Simulation Output Research Articles

A NOVEL FAULT-TOLERANT GENERALIZED SYMMETRICAL TOPOLOGY FOR RENEWABLE ENERGY AND ELECTRIC VEHICLE APPLICATIONS

Multilevel inverters are a versatile and powerful technology for applications such as motor drives, high voltage direct current transmission (HVDC), renewable energy systems, and grid-connected applications. As the demand for high-quality, clean energy and low distortion continues to grow, multilevel inverters are preferred over traditional square wave inverters. This paper proposes a generalized 13-level symmetrical topology to meet the power quality requirements. It consists of six DC sources and twelve semiconductor devices. Further, the topology has cascaded to increase the number of levels, but the cost and circuit complexity increase with the number of units. The gate pulses for switches are produced by the nearest-level modulation technique. The performance comparison of the topology with recent literature provides comprehensive information about the novelty of the configuration. The MLI performance parameters are number of switches, number of gates driving circuits, total harmonic distortion (THD), cost function per level (CCPL), total standing voltage (TSV), and efficiency (η). MATLAB/Simulink investigates the proposed topology, and further, to validate its simulation output, the hardware prototype model has been implemented in the laboratory.

Achieving successful gas plant construction: A risk modelling framework with quantitative risk-based approach to schedule management

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

Data-driven surrogate model for wind turbine damage equivalent load

Abstract. Aeroelastic simulations are employed to assess wind turbines in accordance with IEC standards in the time domain. These analyses enable the evaluation of fatigue and extreme loads experienced by wind turbine components. Such simulations are essential for several reasons, including but not limited to reducing safety margins in wind turbine component design by accounting for a wide range of uncertainties in wind and wave conditions and fulfilling the requirements of the digital twin, which necessitates a comprehensive set of simulations for calibration. Thus, it is essential to develop computationally efficient yet accurate models that can replace costly aeroelastic simulations and data processing. To address this challenge, we propose a data-driven approach to construct surrogate models for the damage equivalent load (DEL) based on aeroelastic simulation outputs. Our method provides a quick and efficient way to calculate DEL using wind input signals without the need for time-consuming aeroelastic simulations. Our study focuses on utilizing a sequential machine learning (ML) method to map wind speed time series to DEL. Additionally, we demonstrate the versatility of the developed and trained surrogate models by testing them on a wind turbine in the wake and applying transfer learning to enhance their predictive accuracy.

Design, synthesis, biological evaluation and computational studies of 4-Aminopiperidine-3, 4-dihyroquinazoline-2-uracil derivatives as promising antidiabetic agents

A novel series of 4-aminopiperidin-3,4-dihyroquinazoline-2-uracil derivatives (9a-9 L) were logically designed and synthesized as potent DPP4 inhibitors as antidiabetic agents. Chemical structure of all new compounds were confirmed by different spectroscopic methods. The designed compounds were evaluated using a MAK 203 kit as DPP4 inhibitors in comparison with Sitagliptin. The biological evaluation revealed that compound 9i bearing chloro substitution on phenyl moiety of 6-bromo quinazoline ring had promising inhibitory activity with IC50 = 9.25 ± 0.57 µM. The toxicity test of all compounds confirmed safety profile of them. Kinetic studies showed that compound 9i exhibited a competitive-type inhibition with a Ki value of 12.01 µM. Computational approach supported the rationality of our design strategy, as 9i represented appropriate binding interactions with the active sites of DPP4 target. MD simulation outputs validated the stability of ligand 9i at DPP4 active site. Also, Density functional theory (DFT) including HOMO-LUMO energies, ESP map, thermochemical parameters, and theoretical IR spectrum was employed to study the reactivity descriptors of 9i and 9a as the most and least potent compounds respectively. Based on the DFT study, compound 9i was softer and, as a result, more reactive than 9a. Taken together, our results showed the potential of 4-aminopiperidin-3,4-dihyroquinazoline-2-uracil derivatives as promising candidates for developing some novel DPP4 inhibitors for managing of type 2 diabetes.

