Reference Solution Research Articles

Influence of Proteins and Building Direction on the Corrosion and Tribocorrosion of CoCrMo Fabricated by Laser Powder Bed Fusion.

Cobalt-chromium-molybdenum (CoCrMo) alloys are common wear-exposed biomedical alloys and are manufactured in multiple ways, increasingly using additive manufacturing processes such as laser powder bed fusion (LPBF). Here, we investigate the effect of proteins and the manufacturing process (wrought vs LPBF) and building orientation (LPBF-XY and XZ) on the corrosion, metal release, tribocorrosion, and surface oxide composition by means of electrochemical, mechanical, microscopic, diffractive, and spectroscopic methods. The study was conducted at pH 7.3 in 5 g/L NaCl and 5 mM 2-(N-morpholino) ethanesulfonic acid (MES) buffer, which was found to be necessary to avoid metal phosphate and metal-protein aggregate precipitation. The effect of 10 g/L bovine serum albumin (BSA) and 2.5 g/L fibrinogen (Fbn) was studied. BSA and Fbn strongly enhanced the release of Co, Cr, and Mo and slightly enhanced the corrosion (still in the passive domain) for all CoCrMo alloys and most for LPBF-XZ, followed by LPBF-XY and the wrought CoCrMo. BSA and Fbn, most pronounced when combined, significantly decreased the coefficient of friction due to lubrication, the wear track width and severity of the wear mechanism, and the tribocorrosion for all alloys, with no clear effect of the manufacturing type. The wear track area was significantly more oxidized than the area outside of the wear track. In the reference solution without proteins, a strong Mo oxidation in the wear track surface oxide was indicative of a pH decrease and cell separation of the anodic and cathodic areas. This effect was absent in the presence of the proteins.

Cheap and effective potentiometric PVC sensor for the determination of imidazolium based ionic liquids in aqueous and aqua-organic media

Owing to the extensive research on imidazolium based ionic liquids; the present study aims to design a potentiometric sensor for alkyl methylimidazolium ion. For this, poly(vinyl chloride) PVC membrane based on the neutral ion-pair complexes of cetylpyridinium chloride and sodium dodecyl sulfate (CPy+DS−) has been developed. The synthesized ion selective electrode (ISEs) have been found to show linear response with near-Nernstian slope for CnMeImCl (C10MeImCl, C12MeImCl and C14MeImCl). The proposed ISEs show quick response (5–6 s) and provide wide pH range for working. The influence of various ions on the performance of ion-selective electrode is investigated in terms of potentiometric selectivity coefficients, which were determined by separate solution method. The performance of synthesized ISE in examining the critical micellar concentration (cmc) of CnMeImCl in aqueous, aqua-organic media and in the presence of amino acids (glycine, L-glutamic acid and L-phenylalanine) has been found to be satisfactory and confirmed through conductivity and fluorescence measurements. This emphasizes its worth for in-situ analysis of alkyl methylimidazolium ions, during an organic reaction, and hence possible reaction mechanism evaluation. Different thermodynamic parameters such as Standard Gibbs energies of micellization (ΔGm∘), degree of counterion dissociation (α), amphiphile tail transfer Gibbs free energy (ΔGm,trans∘) have been evaluated. Further, the analytical application of synthesized electrode as end point indicator in the potentiometric titration of C14MeImCl with SDS has also been explored, which compared well with improved two-phase titration method. The current study is novel as it involves the use of a cheaper material (CPyCl) as ionophore and that the same electrode can be used for different chain alkyl methylimidazolium ions simply by changing the internal reference solutions.

Hybrid AI-Analytical Modeling of Droplet Dynamics on Inclined Heterogeneous Surfaces

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

Coupling of kinematics for the analysis of composite beam based on the partition of the unity method

Within the context of composite beam structure modeling, a refined model is combined with a simpler one solely within a region of interest, aiming to enhance result accuracy. The purpose is to gain computational cost without loss of precision. For that, the Partition Unity Method (PUM) is used to ensure the continuity condition on the displacement field. A 1D refined Sinus model and a 2D approach are involved to demonstrate the capability of the method. It is assessed on different numerical examples involving both laminated and sandwich beams. The accuracy of the results is shown using reference solutions and computations using a mono-model. The interest of the method is further demonstrating by estimating the computational cost with or without the coupling. The results are rather very promising.

