Reliable Set Of Experimental Data Research Articles

KHz Rate Fs/Ps-CARS Thermometry For Supersonic H_2/Air Combustion Studies

Accurate flow field measurements are essential to probe the phenomena involved in aeronautical engines undergoing high temperature and high turbulences. Indeed, reliable experimental datasets are needed to compare and validate numerical models. Among other laser diagnostics, Coherent Anti-stokes Raman Scattering (CARS) is a well-established non-linear spectroscopic technique used to probe harsh environments, since it provides non-intrusive local point measurements of chemical composition and temperature. We report here hybrid fs/ps Coherent Anti-Stokes Raman Scattering (fs/ps-CARS) thermometry campaign performed on N 2 in a supersonic H2 /air combustion. We used a mobile and compact laser architecture that shapes, synchronize and generates the laser beams necessary for CARS spectroscopy. The instrument was moved to a large-scale facility representative of a scramjet combustor (ONERA LAERTE/LAPCAT-II bench). H2 /air was injected during several burst at 0,12 and 0,15 equivalent ratios. Using kHz single-shot measurements combined with motorized translation stages, spatial and temporal resolution were obtained to probe the flow and the combustion at various positions upstream and downstream the hydrogen injectors. At these positions, horizontal and vertical temperature profiles were retrieved and compared with a numerical unsteady fluid dynamic simulation using delayed detached eddy simulations (DDES) developed at ONERA (CEDRE). The position of the combustion inside the combustion chamber could be identified through several horizontal and vertical profile temperature measurements. The signal-to-noise ratio of the detection was optimized by empirically adjusting the number of averaged laser shots based on the level of flow turbulence. For instance, far downstream the H2 injection, averaging over 10 to 100 pulses was possible as the heated air flow was steady. This was confirmed by the good agreement with numerical simulations although some distortion could still be observed and will be used to adjust the numerical simulation algorithm. However, averaging was no longer possible close to the injectors, where a highly turbulent H2 /air combustion was observed. Nevertheless, taking advantages of the high rate single-shot capacity of the spectroscopic technique, temperature could still be retrieved inside this area. These measurements provided a valuable experimental database for comparing and refining computational fluid dynamics modeling.

OptADMET: a web-based tool for substructure modifications to improve ADMET properties of lead compounds.

Lead optimization is a crucial step in the drug discovery process, which aims to design potential drug candidates from biologically active hits. During lead optimization, active hits undergo modifications to improve their absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles. Medicinal chemists face key questions regarding which compound(s) should be synthesized next and how to balance multiple ADMET properties. Reliable transformation rules from multiple experimental analyses are critical to improve this decision-making process. We developed OptADMET ( https://cadd.nscc-tj.cn/deploy/optadmet/ ), an integrated web-based platform that provides chemical transformation rules for 32 ADMET properties and leverages prior experimental data for lead optimization. The multiproperty transformation rule database contains a total of 41,779 validated transformation rules generated from the analysis of 177,191 reliable experimental datasets. Additionally, 146,450 rules were generated by analyzing 239,194 molecular data predictions. OptADMET provides the ADMET profiles of all optimized molecules from the queried molecule and enables the prediction of desirable substructure transformations and subsequent validation of drug candidates. OptADMET is based on matched molecular pairs analysis derived from synthetic chemistry, thus providing improved practicality over other methods. OptADMET is designed for use by both experimental and computational scientists.

Predicting the effect of voids generated during RTM on the low-velocity impact behaviour by machine learning-based surrogate models

The main objective of the present paper is to demonstrate the feasibility of machine-learning-based surrogate models for predicting low-velocity impact behaviour considering void content and location generated during the resin transfer moulding process. Generating reliable experimental datasets for training those models is almost impossible, therefore an adapted finite element model was implemented providing reliable results to generate the synthetic datasets. The optimum hyperparameter combination for training the Random Forest model was found based on the grid search technique. The accuracy of the classification, single-output regression and multi-output regression models was sufficient. It was concluded that the multi-output regression model, which predicts the force-time, displacement-time, and energy-time curves, provides the best information, and is sufficiently accurate (R2 > 0.995) and fast (5 s per sample) as an online structural performance monitoring tool.

