Benchmark Experimental Data Research Articles

A multi-stage sub-structural damage localization approach using multi-label radial basis function neural network and auto-regressive model parameters

This paper proposes a Structural Health Monitoring (SHM) approach to detect localized damage by employing a Multi-Label Radial Basis Function Neural Network (ML-RBFNN). The proposed research methodology aims to identify structural damage more efficiently. This involves dividing the main structure into smaller sub-structures known as zones instead of employing a global search for damage across the entire structure. Each zone is further divided into smaller groups, including sub-zones, nodes, or elements. To reduce the complexities associated with feature extraction, auto-regressive (AR) model parameters extracted from raw time series are considered damage-sensitive features. Considering the simultaneous effects of multiple sensors can increase the number of inputs to the networks, making it challenging to reduce the complexity of the dataset. To address this problem, the principal component analysis (PCA) method is utilized to decrease the dimension of feature space. The effectiveness of the proposed approach is evaluated through a comprehensive comparative analysis against conventional single-stage damage detection methods, with a focus on computational costs and accuracy. Furthermore, the noise-sensitivity characteristics of the proposed method are explored, providing insights into their robustness under various conditions. The validation of the approach is carried out through numerical modeling using the ASCE benchmark structure and experimental data collected from the Qatar University Grandstand Simulator. The application of ML-RBFNN with AR parameters as features yielded a remarkable accuracy exceeding 98% in diagnosing damage occurrence and location for both multi-stage and single-stage SHM methods. Moreover, our proposed approach demonstrates enhanced computational efficiency compared to the single-stage method.

Separation of isotopologues of amphetamine with various degree of deuteration on achiral and polysaccharide-based chiral columns in high-performance liquid chromatography

Hydrogen/deuterium (H/D) isotope effects are not unusual in chromatography and such phenomena have been observed in both gas- and liquid-phase separations. Despite the numerous reports on this topic, the understanding of mechanisms and the underlying noncovalent interactions at play remains rather challenging. In our recent study, we reported baseline separation of isotopologoues of some amphetamine (AMP) derivatives on achiral and polysaccharide-based chiral columns, as well as some correlations between the degree of separation of enantiomers and isotopologues on (the same) polysaccharide-based chiral column(s). Following our previous findings on isotope effects in high-performance liquid chromatography, we report herein a comparative study on the isotope effects observed with AMP and methamphetamine (MET). The impact of some pivotal factors such as the number of deuterium atoms part of AMP isotopologues, the structure of its isotopomers, the chemical structure of the achiral and chiral stationary phases used in this study, and the use of methanol- vs acetonitrile-containing mobile phases on the isotope effects was examined and discussed. Quantitative correlations between the observed isotope effects and the enantioselectivity of the chiral columns used are also shortly discussed. Furthermore, considering the chromatographic results as benchmark experimental data, we attempted to elucidate the molecular bases of the observed phenomena using quantum mechanics calculations.

Gene expression programming (GEP) as novel tool for thermal analysis and kinetic modeling of pyrolysis reactions: coal pyrolysis case study

PurposeIn this study, a different approach is introduced to generate the kinetic sub-model for the modeling of solid-state pyrolysis reactions based on the thermogravimetric (TG) experimental data over a specified range of heating rates. Gene Expression Programming (GEP) is used to produce a correlation for the single-step global reaction rate as a function of determining kinetic variables, namely conversion, temperature, and heating rate.Design/methodology/approachFor a case study on the coal pyrolysis, a coefficient of determination (R2) of 0.99 was obtained using the generated model according to the experimental benchmark data. Comparison of the model results with the experimental data proves the applicability, reliability, and convenience of GEP as a powerful tool for modeling purposes in the solid-state pyrolysis reactions.FindingsThe resulting kinetic sub-model takes advantage of particular characteristics, to be highly efficient, simple, accurate, and computationally attractive, which facilitates the CFD simulation of real pyrolizers under isothermal and non-isothermal conditions.Originality/valueIt should be emphasized that the above-mentioned manuscript is not under evaluation in any journals and submitted exclusively for consideration for possible publication in this journal. The generated kinetic model is in the final form of an algebraic correlation which, in comparison to the conventional kinetic models, suggests several advantages: to be relatively simpler, more accurate, and numerically efficient. These characteristics make the proposed model computationally attractive when used as a sub-model in CFD applications to simulate real pyrolizers under complex heating conditions.