Increasing the Sensitivity of pH Glass Electrodes with Constant Potential Coulometry at Zero Current.

It has recently become possible to increase the sensitivity of ion-selective electrodes (ISEs) by imposing a constant cell potential, allowing one to record current spikes with a capacitor placed in series in the circuit. The approach requires a transient current to pass through the measurement cell, which unfortunately may introduce measurement errors and additionally excludes the use of high-impedance indicator electrodes, such as pH glass electrodes. We present here an electronic circuit that overcomes these limitations, where the cell is measured at zero current in combination with a voltage follower, and the current spike and capacitor charging occur entirely within the instrument. The approach avoids the need for a counter electrode, and one may use any electrode useful in potentiometry regardless of its impedance. The characteristics of the circuit were found to approach ideality when evaluated with either an external potential source or an Ag/AgCl electrode. The current may be linearized and extrapolated to further reduce the measurement time. The circuit is further tested with the most common yet very challenging electrode, the pH glass electrode. A precision of 64 μpH was obtained for 0.01 pH change up to 0.05 from a reference solution. Similar pH changes were also measured reliably further away from the reference solution (0.5-0.55) and resulted in a precision of 377 μpH. The limitations of this experimental setup were explored by performing pH calibrations within the measuring range of the probe.

DCMIP2016: the tropical cyclone test case

Abstract. This paper describes and analyzes the Reed–Jablonowski (RJ) tropical cyclone (TC) test case used in the 2016 Dynamical Core Model Intercomparison Project (DCMIP2016). This intermediate-complexity test case analyzes the evolution of a weak vortex into a TC in an idealized tropical environment. Reference solutions from nine general circulation models (GCMs) with identical simplified physics parameterization packages that participated in DCMIP2016 are analyzed in this study at 50 km horizontal grid spacing, with five of these models also providing solutions at 25 km grid spacing. Evolution of minimum surface pressure (MSP) and maximum 1 km azimuthally averaged wind speed (MWS), the wind–pressure relationship, radial profiles of wind speed and surface pressure, and wind composites are presented for all participating GCMs at both horizontal grid spacings. While all TCs undergo a similar evolution process, some reach significantly higher intensities than others, ultimately impacting their horizontal and vertical structures. TCs simulated at 25 km grid spacings retain these differences but reach higher intensities and are more compact than their 50 km counterparts. These results indicate that dynamical core choice is an essential factor in GCM development, and future work should be conducted to explore how specific differences within the dynamical core affect TC behavior in GCMs.

Testing Approximate Infrared Scattering Radiative-transfer Methods for Hot Jupiter Atmospheres

The calculation of internal atmospheric (longwave) fluxes is a key component of any model of exoplanet atmospheres that requires radiative-transfer (RT) calculations. For atmospheres containing a strong scattering component such as cloud particles, most 1D multiple-scattering RT methods typically involve numerically expensive matrix inversions. This computational bottleneck is exacerbated when multitudes of RT calculations are required, such as in general circulation models (GCMs) and retrieval methods. In an effort to increase the speed of RT calculations without sacrificing too much accuracy, we investigate the applicability of approximate longwave scattering methods developed for the Earth science community to hot Jupiter atmospheres. We test the absorption approximation and variational iteration method (VIM) applied to typical cloudy hot Jupiter scenarios, using 64-stream DISORT calculations as reference solutions. We find the four-stream VIM variant is a highly promising method to explore for use in hot Jupiter GCM and retrieval modeling, and it shows excellent speed characteristics, with typical errors ∼1% for outgoing fluxes and within ∼50%, but with larger errors in the test case of a deep cloud layer, for vertical heating rates. Other methods explored in this study were found to typically produce similar error characteristics in vertical heating rates.

