrival for the breakthrough curves and a shorter time to reach maximum relative concentration at lower pH. The methodology presented in this study, combining column experiment and modeling transport using the Hydrus-1D model, successfully assessed the removal of Cr(VI) from polluted soils, offering innovative, cost-effective, and environmentally friendly remediation methodologies.

Airblast observations and near-field modeling of the large surface explosion coupling experiment

Seismoacoustic wave generation for two consecutive surface chemical explosions of the same yield (approximately 1 ton TNT-equivalent) was studied during the Large Surface Explosion Coupling Experiment (LSECE) conducted at Yucca Flat on the Nevada National Security Site (NNSS) site in alluvium geology. We have performed numerical simulations for both chemical explosions to investigate how the non-central source initiation, site topography and soil mechanical properties affect the evolution of the explosion (fireball and cloud), its crater, and variations in the generated blast waves. The results can be used to improve the understanding of surface explosions and their effects and how those effects can be used to infer source information such as explosive yield and emplacement. We find that the non-central detonation of the explosive cube results in non-axisymmetric blast overpressures which persist through the strong and weak shock regimes, in this case out to 200 m and more. The pattern of the secondary shock (i.e., shock created due to slowing explosive products within the expanding fireball) is also affected and its arrival relative to the main shock and may be indicative of explosive type due to its dependence on the explosive products ratio of heats. Small reflections are visible within the overpressure signal that are most probably due to small artifacts in blast path. Importantly, the fireball growth, cavity generation, and cloud formation also depart from spherical and ideal approximations due to ground interactions and material dependence, which shows the importance of realistic geomaterial models for accurate prediction. The asymmetry in peak overpressure is diminished for the second chemical explosion, which was placed in the crater of the first. Numerical modeling shows that the explosive jetting created by the non-central detonation is reduced upon interaction with the crater walls and this has the effect of making the blast generation more axisymmetric.

The odd-number cyclo[13]carbon and its dimer, cyclo[26]carbon

Molecular rings of N carbon atoms (cyclo[ N ]carbons, or C N ) are excellent benchmarking systems for testing quantum chemical theoretical methods and valuable precursors to other carbon-rich materials. Odd- N cyclocarbons, which have been elusive to date, are predicted to be even less stable than even- N cyclocarbons. We report the on-surface synthesis of cyclo[13]carbon, C 13 , by manipulation of decachlorofluorene with a scanning probe microscope tip. We elucidated the properties of C 13 by experiment and theoretical modeling. C 13 adopts an open-shell configuration with a triplet ground state and a kinked geometry, which shows different extents of distortion and carbene localization depending on the molecular environment. Moreover, we prepared and characterized the C 13 dimer, cyclo[26]carbon, demonstrating the potential of cyclocarbons and their precursors as building blocks for carbon allotropes.

Assessing the Performance of a Thermoresponsive Liquid‐Based Smart Windows for Building Thermal Regulation through Outdoor Experimentation and Computational Modeling

AbstractSmart windows have the potential to respond dynamically and passively to external stimuli, controlling the amount of light passing through the window. When a smart window switches from a clear to a translucent state, energy flow through the window is partially attenuated, allowing a room to cool down passively, thereby reducing the energy and fossil fuel consumption for air conditioning. The smart window demonstrated here consists of a thermoresponsive liquid consisting of Tergitol 15‐S‐7, which can dynamically and passively switch the window's transmittance when a temperature of 39 °C is reached. It is also demonstrated how the transition temperature can be lowered by adding salts. Outdoor experiments in realistic environments show that the temperature of a model house built with a thermo‐responsive window can achieve an indoor temperature of 7 °C less than a control house with an ordinary window. This study quantifies the energy savings possible using such windows at the building scale for cooling and heating in different climates and times of the year.