Validation of 3D thermal simulations of the Double C Block, a novel composite masonry unit, using in-situ U-value measurements

Energy efficient building envelopes, particularly innovative prefabricated building components, can significantly contribute to lower the building industry energy-related emissions. In this research the composite masonry unit Double C Block (DCB) is quantitatively tested by means of three-dimensional thermal simulation validated by in-situ U-value measurements carried out in both summer and winter conditions.The simulations were enhanced by the experimental measurement of the dry thermo-physical material properties (specific heat capacity, thermal conductivity, and density) in laboratory conditions for both concrete and polyurethane. The samples were extracted from real-scale DCB prototypes. Furthermore, site-specific boundary conditions of Malta — representative a case study of a Mediterranean climate − were utilised in the simulations. Both laboratory and in-situ assessments of thermal transmittance, or U-value, had their uncertainty specified as part of the calculations.The validation exercise was performed by means of hourly recorded surface temperature and the selected indices were the root mean square error and mean absolute error. Results showed that these indices were within the recommended accuracy thresholds during both winter and summer assessments. For the hourly-based heat flux measurements, the mean bias error and coefficient of variation of the root mean square error were found lower than values reported in literature, thus supporting the robustness of the overall simulation outputs. Hence the 3D CVM simulation was experimentally validated by in-situ assessments.The convergence value of the experimental U-value provided respectively 1.51 ± 0.20 W/m2K (winter) and 1.44 ± 0.19 W/m2K (summer). Then, the validated 3D CVM model was adapted to calculate the theoretical U-value of DCB, using monthly values of recorded surface temperatures obtained by the test cells (in both seasons). The predicted thermal transmittance was of 1.47 W/m2K, in both conditions, and this almost matches the above-mentioned experimental assessments and thresholds established in previous works.

Simulation model calibration with dynamic stratification and adaptive sampling

ABSTRACT Calibrating simulation models that take large quantities of multi-dimensional data as input is a hard simulation optimization problem. Existing adaptive sampling strategies offer a methodological solution. However, they may not sufficiently reduce the computational cost for estimation and solution algorithm’s progress within a limited budget due to extreme noise levels and heteroscedasticity of system responses. We propose integrating stratification with adaptive sampling for the purpose of efficiency in optimization. Stratification can exploit local dependence in the simulation inputs and outputs. Yet, the state-of-the-art does not provide a full capability to adaptively stratify the data as different solution alternatives are evaluated. We devise two procedures for data-driven calibration problems that involve a large dataset with multiple covariates to calibrate models within a fixed overall simulation budget. The first approach dynamically stratifies the input data using binary trees, while the second approach uses closed-form solutions based on linearity assumptions between the objective function and concomitant variables. We find that dynamical adjustment of stratification structure accelerates optimization and reduces run-to-run variability in generated solutions. Our case study for calibrating a wind power simulation model, widely used in the wind industry, using the proposed stratified adaptive sampling, shows better-calibrated parameters under a limited budget.

Simulating Daily Large Fire Spread Events in the Northern Front Range, Colorado, USA

Extreme spread events (ESEs), often characterized by high intensity and rapid rates of spread, can overwhelm fire suppression and emergency response capacity, threaten responder and public safety, damage landscapes and communities, and result in high socioeconomic costs and losses. Advances in remote sensing and geospatial analysis provide an improved understanding of observed ESEs and their contributing factors; however, there is a need to improve anticipatory and predictive capabilities to better prepare, mitigate, and respond. Here, leveraging individual-fire day-of-arrival raster outputs from the FSim fire modeling system, we prototype and evaluate methods for the simulation and categorization of ESEs. We describe the analysis of simulation outputs on a case study landscape in Colorado, USA, summarize daily spread event characteristics, threshold and probabilistically benchmark ESEs, spatially depict ESE potential, and describe limitations, extensions, and potential applications of this work. Simulation results generally showed strong alignment with historical patterns of daily growth and the proportion of cumulative area burned in the western US and identified hotspots of high ESE potential. Continued analysis and simulation of ESEs will likely expand the horizon of uses and grow in salience as ESEs become more common.