Bio inspired heuristic computing scheme for the human liver nonlinear model

In this research, a bio-inspired heuristic computing approach has been developed to solve the nonlinear behavior of the human liver, which is categorized into the liver and blood. The solutions of the human liver model are presented by using the stochastic computation procedure based on the artificial neural network (ANN) along with the optimization of genetic algorithm (GA) and interior-point (IP). A fitness function is designed through the differential form of the nonlinear human liver model and then optimized by using the hybrid competency of GAIP scheme. The correctness and exactness of the proposed approach are observed through the overlapping of the obtained (GAIP) and reference (Adams scheme) solutions, while the calculated absolute error values in good order enhance the worth of the proposed solver. The log-sigmoid transfer function together with ten numbers of neurons is executed to perform the solutions of the human liver nonlinear model. Furthermore, the statistical approaches have been applied in order to observe the reliability of the designed approach for solving the nonlinear human liver model.

Emulating the interstellar medium chemistry with neural operators

Context. The study of galaxy formation and evolution critically depends on our understanding of the complex photo-chemical processes that govern the evolution and thermodynamics of the interstellar medium (ISM). In a computational sense, resolving the chemistry is among the weightiest tasks in cosmological and astrophysical simulations. Aims. Astrophysical simulations can include photo-chemical models that allow for a wide range of densities (n), abundances of different species (ni/n) and temperature (T), and plausible evolution scenarios of the ISM under the action of a radiation field (F) with different spectral shapes and intensities. The evolution of such a non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim to substitute such procedural solvers with fast, pre-trained emulators based on neural operators. Methods. We emulated a non-equilibrium chemical network up to H2 formation (9 species, 52 reactions) by adopting the DeepONet formalism, namely: by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks (named branch and trunk). We used KROME to generate a training set, spanning −2 < log(n/cm−3) ≤ 3.5, log(20) ≤ log(T/K) ≤ 5.5, −6 ≤ log(ni/n) < 0, and adopting an incident radiation field, F, sampled in 10 energy bins with a continuity prior. We separately trained the solver for T and each ni for ≃4.34 GPUhrs. Results. Compared with the reference solutions obtained by KROME for single-zone models, the typical precision obtained is of the order of 10−2, that is, it is 10 times better when using a training that is 40 times less costly, with respect to previous emulators that only considered a fixed F. DeepONet also performs well for T and ni outside the range of the training sample. Furthermore, the emulator aptly reproduces the ion and temperature profiles of photo dissociation regions as well; namely, by giving errors that are comparable to the typical difference between various photo-ionization codes. The present model achieves a speed-up of a factor of 128× with respect to stiff ODE solvers. Conclusions. Our neural emulator represents a significant leap forward in the modelling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency. Nevertheless, further work is required to address the challenges represented by the extrapolation beyond the training time domain and the removal of potential outliers.

A methodology for applying isogeometric inverse finite element method to the shape sensing of stiffened thin-shell structures

The capability of reconstructing the displacement field of a structure based on in-situ strain measurements is referred to as “shape sensing” and constitutes a fundamental unit for the real-time monitoring of critical structural components. The Inverse Finite Element Method (iFEM) is one of the most promising innovative techniques for accomplishing this task. This study explores the application of iFEM in conjunction with Isogeometric Analysis (IGA) for the shape sensing of stiffened thin-shell structures. The use of IGA allows for the exact representation of computational geometry, simplifies mesh refinement, and potentially reduces the number of installed sensors. In the context of IGA, when the geometry of a structure is composed of multiple surfaces, non-conforming interfaces are typically involved. To overcome this issue, we propose an assembly process based on the Lagrange multiplier method to jointly apply iFEM and IGA to shell structures assemblies. The methodology is numerically validated using FEM models both to generate the in-situ strain data and as a comparison for iFEM reconstruction results. The case studies include a T-beam, a reinforced hyperbolic paraboloid, and a wingbox. Convergence analyzes are performed to investigate the shape sensing accuracy as the number of inverse elements increases. Additionally, contour plots of displacement field components are compared with the reference solution, revealing a high degree of agreement.

