Baseline Simulation Research Articles

A data‐driven turbulence modeling for the Reynolds stress tensor transport equation

AbstractThe long lasting demand for better turbulence models and the still prohibitively computational cost of high‐fidelity fluid dynamics simulations, like direct numerical simulations and large eddy simulations, have led to a rising interest in coupling available high‐fidelity datasets and popular, yet limited, Reynolds averaged Navier–Stokes simulations through machine learning (ML) techniques. Many of the recent advances used the Reynolds stress tensor or, less frequently, the Reynolds force vector as the target for these corrections. In the present work, we considered an unexplored strategy, namely to use the modeled terms of the Reynolds stress transport equation as the target for the ML predictions, employing a neural network approach. After that, we solve the coupled set of governing equations to obtain the mean velocity field. We apply this strategy to solve the flow through a square duct. The obtained results consistently recover the secondary flow, which is not present in the baseline simulations that used the model. The results were compared with other approaches of the literature, showing a path that can be useful in the seek of more universal models in turbulence.

Glaciers determine the sensitivity of hydrological processes to perturbed climate in a large mountainous basin on the Tibetan Plateau

Abstract. The major rivers on the Tibetan Plateau supply important freshwater resources to riparian regions but have been undergoing significant climate change in recent decades. Understanding the sensitivities of hydrological processes to climate change is important for water resource management, but large divergences exist in previous studies because of the uncertainties of hydrological models and climate projection data. Meanwhile, the spatial pattern of local hydrological sensitivities was poorly explored despite the strong heterogeneity on the Tibetan Plateau. This study adopted the climate perturbation method to analyze the hydrological sensitivities of a typical large mountainous basin (Yarlung Tsangpo River, YTR) to climate change. We utilized the tracer-aided hydrological model Tsinghua Representative Elementary Watershed-Tracer-aided version (THREW-T) to simulate the hydrological and cryospheric processes in the YTR basin. Multiple datasets and internal stations were used to validate the model to provide confidence in the baseline simulation and the sensitivity analysis. Results indicated that (1) the THREW-T model performed well in simulating the streamflow, snow cover area (SCA), glacier mass balance (GMB) and stream water isotope, ensuring good representation of the key cryospheric processes and a reasonable estimation of the runoff components. The model performed acceptably in simulating the streamflow at eight internal stations located in the mainstream and two major tributaries, indicating that the spatial pattern of hydrological processes was reflected by the model. (2) Increasing temperature led to decreasing annual runoff, smaller inter-annual variation, more even intra-annual distribution and an earlier maximum runoff. It also influenced the runoff regime by increasing the contributions of rainfall and glacier melt overland runoff but decreasing the subsurface runoff and snowmelt overland runoff. Increasing precipitation had the opposite effect to increasing temperature. (3) The local runoff change in response to increasing temperature varied significantly, with a changing rate of −18.6 % to 54.3 % for 5∘ of warming. The glacier area ratio (GAR) was the dominant factor in the spatial pattern of hydrological sensitivities to both perturbed temperature and precipitation. Some regions had a non-monotonic runoff change rate in response to climate perturbation, which represented the most dynamic regions within the basin, as they kept shifting between energy- and water-limited stages. The GAR and mean annual precipitation (MAP) of the non-monotonic regions had a linear relation and formed the boundary of regions with different runoff trends in the GAR–MAP plot.

Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation

AbstractParameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and details of orography. This study uses machine learning to replace the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse‐grid (200 km) global atmosphere model, using training data obtained by spatially coarse‐graining a 40‐day realistic geography global storm‐resolving simulation. The training targets are the three‐dimensional fields of effective heating and moistening rates, including the effect of grid‐scale motions that are resolved but imperfectly simulated by the coarse model. A neural network is trained to predict the time‐dependent heating and moistening rates in each grid column using the coarse‐grained temperature, specific humidity, surface turbulent heat fluxes, cosine of solar zenith angle, land‐sea mask and surface geopotential of that grid column as inputs. The coefficient of determination R2 for offline prediction ranges from 0.4 to 0.8 at most vertical levels and latitudes. Online, we achieve stable 35‐day simulations, with metrics of skill such as the time‐mean pattern of near‐surface temperature and precipitation comparable or slightly better than a baseline simulation with conventional physical parameterizations. However, the structure of tropical circulation and relative humidity in the upper troposphere are unrealistic. Overall, this study shows potential for the replacement of human‐designed parameterizations with data‐driven ones in a realistic setting.