Study on multi‐cycle gas‐water displacing mechanism in underground gas storage of low‐permeability reservoir based on PNM

AbstractA deep understanding of the pore‐scale multi‐cycle two‐phase seepage mechanism of gas‐water systems in low‐permeability reservoirs is crucial for enhancing oil and gas recovery and optimizing the operating conditions of underground gas storage. A pore network model was reconstructed using micro‐CT images of low‐permeability core samples from the Dagang Oilfield, China. The mathematical models of gas‐water flow in the pore‐scale models were established based on Poiseuille's law and the quasi‐static displacement theory, considering the gas compressibility and slip effects. The porosity, pore size, absolute permeability, and relative permeability (Kr) of oil‐water and gas‐water were calculated using pore network simulations and validated against experimental benchmark data of the same samples. The effects of multi‐cycle displacement, rock wettability, and average pore pressure on the gas‐water two‐phase flow were simulated and analyzed. The results showed that the relative permeability of gas (Krg) at the same water saturation level significantly increased when the gas compressibility and slip effects were considered. With an increase in the number of gas‐water displacement cycles, Krg at the same water saturation level showed a decreasing trend, indicating a reduction in the gas seepage capability. Krg decreased with increasing water contact angle. With an increase in the average pore pressure, Krg decreased and gradually increased when the average pressure exceeded 25 MPa.

Selective Tautomer Production and Cryogenic Ion Spectroscopy of Radical Cations: The Uracil and Thymine Cases.

The vibrational and electronic spectroscopy of the radical cations of two nucleobases (NB) (uracil and thymine) was studied by cryogenic ion photodissociation spectroscopy. The radical cations have been generated from the photodissociation of NB-Ag+ complexes. A charge transfer process from the NB to Ag+ governs the deactivation mechanism, leading to the formation of the radical cation without further tautomerization. Single- and double-resonance spectroscopy allows for structural assignments of both the silver complexes and the radical cations by comparison with DFT-based calculations. Interestingly, a tautomer-dependent fragmentation is observed in the thymine enol form that involves the loss of NCO, a fragment which was never reported before for this NB. This selective photodissociation of silver complexes containing aromatic chromophore greatly expands the current technique to produce isomer-selected radical cations in the gas phase providing benchmark experimental data to assess calculations of open-shell species.

On conjugate heat transfer in microchannel heat sinks

This work aims to study the anisotropic thermal impacts of employing liquid metal fluids including Na and NaK as coolants in two metallic microchannel heat sinks. The two studied microchannel heat sinks have aspect ratios of 1 and 2 while their hydraulic diameters are 267 µm and 400 μm. In the first stage, the accuracy and reliability of the developed numerical model are verified against benchmark problems and experimental data. In the second stage, the utilized verified and validated numerical model is extended to forced convection of Na and NaK in metallic microchannel heat sinks. The investigated metallic microchannel heat sinks are assumed to be out of stainless steel and Nickel with thermal conductivities of 17 W/mK and 90 W/mK, respectively. Obtained results revealed that NaK provides higher Nusselt numbers than Na regardless of the microchannel aspect ratio and hydraulic diameter. The effect of the microchannel heat sink solid substrate thermal conductivity on the temperature and heat flux distributions over the microchannel walls are also investigated comprehensively. For the investigated steady state Na and NaK flow within metallic microchannel heat sinks, the thermal conductivity of the heat sink solid substrate is observed to have a significant effect on the local temperature and heat flux distributions over microchannel walls.