Numerical homogenization of the gas filtration problem

The non-stationary gas flow equation in homogeneous and heterogeneous media is considered. Areas of this type are often encountered when considering gas production processes from a gas-bearing reservoir. The heterogeneous structure of the reservoir can strongly affect the extraction processes. For the numerical solution of the problem under consideration, we construct an approximation of the equations on a coarse grid using the numerical averaging method. The method allows us to solve the problem using less computational power and in a shorter time. A numerical comparison of the results of solving the model problem is carried out in a two-dimensional domain for the cases of linear and nonlinear variants of the equations, as well as in a homogeneous and heterogeneous medium. The finite element solution on a fine mesh was taken as a reference solution. The computational realization was performed using the FEniCS library. The construction of the geometric computational domain was performed in the program Gmsh. Paraview and the Matplotlib library in Python were used for data visualization.

A Case Study on Advanced CPT Data Interpretation: From Stratification to Soil Parameters

The cone penetration test (CPT) is considered as one of the most reliable in-situ tests and has found numerous applications in the geotechnical engineering field. Traditional CPT interpretation includes, but are not limited to the identification of the soil stratification and the determination of soil parameters. This paper presents a case study concerning a test site located in Salzburg, Austria, in which we focus on the interpretation of CPTs from different perspectives. The manuscript is divided into three main sections dealing with three different aspects of CPT interpretation, namely stratification, ground variability and soil parameters. The first strategy introduces a machine learning based stratification identification strategy to detect soil layer boundaries from CPT measurements. A comparison with reference solutions demonstrates relative merits of this approach to classical filter algorithms based on empirical CPT classifications. The second strategy introduces an intuitive approach to evaluate the ground variability. This is achieved by calculating the level of fluctuation on the basis of CPT measurements and could be used as a data-driven decision-making tool for the improved design of CPT investigation layouts. The third strategy is embedded in an ongoing research project that aims to determine constitutive model parameters from in-situ tests using a graph-based methodology. In the present work, the developed automated parameter determination framework is applied to evaluate the soil parameters of one selected soil layer identified from the CPT interpretations. Potential lines of research in the context of CPT interpretation are explored throughout this work and may serve as valuable reference in future research.

Real-time Welding Machine Status Recognition using Embedded Perceptron Color Classification Model

This paper aims to utilize the perceptron model in machine learning to achieve precise classification of the working status of welding machines by analyzing image data. We collected static images of welding and non-welding operations, extracted the R, G, B color values of the images, and labeled them accordingly (+1 for welding status, -1 for idle status). By employing the stochastic gradient descent method for training the perceptron model, we obtained the weights w and bias b of the model under the condition that all data points in the training set are correctly classified. The model's recognition accuracy was validated on the test set. Ultimately, we applied this model to the STM32f103zet6 microcontroller chip and successfully achieved real-time recognition of the welding machine's operational status during the welding process via a camera. Through testing, the classification accuracy reached 83.6%. Further optimization of classification criteria can be combined with relevant expert suggestions, providing a reference solution for monitoring and identifying the working status of welding machines in steel pipe production.

An innovative end-to-end PINN-based solution for rapidly simulating homogeneous heat flow problems: An adaptive universal physics-guided auto-solver

In contemporary heat flow computations, the widespread application of deep learning, specifically Physical Informed Neural Networks (PINN), has been noted. However, existing PINN methods often exhibit limited applicability to specific operational conditions and are hindered by prolonged training times, rendering them unsuitable for engineering scenarios requiring frequent changes in operational parameters. This study addresses the imperative of enhancing the computational efficiency of conventional PINN methodologies and introduces an innovative end-to-end PINN-based solution—Adaptive Universal Physics-Guided Auto-Solver (AUPgAS) for swift simulation of homogeneous heat flow problems. AUPgAS introduces the operational condition as a variable within the neural network, thereby enhancing the model's versatility in addressing homogeneous problems. Simultaneously, AUPgAS refines training accuracy through the incorporation of adaptive sampling methods. The efficacy of this method is demonstrated through validations on two application cases of laminar incompressible flow with viscosity, namely the flow around a cylinder and the flow around two cylinders. After training, the model demonstrates the capability to predict pressure and velocity fields of the flow around a single cylinder in different locations in case Ⅰ, as well as simulate the flow field when the distance between two cylinders rapidly changes in case Ⅱ. Comparative analyses with traditional computational fluid dynamics methods show that the AUPgAS greatly reduces the solution time, the average time taken by the trained AUPgAS to solve for each problem is 3.4 s, which is a significant improvement in efficiency compared to the average time taken by the traditional fluid dynamics methods, which is 910 s. Furthermore, the agreement of the results obtained by AUPgAS with the reference solution is commendable, with a minimum average error of 13.55%. This end-to-end approach presents an innovative and efficient solution for the rapid simulation of homogeneous heat flow problems, offering promising advancements in the realm of engineering computational efficiency.