Toward Implementation of the Pressure-regulated, Feedback-modulated Model of Star Formation in Cosmological Simulations: Methods and Application to TNG

Traditional star formation subgrid models implemented in cosmological galaxy formation simulations, such as that of V. Springel & L. Hernquist (hereafter SH03), employ adjustable parameters to satisfy constraints measured in the local Universe. In recent years, however, theory and spatially resolved simulations of the turbulent, multiphase, star-forming interstellar medium (ISM) have begun to produce new first-principles models, which when fully developed can replace traditional subgrid prescriptions. This approach has advantages of being physically motivated and predictive rather than empirically tuned, and allowing for varying environmental conditions rather than being tied to local-Universe conditions. As a prototype of this new approach, by combining calibrations from the TIGRESS numerical framework with the pressure-regulated feedback-modulated (PRFM) theory, simple formulae can be obtained for both the gas depletion time and an effective equation of state. Considering galaxies in TNG50, we compare the “native” simulation outputs with postprocessed predictions from PRFM. At TNG50 resolution, the total midplane pressure is nearly equal to the total ISM weight, indicating that galaxies in TNG50 are close to satisfying vertical equilibrium. The measured gas scale height is also close to theoretical equilibrium predictions. The slopes of the effective equations of states are similar, but with effective velocity dispersion normalization from SH03 slightly larger than that from current TIGRESS simulations. Because of this and the decrease in PRFM feedback yield at high pressure, the PRFM model predicts shorter gas depletion times than the SH03 model at high densities and redshift. Our results represent a first step toward implementing new, numerically calibrated subgrid algorithms in cosmological galaxy formation simulations.

Neural network based estimates of the climate impact on mortality in Germany: application to storyline climate simulations

The aim of this work is the prediction of heat-related mortality for Germany under future, i.e. hotter, climate conditions. The prediction is made based on 2m temperature data from climate storyline simulations using machine learning techniques. We use an echo state network for linking the outputs of storyline climate simulations to the target data. The target data are all-cause mortality rates of Germany for all ages. The network is trained with present day climate model outputs. Model outputs of future, i.e. 2K and 4K warmer, storylines are used to predict mortality rates under such climatic conditions. We find that we can train an echo state network with recent temperature data and mortality and make plausible predictions about expected developments of mortality in Germany based on future climate storylines. The trained network can successfully predict mortality rates for future climate conditions. We find increased mortality during the summer months which is attributed to the presence of more severe heat waves. The mortality decrease found during winter can be explained milder winters leading to fewer deaths caused by respiratory diseases. However, mortality in winter is largely influenced by other factors such as influenza waves or vaccination rate and explainability due to temperature is limited.

Enhancing 3D planetary atmosphere simulations with a surrogate radiative transfer model

Abstract This work introduces an approach to enhancing the computational efficiency of 3D atmospheric simulations by integrating a machine-learned surrogate model into the OASIS global circulation model (GCM). Traditional GCMs, which are based on repeatedly numerically integrating physical equations governing atmospheric processes across a series of time-steps, are time-intensive, leading to compromises in spatial and temporal resolution of simulations. This research improves upon this limitation, enabling higher resolution simulations within practical timeframes. Speeding up 3D simulations holds significant implications in multiple domains. Firstly, it facilitates the integration of 3D models into exoplanet inference pipelines, allowing for robust characterisation of exoplanets from a previously unseen wealth of data anticipated from JWST and post-JWST instruments. Secondly, acceleration of 3D models will enable higher resolution atmospheric simulations of Earth and Solar System planets, enabling more detailed insights into their atmospheric physics and chemistry. Our method replaces the radiative transfer module in OASIS with a recurrent neural network-based model trained on simulation inputs and outputs. Radiative transfer is typically one of the slowest components of a GCM, thus providing the largest scope for overall model speed-up. The surrogate model was trained and tested on the specific test case of the Venusian atmosphere, to benchmark the utility of this approach in the case of non-terrestrial atmospheres. This approach yields promising results, with the surrogate-integrated GCM demonstrating above 99.0% accuracy and 147 factor GPU speed-up of the entire simulation compared to using the matched original GCM under Venus-like conditions.