An enhanced perception health state assessment method for subway sliding plug door transmission system

The monitoring data (current and rotational speed) of the subway sliding plug door transmission system changed positively and negatively with the acceleration and weight of the door. How to perceive the changes is a challenging problem in the health state assessment of transmission system. To address this problem, an enhanced perception health state assessment method was proposed for the transmission systems. In the method, firstly, the equivalent resistance force is calculated by monitoring the current and rotational speed data according to mechanical dynamic knowledge. Secondly, the sensitive features of normal and abnormal states are screened out from the enhanced dataset constructed by current, rotational speed data, and equivalent resistance force data. Finally, the health state of the transmission system is assessed using an integrated learning algorithm. The effectiveness of the method is verified by benchmark experimental data, and the results indicate that the method has a higher accuracy with four classifiers and a broader suitable range with varying door acceleration and weight.

Accurate Measurements of NH3 Differential Adsorption Heat Unveil Structural Sensitivity of Brønsted Acid and Brønsted/Lewis Acid Synergy in Zeolites.

Differential adsorption heats of NH3 on a series of zeolites, including MOR, MFI, FER, and BEA, are accurately measured to probe their acidity using flow-pulse adsorption microcalorimetry. Initial adsorption heats of NH3 at Brønsted acid sites (BAS) vary between 105 to 136 kJ/mol, depending on framework aluminum amounts and topography structures of zeolites. A Brønsted/Lewis acid synergy between BAS and proximate tricoordinated framework-associated aluminum species is identified to generate super acid sites with initial adsorption heats of NH3 around 150 kJ/mol, but occurs only in the MFI zeolites and sensitively depends on the Si/Al ratio. These accurate data of NH3 differential adsorption heats unveil structural sensitivity of BAS and Brønsted/Lewis acid synergy in zeolites and provide experimental benchmark data for fundamental understanding of acidity and acid-catalysis of zeolites.

Prediction of CHF location through applied machine learning

The prediction of critical heat flux (CHF) location is a challenging issue in power generation industries. Several factors viz. geometric parameters and operating conditions influence the occurrence of CHF, and many mechanisms have been proposed to better explain the physics behind the occurrence of CHF. As CHF is a complex phenomenon, its dependency on these factors exhibit non-linear relationship. Further, it is challenging to account for all the factors simultaneously in a single model. Moreover, the benchmark experimental data has to be available to validate the constructed model with all the factors viz., operating pressure and power, inlet mass flux and subcooling etc. Due to these reasons, the influence of these factors remain unresolved. Considering the above issues, the present study employs different machine learning models to predict CHF location and identify the accurate model based on the comparisons against the chosen benchmark experimental data. Becker et al. (1983) data bank was selected for machine learning model training as well as the testing due to the wide range of operating conditions covered. Hyperparameter tuning is applied to optimize the performance of the models. Among all the implemented models, the artificial neural network (ANN) model is found to perform well with the training and the test datasets. The mean absolute percentage error (MAPE) of the training and testing data points are 4.01% and 6.04%, respectively. The accuracy score of the ANN model is approximately 96% for the training and 94% for the testing datasets. The proposed model is helpful for design engineers and analysts of power plants for design optimization and to reduce the computational time.

Analysis of loss of flow without scram test in the FFTF reactor – Part I: Preparation of neutronics data

This study presents a benchmark analysis of an unprotected loss of flow transient in a sodium-cooled fast reactor at the Fast Flux Test Facility (FFTF), carried out as part of an IAEA coordinated research project. Three codes, namely Serpent (Monte Carlo), DYN3D (3D nodal diffusion) and ATHLET (system thermal hydraulics), were employed in the benchmark exercise. Two distinct modeling approaches were utilized: 1) stand-alone ATHLET with point kinetics (PK) and system thermal hydraulics (TH); and 2) coupled DYN3D/ATHLET with spatial kinetics (SK) and system TH. Neutronics data essential for both approaches were generated using Serpent. The study is organized into three parts.Part I presents a summary of the preparation of neutronics data for PK or coupled SK/TH simulations and includes the outcomes of the static neutronics stage of the benchmark. The main focus lies on verifying the cross-section generation method for DYN3D by comparing its results against the reference Monte Carlo solutions obtained with Serpent.Part II will provide a detailed description of the ATHLET TH model of the system. This model will be thoroughly evaluated by comparing the results with the ANL benchmark solution and experimental data for the transient. Furthermore, a sensitivity analysis will be conducted to explore various modeling options and assess their impact on the simulation results.Part III will showcase the transient calculation results using the two modeling approaches. Additionally, an adaptive decay heat model for nodal codes will be introduced. The performance of both modeling approaches will be assessed by comparing their results to the available experimental data.