Evaluating and Enhancing Snow Compaction Process in the Noah‐MP Land Surface Model

AbstractThe accuracy of snow density in land surface model (LSM) simulations impacts the accuracy of simulated terrestrial water and energy budgets. However, there has been little research that has focused on enhancing snow compaction in operationally used LSMs. A baseline snow simulation with the widely used Noah‐MP LSM systematically overestimates snow depth by 55 mm even after removing daily snow water equivalent (SWE) biases. To reduce uncertainties associated with snow compaction, we enhance the most sensitive Noah‐MP snow compaction parameter—the empirical parameter for compaction due to overburden (Cbd)—such that Cbd is calculated as a function of surface air temperature as opposed to a fixed value in the baseline simulation. This enhancement improves accuracy in simulated snow compaction across the majority of western U.S. (WUS) SNOTEL test sites (biases reduced at 88% of test sites), with modest bias reductions in cooler accumulation periods (biases reduced at 70% of test sites) and substantial improvements during warmer ablation periods (biases reduced at 99% of test sites). Relatively larger improvements during warm conditions are attributable to the default Cbd value being reasonable for cold temperatures (≤−5°C). Improvements in simulated snow depth and density with observations outside of the training sites and optimization periods support that the snow compaction enhancement is transferable in space and time. Differences between enhanced and baseline gridded simulations across the total WUS support that the enhancement can have important impacts on snowpack evolution, snow albedo feedback, and snow hydrology.

Foundational Mechanics and Baseline Simulation of the Diaphragm

Foundational Mechanics and Baseline Simulation of the Diaphragm

Modeling study of PM2.5 pollution episode of early spring 2019 in Hokkaido, Japan caused by biomass burning in Northeast China

Biomass burning (BB), in particular agricultural waste burning (Agri-BB), occurs at random locations, scales, and times. These factors make it challenging to detect Agri-BB accurately through satellite observations. Thus, the BB emission inventories using satellite observation data have uncertainties for their emission estimation approach and cause poor model performance for air pollutants including PM2.5. We utilized the two BB emission inventories, GFEDv4.1s and FINNv2.5 with the CMAQ model to simulate the PM2.5 heavy pollution episode in Hokkaido 2019. To estimate Agri-BB contributions, we conducted three simulation cases for each BB emission inventory: with and without Agri-BB emission, and the boosted Agri-BB emission cases. The baseline simulation failed to capture the temporal and spatial variation patterns of PM2.5. Meanwhile, the boosted Agri-BB case could show favorable performance for PM2.5 concentrations. These results indicated that the two BB emission inventories underestimated Agri-BB emissions. In the two boosted Agri-BB cases, the PM2.5 contributions from Agri-BB accounted for more than 50% during the episode. Moreover, high PM2.5 emissions were found in Northeast China and its surrounding regions similar to the two boosted Agri-BB cases. Consequently, the results revealed that Agri-BB emissions during the episode were significantly derived from the agricultural areas in Northeast China.

The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe

AbstractData‐driven methods have been extensively applied to predict atmospheric compositions. Here, we explore the capability of a deep learning (DL) model to make ozone (O3) predictions across continents in China, the United States (US) and Europe. The DL model was trained and validated with surface O3 observations in China and the US in 2015–2018. The DL model was applied to predict hourly surface O3 over three continents in 2015–2022. Compared to baseline simulations using GEOS‐Chem (GC) model, our analysis exhibits mean biases of 2.6 and 4.8 μg/m3 with correlation coefficients of 0.94 and 0.93 (DL); and mean biases of 3.7 and 5.4 μg/m3 with correlation coefficients of 0.95 and 0.92 (GC) in Europe in 2015–2018 and 2019–2022, respectively. The comparable performances between DL and GC indicate the potential of DL to make reliable predictions over spatial and temporal domains where a wealth of local observations for training is not available.