Changes in Extreme Daily Precipitation over the Contiguous United States from Convection-Permitting Simulations

Abstract The potential for changes in extreme precipitation events due to anthropogenic climate change may have significant societal impacts (e.g., agricultural productivity, property loss, mortality). This project uses a dynamically downscaled, convection-permitting regional climate model to investigate extreme daily precipitation in the CONUS, defined explicitly as the 99th percentile 24-h accumulated value. The simulation output includes a historical baseline (HIST; 1990–2005) and two epochs at the end of the twenty-first century (EOC; 2085–2100) under intermediate and pessimistic emissions scenarios. Independent observations illustrate that HIST admirably represents extreme precipitation climatology for most locations in the domain. Comparisons between HIST and the two EOC scenarios for the 99th percentile of daily precipitation show statistically significant increases during Dec–May across the Midwest and Ohio Valley and statistically significant decreases for the southern Great Plains during Dec–Feb. Extreme value analysis further reveals increasing variability in precipitation extremes for eight climatologically unique cities across the CONUS by the end of the twenty-first century and significant increases in return period precipitation amounts for most cities examined. These results provide additional guidance for stakeholders to reduce societal impacts and economic loss from daily precipitation extremes and create a more climate-resilient society.

A multilabel deep learning model for the detection of conduction and rhythm disorders from PDF outputs of a widely available portable ECG Device

Abstract Background Wearable and portable devices with ECG capabilities can provide broad access to screening for cardiovascular disorders. However, most are approved for the detection of only atrial fibrillation. Most algorithms for detecting rhythm and conduction disorders are trained and tested on 12-lead ECGs or the lead I of clinical ECGs, with an unclear role for extending the diagnostic range of wearable and portable devices. Moreover, no current algorithms enable cardiovascular diagnosis on data available to end users, who largely have access to the PDF outputs of their data. Purpose To validate a novel artificial intelligence (AI) model trained to identify conduction disorders and arrhythmias on synthetic ECG outputs from lead I data of clinical ECGs, to detect the same disorders on cardiologist-annotated PDF outputs from the Alivecor KardiaMobile single-lead ECGs (Kardia ECGs). Methods We extracted lead I data from 3,076,352 clinical ECGs across 5 hospitals of a large U.S.-based hospital system from 2000-2024 and simulated wearable PDF outputs from commercial devices using a novel plotting approach that replicated the variations in plotted formats. Gold-standard cardiologist written diagnosis statements were processed to generate 8 clinical labels spanning conduction and rhythm disorders, and we developed a multilabel model to predict these labels using an EfficientNet-B3 convolutional neural network. We examined the performance of the model on 24,808 Kardia ECGs annotated by cardiologists. Results The model achieved a high performance on clinical ECGs in a held-out subset of the synthetic lead I ECG printouts resembling wearable outputs (AUC > 0.9 for all 8 labels). In the independent 24,808 Kardia ECGs, annotated by experts, the model achieved high performance across all labels. For the rhythm disorders atrial fibrillation or atrial flutter, premature atrial contraction, and premature ventricular contraction, the model had AUROCs of 0.99, 0.91, and 0.95, respectively. For the conduction disorders 1st-degree AV block and 2nd or 3rd-degree AV blocks, the model had AUROCs of 0.94 and 0.99, respectively. Finally, for supraventricular tachycardias, wide QRS, and paced ECGs, the model had AUROCs of 0.998, 0.98, and 0.98, respectively. Conclusions We demonstrate that a deep learning model trained to identify rhythm and conduction disorders based on synthetic ECGs where lead I data from clinical 12 lead ECGs are used to replicate real-world PDF outputs of wearable ECGs, generalizes to real-world wearable and handheld single-lead devices.