Failure analysis of scouring at pile groups exposed to steady-state flow: On the assessment of reliability-based probabilistic methodology

This research aims to present a probabilistic model to assess scour depth around the pile group. In this way, the limit state function was back on the regression-based equation govern by the steady-state flow variables, motion states of bed sediments, and geometric properties of pile groups. Reliable experimental datasets, used in developing the probabilistic model, have intrinsic uncertainties due to randomness in influential factors. Firstly, the influential factors extracted from experimental observations have been grouped into random and deterministic variables. To understand the variation of the random variables in terms of quantity, the probabilistic distribution has been provided. Hence, a probabilistic model has been implemented by means of the Model Tree (MT) technique to estimate the local scour depth. In the next step of the current study, the proposed equation given by MT has been applied to the probabilistic framework through Monte Carlo sampling analysis in order to evaluate the influence of different variables on the exceedance probability of scour depth. Results indicated that regions with a low probability for the exceedance of scour depth from the desired value are proposed for the design purpose. As well, methods were proposed to reduce the exceedance probability of scour depth around marine structures' piles.

Experimental investigations and developing multilayer neural network models for prediction of CO2 solubility in aqueous MDEA/PZ and MEA/MDEA/PZ blends

AbstractIn this research, a new set of experimental data for CO2 solubility in aqueous blended amine solvents were investigated experimentally over the CO2 partial pressure range from 8 to 100 kPa at 40 °C and were compared with the benchmark aqueous 30 wt.% MEA solution. This work developed two multilayer neural network models named models A and B, for predicting the CO2 solubility in various aqueous blended amine solvents including 36 wt.% MDEA + 17 wt.% PZ, 24 wt.% MDEA + 26 wt.% PZ, and 6 wt.% MEA + 25 wt.% MDEA + 17 wt.% PZ. Models A and B were developed by using Levenberg–Marquardt back propagation algorithm with 427 and 301 of reliable experimental data sets gathered from the published data, respectively. The results indicate that the high accuracy prediction of the CO2 solubility in Methyldiethanolamine/Piperazine (MDEA/PZ) blends could be obtained by the network developed by Tan‐sigmoid transfer function with two hidden layers consist of eight and four neurons, while the network developed by Tan‐sigmoid transfer function with three hidden layers consist of 20, 10, and five neurons provided the highest accuracy for predicting the CO2 solubility in MEA/MDEA/PZ blends comparing to other model structures. The comparison results show that the neural network modeling provided more closer predictions to the experimental results than the simulator and other thermodynamic models when predicting the CO2 equilibrium solubility in blended amine solvents. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd.

Elucidating the constitutive relationship of calcium\u2013silicate\u2013hydrate gel using high throughput reactive molecular simulations and machine learning

Prediction of material behavior using machine learning (ML) requires consistent, accurate, and, representative large data for training. However, such consistent and reliable experimental datasets are not always available for materials. To address this challenge, we synergistically integrate ML with high-throughput reactive molecular dynamics (MD) simulations to elucidate the constitutive relationship of calcium–silicate–hydrate (C–S–H) gel—the primary binding phase in concrete formed via the hydration of ordinary portland cement. Specifically, a highly consistent dataset on the nine elastic constants of more than 300 compositions of C–S–H gel is developed using high-throughput reactive simulations. From a comparative analysis of various ML algorithms including neural networks (NN) and Gaussian process (GP), we observe that NN provides excellent predictions. To interpret the predicted results from NN, we employ SHapley Additive exPlanations (SHAP), which reveals that the influence of silicate network on all the elastic constants of C–S–H is significantly higher than that of water and CaO content. Additionally, the water content is found to have a more prominent influence on the shear components than the normal components along the direction of the interlayer spaces within C–S–H. This result suggests that the in-plane elastic response is controlled by water molecules whereas the transverse response is mainly governed by the silicate network. Overall, by seamlessly integrating MD simulations with ML, this paper can be used as a starting point toward accelerated optimization of C–S–H nanostructures to design efficient cementitious binders with targeted properties.