Effect of high Hartmann number on thermal performance of SWCNT-Fe3O4 hybrid nanoenhanced PCM in magnetized minichannel

Numerical investigations are conducted to investigate the impact of the magnetic field on the molten phase formation and thermal performance of phase change material (PCM) incorporated with hybrid nanoparticles within a three-dimensional vertical channel subjected to differential heating. The PCM, lauric acid, is dispersed using nanoparticle pairings consisting of iron oxide combined with aluminum oxide, titanium dioxide, and single-wall carbon nanotubes. This study focuses on examining the melting characteristics under turbulent conditions, specifically at high Rayleigh (Ra) and Hartmann number (Ha) within the specified range of 106≤Ra≤1010 and 0≤Ha≤1500. The enthalpy-porosity method is being considered for evaluating the phase change characteristics with an in-house code. For Ha of 1500, the melting process influenced by thermal convection was effectively suppressed, leading to significant delays in the melting rates. The melting time and molten fraction of the PCM exhibit a decreasing trend in response to the applied magnetic field, which results in an 82.14% reduction in the heat transfer rate. The SWCNT-Fe3O4 nanoparticle pairs demonstrated superior thermal performance, as the melting fraction and convective heat transfer were increased by 84.03% and 43.83%, respectively. The thermal performance exhibits a decreasing relationship with the Hartmann number, resulting in a 23.87% reduction in the heat transfer of nanoenhanced lauric acid. The accuracy of the present mathematical model is confirmed through validation against experimental benchmark data.

Efficient, nonparametric removal of noise and recovery of probability distributions from time series using nonlinear-correlation functions: Additive noise.

Single-molecule and related experiments yield time series of an observable as it fluctuates due to thermal motion. In such data, it can be difficult to distinguish fluctuating signal from fluctuating noise. We present a method of separating signal from noise using nonlinear-correlation functions. The method is fully nonparametric: No a priori model for the system is required, no knowledge of whether the system is continuous or discrete is needed, the number of states is not fixed, and the system can be Markovian or not. The noise-corrected, nonlinear-correlation functions can be converted to the system's Green's function; the noise-corrected moments yield the system's equilibrium-probability distribution. As a demonstration, we analyze synthetic data from a three-state system. The correlation method is compared to another fully nonparametric approach-time binning to remove noise, and histogramming to obtain the distribution. The correlation method has substantially better resolution in time and in state space. We develop formulas for the limits on data quality needed for signal recovery from time series and test them on datasets of varying size and signal-to-noise ratio. The formulas show that the signal-to-noise ratio needs to be on the order of or greater than one-half before convergence scales at a practical rate. With experimental benchmark data, the positions and populations of the states and their exchange rates are recovered with an accuracy similar to parametric methods. The methods demonstrated here are essential components in building a complete analysis of time series using only high-order correlation functions.

