Simulation Model Research Articles

The LTAR Grazing Land Common Experiment at Walnut Gulch Experimental Watershed.

The Walnut Gulch Experimental Watershed (WGEW) Long-Term Agroecosystem Research (LTAR) network common experiment addresses the aspirational practice of brush management (BM) to reverse the prevailing condition of woody plant encroachment (WPE) and increase perennial native grass production. Across the western United States, the decision to implement BM includes consideration of management objectives, cost, and the expected impact on a diverse suite of ecosystem services. Maintaining or restoring grass cover will help meet the LTAR sustainable production, economic, and social goals, and averting degradation will meet environmental goals. This common experiment, focused on hydrologic and erosion impacts of BM, aims to inform land management decisions on three major plant communities in the Southwestern United States: creosote bush (Larrea tridentata), mesquite (Prosopis velutina), and pinyon-juniper (PJ, Pinus and Juniperus spp.). On the WGEW, applying tebuthiuron pellets to creosote bush increased grass cover and reduced runoff and erosion. The 2016BM experiment on the Santa Rita Experimental Range applied a commonly used liquid herbicide cocktail but achieved only 7% mortality on mesquite, probably because of the timing of the aerial application. Experiments manipulating rainfall amount and intensity on plots receiving fire, chemical, or mechanical BM treatments on PJ communities aim to improve process representation in simulation models. The deliverables of these BM experiments will be to (i) improve the performance of runoff and erosion models, (ii) enhance our ability to identify areas most at risk from reduced hydrologic function and soil erosion after shrub proliferation, and (iii) better predict how landscapes will respond to BM interventions. Ranchers, land management agencies, and watershed conservation organizations will benefit from training and availability of improved tools to focus treatments on areas where greatest net benefits might be realized.

Research on the evaluation method for safety cognitive ability of workers in high-risk construction positions

PurposeSafety cognitive ability is a key factor influencing unsafe behavior. However, the existing achievements have not yet involved the division of the hierarchical relationship of factors influencing safety cognition and lack a quantitative evaluation system of safety cognitive ability. The purpose of this paper is to find out the deficiencies in the safety cognition of workers in high-risk construction positions and to provide practical suggestions for improving their safety cognitive ability and reducing unsafe behavior.Design/methodology/approachBased on the iceberg model, the factors influencing the safety cognitive ability of workers in high-risk construction positions and their hierarchical relationship were determined, and an evaluation index system consisting of four primary indicators and 20 secondary indicators was constructed. The game theory algorithm was used to optimize the subjective and objective weights of the indicators calculated by the sequential analysis method (G1) and the entropy weighting method (EWM) to obtain the optimal combination weight value. The Matlab software was used for cloud mapping and similarity calculation to determine the safety cognitive ability level of the object to be evaluated.FindingsThe research results indicate that the comprehensive level of safety cognitive ability of scaffolders in this construction project is at “Level III”, the fundamental factors and compliance factors are at “Level IV”, the auxiliary factors and driving factors are at “Level III”. This conclusion aligns with the situation learned from the previous field investigation, which validates the feasibility and scientificity of the proposed evaluation method.Research limitations/implicationsConsidering that the safety cognitive ability of construction workers is constantly changing, this study has not yet delved into the specific impacts of various influencing factors on the level of safety cognitive ability. Future research can utilize simulation software, such as MATLAB and Vensim, to construct dynamic simulation models that accurately simulate the changing rules of construction workers’ safety cognitive ability under the influence of different factors.Practical implicationsThis research broadens the application scope of the iceberg model, enriches the analysis model of the factors influencing the safety cognitive ability of workers in high-risk construction positions and provides a novel perspective for similar research. The safety cognitive ability evaluation method proposed in this paper can not only accurately evaluate the safety cognitive ability level of workers in high-risk positions such as scaffolders but also provide practical suggestions for improving the safety cognitive ability of workers, which is of great significance to improve the safety management level and reduce unsafe behavior in the construction field.Originality/valueThis research fills the research gap of workers in high-risk construction positions and the quantification of safety cognitive ability. The iceberg model is used to realize the hierarchical division of the factors influencing safety cognitive ability. Additionally, an evaluation method for the safety cognitive ability of workers in high-risk construction positions based on the game theory combination weighting method and cloud model is proposed, which realizes the quantitative evaluation of safety cognitive ability.

