Experiment Modeling Research Articles

Topological and site disorder in boron nitride networks.

Amorphous boron nitride (a-BN) is modelled over a wide range of densities using a relatively simple potential model augmented with site charges. The local topology (defined, for example, through the total nearest-neighbour coordination number), appears near-constant across a wide range of densities and site charges. Furthermore, total scattering and total pair distribution functions also show few changes as a function of either density or site charge. Variation of the site charges directly controls the level of site (rather than topological) disorder meaning that although total pair functions may be near-constant, the underlying partial contributions may be very different. Direct contact is made with both experiment and (more recent) density-functional theory-based modelling work.&#xD.

Investigating formability of AZ31B magnesium alloy sheet with different texture and thickness in combination of the Erichsen experiment and CPFEM model

In this study, the formability of the AZ31B magnesium alloy sheets was investigated in combination of experiment and CPFEM modelling. The deformation mechanisms during the Erichsen forming were revealed and the effect of the related parameters were discussed. It was shown that the simulation results based on the normalized Cockcroft-Latham criterion are consistent with the experimental results. The maximum stress and strain concentrate at the center of dome, and exhibit an elliptical distribution shape. The difference of asymmetry situation between the sheets results from the different textures. The basal <a> slip is the dominant mode, and the prismatic <a> slip is also active to accommodate plastic strain in sheet plane. The relative activity of the pyramidal <c+a> slip is low, but very effective to accommodate the plastic strain in thickness direction. The sheet with higher relative activity of the prismatic <a> and pyramidal <c+a> slips exhibit higher formability. The sheet thickness influences the formability through changing the stress-strain response, as well as the orientation-relationship between stress state and grains which can affect the deformation mechanisms. The formability depends on the plastic strain accommodation ability that is comprehensively related to the averaged plasticity, orientation-relationship between stress state and grains, r-value and n-value. CPFEM modeling is convenient pathway to predict the formability.

Optimization of Polysaccharide Extraction from Polygonatum cyrtonema Hua by Freeze–Thaw Method Using Response Surface Methodology

Polygonatum cyrtonema polysaccharides have a variety of pharmacological effects. The commonly used extraction methods include traditional hot water extraction, alkaline extraction, enzymatic hydrolysis method, ultrasonic-assisted extraction, etc., but there are problems such as low yield, high temperature, high cost, strict extraction conditions, and insufficient environmental protection. In this study, crude polysaccharide extraction from the Polygonatum cyrtonema Hua was performed using the freeze–thaw method. Response surface methodology (RSM), based on a three-level, three-variable Box–Behnken design (BBD), was employed to obtain the best possible combination of water-to-raw material ratio (A: 30–50), freezing time (B: 2–10 h), and thawing temperature (C: 40–60 °C) for maximum polysaccharide extraction. Using the multiple regression analysis and analysis of variance (ANOVA), the experimental data were fitted to a second-order polynomial equation and were used to generate the mathematical model of optimization experiments. The optimum extraction conditions were as follows: a water-to-raw material ratio of 36.95:1, a freezing time of 4.8 h, and a thawing temperature of 55.99 °C. Under the optimal extraction conditions, the extraction rate of Polygonatum cyrtonema Hua polysaccharide (PCP) was 65.76 ± 0.32%, which is well in close agreement with the value predicted by the model, 65.92%. In addition, PCP has significant antioxidant activity. This result shows that the freeze–thaw method can improve the extraction efficiency, maintain the structural integrity of polysaccharides, simplify the extraction process, promote the dispersion of polysaccharides, and is suitable for large-scale industrial production.

Modeling of surface generation and process experiments for grinding Ti-6Al-4V with small-scale grinding tool with regular array structure

Modeling of surface generation and process experiments for grinding Ti-6Al-4V with small-scale grinding tool with regular array structure

Stability of slender GFRP tube-confined UHPC-filled steel-encased column under axial compression: Experiment and mesoscale modeling

