Computer Simulation Research Articles

Thermal analysis of five flat air solar collectors in Epinal (France) using modelling and simulation

ABSTRACT The purpose of this study is to investigate the effect of the position of the solar collector absorber and stagnant air relative to the airflow path on the thermodynamic properties of traversing air and the overall thermal performance of the collector. A numerical model was developed and validated using five different absorber design positions. Numerical results are validated using experimental data obtained from Epinal (France). The model was subsequently used to parametrically study the influence of these absorber positions on the density, temperature, velocity variations of the airflow through the collector and the thermal efficiency of each case scenario. The study showed the importance of stagnant air in solar collector thermal performance. Also, it showed that when one absorber is used, it is better to locate the absorber on the top of the airflow path than below the airflow path. Sandwiching the airflow path between double absorbers with the upper absorbers having stagnant air between it and the plexiglass cover and the down absorber having stagnant air between it and the bottom of the collector can produce a thermal efficiency of 31% which is 2% higher than the closest case and 13% higher than the worst-case design.

Using Expert Participation to Evaluate the Accuracy of Hand-Drawn Water-Table Maps.

Water-table maps are fundamental to hydrogeological studies and a manual, hand-drawn method is still commonly used to produce them. Despite this, the accuracy and variability of such maps have received little attention in international literature. In a unique experiment, 63 groundwater professionals drew water-table equipotential contours based on the same dataset of point measurements and were asked to infer flow directions and predict groundwater elevations at predefined locations. The root mean squared error (RMSE) for the average map calibration data was 10.5 m, which is accuracy comparable to numerical groundwater models. This study confirmed that to produce hand-drawn water-table maps, practitioners seek to not only fit the spatial data, but also to conform to their own cognitive model of hydrogeological concepts and processes. The calibration accuracy increased with experience; from a RMSE of 13.3 m for practitioners with 0-3 years of experience to a RMSE of 9.2 m for those with four or more years. Despite considerable variability in the style of the hand-drawn water-table maps, the maps were consistent in their representation of the dominant regional groundwater flow directions. There was less consensus, however, in predicting the direction of surface water-groundwater interaction for a stream reach. Hand-drawn water-table mapping remains useful and valid, especially as a starting point for hydrogeological conceptualization, yet further work is required to resolve issues around transparency, repeatability, and reproducibility.

Tsunami inundation and flow velocity within a partially sheltered structural array

ABSTRACT This work examines tsunami run-up processes within a building array partially sheltered by a high-elevation, finite length, seawall. Work was performed using numerical modeling of 2D shallow water equations with varying seawall length, wave height, time scale, and structure size. Inundation depth and flow velocity were output at many locations of interest throughout the structural array. The most important influence on results when compared to the no-wall case was the sheltering or clearance angle as defined from the end of the wall. Behind the wall, inundation depth and cross-shore velocity declined significantly as sheltering angles increased (more sheltered). Alongshore inundation velocity (parallel to the seawall) increased strongly with increasing sheltering angle. A longer wave time scale resulted in significant increase in inundation depth and velocity especially at locations sheltered by the wall. Larger structure size or blockage ratio also led to increased inundation depth and flow velocity. However, varying wave height gave little change in inundation and velocity patterns. These results will serve to provide a better understanding of the wave inundation within a coastal urban region in a tsunami event when the seawall is breached, as well as the influence of coastal structure size and spacing on the tsunami inundation.

Tailored Postsynthetic Nitration of a Hypercrosslinked Polymer for Single-Step Ethylene Purification from a Ternary C2 Gas Mixture.

Purifying C2H4 from a ternary C2H2/C2H4/C2H6 mixture poses a substantial industrial challenge due to their close physical and chemical properties. In this study, we introduce an innovative design approach to regulate and optimize the nitration degree of a hypercrosslinked polymer to achieve targeted separation performance. We synthesized a porous organic polymer (HCP) using the solvent knitting method and carried out its postsynthetic nitration, resulting in HCP-NO2-1 and HCP-NO2-2 with different nitration degrees. Notably, the adsorption capacity shifted from C2H6 > C2H4 ≈ C2H2 for HCP to C2H2 > C2H6 > C2H4 for HCP-NO2-1 and to C2H2 > C2H4 ≈ C2H6 for HCP-NO2-2, demonstrating the controllable nature of the separation process via the polar nitro group insertion. Remarkably, HCP-NO2-1 exhibited a desirable, selective separation of C2H4 from the C2H6/C2H4/C2H2 mixture thanks to an exquisite combination of the acidic proton-polar nitro group and nonpolar C-H∙∙∙π interactions. Separation capability was further corroborated by computational simulations and breakthrough tests. This work marks a significant advancement as the first successful postsynthetic functionalization strategy for C2H4 purification from a ternary gas mixture among porous organic polymers.