Quantifying the potential of evidence-based planting-pattern for reducing the outdoor thermal stress from a bio-meteorological perspective in tropical conditions of Indian cities.

The impact of declined natural greenery and increased built surfaces exacerbates heat stress in urban areas causing limited usage of outdoor spaces. Greenery strategies such as trees are capable of mitigating outdoor thermal stress gain because of their phytological properties. While urban greenery guidelines have suggested the ad-hoc procedure of tree planting-schemes based on aesthetic-value, soil-water preservation etc., understanding of their morphological character help in regulating extreme thermal condition. Hence, this study aims to investigate the most efficient planting pattern based on canopies densities and trees clusters for reducing the outdoor thermal stress from bio-meteorological perspective.It initiates with the measurement of the site's morphological and meteorological attributes in existing commercial market of Bhopal City which has a humid sub-tropical climate (Aw, Koppen climate categorization). Furthermore, it leads to the development of 4-different iterated clusters incorporating moderate to high-density canopies and their overlaps pattern to estimate reduction potential in outdoors using field surveys and validated simulation model. The reduction potential in terms of magnitude and duration of thermal stress is quantified across 3-thermal variables i.e., air temperature, mean radiant temperature and universal thermal climate index. Results indicate highly-dense canopies are more effective in reducing greater magnitude of thermal stress along longer duration. Also overlapped planting pattern within the same canopy density does not make significant difference in stress reduction as compared to the changing the densities. This study will help planners and landscape architects to adopt evidence-based planting-pattern strategies for improving outdoor microclimate.

Optimizing key parameters (biological-control factor) of soil erosion simulation models at a regional scale– The case of Jiangxi Province, China

Unveiling the spatial information of actual soil erosion is a prerequisite for subsequent soil erosion research and is critical for soil and water conservation as well as ecological resource protection. However, the existing regional-scale soil erosion simulation models often ignore the vertical structure of vegetation, particularly near-surface vegetation information, which leads to discrepancies with the real-world conditions. This study aims to optimize the biological-control factor (B factor) of the Chinese Soil Loss Equation (CSLE) model to improve simulation accuracy. The Global Ecosystem Dynamics Investigation (GEDI) can provide accurate layered vegetation data. Thus, this study focuses on the forest of Jiangxi Province, simulating the understory vegetation structure by vegetation type using a combination of GEDI data and sampled data. By considering the soil and water conservation benefits of different vegetation layers, we developed a B factor optimization method to simulate soil erosion in forests at a regional scale. Compared to previous methods, the optimized B factor mean value for Jiangxi Province’s forested areas increased from 0.0034 to 0.0096. The estimated soil erosion changed from 38 t·km-2a-1 (with 86.6 % of forests exhibiting no soil erosion) to 166.7t·km-2a-1 (with 28.2 % of forests exhibiting no soil erosion). Our findings indicate that the optimized assessment results are more consistent with the actual situation. The optimization method is a good attempt at regional-scale B factor simulation and can provide a more accurate depiction of the spatial information of soil erosion in forest at the regional scale. This study can lay a solid foundation for future regional-scale soil erosion assessments and offer valuable data support and methodological references for the regional-scale dynamic monitoring of soil and water loss, as well as research in soil and water conservation.

A framework to model charge sharing and pulse pileup for virtual imaging trials of photon-counting CT.