The stability of GFRP tube-confined UHPC-filled steel-encased columns (FUSCs) is critical for optimal utilization of its superior cross-sectional load-bearing capacity that guarantees overall structural safety. This study conducted an experimental investigation on 14 slender FUSC specimens under uniaxial compression for different aspect ratios and aggregate contents. The pressure-sensing films were applied to measure the contact pressure between the GFRP tube and UHPC, and a mesoscale finite element model was developed to uncover the role of randomly distributed fibers and aggregates in the UHPC during the failure process. Accordingly, the stability performance of slender FUSC was analyzed, and specific discussions were held on the mutual interaction mechanism between GFRP and UHPC during the loading process. The results showed that the stability of FUSCs decreased faster with an aspect ratio of l0/D > 8 (λ>30.77), resulting in the decrease of load-carrying capacity for up to 25 %. By including coarse aggregate, the distribution of steel fibers was densified, and Young's Modulus of UHPC was increased, considerably improving the stability of FUSCs. Moreover, the GFRP tube with a fixed winding angle of 50° remarkably enhanced the stability of the specimen due to the sharing of bending moment instead of providing confinement. This research outcome offers a basis for the stability control of slender FUSCs.

In situ combustion performance in heavy oil carbonate reservoirs: A triple-porosity numerical model

This study aims to address the challenges of oil recovery in highly heterogeneous carbonate reservoirs by employing a novel triple-porosity numerical model approach for in-situ combustion (ISC). The primary focus is on the performance of ISC within isolated vuggy pore spaces, a characteristic feature of such reservoirs. By simulating triple-porosity through artificially induced vuggs, core matrix, and fractures, we evaluated the feasibility of the ISC technique for heavy oil recovery in a dolomite reservoir using a combustion tube setup.The numerical model, calibrated through history matching, incorporates an extended kinetic reaction scheme to better represent high-temperature oxidation processes. Experimental and simulation results show a strong correlation in temperature profiles, ignition timing, and fluid production, effectively reproducing the observed gas compositions. This integrated experimental and numerical approach demonstrates the potential for applying ISC in triple-porosity systems, offering valuable insights for field-scale implementations in similarly complex carbonate reservoirs. The novelty of this research lies in its comprehensive modeling of the first ISC experiment in a triple-porosity framework, providing a deeper understanding of the interactions between matrix, fracture, and vugg networks, and paving the way for enhanced oil recovery (EOR) strategies in challenging reservoir environments.

Improving Bayesian Optimization via Hierarchical Variation Modeling for Combinatorial Experiments Given Limited Runs Guided by Process Knowledge

Active learning based on Bayesian optimization (BO) is a popular black-box combinatorial search method, particularly effective for autonomous experimentation. However, existing BO methods did not consider the joint variation caused by the process degradation over time and input-dependent variation. The challenge is more significant when the affordable experimental runs are very limited. State-of-the-art approaches did not address allocating limited experimental runs that can jointly cover (1) representative inputs over large search space for identifying the best combination, (2) replicates reflecting the true input-dependent testing variation, and (3) process variations that increase over time due to process degradation. This paper proposed Empirical Bayesian Hierarchical Variation Modeling in Bayesian Optimization (EHVBO) guided by the process knowledge to maximize the exploration of potential combinations in sequential experiments given limited experimental runs. The method first mitigates the process degradation effect through generalized linear modeling of grouped variations, guided by the knowledge of the re-calibration cycle of process conditions. Then, EHVBO introduces an empirical Bayesian hierarchical model to reduce the replicates for learning the input-dependent variation, leveraging the process knowledge of the common structure shared across different testing combinations. This way can reduce the necessary replicates for each input condition. Furthermore, the paper developed a heuristics-based strategy incorporated in EHVBO to improve search efficiency by selectively refining the search space over pivotal regions and excluding less-promising regions. A case study based on real experimental data demonstrates that the proposed method outperforms testing results from various optimization models.

Abnormally narrow peaks in TPR-H2 over Pt/CeO2: Experiment and mathematical modelling

The application and development of methods using hydrogen as a gas for testing active surface sites is of great interest in the study of oxide catalysts. In this work a systematic study of Pt/CeO2 catalysts, depending on the platinum loading and calcination temperature was carried out using the method of temperature-programmed reduction by hydrogen (TPR-H2). Experimental TPR-H2 curves demonstrated a complex structure over a wide temperature range, including the presence of abnormally narrow consumption peaks (ANCP-H2) at low temperatures. To describe the kinetics, mathematical modeling of H2 consumption by various platinum active centers has been used, including isolated platinum ions (single atoms) and PtOx clusters of 2D and 3D structures on the surface of ceria nanoparticles. We proposed a criterion that makes it possible to extend the analysis of the TPR-H2 curves for reducible metal-supported oxide systems and unambiguously establish the action of the autocatalytic mechanism in hydrogen consumption. The kinetics of ANCP-H2 by cluster PtOx centers with a half-width of several degrees on the base of the autocatalysis mechanism was described on the base of the proposed model. The simulations show that ANCP-H2 are described by synchronous fast formation of metallic centers that provide a sharp acceleration of H2 consumption. In general, the obtained results establish the fundamental aspects of the H2 interaction with heterogeneous catalysts and are of particular interest in the light of various practical applications of hydrogen energy.