Triaxial behavior of unreinforced and geogrid-reinforced calcareous gravelly sand: Experiment and discrete element modelling

Geogrid reinforcement is a feasible technique for improving the mechanical and deformation behaviors of calcareous gravelly sand that is predominantly distributed in reclaimed foundations as construction materials. However, the shear behavior of geogrid-reinforced calcareous gravelly sand still remains unclear. In this paper, a series of large-scale drained consolidated triaxial compression tests (the specimen 300 mm in diameter by 600 mm in height) were conducted to investigate the effects of confining pressure, moisture content and geogrid inclusion on the mechanical and deformation behaviors of calcareous gravelly sand. The dilation effect, strain-softening behavior, reinforcing effect and particle breakage characteristics of unreinforced and geogrid-reinforced calcareous gravelly sand were comprehensively analyzed and discussed. Additional analysis was also performed from a microscopic perspective using the three-dimensional discrete numerical modelling. The experimental results indicate that calcareous gravelly sand exhibited a greater resistance to strain-softening tendency, and produced a higher susceptibility to crushing under the same input energy than calcareous coarse sand. Geogrid inclusion can effectively mitigate the susceptibility of calcareous gravelly sand to volumetric dilation through constraining the lateral expansion within the reinforced zone, and thus substantially increased the peak shear strength. Moreover, particle breakage appeared to be insensitive to geogrid reinforcement and water content, but increased significantly with increasing confining pressures. The numerical results further indicate that the mechanical response of geogrids was regulated by axial strain and confining pressure. As axial strain was greater than 10%, increased confining pressure resulted in a wider area of stress concentration within the geogrid meshes, and the upper geogrid sustained a greater contact force and tensile strain. The findings in this study are of practical significance for the design and construction of structures in calcareous gravelly sand reinforced with geogrids.

Evaluation and associated mechanism of mitigating Cr depletion by vanadium in modified 9Cr-1Mo steel: Experiment and phase-field modelling

The 9Cr-1Mo steel is extensively utilized in nuclear heat exchangers due to its exceptional corrosion resistance properties. The Chromium (Cr) element plays a pivotal role in enhancing the corrosion resistance. The Cr accumulates at grain boundaries (GBs) where defects and mismatches are located during long-term usage, leading to the depletion of Cr within the grain interior. This results in the Cr-rich carbide precipitates form along GBs, with a corresponding the depletion zone residing in the adjacent regions. In this study, we employed aberration-corrected high-resolution electron microscopy in high-angle annular dark field microscopy (HAADF) mode, equipped with energy dispersive spectroscopy (EDS), to analyse carbides formation along GBs and obtain elemental distribution information. Our investigation reveals that the Cr-rich carbide promotes the formation of chromium-depletion zone (CDZ) by consuming the surrounding Cr in the matrix, and the presence of vanadium-rich carbide inhibits the formation of (CDZ) from atomic scale.A phase-field modelling approach is employed to investigate the diffusive formation process in a polycrystalline system. Our modelling results are consistent with experimental observations. Cr preferentially diffuses towards grain boundaries (GBs) and accumulates, facilitating the formation of Cr-rich phases by consuming adjacent Cr in the surrounding region. The bilateral region of GBs serves as a source for supplying Cr to these newly formed phases while depleting Cr content, ultimately leading to the development of CDZ.By employing experimental techniques and phase-field method, this research provides valuable insights into the role of Cr, carbide precipitates, influence of vanadium, and CDZ formation.

Deciphering global patterns of forest canopy rainfall interception (FCRI): A synthesis of geographical, forest species, and methodological influences