This study describes the development, validation, and integration of a detector response model that accounts for the combined effects of x-ray crosstalk, charge sharing, and pulse pileup in photon-counting detectors. The x-ray photon transport was simulated using Geant4, followed by analytical charge sharing simulation in MATLAB. The analytical simulation models charge clouds with Gaussian-distributed charge densities, which are projected on a 3x3 pixel neighborhood of interaction location to compute detected counts. For pulse pileup, a prior analytical method for redistribution of energy-binned counts was implemented for delta pulses. The x-ray photon transport and charge sharing components were validated using experimental data acquired on the CdTe-based Pixirad-1/Pixie-III detector using monoenergetic beams at 26, 33, 37, and 50 keV. The pulse pileup implementation was verified with a comparable Monte Carlo simulation. The model output without pulse pileup was used to generate spatio-energetic response matrices for efficient simulation of scanner-specific photon-counting CT (PCCT) images with DukeSim, with pulse pileup modeled as a post-processing step on simulated projections. For analysis, images for the Gammex multi-energy phantom and the XCAT chest phantom were simulated at 120 kV, both with and without pulse pileup for a range of doses (27-1344 mAs). The XCAT images were evaluated qualitatively at 120 mAs, while images for the Gammex phantom were evaluated quantitatively for all doses using measurements of attenuation coefficients and Calcium concentrations. Reasonable agreement was observed between simulated and experimental spectra with Mean Absolute Percentage Error Values (MAPE) between 10%-31% across all incident energies and detector modes. The increased pulse pileup from increased dose affected attenuation coefficients and calcium concentrations, with an effect on calcium quantification as high as MAPE of 28%. The presented approach demonstrates the viability of the model for enabling VITs to assess and optimize the clinical performance of PCCT.

Simulation and Experiments on Optimization of Vortex-Induced Vibration Power Generation System Based on Side-by-Side Double Blunt Bodies

In order to improve the utilization efficiency of converting low-flow current energy into electric energy for Reynolds number 10,000 ≤ Re ≤ 40,000, this paper proposes a vortex-induced vibration power generation system based on a side-by-side double blunt body. In this system, the side-by-side double blunt body structure is used in the current energy capture part to enhance the collection of low-flow current energy; the permanent magnet linear motor is used in the electric energy conversion part to improve the efficiency of electric energy conversion; and a laboratory device is constructed for testing. The effects of the blunt body structure parameters and the center spacing ratio on the energy harvesting performance of the system are qualitatively explained by constructing a simulation model. Compared with the single blunt body energy capture structure, the side-by-side double blunt body structure increases the vibration amplitude by 1.04 times and the lift by 1.14 times at the center spacing S/D = 2.4. Meanwhile, energy harvesting can be realized at a lower flow velocity, increasing the vortex-induced vibration’s energy capture range. Finally, the power generation system was experimentally verified in the laboratory, and the results showed that the vibration amplitude of the double blunt body structure was increased by 1.12 times compared to the single blunt body. The maximum output power of the generator is 10.55 W when the water velocity is 0.7 m/s. The energy conversion efficiency of the power generation system can reach a maximum of 52.93%, which is 12.33% higher than that of a single blunt body structure, which proves that the system has a higher power conversion efficiency than that of a conventional single conversion system.

Misconceptions about randomisation harm validity of randomised controlled trials.

The coherence theory of truth, the epistemology of evidence-based medicine, mathematical statistics, and axiomatic mathematics. To explore mathematical misconceptions inhering in randomised controlled trial designs, suggest improvements, encourage meta-methodological discussions and call for further interdisciplinary studies. Mathematical-statistical analyses and science-philosophical considerations. Randomisation does not (necessarily) generate equal samples, ergo, outcomes of usual RCTs are not as reliable as they claim. Moreover, ignoring initial sample discrepancies may cause inaccuracies similar to type I and type II errors. Insufficient awareness of these flaws harms final RCT statements about significance and evidence levels, hence their loss of trustworthiness. Statistical parameters such as the standard error of the mean may help to estimate the expected distinction between random samples. Researchers in EBM should be aware of systemic misconceptions in RCT standards. Pre-measurement can reduce shortcomings, e.g. through calculation how sample differences impact on usual RCT processing, or randomisation is given up in favour of mathematical minimisation of sample differences, i.e. optimising statistical sample equality. Moreover, the promising future of dynamic simulation models is highlighted.

Cost-Effectiveness Analysis Comparing Conventional and Digital Software Supported Management for Hypothyroidism.