A Bubble Column Dehumidifier using Ionic Liquid Desiccant for Low-Humidity Industries: Insights into Transfer Processes Integrating Experiment and CFD Modelling

A Bubble Column Dehumidifier using Ionic Liquid Desiccant for Low-Humidity Industries: Insights into Transfer Processes Integrating Experiment and CFD Modelling

Investigating the effect of MXene reinforcement on the mechanical and vibrational behaviour of honeycomb core sandwich composite structures

Honeycomb core sandwich composite panels are now being employed in various industries and possess huge potential owing to the fact that they provide almost similar properties as that of traditional metallic materials with substantial reduction in weight and are extremely corrosion resistant. MXene nanosheets have caught attention of the scientific community owing their extraordinary physical, chemical and mechanical properties. In the present work, the effect MXene reinforcement on the bending and vibrational behaviour of honeycomb cored sandwich composite structures has been investigated using experimentation and finite element (FE) modeling. The honeycomb cored sandwich plates were fabricated and samples were tested via three-point bending and vibration test under various testing conditions. The SEM images revealed that the MXene nanosheets were well dispersed within the epoxy matrix when their mixture was prepared and the homogenous dispersion was also maintained in the fabricated honeycomb core. Also, the MXene and glass fibres were found to be well bonded with the matrix. As far as bending behaviour is concerned, the sandwich structure containing MXene reinforced honeycomb core displayed superior load bearing capacity and exhibited higher values of core shear strength and face sheet bending strength at all testing temperatures. Furthermore, the reinforcement of MXene resulted in an increase in the natural frequency for all the considered modes irrespective of the end conditions. For instance, the natural frequency of the honeycomb core sandwich composite plate at first mode increased by 5.5 % and 10.2 % under CFFF and CFCF boundary conditions, respectively, upon addition of MXene.

Grain size dependence of twin nucleation in magnesium alloys

Twinning plays a prominent role in the plastic deformation of low-symmetry crystals such as magnesium alloys. However, the capture of twin nucleation has been challenging since the process is rapid and difficult to predict, making it impossible to observe directly. Because of this, the understanding of the fundamental twinning mechanism through experiments and simulation techniques incorporating twinning has progressed slowly. In this study, a quantitative analysis integrating an in-situ three-point bending experiment and crystal plasticity modeling was conducted on the twin nucleation in AZ31 magnesium alloys with different grain sizes. The threshold for triggering twin nucleation has been quantified. The prediction ability of three widely used criteria for twin nucleation, i.e., geometrically necessary dislocation (GND) density, von Mises stress, and stored energy density, is evaluated. The results suggest that only the stored energy density is able to capture the twin nucleation location accurately in all the considered grain sizes. The critical value exhibits an exponential reduction with the increase in grain size. Based on the quantitative analysis, the mechanism of the grain size effect on twin nucleation is proposed.

Fuzzy assessment of process parameter interactions on warpage defect modeling in plastic injection molding

AbstractStudying the interactions among major plastic injection molding process parameters is necessary to understand how they collectively influence major defects such as warpage hence enabling optimization of the process for improved product quality. Existing process parameter interaction studies have used statistical approximations, which have limitations such as reduced predictive power and limited accuracy. To overcome these limitations, this study presents an alternative method of analysis of the interactions among process parameters based on fuzzy logic intelligent algorithm. Through computer aided engineering, factorial design of experiment and fuzzy logic modelling, the study evaluated the effects of major injection molding process parameter interactions on warpage. The results obtained indicated a general increase in warpage with increase in parameters such as melt temperature, mold temperature, injection pressure and cool time whereas an increase in parameters such as ambient temperature and packing pressure decreased warpage. Parameter interactions were obtained both statistically and based on fuzzy logic model and their significance tested through ANOVA. Ambient temperature (30.6%) and melt temperatures (18.7%) had the greatest effect on warpage all with P-values of 0.000 whereas cooling time (0.1%) had the least effect with P-value of 0.250. The largest two way interaction affecting warpage involved melt temperature and cooling time with a contribution of 12.2% whereas the largest three way interaction involves ambient temperature, packing pressure and injection pressure with a contribution of 2.7%. Also, despite cooling time having the least mains effect, most interaction terms with greater effect on warpage involved cooling time and melt temperature. The results from this study provides an insight on targeted injection molding process parameter control for defect minimization.