Bubble growth and departure behavior in subcooled flow boiling regime

Understanding the detailed dynamics of bubble ebullition cycle is central to effectively exploit coolant phase change in subcooled flow boiling. In the present study, the bubble growth rate and the bubble departure mechanism in subcooled flow boiling conditions are investigated. The bubble growth rate is estimated by employing an energy balance model, which ensures that the applied wall heat flux contribution to components, such as (i) microlayer evaporation, (ii) conduction through superheated layer region, and (iii) condensation heat transfer, are accounted. The bubble departure diameter and the type of departure (i.e., sliding or lift-off) are predicted using a simple force balance model on the vapor bubble. The implemented models are thoroughly validated against the benchmark experimental data. Furthermore, the influence of operating conditions, viz., pressure (1–3 bar), mass flux (200–1000 kgm−2s−1), heat flux (200–500 kWm−2), and the degree of subcooling (20–30 K) on the bubble dynamics, is investigated for subcooled flow boiling conditions. Based on the parameters considered, a flow regime map is developed to identify the bubble departure type and diameter for a given set of operating conditions. It was noticed that the bubble departure diameter was maximum at low pressure (1 bar), low mass flux (200 kgm−2s−1), high heat flux (500 kWm−2), and low degree of subcooling (20 °C). For all the values of pressure and degree of subcooling, the sliding mode of departure was noticed at low and high mass flux values, whereas bubble departs by lift-off for moderate values of mass flux.

A plasma chemistry model for H2/SiH4 mixtures used in PECVD processes

Plasma chemical processes in H2/SiH4 discharges are critically reviewed. A model set of reactions is proposed which includes temperature and pressure-dependent reaction rates and describes SiyHx (y ≤ 3) chemistry. Using a 2D fluid plasma simulator, the model has been tested under three different set of operating conditions. First, it has been validated against the experimental benchmark data (Horvath and Gallagher (2009) J. Appl. Phys. 105, 13304). Based on considerations of atomic hydrogen content, the branching of SiH4 dissociation channels and the H surface loss probability have been defined more accurately. Then, simulations have been also performed for the plasma source of a PECVD tool from Meyer Burger Germany. A very good agreement between the computed and experimentally determined deposition rates can be stated.

Experimental benchmark data for Monte Carlo simulated radiation effects of gold nanoparticles. Part II: comparison of measured and simulated electron spectra from gold nanofoils

Electron emission spectra of a thin gold foil after photon interaction were measured over the energy range between 50 eV and 9500 eV to provide reference data for Monte Carlo radiation-transport simulations. Experiments were performed with the HAXPES spectrometer at the PETRA III high-brilliance beamline P22 at DESY (Hamburg, Germany) for photon energies just below and above each of the gold L-edges, that is, at 11.9 keV, 12.0 keV, 13.7 keV, 13.8 keV, 14.3 keV, and 14.4 keV. The data were analyzed to obtain the absolute values of the particle radiance of the emitted electrons per incident photon flux. Simulations of the experiment were performed using the Penelope and Geant4 Monte Carlo radiation-transport codes. Comparison of the measured and simulated results shows good qualitative agreement. On an absolute scale, the experiments tend to produce higher electron radiance values at the lower photon energies studied as well as at the higher photon energies for electron energies below the energy of the Au L3 photoelectron. This is attributed to the linear polarization of the photon beam in the experiments, something which is not considered in the simulation codes.

Experimental benchmark data for Monte Carlo simulated radiation effects of gold nanoparticles. Part I: Experiment and raw data analysis

Electron emission spectra of gold nanoparticles (AuNPs) after photon interaction were measured over the energy range between 50 eV and 9500 eV to provide reference data for Monte Carlo radiation-transport simulations. Experiments were performed with the HAXPES spectrometer at the PETRA III high-brilliance beamline P22 at DESY (Hamburg, Germany) for photon energies below and above each of the gold L-edges, that is, at 11.9 keV, 12.0 keV, 13.7 keV, 13.8 keV, 14.3 keV, and 14.4 keV. The study focused on a sample with gold nanoparticles with an average diameter of 11.0 nm on a thin carbon foil. Additional measurements were performed on a sample with 5.3 nm gold nanoparticles and on reference samples of gold and carbon foils. Further measurements were made to calibrate the photon flux monitor, to characterize the transmission function of the electron spectrometer, and to determine the size of the photon beam. This allowed the determination of the absolute values of the spectral particle radiance of secondary electrons per incident photon flux. The paper presents the experimental and raw data analysis procedures, reviews the data obtained for the nanoparticle samples, and discusses their limitations.