Wearable devices can now leverage the established correlation between thyroid function and heart rate to monitor thyroid function alongside exercise levels and heart rate. The objective was to assess the cost-effectiveness of introducing a wearable/mobile-based thyroid function digital monitoring solution for the management of hypothyroidism compared to the conventional management approach. A decision-analytic Markov state-transition simulation model employed for utilizing a simulated cohort of 10,000 40-year-old patients with hypothyroidism to estimate costs and health outcomes. Cost-effectiveness from the healthcare sector perspective was evaluated using a 4.5% annual discount rate and the costs adjusted to 2022 levels, and lifetime outcomes were presented through incremental cost-effectiveness 49 ratios (ICERs). Deterministic and probabilistic sensitivity analyses evaluated the robustness of the results. The digital monitoring solution supported group yielded an additional 0.65 QALYs with an incremental cost of $11700.87, resulting in an ICER value of $17988.97 per QALY gained. Digital-powered software could be an optimal strategy in 99% of iterations against willingness-to-pay thresholds of $32,255/QALY gained. The ICER was most sensitive to the annual cost of a digital monitoring solution for hypothyroidism. The incorporation of the digital monitoring solution has demonstrated positive cost-effectiveness in hypothyroidism management when compared to the standard care. The cost of the digital monitoring solution and its sensitivity are key factors in determining cost-effectiveness. Striking a balance among the cost of digital monitoring support, the precision of hormonal level monitoring, and its effectiveness for the specific group of hypothyroid patients in real-world clinical practice is essential.

Comparative analysis of mechanical and drilling properties: Human skull vs. 3D-printed replicas for neurosurgical training

Neurosurgery is one of the most difficult surgical disciplines, making neurosurgical simulation crucial for learners. 3D printing is increasingly used for this simulation due to its accessibility and cost-effectiveness compared to conventional methods. Previous studies have qualitatively evaluated the efficacy of 3D printing models for burr hole and craniotomy simulation using surgeon feedback. This study quantitatively evaluates the mechanical properties of human skulls and compares them with 3D-printed skulls to identify the necessary materials and technologies for accurate replication in neurosurgical training. Vickers hardness, compression, and drilling simulation tests were conducted on human cadaveric skulls and 3D-printed models manufactured using Fused Filament Fabrication (FFF), Stereolithography (SLA), and Material Jetting (MJ) technologies. Uniaxial force during a simulated burr hole procedure was measured as drilling progressed through the outer table (OT), diploë and inner table (IT) of the skull. The results showed that different 3D printing technologies and materials have qualities corresponding to each of the three layers of the human skull. For instance, the White Resin 40 model was the closest match to the OT of human skull, exhibiting the lowest mean difference of drilling modulus of 1.98. The OT of the human skull had a Vickers hardness of 38.6 ± 5 HV0.05. The closest 3D-printed models were SLA White at 16.6 ± 0.3 HV0.05 and Rigid 10K at 65.7 ± 3 HV0.05. In terms of mechanical properties, the human skull demonstrated a compression modulus that closely matched the SkullSTN 2, PC 60, PEEK 40, and PEEK 60, with differences of less than 0.1 GPa. These findings demonstrate the effectiveness of drilling simulation tests in evaluating various materials and infills to refine models before neurosurgeon evaluation, enabling the identification of an optimal 3D-printed training model for simulating burr hole procedures.

Modelling and Transmission Characteristics Analysis of APU Pneumatic Servo System