Bayesian modeling and optimization for split-plot experiments with multiple responses

In many industrial processes, cost or time constraints make some input variables harder to change or control than others. An appropriate experimental design method is restricted randomization, which results in split-plot experiments. Empirical models that connect multiple quality characteristics with input variables play a crucial role in robust parameter design for split-plot experiments. At present, many modeling methods typically adopt the single response model for analyzing industrial processes in the split-plot experiments without considering correlation among multiple responses, correlation among whole plots, and uncertainty of model parameters. However, ignoring these issues can lead to poor product or process design. To solve these issues, this paper suggests a novel Bayesian modeling and optimization approach. We first construct a Bayesian multi-response linear mixed-effects model and obtain the posterior distribution for model parameters by employing Bayesian theorem. Then, the Gibbs sampling procedure is employed for the estimation of model parameters. Finally, the overall weighted desirability optimization function meeting the specification is developed to avoid acquiring ideal input settings with outliers. A simulation and an engineering case study demonstrate the validity of the proposed method. In comparison to existing methods, the optimization results given the proposed method are more robust and reliable.

Experiment and constitutive modelling of creep deformation in the frozen silt-concrete interface

Experiment and constitutive modelling of creep deformation in the frozen silt-concrete interface

Experiment Modelling of Automated Verification Control on Environmental Conditions During Crop Storage

The study introduces a preliminary approach to enhance the supervision of food processing operators. Specifically, it showcases a digital model of an online compliance validation system with environmental conditions designed for food processing and preservation items that are sensitive to climate factors. The technology demonstration involves four fundamental stages carried out in a complete experimental cycle, where intelligent sensors gather and transmit data through a low-power wireless wide-area network from a farm specializing in sunflower seed storage. Subsequently, six consecutive scripts are executed, five of which are coded in Python. The integration of distributed data technology brings objectivity to the process of securely storing environmental parameters and objectively verifying their compliance with recommended hygienic, bio certification standards. As a result, monitoring the food production process becomes more publicly accessible and with higher rates of objectivity in decisions, enabling prospective buyers and certification agencies to assess the quality of food products with great precision. Therefore, the suggested digital model has the potential to ensure the utmost transparency in the food processing sector.

FEATURES OF STRUCTURAL REORGANISATION OF THE HYPOTHALAMIC SUPRAOPTIC NUCLEUS IN RATS DURING THE EARLY STAGES OF DIABETES DEVELOPMENT

The objective of the study – to ascertain the structural organisation of the hypothalamicsupraoptic nucleus in rats with streptozotocin- induced diabetes.Materials and Methods. Mature rats, modelled with streptozotocin- induced diabetes,were used in the experiment. On the 14th day of the experimental diabetes developmentin rats, quantitative cyto-myeloarchitecture parameters, histological and ultrastructuralchanges in the anterior hypothalamic supraoptic nucleus were determined.Results. After 14 days of the experiment modelling, the histological structure of thehypothalamic supraoptic nucleus remains practically unchanged and identical to the intactanimals. However, occasional occurrences of central chromatolysis were observed in thelight neurons of the supraoptic nucleus. The perikarya of the dark neurons were denselyfilled with Nissl substance. The ultrastructural feature of the dark neurosecretory cellsis the presence of highly developed granular endoplasmic reticulum in their peripheralzone, which was represented by flat elongated cisterns. These cisterns had a large numberof ribosomes on their surface. Additionally, free ribosomes and polysomes were present inthe hyaloplasm between the cisterns. A similar pattern has been observed in light neurons.Furthermore, an increase in the volumetric density of neurosecretory granules comparedto intact indicators of up to 2.36 ± 0.12 % (p ≤ 0.01) has been detected. In the neuropil of the hypothalamus, most of the axons were filled with neurosecretory granules (NG)of moderate electron density. Alongside axons that were expanded and filled withneurosecretory granules, axons of neurosecretory cells with usual structure containingmitochondria, synaptic vesicles, neurotubules, and neurofilaments have been observed.Conclusions. 1. The hypothalamic supraoptic nucleus contained light and darkneurosecretory cells with a well-developed protein synthesis apparatus. 2. Ultrastructuralchanges of the neuro-glio-capillary complexes in the investigated hypothalamic nuclei weredetected on the 14th day of the experimental streptozotocin- induced diabetes, indicatingthe activation of neurosecretion synthesis and the transport to the neurohypophysis. Anincrease in the volumetric density of neurosecretory granules compared to intact animalswas found to be 2.36±0.12 % (p≤0.01).