Impact of stochastic vehicle generation on traffic microsimulation model output

One of the most important characteristics of microsimulation is the ability to model the temporal and spatial nature of traffic demand. Every traffic microsimulation has a vehicle generation model that determines how and when the driver–vehicle units are introduced in the simulation. While microsimulation use has become increasingly popular, it is unclear how the vehicle generation options affect the final result. The purpose of this paper is to examine the stochastic component of the vehicle generation model and how it may affect the simulation output. Three scenarios, including the passenger car equivalent (PCE) model of the Highway Capacity Manual (HCM-7), were used for assessing stochastic volumes and their impacts on performance measures. Results indicate a statistically significant impact of the variability of stochastic volumes on the estimation of performance measures at the breakdown flow phase and synchronized flow phase for interrupted and uninterrupted flow conditions, respectively. This finding highlights the importance of reporting vehicle generation as a calibration parameter, enabling others to replicate the experiments. When utilizing microsimulations for the assessment of roadway/system performance, it is crucial for users to have a thorough understanding of demand generation.

Synthesis, Antimicrobial and Antioxidant Activity with in silico ADMET Prediction, Molecular Docking and Dynamics Studies of Carbazole Ring Containing Thiazole Schiff Bases

AbstractTwelve thiazole hybrids (2 a–2 l) were synthesized in this study incorporating with Schiff base and carbazole scaffolds and characterized by spectral analyses. Antimicrobial activity and antioxidant potency were investigated by agar disc diffusion method and DPPH free radical scavenging assays where trimethyldihydrothiazole 2 c had revealed its higher efficacy in both biological studies. It had displayed IC50 of 25.55±0.40 μg/mL which is higher than the standard ascorbic acid (IC50=41.31±1.83 μg/mL) as well as dihydrothiazoylchromenone 2 e showed promising antioxidant activity with IC50 value of 40.82±0.54. The compound 2 c satisfied all the ADME properties and toxicological parameters with maximum drug score value (0.39) among all derivatives. Molecular docking was undertaken for higher bioactive compounds where 2 c revealed its wide range of interactions with different amino acid residues which was also endorsed with the output of molecular dynamics simulation.

Research on reaction mechanisms on chemical looping gasification of naphthalene over NiFe2O4 for syngas production: An experimental and theoretical study

The effective removal of tar during biomass gasification is a significant challenge that can be addressed by chemical looping gasification (CLG), which offers a promising alternative for converting biomass into syngas with reduced tar generation. In this study, we investigated the mechanism of CLG of naphthalene as a model biomass tar compound over NiFe2O4 to produce syngas. Both experimental and simulation results indicate the optimal operating conditions for syngas production are 1000℃ and an O/C ratio of 1:1. Under the optimal reaction conditions, the experimental and simulated CO yields were 67 vol% and 73 vol%, respectively, while H2 yields were 22 vol% and 24 vol%. Additionally, carbon conversion rates reached 78% and 76%. Thermodynamic calculations and molecular dynamics simulations revealed that the reactions proceed spontaneously, with increasing temperature favoring naphthalene conversion and syngas production. Density functional theory results indicate that the reaction pathway centered on the carbon at the α-position is the key to naphthalene and NiFe2O4 conversion. The optimal reaction pathways for H2 and CO formation were explored. This study on the CLG of naphthalene over NiFe2O4 provides a theoretical basis for the development of more efficient CLG processes with reduced tar formation.