Analytical and numerical modeling on strengths of aluminum and magnesium micro-lattice structures fabricated via additive manufacturing

Analytical and numerical modeling on strengths of aluminum and magnesium micro-lattice structures fabricated via additive manufacturing

Comparative assessment of thermal oils and water as working fluids in parabolic trough collectors for enhanced solar power generation

Examining the thermal behavior of specific working fluids, namely Syltherm800 and TherminolVP-1, in parabolic trough collectors (PTCs) is imperative for enhancing power generation. This study addresses a crucial gap by conducting computational fluid dynamics simulations through COMSOL Multiphysics software and experimental tests to explore the advantages of utilizing thermal oils over water as a working fluid in PTCs. Experimental tests were performed on a water-based PTC in Iraq to validate the numerical model, considering various operating conditions such as input temperature (323.15–423.15 K) and mass flow rates (0.00926–0.0556 kg/s). Key parameters including output temperature, thermal efficiency, useful heat, and total heat losses were evaluated. The numerical model was validated against experimental data, showing good agreement with an overall discrepancy of 1.7% for the current experiments and 3.18% for literature results. The results indicated that Syltherm800, particularly with a high mass flow rate, outperformed TherminolVP-1 and water in terms of overall thermal performance. The optimal PTC thermal efficiency was achieved in July with a mass flow rate of 0.0556 kg/s and an input temperature of 348.15 K. The optimal range for PTC thermal efficiency over four months was between 50% and 70%. The endorsement of thermal oils in PTCs is supported by their low vapor pressure, superior thermal stability, and extended lifespan.

Determining the optimization of seawater concentrate discharge of coastal desalination plants into the marine environment, based on numerical modeling

Determining the optimization of seawater concentrate discharge of coastal desalination plants into the marine environment, based on numerical modeling

On Perceiving Molecular Time: Computational Chemical Simulations and the Moving Image

On Perceiving Molecular Time: Computational Chemical Simulations and the Moving Image

Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions.

The success of machine learning in predicting material properties is largely dependent on the design of the model. However, the current designs of deep learning models in materials science have the following prominent problems. First, the model design lacks a rational guidance strategy and heavily relies on a large amount of trial and error. Second, numerous deep learning models are utilized across various fields, each with its own advantages and disadvantages. Therefore, it is important to incorporate a fusion strategy to fully leverage them and further expand the design strategies of the models. To address these problems, we analyze that the main reason is the lack of a new feedback method rich in physical insights. In this study, we developed a feedback method called the Chemical Environment Clustering Vector (CECV) of compounds at different thresholds, which is rich in physical insights. Based on CECV, we rationally designed the Long Short-Term Memory and Gated Recurrent Unit fused with Deep Convolutional Neural Network (L-G-DCNN) to explore the field of structure-agnostic material property predictions. L-G-DCNN accurately captures the interactions between elements in compounds, enabling more accurate and efficient predictions of the material properties. Our results demonstrate that the performance of the L-G-DCNN surpasses the current state-of-the-art structure-agnostic models across 28 benchmark data sets, exhibiting superior sample efficiency and faster convergence speed. By employing different visualization methods, we demonstrate that the fusion strategy based on CECV significantly enhances the comprehension of the L-G-DCNN model design and provides a fresh perspective for researchers in the field of materials informatics.

Dataset of Post-Event Survey of the 2024 Noto Peninsula Earthquake Tsunami in Japan

An earthquake with a moment magnitude of 7.5 (Mw) struck the northern Noto Peninsula, Ishikawa Prefecture, Japan, at 16:10 local time on January 1, 2024. This earthquake triggered a tsunami that propagated along the coastline of Ishikawa, Toyama, and Niigata Prefectures facing the Sea of Japan and significantly damaged coastal communities and infrastructure. Approximately 70 researchers from 23 universities or other institutes throughout Japan formed a joint research group to conduct a post-tsunami survey along a 340 km stretch of the coast. Based on the watermarks and traces of the tsunami, the inundation and run-up heights were surveyed using total stations, automatic optical levels, laser range finders, and a real-time kinematic (RTK) Global Navigation Satellite System (GNSS). The tidal correction was adjusted using astronomical tidal tables. In total, 303 survey records have been compiled, generating the NP2024TS (Noto Peninsula 2024 Tsunami Survey) dataset. This dataset provides comprehensive information on the inundation and run-up heights of the tsunami, which is useful for understanding tsunami characteristics and validating numerical tsunami models.