Engagement opportunities in OECD NEA benchmark development

A myriad of opportunities is available to collaborate via international benchmark exercises and experimental data preservation activities. Many such opportunities abound under the auspices of the Nuclear Science Committee of the Organisation for Economic Co-operation and Development Nuclear Energy Agency (NEA). Key projects and activities of relevance to the development of advanced reactors design include the International Criticality Safety Benchmark Evaluation Project (ICSBEP), the International Reactor Physics Experiment Evaluation Project (IRPhEP), the International Assay Data of Spent Nuclear Fuel Database (SFCOMPO), the Shielding Integral Benchmark and Archive Database (SINBAD), and The International Experimental Thermal HYdraulicS Database (TIETHYS), and various cooperative benchmark exercises. Interested participants are encouraged to contact the leadership and secretariat of the various Technical Working Groups and Working Parties to become more engaged. This paper provides a summary of the current benchmark exercises and experimental databases available for international participation.

Modelling of Tendon-Driven Continuum Robot Based on Constraint Analysis and Pseudo-Rigid Body Model

Quasi-static models of tendon-driven continuum robots (TDCR) require consideration of both the kinematic and static conditions simultaneously. While the Pseudo-Rigid Body (PRB-3R) model has been demonstrated to be efficient, existing works ignore the mechanical effect of the tendons such as elongation. In addition, the static equilibrium equations for the partially constrained tendons have been expressed in different forms within the literature. This leads to inconsistent simulation results which have not been validated by experimental data when external loads are applied. Furthermore, the inverse problem for solving the required inputs for a prescribed end effector pose has not been studied for the PRB-3R model. In this work, we introduce a new modelling approach based on constraint analysis (CA) of a multi-body system and Lagrange multipliers to systematically derive all the relevant governing equations required for a planar TDCR. This method can include tendon mechanics and efficiently solve for the direct and inverse kinetostatic models with either forces or displacements as the actuation inputs. We validate the proposed CA method using numerical simulation of a benchmark model and experimental data.

Contributions to Integral Nuclear Data in ICSBEP and IRPhEP since ND 2019

The status of the two neutronics international benchmark projects sanctioned by the Organisation for Economic Co-operation and Development Nuclear Energy Agency (OECD NEA), the International Criticality Safety Benchmark Evaluation Project (ICSBEP) and the International Reactor Physics Experiment Evaluation Project (IRPhEP), was last directly discussed with the international nuclear data community at the 14th International Conference on Nuclear Data for Science and Technology (ND2019) in Beijing, China. Since ND2019, the quantity of available integral benchmark experiment data has increased. The primary purpose of the ICSBEP and IRPhEP is to provide extensively peer-reviewed benchmark data to the international nuclear community in support of validation and testing of nuclear data and models. A total of 28 countries have contributed to the past and continued success of these projects as benchmark evaluations, technical reviews, and experimental data using their own time and resources: 26 to the ICSBEP and 25 to the IRPhEP. Key contributions to the handbooks over the past three years can only be highlighted within this paper. Full technical details and benchmark experiment descriptions can be located within the benchmark reports distributed within recent editions of the handbooks.

Continuous-time modeling of financial returns based on Limit Order Book data

The Limit Order Book (LOB) of a financial security contains the list of buy and sell orders placed by traders on a single or across many exchanges for a given stock. As such, the amount of information contained in the LOB is much richer than that contained in the time history of the mean price, so it can potentially be exploited to model and predict more accurately the future returns. As far as we are aware, this work offers - for the first time - a dynamical systems perspective on the latter modeling problem, leading to a two-fold contribution: (i) we will define a new regressor, called Base Imbalance, and we will show that its predictive potential is more significant than the ones usually employed in the literature; (ii) we will discuss that the most suitable way to derive a model of the LOB dynamics is via continuous-time system identification, due to the event-based nature of the phenomenon at hand. The effectiveness of the proposed modeling strategy to be used in trading policies will be illustrated by means of a benchmark experimental data.