A robust experimental-based artificial neural network approach for photovoltaic maximum power point identification considering electrical, thermal and meteorological impact

This paper aims to develop a robust and practical photovoltaic (PV) Maximum Power Point (MPP) identification tool developed using reliable experimental data sets. The correlations between the voltage and the current (Vmp and Imp) at maximum power from one side, and the irradiance information, electrical parameters, thermal parameters and weather parameters from another side, are investigated and compared. A comparative study between a number of input scenarios is conducted to minimize the MPP estimation error. Four scenarios based on a combination of various PV parameters using various Artificial Neural Network (ANN)-based MPP identifiers are presented, evaluated using the most common regression measure (Mean Squared Error (MSE)), improved in terms of the accuracy of the identification of MPP, and then compared. The first scenario is divided into two parts I(a) and I(b) and considers the irradiance information in addition to the highest correlated parameters with Imp and Vmp, which are circuit current (Isc) and open-circuit voltage (Voc), respectively. The second scenario considers irradiance information and the electrical parameters only. The irradiance information, in addition to the electrical, thermal, and weather parameters, are considered in the third scenario using a single layer network, while the irradiance information, in addition to the electrical, thermal, and weather parameters, are considered in the fourth scenario using a two-layer ANN network. Although the correlation study shows that the Vmp and Imp have the best correlation with the open-circuit voltage and the short circuit current (scenario I), respectively. Nonetheless, the consideration of irradiance, electrical, thermal, and weather parameters (scenario IV) yielded higher identification accuracy. The results showed a decrease in the MSE of Vmp by 74.3% (from 1.6 V to 0.411 V), and in the MSE of Imp by 95% (from 4.4e−6 A to 2.16e−7 A), respectively. In comparison to the conventional methods, the proposed concept outperforms their performances and dynamic responses. Moreover, it has the potential to eliminate the oscillations around the MPP in cloudy days. The MPP prediction performance is 99.6%, and the dynamic response is 276 ms.

Fuzzy Modeling to Simulate Surface Roughness in CNC Turning of AISI 1045 Steel

The objective of the present work is to simulate surface roughness in Computer Numerical Controlled (CNC) machine by Fuzzy Modeling of AISI 1045 Steel. To develop the fuzzy model; cutting depth, feed rate and speed are taken as input process parameters. The predicted results are compared with reliable set of experimental data for the validation of fuzzy model. Based upon reliable set of experimental data by Response Surface Methodology twenty fuzzy controlled rules using triangular membership function are constructed. By intelligent model based design and control of CNC process parameters, we can enhance the product quality, decrease the product cost and maintain the competitive position of steel.

A reinterpretation of the Odar and Hamilton data on the unsteady equation of motion of particles

AbstractThe experimental data and correlations derived by Odar and Hamilton have been the basis of studies that included the history term in the expression of the unsteady drag. Recent studies have shown that the value of the added mass coefficient is constant and equal to ½ over a very large range of Reynolds numbers. Recent studies on the history term have proven that its form is not correct at high Re. However, the experimental data are accurate at low Re and, most probably, they represent the most reliable set of experimental data on the unsteady force on solid spheres. We conducted a study to re‐calculate the functional form of the history term in the unsteady equation of motion at low Re and to derive a new correlation for the so‐called “history force coefficient,” ΔH. The new correlation is expressed in terms of the Reynolds and Strouhal numbers of the particle. © 2010 American Institute of Chemical Engineers AIChE J, 2011

Experiments for Combustion-LES Validation

Designs of future combustors increasingly rely on numerical combustion simulations. Large-eddy simulation (LES) emerged as a method to better describe turbulence than Reynolds-averaged Navier-Stokes (RANS) approaches. Processes at the subgrid scale, however, need to be modelled. Validation using comprehensive and reliable experimental data sets is therefore a crucial part in the development of combustion-LES. Using well-defined benchmark flames and advanced laser diagnostics, different physical and chemical quantities can be precisely measured with high temporal and reasonable spatial resolution. In this contribution, quantities important to combustion-LES validation and the most frequently used laser diagnostic methods are briefly reviewed. New challenges in the field of diagnostics at high repetition rates and at walls are highlighted. In a closing chapter, some critical aspects on the comparison of experimental measurands and combustion-LES-predicted counterparts are discussed.