Systematic Evaluation of Hematoma Expansion Models in Spontaneous Intracerebral Hemorrhage: A Meta-Analysis and Meta-Regression Approach.

Accurate prediction of hematoma expansion (HE) in spontaneous intracerebral hemorrhage (sICH) is crucial for tailoring patient-specific treatments and improving outcomes. Recent advancements have yielded numerous HE risk factors and predictive models. This study aims to evaluate the characteristics and efficacy of existing HE prediction models, offering insights for performance enhancement. A comprehensive search was conducted in PubMed for observational studies and randomized controlled trials focusing on HE prediction, written in English. The prediction models were categorized based on their incorporated features and modeling methodology. Rigorous quality and bias assessments were performed. A meta-analysis of studies reporting C-statistics was executed to assess and compare the performance of current HE prediction models. Meta-regression was utilized to explore heterogeneity sources. From 358 initial records, 22 studies were deemed eligible, encompassing traditional models, hematoma imaging feature models, and models based on artificial intelligence (AI) or radiomics. Meta-analysis of 11 studies, involving 12,087 sICH patients, revealed an aggregated C-statistic of 0.74 (95% CI: 0.69 - 0.78) across seven HE prediction models. Eight characteristics related to development cohorts were identified as key factors contributing to performance variability among these models. The findings indicate that the current predictive capacity for HE risk remains suboptimal. Enhanced accuracy in HE prediction is vital for effectively targeting patient populations most likely to benefit from tailored treatment strategies.

Beneficial normalization of cardiac repolarization by carnitine in transgenic SQT1 rabbit models.

Short-QT-syndrome type 1 (SQT1) is a genetic channelopathy caused by gain-of-function variants in HERG underlying the rapid delayed-rectifier K+ current (IKr), leading to QT-shortening, ventricular arrhythmias, and sudden cardiac death. Data on efficient pharmaco-therapy for SQT1 are scarce. In patients with primary carnitine-deficiency, acquired-SQTS has been observed and rescued by carnitine-supplementation. Here, we assessed whether carnitine exerts direct beneficial (prolonging) effects on cardiac repolarization in genetic SQTS. Adult wild-type (WT) and transgenic SQT1 rabbits (HERG-N588K, gain of IKr) were used. In vivo ECGs, ex vivo monophasic action potentials (APs) in Langendorff-perfused hearts, and cellular ventricular APs and ion currents were assessed at baseline and during L-Carnitine/C16-Carnitine-perfusion. 2D computer simulations were performed to assess reentry-based VT-inducibility.L-Carnitine/C16-Carnitine prolonged QT intervals in WT and SQT1, leading to QT-normalization in SQT1. Similarly, monophasic and cellular AP duration (APD) was prolonged by L-Carnitine/C16-Carnitine in WT and SQT1. As underlying mechanisms, we identified acute effects on the main repolarizing ion currents: IKr-steady, which is pathologically increased in SQT1, was reduced by L-Carnitine/C16-Carnitine and deactivation kinetics were accelerated. Moreover, L-Carnitine/C16-Carnitine decreased IKs-steady and IK1. In silico modelling identified IKr-changes as main factor for L-Carnitine/C16-Carnitine-induced APD-prolongation. 2D-simulations revealed increased sustained reentry-based arrhythmia formation in SQT1 compared to WT, which was decreased to the WT-level when adding carnitine-induced ion current changes. L-Carnitine/C16-Carnitine prolong/normalize QT and whole heart/cellular APD in SQT1 rabbits. These beneficial effects are mediated by acute effects on IKr. L-Carnitine may serve as potential future QT-normalizing, anti-arrhythmic therapy in SQT1.

Evaluation of ride quality in a passenger car with non-uniform tyre radial stiffness

This study is dedicated to examining the influence of varying radial stiffness along the circumference of tyres on a road vehicle ride quality and with a specific focus on aspects relevant to maintenance. Computer simulations employing a three-dimensional road-vehicle model, developed within commercial multi-body software, were conducted to explore the multifaceted factors influencing vehicle behaviour. These factors encompass road unevenness, driving speed, and the range of radial force variation resulting from harmonic changes in tire radial stiffness. Furthermore, simulation tests were carried out using a model of a diagnostic wheel balancing machine, which holds significance in vehicle maintenance practices. Measurements obtained from a passenger car equipped with standard tires with minimal non-uniformity, served as reference values for assessing the car's vibration levels. Additionally, selected scenarios were examined, involving vehicle with multiple wheels exhibiting non-uniform radial stiffness. The findings underscore that non-uniform radial stiffness can magnify the detrimental effects of road irregularities, potentially leading to amplified vibration levels and a heightened need for maintenance considerations.