Climate-adaptative management strategies for soybean production under ENSO scenarios in Southern Brazil: An in-silico analysis of crop failure risk

CONTEXTSoybeans (Glycine max L.) are a crucial crop for global food security, and the state of Rio Grande do Sul (RS), Brazil, plays a significant role. However, climate instability, particularly water stress (WS), is a major concern in this region, causing large interannual yield variability. OBJECTIVEThis study aims to address this issue using an in-silico approach to: i) characterize WS and spatial patterns and their frequency within ENSO events; and ii) explore climate-adaptative management strategies such as planting dates and maturity groups to mitigate the risk of crop failure and maximize seed yield and profits. METHODSCrop growth simulations were performed testing three soybean maturity groups (MG, 5.0, 5.8, and 6.4) and eight planting dates (from October 5th to January 20th) over 30 years at 187 locations in RS, Brazil, using APSIM Next Generation. Failure risk was calculated as the percentage of simulations that yielded less than the economic break-even soybean yield in a given scenario. RESULTS AND CONCLUSIONSThe simulated yields were clustered into four regions: Northeast, North, Central, and Southwest. Four WS seasonal patterns were then defined (no stress, early stress, late stress, and whole season stress). On average, WS reduced yields up to 2 Mg ha-1 (∼50 % relative to the maximum). WS varied among regions, with the SW experiencing more frequent and severe stress (up to 50 % of whole season stress during La Nina). ENSO events influenced WS frequency, with El Niño events associated with reduced stress and La Niña events to increased stress. The MG 5.0 resulted in a higher probability of failure risk in all regions. Early planting dates resulted in the highest yield variability (up to 5 Mg ha-1). Climate-adaptative management strategies, such as optimizing planting dates and maturity groups, resulted in a 15 % reduction in crop failure. SIGNIFICANCEOur findings provide valuable insights for developing targeted approaches to enhance soybean yield stability, thereby increasing the resilience of agriculture in the face of future climate uncertainties.

Composition and Injection Rate Co-Optimization Method of Supercritical Multicomponent Thermal Fluid Used for Offshore Heavy Oil Thermal Recovery

Supercritical multicomponent thermal fluid injection is a new technology with great potential for offshore heavy oil thermal recovery. In the process of thermal fluid generation, the reaction conditions including temperature, pressure, and the organic mass concentration in the reaction material will significantly affect its composition and injection rate and will further affect the thermal recovery and development quality of heavy oil. However, there is a lack of relevant research on the variation rules and control methods of the composition and injection rate of supercritical multicomponent thermal fluids, resulting in a lack of technical mechanisms for effective optimization. To fill this gap, a reaction molecular dynamics simulation method was used to simulate thermal fluid generation under different temperatures, pressures, and organic mass concentrations. The changes in thermal fluid composition and yield with reaction conditions were studied, and a control model of thermal fluid composition and yield was established. The proportional relationship between the thermal fluid generation scale of an offshore heavy oil platform and the simulated thermal fluid generation scale is analyzed, and a collaborative optimization method of thermal fluid composition and injection rate in field applications is proposed. The results show the following: (1) The higher the mass concentration of organic matter, the higher the content of supercritical carbon dioxide and supercritical nitrogen in thermal fluids, and the lower the content of supercritical water. (2) The higher the temperature and pressure, the higher the thermal fluid yield, and the higher the organic mass concentration, the lower the thermal fluid yield. (3) The component fitting model conforms to the power function relationship, and the coefficient of determination R2 is greater than 0.9; the yield fitting model conforms to the modified inverse linear logarithmic function relationship, the determination coefficient R2 is greater than 0.8, and the fitting degree is high. (4) The ratio between the actual injection rate of thermal fluids in the mine field and the molecular simulated thermal fluid yield is the ratio of organic matter mass in the platform thermal fluid generator and organic matter mass in the simulated box. (5) Based on the composition and yield control model, combined with the simulation of the ratio relationship between yield and injection rate in the field, a collaborative optimization method of thermal fluid composition and injection rate was established. The research results can provide an effective technical method for predicting, controlling, and optimizing the composition and injection rate of supercritical multicomponent thermal fluids.

Symmetry-Engineered BINOL-Based Porous Aromatic Frameworks for H2O2 Production via Artificial Photosynthesis and In Situ Degradation of Pharmaceutical Pollutants.