Chord length sampling with memory effects for spatially heterogeneous Markov media: Application to the rod model.

In this work we propose a modified Chord Length Sampling (CLS) algorithm, endowed with two layers of "memory effects," aimed at solving particle transport problems in one-dimensional spatially nonhomogeneous Markov media. CLS algorithms are a family of Monte Carlo methods which account for the stochastic nature of the media by sampling on-the-fly the random interfaces between material phases during the particle propagation. The possibility for the particles to remember the last crossed interfaces increases the accuracy of these models with respect to reference solutions obtained by solving the Boltzmann equationon a large number of realizations of the Markov media. In previous investigations, CLS models with memory have been tested exclusively for spatially uniform stochastic media: in this paper we extend this class of Monte Carlo methods to the case of spatially nonhomogeneous configurations. The effectiveness and the robustness of the modified CLS are probed considering several benchmark problems with varying material cross sectionsand Markov media densities. The obtained results are a stepping stone towards a generalization to three-dimensional models.

Green recovery of platform chemicals from hydrothermal carbonization process water

This study explores the side-recovery of platform chemicals in the hydrothermal carbonization waste-to-energy chain. Up to 90 % of target chemicals, furfural and 5-hydroxymethylfurfural, were green extracted from the process waters using the hydrophobic deep eutectic solvent decanoic acid/thymol. Wheat straw and rice husk reacted (1/4 solid/water ratio) for up to 120 min and at 180 and 230 °C. The dissolved chemicals built up to 5.9 g/L, accounting for 12 % of the total carbon. Extractions from the process waters and those from reference solutions behaved similarly. The solvent affinity toward furfural is 3.5 times higher than that of 5-hydroxymethylfurfural. The aqueous raffinates chromatographic outline demonstrated the recyclability for prosecuting the HTC. Focused experiments mimicked the functioning of an industrial cascade of two crosscurrent mixer settlers. Extraction yields were 90.7 and 65.8 % for furfural and 5-hydroxymethylfurfural. The counterflow arrangement, simulated by the Kremser method, gave corresponding higher yields, 96.9 and 73.4 %, respectively.

Design and Demonstration of a Tandem Dual-Rotor Aerial–Aquatic Vehicle

Aerial–aquatic vehicles (AAVs) hold great promise for marine applications, offering adaptability to diverse environments by seamlessly transitioning between underwater and aerial operations. Nevertheless, the design of AAVs poses inherent challenges, owing to the distinct characteristics of different fluid media. This article introduces a novel solution in the form of a tandem dual-rotor aerial–aquatic vehicle, strategically engineered to overcome these challenges. The proposed vehicle boasts a slender and streamlined body, enhancing its underwater mobility while utilizing a tandem rotor for aerial maneuvers. Outdoor scene tests were conducted to assess the tandem dual-rotor AAV’s diverse capabilities, including flying, hovering, and executing repeated cross-media locomotion. Notably, its versatility was further demonstrated through swift surface swimming on water. In addition to aerial evaluations, an underwater experiment was undertaken to evaluate the AAV’s ability to traverse narrow underwater passages. This capability was successfully validated through the creation of a narrow underwater gap. The comprehensive exploration of the tandem dual-rotor AAV’s potential is presented in this article, encompassing its foundational principles, overall design, simulation analysis, and avionics system design. The preliminary research and design outlined herein offer a proof of concept for the tandem dual-rotor AAV, establishing a robust foundation for AAVs seeking optimal performance in both water and air environments. This contribution serves as a valuable reference solution for the advancement of AAV technology.