2D and 3D numerical modelling for preliminary assessment of long-term deterioration in Irish glacial till geotechnical slopes

ABSTRACT Long-term geotechnical slope deterioration, influenced by weathering and meteorological factors, presents stability challenges to infrastructure. Wetting and drying cycles lead to pore water pressure variations, causing deformations and slope failure. Studies on glacial tills which investigate deterioration in geotechnical slopes focus on variables like pore water pressure, soil water retention, compaction, freeze–thaw cycles and cracking. This research conducts a preliminary assessment of an Irish glacial till-cut slope, establishing a data-driven foundation for long-term slope behaviour studies. Data analysis, geospatial modelling and numerical simulations were performed on a cut slope in Castleblayney (Ireland), considering short-term/undrained and long-term/drained conditions. FLAC/Slope and Scoops3D were used for 2D and 3D slope stability analyses, applying the First-Order Second Moment (FOSM) probabilistic approach to assess how minor soil property changes affect slope stability, including long-term deterioration scenarios. The study underscores the importance of precise instrument placement within Irish glacial till geotechnical cut slopes, particularly at the uppermost part where shallow and deep failures coincide under long-term and short-term scenarios. This informs strategic instrument positioning for accurate slope deterioration investigations. This research lays the groundwork for understanding mechanisms driving geotechnical slope deterioration and provides insights for future studies on geotechnical asset deterioration models in Irish glacial tills.

Convergence, sampling and total order estimator effects on parameter orthogonality in global sensitivity analysis.

Dynamical system models typically involve numerous input parameters whose "effects" and orthogonality need to be quantified through sensitivity analysis, to identify inputs contributing the greatest uncertainty. Whilst prior art has compared total-order estimators' role in recovering "true" effects, assessing their ability to recover robust parameter orthogonality for use in identifiability metrics has not been investigated. In this paper, we perform: (i) an assessment using a different class of numerical models representing the cardiovascular system, (ii) a wider evaluation of sampling methodologies and their interactions with estimators, (iii) an investigation of the consequences of permuting estimators and sampling methodologies on input parameter orthogonality, (iv) a study of sample convergence through resampling, and (v) an assessment of whether positive outcomes are sustained when model input dimensionality increases. Our results indicate that Jansen or Janon estimators display efficient convergence with minimum uncertainty when coupled with Sobol and the lattice rule sampling methods, making them prime choices for calculating parameter orthogonality and influence. This study reveals that global sensitivity analysis is convergence driven. Unconverged indices are subject to error and therefore the true influence or orthogonality of the input parameters are not recovered. This investigation importantly clarifies the interactions of the estimator and the sampling methodology by reducing the associated ambiguities, defining novel practices for modelling in the life sciences.

Quantitative stakeholder-driven assessment of radiation protection issues via a PIANOFORTE online survey.

To enhance stakeholder engagement and foster the inclusion of interests of citizens in radiation protection research, a comprehensive online survey was developed within the framework of the European Partnership PIANOFORTE. This survey was performed in 2022 and presented an opportunity for a wide range of stakeholders to voice their opinions on research priorities in radiation protection for the foreseeable future. Simultaneously, it delved into pertinent issues surrounding general radiation protection. The PIANOFORTE e-survey was conducted in the English language, accommodating a diverse range of participants. Overall, 440 respondents provided their insights and feedback, representing a broad geographical reach encompassing 29 European countries, as well as Canada, China, Colombia, India, and the United States. To assess the outcomes, the Positive Matrix Factorization numerical model was applied, in addition to qualitative and quantitative assessment of individual responses, enabling the discernment of four distinct stakeholder groups with varying attitudes. While the questionnaire may not fully represent all stakeholders due to the limited respondent pool, it is noteworthy that approximately 70% of the participants were newcomers to comparable surveys, demonstrating a proactive attitude, a strong willingness to collaborate and the necessity to continuously engage with stakeholder groups. Among the individual respondents, distinct opinions emerged particularly regarding health effects of radiation exposure, medical use of radiation, radiation protection of workers and the public, as well as emergency and recovery preparedness and response. In cluster analysis, none of the identified groups had clear preferences concerning the prioritization of future radiation protection research topics.