The auxiliary power unit (APU), which is a compact gas turbine engine, is employed to provide a stable compressed air supply to the aircraft. This compressed air is introduced into the various aircraft components via the pneumatic servo system, thereby ensuring the normal operation of the aircraft’s systems. The objective of this study is to examine the impact of parameter variation on the transmission characteristics of an APU pneumatic servo system, with a particular focus on the aerodynamic moment associated with the operating process of a butterfly valve. To this end, a mathematical model of the pneumatic servo system has been developed. The accuracy of the mathematical model was verified by means of numerical simulation and comparative analysis of experiments. The simulation model was established in the Matlab/Simulink environment. Furthermore, the effects of throttling area ratio, fixed throttling hole diameter, rodless chamber volume of actuator cylinder and gas supply temperature on the transmission characteristics of the system were discussed in greater detail. The findings of the research indicate that the throttle area ratio is insufficiently sized, which results in a deterioration of the system’s linearity. Conversely, an excessively large throttle area ratio leads to a reduction in the controllable range of the load axis and is therefore detrimental to the servo mechanism of the flow control. An increase in the diameter of the fixed throttling hole or a decrease in the volume of the rodless cavity of the actuator cylinder facilitates a rapid change in flow rate within the rodless cavity and an increase in the response speed of the load-rotating shaft of the servomechanism. An increase in the temperature of the gas supply from 30 °C to 230 °C results in a reduction in the response time of the system by a mere 0.2 s, which has a negligible impact on the transmission characteristics of the system.

A Comparative Study on the Prediction of Hydrogen, Nitrogen, and Water Vapor of Regenerative Hydrogen Pump With Different Turbulence Models

Abstract The flow in a regenerative hydrogen pump used in a proton exchange membrane (PEM) fuel cell system is complex, including velocity mixing, streamline distortion, swirling flow, corner flow, and possible boundary separation. Experimental measurement and computational fluid dynamics (CFD) prediction are the main methods used to evaluate such performance. In this work, regenerative hydrogen designed for fuel cell electric vehicles with an impeller diameter of 91 mm and a shaft speed of 22,000 rpm is tested with a mixture of nitrogen, hydrogen, and water vapor. During the test, the flowrate and shaft speed ranges are 151–881 L/min and 8000–22,000 rpm, respectively. The inlet pressure and temperature are 171 kPa and 66 °C, respectively. Then, the flow is predicted by the k − ε model, the renormalization group (RNG) k − ε model, the explicit algebraic Reynolds stress models (EARSM) and the shear-stress transport (SST) detached eddy simulation (DES) model. The differences between the tested and predicted performances are compared. With these results, the velocity distribution in the side channel, the radial force of the impeller and casting, and the changes in the downstream pressure and velocity are analyzed in consideration of the ability of the turbulence models to simulate each characteristic flow. On this basis, the differences and abilities of the turbulence models for predicting the flow in regenerative hydrogen pumps are summarized.

Energy management strategy for extended range electric logistics vehicle based on type-2 fuzzy control

To overcome the limitations of fuzzy energy management strategy (EMS) in describing uncertainty, this paper proposes an EMS based on type-2 fuzzy (T2F), which optimizes energy consumption for extended range electric logistics vehicle (ERELV). Firstly, establish an energy consumption simulation model for ERELV. Then, an EMS based on T2F controller is designed. The strategy takes the state of charge (SOC) and the demand power as the input, and the range extender power as the output. This paper analyses the influence of T2F control strategy on the energy consumption of the ERELV, and uses the model-based design method to verify the EMS in the Hardware-in-the-loop (HIL) simulation test. The test results show that compared with the traditional fuzzy control strategy, the T2F strategy can reduce the engine’s energy loss by 12.58%. Finally, actual fuel consumption tests were conducted on the established ERELV model. By comparing the results of C-WTVC and CHTC-HT operating conditions, the model was verified to meet the research requirements of EMSs in this paper and has practical significance.

Cost and environmental impact analysis of slag repurposing as alternative solution to bricks

The world’s leading producer of Platinum Group Metal is South Africa and accounts for 94 percent of world’s platinum. Platinum smelters produce around 10 525,90 tons of slag annual of which accumulates as stockpile, never utilised. More storage area is required for continuous operation of the smelters to store slag. The aim of the research is to investigate the cost and environmental impact of repurposing slag into bricks for low-cost housing. AnyLogic simulation model specifically the Discrete Event Modelling (DEM) was employed for the modelling and simulation because of its capabilities compared to other models such as Flexsim, Vensim, ANSYS. The results indicate that 92 human resources are required to have functioning business that is able to manufacture and deliver bricks enough to build 326 houses per month. The operation is labour intensive to ensure more job opportunities are created and a total 368 personnel are employed permanently on all sites. The smelter potentially saves over R1,5 billion per operation and R5,6 billion across 4 sites over a decade by giving the slag away for free. The environmental pollution is improved as five heavy plant equipment is removed from the operation. The operation has potential to generate R392,2 million profit over 10-years.