Forest canopy rainfall interception (FRCI) is an essential hydrological process that governs water and biogeochemical cycles in forest ecosystems. Identifying patterns and relationships of FCRI using a systematic review is key to improving our knowledge supporting new experiment research, modeling, and application. In this meta-analysis, we aimed to delineate the canopy interception (CI), throughfall (TF), and stemflow (SF) concerning geographical and forest variables and experimental methodologies. We leveraged peer-reviewed 170 articles across 234 sites globally, extracting TF, CI, SF, geographical, forest, and experimental aspects. We applied multivariate statistical procedures to discern the principal influences on TF, CI, and SF and examined their multicollinearity. In addition, we developed Generalized Linear Models (GLM) for CI and TF. Global TF experiments indicate that the predominant rainfall devices, number of sample trees, number of events, and monitoring length are 10–20 devices (81% fixed), 3–6 trees, 30–50 events, and 10–30 months. Predominant global values of TF, CI, and SF are 70–80% (median = 73%), 20%–30% (median = 23.9%), and <1.0% (median = 1.87%), respectively. Global models of CI and TF were responsive to T, LAI, and D (respectively, R2adj of 0.196** and 0.206**). Temperate forests mirrored the global model (R2adj of 0.274** and 0.31**, respectively). The Subtropical CI model was fitted based on P and DBH (R2adj = 0.245*), and the TF model was based on E, D, and LAI (R2adj = 0.532**); the Mediterranean CI model was based on T, Basal, and LAI (R2adj = 0.45*), while TF was based on P, Basal, and LAI (R2adj = 0.671**). The Tropical CI model was based on T and H (R2adj = 0.396*), and the TF model, LAI, and P (R2adj = 0.35*). This meta-analysis underscores the importance of comprehending the hydrological processes in forested areas as they are pivotal in mitigating climate change impacts.

Enhancing municipal solid waste leachate treatment efficiency: AI-based prediction of electrocoagulation/flocculation recovery using iron electrodes

ABSTRACT This study addresses a gap in municipal leachate (MUPL) treatment by introducing a pioneering application of artificial intelligence (AI) in the electrocoagulation/electroflocculation (EC/EF) process utilizing iron electrodes. The overarching aim is to demonstrate the efficacy of AI, particularly a multi-layer perceptron (MLP)-based feed-forward artificial neural network (ANN) incorporating the Levenberg-Marquardt (LMb) algorithm, in predicting and optimizing EC/EF outcomes for turbidity (TDY) removal. The research methodology involved experimentation and robust ANN data modeling. The significance of this work emerges from the successful integration of AI, showcasing its potential in advancing wastewater, demonstrated through a strong positive correlation (0.994) between the ANN model predictions and experimental outcomes. The study achieves a remarkable 99.4% TDY removal at an electrolysis time of 10 min and contributes valuable insights into the critical parameters influencing the EC/EF process. Results from the ANN modeling exhibit high predictive accuracy, supported by elevated R-squared values and minimal mean square error. Statistical analyses underscore the significance of key process parameters, highlighting the influential roles of current intensity and settling time. The study emphasized the favourable impact of maintaining an acidic pH range, as it reduced electrostatic repulsion between particles, facilitating pollutant agglomeration, and identified electrolysis time as a key factor in enhancing treatment efficiency, supported by a strong positive correlation between electrolysis time and TDY reduction. Energy cost savings were realized by not requiring temperature elevation. Achieving a 99.4% TDY removal translates to substantial reductions in other pollutants present in the MUPL, thereby elevating water quality and ensuring compliance.

Bayesian modeling of binomial experiments in sociology: problem analysis

The article is devoted to Bayesian modeling of simple comparative binomial experiments with binary data sets (of “hit” and “miss” format) in sociology and other social sciences. The main methodological foundations of application of Bayesian approach in statistics are briefly reviewed: the use of priors in analysis, features of Bayesian statistical inference, differences in frequency and Bayesian confidence intervals, features of hypothesis testing in Bayesian statistics. A Bayesian model of a comparative binomial experiment has been constructed. It supports comparison of independent and dependent samples of binomial variables, and also allows for differences in sizes of the compared samples. The capabilities of the model, as well as the principles of the Bayesian hypothesis testing, were demonstrated on test data using PyMC and ArviZ, contemporary free packages of the Bayesian modeling and analysis. The use of these tools allows implementing direct tensor operations with the obtained posterior distributions and provides the researcher with an effective way to calculate the effect size when comparing two binomial samples without having to resort to complicated forms of calculating this parameter. The possibilities and limitations of the Bayesian approach are shown in the context of comparative analysis of the results of binomial experiments in social sciences by estimating the probability of hypotheses via finding and comparing the area of intervals of posterior distributions

Toward a high-fidelity tritium transport modeling for retention and permeation experiments