Optimising synthetic datasets for machine learning-based prediction of building damage due to tunnelling

Assessment of tunnelling-induced building damage is a complex Soil-Structure Interaction (SSI) probelm, influenced by numerous geometric and material parameters of both the soil and structures, and is characterised by strong non-linear behaviour. Currently, there is a trend towards developing data-driven models using Machine Learning (ML) to capture this complex behaviour. Given the scarcity of real data, which typically comes from specific case studies, many researchers have turned to creating extensive synthetic datasets via sophisticated and validated numerical models like Finite Element Method (FEM). However, the development of these datasets and the training of advanced ML algorithms present significant challenges. poses significant challenges. Reliance solely on parameter domains and ranges derived from case studies can lead to imbalanced data distributions and subsequently poor performance of models in less populated regions. In this paper, we introduce a strategy for designing optimal high-confidence datasets through an iterative procedure. This process begins with a systematic literature review to determine the importance of parameters, their ranges, and dependencies as they pertain to building damage induced by SSI. Starting with several hundred FEM simulations, we generate an initial dataset and assess its quality and impact through Sensitivity Analysis (SA) studies, statistical modelling, and re-sampling in statistically significant regions. This evaluation allows us to refine the model’s input space, seeking scenarios that mitigate output distribution imbalances. The procedure is repeated until the datasets achieve a satisfactory balance for training metamodels, minimising bias effectively. Our findings highlight the success of this approach in identifying an optimal and feasible input space that significantly reduces imbalanced distributions of output features. This approach not only proves effective in our study but also offers a versatile methodology that could be adapted to other disciplines aiming to generate high-quality synthetic datasets.

Verification and validation of a coupled CFD–FEM approach with overset structured grid through free decay and regular wave tests

This paper presents a comprehensive verification and validation study applied to the hydrodynamic analysis of a semi-submersible Floating Offshore Wind Turbine (FOWT) platform. The numerical simulation is conducted using a coupled Computational Fluid Dynamics-Finite Element Method (CFD–FEM) approach. The CFD program incorporates an overset grid interpolation method, facilitating a more systematic grid refinement in the estimation of discretization uncertainties. A mooring analysis program, based on the FEM scheme, is integrated with the CFD program to simulate the dynamic responses of mooring system. The necessity of the nonlinear mooring model is demonstrated by comparing solutions with a comparative test using a linear mooring model. The verification study, based on a least-squares-based extrapolation method, quantifies the spatial and temporal discretization errors of the numerical model. The numerical solutions based on the baseline model are further validated against the model test measurements. A pitch free decay model test and a regular wave model test conducted within the OC5 project are utilized for the verification and validation study. Results from both tests demonstrate the reliability and accuracy of the coupled method.

Анализ исследований роли компьютерных тренажеров в формировании, измерении и совершенствовании навыков командной работы

<p>The article considers the influence of training on computer simulators on the development of teamwork skills of operators of complex systems, in particular, in aviation. The article substantiates the effectiveness of work on computer simulators for the formation of interaction skills in people with disabilities. A review of experimental studies demonstrating the positive impact of using computer simulators on the formation of skills necessary for teamwork: coordinated actions, communication, decision-making and situational awareness is presented. The analysis of methods for assessing teamwork skills is given and the realization of a hardware and software complex in the form of a simulator for training these skills and their quantitative measurement is described.</p>

Evidence of a dual African and Australian biomass burning influence on the vertical distribution of aerosol and carbon monoxide over the southwest Indian Ocean basin in early 2020

Abstract. During the 2020 austral summer, the pristine atmosphere of the southwest Indian Ocean (SWIO) basin experienced significant perturbations. This study examines the variability of aerosols and carbon monoxide (CO) over this remote oceanic region and investigates the underlying processes in the upper troposphere–lower stratosphere (UT-LS). Aerosol profiles in January and February 2020 revealed a multi-layer structure in the tropical UT-LS. Numerical models – the FLEXible PARTicle dispersion model (FLEXPART) and the Modèle Isentropique de transport Mésoéchelle de l'Ozone Stratosphérique par Advection (MIMOSA) – indicated that the lower-stratospheric aerosol content was influenced by the intense and persistent stratospheric aerosol layer generated during the 2019–2020 extreme Australian bushfire events. A portion of this layer was transported eastward by prevailing easterly winds, leading to increased aerosol extinction profiles over Réunion on 27 and 28 January. Analysis of advected potential vorticity revealed isentropic transport of air masses containing Australian biomass burning aerosols from extratropical latitudes to Réunion at the 400 K isentropic level on 28 January. Interestingly, we found that biomass burning (BB) activity in eastern Africa, though weak during this season, significantly influenced (contributed up to 90 % of) the vertical distribution of CO and aerosols in the upper troposphere over the SWIO basin. Ground-based observations at Réunion confirmed the simultaneous presence of African and Australian aerosol layers. This study provides the first evidence of African BB emissions impacting the CO and aerosol distribution in the upper troposphere over the SWIO basin during the convective season.