Simultaneous extraction of several metabolites of energy metabolism and related substances in mammalian cells: Optimization using experimental design

As a basis for the development of predictive mathematical models in systems biology and a quantitative understanding of cellular metabolism, reliable experimental data sets of intracellular metabolites are indispensable. A prerequisite for the acquisition of such data is the identification of a suitable sample preparation method. In this work, the extraction procedure for the simultaneous measurement of a wide range of intracellular metabolites from adherent mammalian cells in culture was optimized. A screening of several commonly used extraction protocols with Madin–Darby canine kidney (MDCK) cells found the methanol/chloroform (MeOH/CHCl 3) and MeOH/Boil methods to be promising candidates for further analysis by anion-exchange chromatography. Both methods were optimized based on experimental design techniques with four response variables: Nucleotide Content, Energy Charge, Fructose 1,6-Bisphosphate content (F16bP), and Absorption at 280 nm. After data evaluation and with the help of desirability functions, an overall optimum for the extraction conditions was found. Using optimal settings, the extraction performances for MDCK and Vero cell cultivations of both methods were compared. Both methods extracted nearly the same absolute amounts of intracellular metabolites, suggesting that these methods are equal. However, recoveries for nucleotide diphosphates were significantly above 100% for both methods. This most likely was due to remaining nucleotide kinase activity during extraction. After combining individual steps of both methods, recoveries close to 100% for all metabolites could be reached. Absolute values of intracellular metabolites extracted with this modified method are comparable to the results of the two previously optimized methods, indicating a good extraction procedure according to the chosen response variables.

Quantitative assessment of thermal–hydraulic codes used for heavy water reactor calculations

The RD-14M large LOCA test, characterized by a reliable set of experimental data, was selected for an international standard problem exercise (SPE) entitled “Intercomparison and validation of computer codes for thermal–hydraulics safety analyses”. The activity was performed within the frame of International Atomic Energy Agency's (IAEAs) Technical Working Group on Advanced Technologies for Heavy Water Reactors (TWG-HWR). In this study, the recently improved fast Fourier transform based method (FFTBM) was used for accuracy quantification of RD-14M large LOCA test B9401 calculations of six participants using four different thermal–hydraulic codes. In addition, developing the capability to calculate the accuracy as a function of time-continues-valued accuracy, did further improvement of FFTBM. Namely, in the past only single valued accuracy parameters for selected time windows and time intervals were calculated. The objective of the study was to demonstrate that the new FFTBM is a powerful tool for quantitative assessment of thermal–hydraulic codes. For demonstration, the test from the facility simulating heavy water reactor was used. The blind accuracy analysis was completed based on solely experimental and calculated data. However, short discussions were held with the representative from Italy (co-author, here) regarding phenomenological windows, variables and void fraction weights selection. In general, the open accuracy analysis confirmed the results obtained in blind accuracy analysis. The main conclusions from accuracy analysis agree with the conclusions from the SPE intercomparison report, which was written independently. Finally, the results suggest that the accuracy of the best calculations of the RD-14M test is comparable with the best calculations of light water reactor experiments.

Analytical E1 strength functions of two-neutron halo nuclei: 11Li and 14Be

An analytical model, recently developed to study the electromagnetic dissociation (EMD) energy spectra of two-neutron halo nuclei, is applied to 11Li and 14Be. We find that a reliable set of experimental data on EMD could help to resolve the problem concerning the structure of the 11Li ground state. For 14Be we find a mutual inconsistency between the existing experimental data on the binding energy, radius and EMD energy spectrum. We also conclude that the structure of 14Be is essentially more complicated than the structure of other Borromean two-neutron halo nuclei.

Finite Element Analysis of a Simulated Total Hip Replacement

This paper presents the results of a finite element analysis of a Stanmore prosthesis cemented into a titanium alloy tube. The problem was analysed since a reliable set of experimental data was available for comparison with the results of analysis. The results achieved demonstrate the importance of using joint elements which permit slip between the prosthesis and cement.