Free Electron Density Gradients Enhanced Biosensor for Ultrasensitive and Accurate Affinity Assessment of the Immunotherapy Drugs.

Accurate affinity assessments play an important role in drug discovery, screening, and efficacy evaluation. Label-free affinity biosensors are recognized as a dependable and standard technology for addressing this challenge. This study constructs a free electron density gradient-enhanced meta-surface plasmon resonance (FED-MSPR) biosensor through a finite-difference time-domain simulation model, the biosensor demonstrates superior detection performance in accurately determining affinity and kinetic rate constants. By controlling the dielectric properties of the metal on the surface of the nanocup arrays, the plasmon resonance effects are easily tuned without changing the nanostructure design. Compared with the single-layer gold chip, the triple-layer FED-MSPR chip demonstrated a four-fold improvement in resolution at the optimal resonance peak. Additionally, the sensitivity and figure of merit (FOM) of the multi-layer chip increased by 3.5 and 7.99 times, respectively. Following modification with high- and low-staggered carboxylation, the noise-signal ratio and baseline stability of the real-time kinetic curves based on these chips are significantly enhanced. The developed carboxylation FED-MSPR platform is successfully used to perform affinity assays for Adalimumab and TNF-α protein, resulting in favorable dynamic curves. These findings validate the proposed FED-MSPR biosensor platform as cost-effective, rapid, sensitive, and label-free, facilitating real-time quality control in drug development.

Earnings Responses to Even Higher Taxes

Abstract We exploit a recent Swedish tax reform, implying higher marginal tax rates for the top 5% of the earnings distribution, to learn about earnings responses to higher taxes. Using a simple and graphical cross sectional method, we estimate an earnings elasticity of 0.16, evaluated four years after the reform. Our analysis indicates that the response is driven by adjustments in wage rates, and not by changes to hours, job switches, or income shifting. We interpret the magnitude and preciseness of the response using a realistically calibrated simulation model in which people face uncertain marginal tax rates due to earnings dynamics.

PSO-FDM (Particle Swarm Optimization-Finite Difference Method)-Based Simulation Model of Temperature and Velocity of Full-Scale Continuous Annealing Furnace

Improving the accuracy of the temperature field prediction model for continuous annealing line strips and enhancing the model’s adaptability to full-size strips are key technical challenges in continuous annealing lines. This paper developed a continuous annealing temperature prediction model based on a variable step-size strategy for the heating section, even-heat section, slow-cooling section, and fast-cooling section of the continuous annealing line. To improve the prediction accuracy for different strip sizes, the PSO optimization algorithm was employed to determine the optimal heat transfer coefficient for each strip size. Additionally, due to the limited production of certain strip gauges, providing insufficient data for optimization, this study introduces a combined file approach to address gauge vacancies. The experimental results indicate that the optimized model with variable step size can control the absolute prediction error to less than 4 °C, improving prediction accuracy by 61.9% and prediction speed by 26.8% compared to the traditional equal-step prediction model. This study verified that the merger method is effective for addressing side gauge vacancies, while the proposed method is suitable for resolving middle gauge vacancies. The main technical contribution of this study is the establishment of a high-precision prediction model for continuous annealing temperature of variable step length strips, ensuring high temperature control accuracy for full-gauge strips when passing through the continuous annealing production line.

Learning protocols for the fast and efficient control of active matter

Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.