Response Surface Methodology Approach for Modeling and Performance Optimization of a PEM Fuel Cell

A Polymer Electrolyte Membrane (PEM) fuel cell is a popular green source of electrical energy and is often used in applications like electric vehicles due to its environmentally friendly operation. This type of fuel cell has a low operating temperature, light weight, and negligible emission of greenhouse gases. However, the PEM fuel cell is a complex multivariable system with a large number of input and output factors, and most of the input factors affect output factors directly or indirectly. As a result, it is conventionally quite difficult to determine which input factor has a major effect on a particular output factor. Statistical methods are very popular for finding the individual and interaction effects of input factors on output factors. In this paper, for the first time, a simple and realistic MATLAB SIMULINK model for a PEM fuel cell is presented to conduct various experimental tests. The developed MATLAB SIMULINK model and statistical design of experiments, Response Surface Methodology (RSM), are used to develop metamodels (mathematical models of the simulation model) for the PEM fuel cell to find the individual and interaction effects of various input factors on output factors. The developed metamodels can be used to find the region for optimum operation of the presented fuel cell. The metamodels are validated by conducting four different statistical tests. The optimum point of operation is presented by calculating stationary points from the metamodels.

Modelling and optimization of an innovative facility for automated sorting of aluminium scraps

The growing demand for aluminium worldwide makes aluminium recycling critical to realising a circular economy and increasing the sustainability of our world. One effective way to improve the impact of aluminium recycling is to develop cost-efficient automated sorting technologies for obtaining pre-defined high-quality aluminium scrap products, thus reducing undesirable downcycling and increasing environmental/economic benefits. In this work, an innovative facility, which includes singulation, sensor scanning, and ejection, is optimised for the automated sorting of aluminium scraps. The sorting facility is computationally studied by a virtual experiment model based on the discrete element method. The model considers particle-scale dynamics of complex-shaped scraps and mimics the automated operation of the facility. Based on virtual experiment modelling, the flow of scrap is optimized by computation, with the feasible operation of the sorting facility being proposed. Accordingly, the sorting facility has been built and model predictions are confirmed in actual operation.

Nanoindentation Study on the Local Evaluation of Hydrogen-Induced Hardening Performance of Ferrite and Austenite in 2205 Duplex Stainless Steel: Experiment and Finite Element Modeling

Nanoindentation Study on the Local Evaluation of Hydrogen-Induced Hardening Performance of Ferrite and Austenite in 2205 Duplex Stainless Steel: Experiment and Finite Element Modeling

Attentional dynamics of evidence accumulation explain why more numerate people make better decisions under risk

In decisions under risk, more numerate people are typically more likely to choose the option with the highest expected value (EV) than less numerate ones. Prior research indicates that this finding cannot be explained by differences in the reliance on explicit EV calculation. The current work uses the attentional Drift Diffusion Model as a unified computational framework to formalize three candidate mechanisms of pre-decisional information search and processing—namely, attention allocation, amount of deliberation, and distorted processing of value—which may differ between more and less numerate people and explain differences in decision quality. Computational modeling of an eye-tracking experiment on risky choice demonstrates that numeracy is linked to how people allocate their attention across the options, how much evidence they require before committing to a choice, and also how strongly they distort currently non-attended information during preference formation. Together, especially the latter two mechanisms largely mediate the effect of numeracy on decision quality. Overall, the current work disentangles and quantifies latent aspects of the dynamics of preference formation, explicates how their interplay may give rise to manifest differences in decision quality, and thereby provides a fully formalized, mechanistic explanation for the link between numeracy and decision quality in risky choice.