Tritium Migration Analysis Program version 8 (TMAP8), the latest version of TMAP, was developed within the framework of the Multiphysics Object-Oriented Simulation Environment (MOOSE). Created at Idaho National Laboratory (INL), MOOSE is an open-source, dimension-agnostic, fully coupled, and fully implicit multiphysics platform featuring massively parallel computation capabilities. Using TMAP8, tritium transport in a divertor monoblock was analyzed to elucidate the effects of pulsed operation (up to fifty 1,600 s plasma discharge and cool-down cycles) on the tritium in-vessel inventory source term and ex-vessel release term (i.e., tritium retention and permeation) for safety analysis. With its built-in Message Passing Interface capability, TMAP8 can, in under 2 h, simulate tritium transport in three different layered materials (i.e., tungsten, copper, and copper-chromium-zirconium alloy) in 2D geometry, using a single device/computer with 10 cores. The MOOSE-based TMAP8 code can leverage other MOOSE tools developed under the Nuclear Energy Advanced Modeling and Simulation program to perform tritium and thermal transport in complex geometries and multiphysics environments. And via its massively parallel computation, MOOSE will enable the fusion pilot plant designers to conduct high-fidelity multiphysics modeling for the design of the divertor and blanket systems as well as for the safety analysis.

Deep carbon recycling viewed from global plate tectonics.

Plate tectonics plays an essential role in the redistribution of life-essential volatile elements between Earth's interior and surface, whereby our planet has been well tuned to maintain enduring habitability over much of its history. Here we present an overview of deep carbon recycling in the regime of modern plate tectonics, with a special focus on convergent plate margins for assessing global carbon mass balance. The up-to-date flux compilation implies an approximate balance between deep carbon outflux and subduction carbon influx within uncertainty but remarkably limited return of carbon to convecting mantle. If correct, carbon would gradually accumulate in the lithosphere over time by (i) massive subsurface carbon storage occurring primarily in continental lithosphere from convergent margins to continental interior and (ii) persistent surface carbon sinks to seafloors sustained by high-flux deep CO2 emissions to the atmosphere. Further assessment of global carbon mass balance requires updates on fluxes of subduction-driven carbon recycling paths and reduction in uncertainty of deep carbon outflux. From a global plate tectonics point of view, we particularly emphasize that continental reworking is an important mechanism for remobilizing geologically sequestered carbon in continental crust and sub-continental lithospheric mantle. In light of recent advances, future research is suggested to focus on a better understanding of the reservoirs, fluxes, mechanisms, and climatic effects of deep carbon recycling following an integrated methodology of observation, experiment, and numerical modeling, with the aim of decoding the self-regulating Earth system and its habitability from the deep carbon recycling perspective.

Dynamic Behavior and Permanent Indentation in S2-Glass Woven Fabric Reinforced Polymer Composites under Impact: Experimentation and High-Fidelity Modeling

This paper studies the behavior of S2-glass woven fabric reinforced polymer composite under low-velocity impact at 18–110 J energy. A macro-homogeneous finite element model for the prediction of their response is implemented, considering the non-linear material behavior and intralaminar and interlaminar failure modes for the prediction of impact damage. The model accurately predicted the permanent indentation caused by impact. By applying the Ramberg-Osgood formulation, different initial stiffness values are examined to assess the post-impact unloading response. This approach reveals the significant role of initial stiffness in inelastic strain accumulation and its consequent effect on permanent indentation depth. A higher initial stiffness correlates with increased inelastic strain, influencing the impactor rebound and resulting in greater permanent indentation. By accurately predicting permanent indentation, and damage accumulation for different impact energies, this study contributes to a better understanding of the impact behavior of composite materials, thereby promoting their wider application.

Towards seamless environmental prediction – development of Pan-Eurasian EXperiment (PEEX) modelling platform

ABSTRACT The Pan-Eurasian Experiment Modelling Platform (PEEX-MP) is one of the key blocks of the PEEX Research Programme. The PEEX MP has more than 30 models and is directed towards seamless environmental prediction. The main focus area is the Arctic-boreal regions and China. The models used in PEEX-MP cover several main components of the Earth’s system, such as the atmosphere, hydrosphere, pedosphere and biosphere, and resolve the physical-chemical-biological processes at different spatial and temporal scales and resolutions. This paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system, online integrated, forward/inverse, and socioeconomical modelling, and other approaches with a particular focus on applications in the PEEX geographical domain. The employed high-performance computing facilities, capabilities, and PEEX dataflow for modelling results are described. Several virtual research platforms (PEEX-View, Virtual Research Environment, Web-based Atlas) for handling PEEX modelling and observational results are introduced. The overall approach allows us to understand better physical-chemical-biological processes, Earth’s system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks, impact, consequences, etc. for population, environment and climate in the PEEX domain. This work was also one of the last projects of Prof. Sergej Zilitinkevich, who passed away on 15 February 2021. Since the finalization took time, the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him.