Monomer extraction from polymers using supercritical CO2

Polymerization reactions often leave unreacted monomers in the polymer. These unreacted monomers can emit vapors at ambient temperatures that cause unpleasant odors, pose both environmental and safety risks, and negatively impacts polymer properties. Therefore, a purification step is necessary after polymerization to remove these residual monomers. High temperature devolatilization is the currently applied technique of removing these monomers, however this treatment can also negatively impact the properties of the polymer. In this study the possibility of using supercritical CO2 (scCO2) as an extraction medium of these residual monomers, which are considered as Volatile Organic Compounds (VOCs), has been investigated experimentally. Polymer samples of either PS or PMMA with 4–7 wt% of residual monomers were extracted continuously with scCO2 at temperatures ranging from 50 to 90 °C and pressures of 100–400 bar. The measured extraction efficiencies, based on 1H NMR analysis, were 99+% and was reached between 4 and 16 h depending on the experimental conditions. In general, higher temperatures and higher pressures lead to enhanced extraction rates and thus higher efficiencies at shorter process times. The experimental results were successfully modelled with an empirical extraction simulation model as originally proposed by Sovová.

Designing an Economical Water Harvesting System Using a Tank with Numerical Simulation Model WASH_2D

Newly incorporated module into the WASH_2D model has enabled simulating a rainwater harvesting system (RWHS) using a tank. The incorporated module in WASH_2D was tested for two field experiments to determine the optimal tank capacity and cultivated area that give the highest net income for farmers. The first experiment was composed of treatments A, B, and C having the same cultivated and harvested areas (plastic sheets) of 24 m2 and 12.5 m2, respectively. The capacity of the tanks for treatments A, B, and C was set at 500, 300, and 200 L, corresponding to storability of 21, 13, and 8 mm, respectively, while in the second experiment we carried out three treatments: F, G, and H having the same tank capacity of 300 L and harvested area of 12.5 m2 with variable cultivated areas as G and H were larger by two and three times than F (10.5 m2), respectively. Water was applied automatically through a drip irrigation system by monitoring soil water suction. Results of the first experiment showed that the optimal storability and seasonal net income simulated by WASH_2D were 17 mm and 5.82 USD yr−1, which were fairly close to 18 mm and 5.75 USD yr−1 observed from field data, respectively. Similarly, the results of the second experiment revealed that simulated net incomes for different cultivated areas agreed well with the observed data. We concluded that the use of the simulation model WASH_2D can be economically useful to promote small-scale irrigation in semi-arid regions and guide planning irrigation or rainwater harvesting investments.

Research on the Inner Surface Discharge of the Insulation Sheath of Electric Locomotive Cable Terminals

Adding an insulating sheath to the exposed metal part of the outer insulation of a roof cable terminal can extend the creepage distance of the leakage current and significantly reduce the probability of pollution flashover on the outer insulation of the equipment. However, during the actual operation of the locomotive, the inner surface of the insulating sheath is discharged, resulting in cable insulation breakdown, the mechanism of which is unclear. This paper establishes a cable terminal–sheath electric field simulation model and studies the interface air gap, the interface with wet pollution, and the distribution of damp pollution on the outer surface and other factors on the electric field distribution of the cable terminal–sheath structure and the interface discharge, revealing the mechanism of discharge on the inner surface of the cable terminal’s insulation sheath. A voltage of 25 kV rms is applied, and the simulation results show that when the outer surface of the cable terminal is clean and there are air gaps and wet dirt on the inner surface, the maximum distortion electric field at the inner surface is 0.8 × 105~3.4 × 105 V/m, and the value of the electric field at this time is not enough to cause a partial discharge; when there is a uniform layer of wet dirt on the outer surface of the cable terminal, the electric field on the inner surface averages 1.5 × 105 V/m, which is about 275% higher than the average electric field when the outer surface is clean; when there is wetting or an air gap on the inner surface at the same time, the maximum aberration electric field on the inner surface is 1.8 × 105~1.9 × 106 V/m. The wetting on the outer surface of the cable terminal strengthens the non-uniformity degree of the distribution of the electric field, and when there is wetting and an air gap on the inner surface, the over-voltage on the cable terminal inevitably leads to a discharge phenomenon in the air gap. This provides a theoretical basis for optimizing the insulation sheath structure to solve the discharge problem.