Accomplishing visible light-driven H2O2 production at millimolar concentrations is practically challenging, particularly for organic semiconductors. In this context, achieving a maximum H2O2 production rate of 31.60 mmol·g-1·h-1 by using porous aromatic frameworks (PAFs) represents a significant accomplishment. We report the unusual photoactivity of tetraphenylmethane-BINOL-linked PAFs in triplet oxygen activation to facilitate the generation of reactive oxygen species (ROS), as confirmed by their optical and electrochemical responses, despite the absence of a conventional chromophoric moiety. Moreover, an in situ BINOL formation strategy was used to synthesize these PAFs during polymerization in contrast to the reported protocols involving chiral BINOLs as precursors. The as-synthesized polymers had a capsule-like morphology (for TPM-BINOL-6), high thermal stability up to 348 °C, and a high Brunauer-Emmett-Teller (BET) surface area of up to 1382 m2/g (for TPM-BINOL-4). Interestingly, they showed sunlight-driven production of H2O2 via an oxygen reduction reaction of up to 17.05 mmol·g-1·h-1 in 1:10 isopropanol in water for TPM-BINOL-6, which was quantified by titration with ceric sulfate. It also exhibited exemplary photocatalytic efficiency with an H2O2 production rate of 6.65 mmol·g-1·h-1 in seawater. Interestingly, the H2O2 production rate reached a maximum of 18.03 mmol·g-1·h-1 with an SCC efficiency of 4.5% under an AM 1.5G solar simulator and apparent quantum yield (AQY) of 15.8% (at λ = 456 nm) for TPM-BINOL-6 in ethanol:water = 1:10. Moreover, the exceptionally high H2O2 production rate of 31.60 mmol·g-1·h-1 was achieved in 1:1 ethanol in water under 50 W blue LED light. Furthermore, these PAFs generated adequate ROS, which were utilized in the photocatalytic degradation of tetracycline via the superoxide intermediate. Additionally, the as-formed H2O2 was further channelized in the pollution abatement catalytic system for the fast degradation of ciprofloxacin (within 4 h) and the reduction of toxic oxometallate Cr(VI) within 10 min, which is one of the earliest reports of utilizing photosynthesized H2O2 for environmental detoxification.

Dynamic model of arriving passengers based on dual value drive

In order to optimize the service process of integrated transportation hubs, the dynamic characteristics of the gathering behavior of arriving passengers were analyzed, and the transformation mechanism of the gathering behavior of arriving passengers in the time dimension of inverse gamma distribution, gamma distribution and chi-square distribution was studied. It was revealed that the gathering behavior was jointly affected by the flight operation time and the utility of the transfer mode, and was converted into the time value and utility value of the arriving passengers respectively. On this basis, a dual-value-driven dynamic model of arriving passengers was established based on the three distributions, and the output was the parameter-adjustable distribution of arriving passengers. The results show that the simulation output with adjustable parameters is consistent with the real distribution, which provides a method and basis for the accurate prediction of the distribution of arriving passengers at hub airports.

Numerical analysis of mixing performance in Y-junction mixers and its impact on yields from supercritical water hydrolysis

This study investigates the mixing behavior in a Y-junction mixer for supercritical water hydrolysis using large eddy simulation with a discrete phase model. Yield changes were simulated using a two-step reaction model with first-order kinetics, based on particles’ temporal temperature data. Effective mixing produced closely matched mass and particle flow temperature distributions, both exhibiting bell-shaped profiles near the mixed temperature. Although variations in flow rate within ±25 % and changes in the inlet temperatures of supercritical water from 350 °C to 430 °C and subcritical water from 100 °C to 170 °C did not significantly affect the overall mixing performance, they did alter the mixed temperature and, subsequently, yield changes. Additionally, backflow occurred when Richardson number for the subcritical inlet reached approximately 7. In effective mixing, simulated yields were approximately 15 % lower than the ideal theoretical yields, calculated using the reaction rate constant at the mixed temperature.