Abnormal multisensory temporal discrimination in Parkinson’s disease

Cognitive deficits are prevalent in Parkinson’s disease (PD), ranging from mild deficits in perception and executive function to severe dementia. Multisensory integration (MSI), the ability to pool information from different sensory modalities to form a combined, coherent perception of the environment, is known to be impaired in PD. This study investigated the disruption of audiovisual MSI in PD patients by evaluating temporal discrimination ability between auditory and visual stimuli with different stimulus onset asynchronies (SOAs). The experiment was conducted with Fifteen PD patients and fifteen age-matched healthy controls where participants were requested to report whether the audiovisual stimuli pairs were temporal simultaneous. The temporal binding window (TBW), the time during which sensory modalities are perceived as synchronous, was adapted as the comparison index between PD patients and healthy individuals. Our results showed that PD patients had a significantly wider TBW than healthy controls, indicating abnormal audiovisual temporal discrimination. Furthermore, PD patients had more difficulty in discriminating temporal asynchrony in visual-first, but not in auditory-first stimuli, compared to healthy controls. In contrast, no significant difference was observed for auditory-first stimuli. PD patients also had shorter reaction times than healthy controls regardless of stimulus priority. Together, our findings point to abnormal audiovisual temporal discrimination, a major component of MSI irregularity, in PD patients. These results have important implications for future models of MSI experiments and models that aim to uncover the underlying mechanism of MSI in patients afflicted with PD.

Modelling a gas injection experiment incorporating embedded fractures and heterogeneous material properties

This study focuses on the modelling of a gas injection experiment to assess the effects of incorporating heterogeneous material properties. The numerical model considers a two-phase flow coupled hydro-mechanical problem, and includes embedded fractures that open with deformation, thereby enhancing permeability. The approach used is integrated in the CODE_BRIGHT software, which allows for the consideration of geomaterials with a spatially correlated heterogeneous field of porosity that follows a normal distribution. This spatial correlation can be either isotropic or anisotropic. A key aspect of this approach is that material properties such as intrinsic permeability, diffusivity or cohesion are defined as a function of porosity. Consequently, these properties also exhibit heterogeneity with spatial correlation and, eventually, anisotropy. The results derived from the numerical model align well with in-situ measurements. The study also includes sensitivity analyses to the variation of critical variables. The calibration of the model has been validated through a similar experiment. The findings indicate that the consideration of heterogeneous material properties can have a significant influence on gas injection problems, particularly when a hydraulic fracture is formed.

Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent

Estrogen receptor positive breast cancer is frequently treated with anti-hormonal treatment such as aromatase inhibitors (AI). Interestingly, a high body mass index has been shown to have a negative impact on AI efficacy, most likely due to disturbances in steroid metabolism and adipokine production. Here, we propose a mathematical model based on a system of ordinary differential equations to investigate the effect of high-fat diet on tumor growth. We inform the model with data from mouse experiments, where the animals are fed with high-fat or control (normal) diet. By incorporating AI treatment with drug resistance into the model and by solving optimal control problems we found differential responses for control and high-fat diet. To the best of our knowledge, this is the first attempt to model optimal anti-hormonal treatment for breast cancer in the presence of drug resistance. Our results underline the importance of considering high-fat diet and obesity as factors influencing clinical outcomes during anti-hormonal therapies in breast cancer patients.

Damage mechanism evolution of red sandstone under drying–wetting cycles: experiment and discrete element modeling

Damage mechanism evolution of red sandstone under drying–wetting cycles: experiment and discrete element modeling