Improved Collision Probability Analysis of the Double-Heterogeneity Problem Based on Matrix Chord Length Correction

Dispersion fuel exhibits excellent safety performance and effectively reduces the risk of radioactive leakage, making it widely applied in high-temperature gas-cooled reactors (HTGRs) and other advanced nuclear reactors. The presence of stochastic media in dispersion fuel leads to the challenging double-heterogeneity problem in neutron transport calculations. Hébert proposed a collision probability analysis model for treating stochastic media, which has been implemented in the DRAGON5 code. As one important basis of derivation, it is assumed in the Hébert model that the neutron transmission probability is identical to the neutron escaping probability in matrix material. In this paper, it is figured out that the assumption is not rigorous for realistic stochastic media. Then, an improved approach based on the Hébert model is proposed to take into account the realistic chord length distribution as well as to ensure the conservation and reciprocity of collision probabilities. The proposed methodology has been implemented in the HTGR lattice physics code XPZ. By numerical analysis against Monte Carlo reference solutions, it is demonstrated that the improved Hébert model with chord length correction gives good accuracy for addressing realistic double-heterogeneity problems.

Assessing the Catalytic Role of Native Glucagon Amyloid Fibrils.

Glucagon stands out as a pivotal peptide hormone, instrumental in controlling blood glucose levels and lipid metabolism. While the formation of glucagon amyloid fibrils has been documented, their biological functions remain enigmatic. Recently, we demonstrated experimentally that glucagon amyloid fibrils can act as catalysts in several biological reactions including esterolysis, lipid hydrolysis, and dephosphorylation. Herein, we present a multiscale quantum mechanics/molecular mechanics (QM/MM) simulation of the acylation step in the esterolysis of para-nitrophenyl acetate (p-NPA), catalyzed by native glucagon amyloid fibrils, serving as a model system to elucidate their catalytic function. This step entails a concerted mechanism, involving proton transfer from serine to histidine, followed by the nucleophilic attack of the serine oxy anion on the carbonyl carbon of p-NPA. We computed the binding energy and free-energy profiles of this reaction using the protein-dipole Langevin-dipole (PDLD) within the linear response approximation (LRA) framework (PDLD/S-LRA-2000) and the empirical valence bond (EVB) methods. This included simulations of the reaction in an aqueous environment and in the fibril, enabling us to estimate the catalytic effect of the fibril. Our EVB calculations obtained a barrier of 23.4 kcal mol-1 for the enzyme-catalyzed reaction compared to the experimental value of 21.9 kcal mol-1 (and a calculated catalytic effect of 3.2 kcal mol-1 compared to the observed effect of 4.7 kcal mol-1). This close agreement together with the barrier reduction when transitioning from the reference solution reaction to the amyloid fibril provides supporting evidence to the catalytic role of glucagon amyloid fibrils. Moreover, employing the PDLD/S-LRA-2000 approach further reinforced exclusively the enzyme's catalytic role. The results presented in this study contribute significantly to our understanding of the catalytic role of glucagon amyloid fibrils, marking, to the best of our knowledge, the first-principles mechanistic investigation of fibrils using QM/MM methods. Therefore, our findings offer fruitful insights for future research into the mechanisms of related amyloid catalysis.

Modelling of the leaching process by the extended quadrature method of moments

An approach based on the extended quadrature method of moments (EQMOM) has been developed to solve the population balance equation written for the evolution of shrinking-core particles in transient leaching processes. It has been shown that the EQMOM can naturally address the dependency of the shrinkage-core model on the unreacted core size and outer size of particles. The approach has been validated by comparing its predictions with either analytical or reference solutions for several test cases. Moreover, the approach has been employed to simulate an experimental leaching process taken from the literature. The obtained results confirm the significance of considering the particle size distribution in the simulation of the leaching process, hence the importance of solution methods such as that presented in this work. Finally, it has been shown that the proposed approach is also useful for the simulation of the dissolution process with shrinking particles.