Spatiotemporal heterogeneity in the increase in ocean acidity extremes in the northeastern Pacific

Abstract. The acidification of the ocean (OA) increases the frequency and intensity of ocean acidity extreme events (OAXs), but this increase is not occurring homogeneously in time and space. Here we use daily output from a hindcast simulation with a high-resolution regional ocean model coupled to a biogeochemical ecosystem model (ROMS-BEC) to investigate this heterogeneity in the progression of OAX in the upper 250 m of the northeastern Pacific from 1984 to 2019. OAXs are defined using a relative threshold approach and using a fixed baseline. Concretely, conditions are considered extreme when the hydrogen ion concentration ([H+]) exceeds the 99th percentile of its distribution in the baseline simulation where atmospheric CO2 was held at its 1979 level. Within the 36 years of our hindcast simulation, the increase in atmospheric CO2 causes a strong increase in OAX volume, duration, and intensity throughout the upper 250 m. The increases are most accentuated near the surface, with 88 % of the surface area experiencing near-permanent extreme conditions in 2019. At the same time, a larger fraction of the OAXs become undersaturated with respect to aragonite (ΩA < 1), with some regions experiencing increases up to nearly 50 % in their subsurface. There is substantial regional heterogeneity in the progression of OAX, with the fraction of OAX volume across the top 250 m increasing in the central northeastern Pacific up to 160 times, while the deeper layers of the nearshore regions experience “only” a 4-fold increase. Throughout the upper 50 m of the northeastern Pacific, OAXs increase relatively linearly with time, but sudden rapid increases in yearly extreme days are simulated to occur in the thermocline of the far offshore regions of the central northeastern Pacific. These differences largely emerge from the spatial heterogeneity in the local [H+] variability. The limited offshore reach of offshore-propagating mesoscale eddies, which are an important driver of subsurface OAX in the northeastern Pacific, causes a sharp transition in the increase in OAX between the rather variable thermocline waters of nearshore regions and the very invariant waters of the central northeastern Pacific. The spatially and temporal heterogeneous increases in OAX, including the abrupt appearance of near-permanent extremes, likely have negative effects on the ability of marine organisms to adapt to the progression of OA and its associated extremes.

Muscle-driven simulations and experimental data of cycling

Muscle-driven simulations have provided valuable insights in studies of walking and running, but a set of freely available simulations and corresponding experimental data for cycling do not exist. The aim of this work was to develop a set of muscle-driven simulations of cycling and to validate them by comparison with experimental data. We used direct collocation to generate simulations of 16 participants cycling over a range of powers (40–216 W) and cadences (75–99 RPM) using two optimization objectives: a baseline objective that minimized muscle effort and a second objective that additionally minimized tibiofemoral joint forces. We tested the accuracy of the simulations by comparing the timing of active muscle forces in our baseline simulation to timing in experimental electromyography data. Adding a term in the objective function to minimize tibiofemoral forces preserved cycling power and kinematics, improved similarity between active muscle force timing and experimental electromyography, and decreased tibiofemoral joint reaction forces, which better matched previously reported in vivo measurements. The musculoskeletal models, muscle-driven simulations, simulation software, and experimental data are freely shared at https://simtk.org/projects/cycling_sim for others to reproduce these results and build upon this research.

The transboundary contribution of PM10 in Korea depended on the dipole surface temperature pattern in the East Asian region during April of 2021

During the spring of 2021, rapid surface cooling in the Arctic area expanded to most of the East Asian region, featuring surface-temperature dipole patterns of the colder west (Mongolia and northern-central China) and the warmer east (eastern-northeastern China, the Yellow Sea, and Korea). Despite the decreasing trend of dusty days within Korea, a notable rise in the occurrence of transboundary mineral dust-induced dusty days was observed in contributing to an increase in PM10 levels in the spring of 2021. This study investigated the PM10 source apportionment of anthropogenic dust, mineral dust, and biomass burning in the East Asian region and its transboundary impact on Korea, within the context of the surface-temperature dipole pattern observed in April of 2021. Source apportionment of PM10 was determined through a comparative analysis between baseline and sensitivity experiments conducted by means of a WRF-Chem simulation. In these sensitivity simulations, emissions from each source were systematically excluded to assess their individual contributions. PM10 Base refers to baseline simulations, PM10 Anthro to anthropogenic emissions, PM10 Dust to mineral dust, and PM10 Biomass to biomass burning. The colder west pattern was linked to increased synoptic-scale cyclonic activity, marked by higher positive potential vorticity, causing a sudden surge in severe dust storms over Mongolia and northern China. PM10 Base levels were most elevated in the higher terrain of Mongolia, comprising predominantly PM10 Dust (95%). However, the dominance of atmospheric-warming-induced anticyclones, represented by enhanced negative potential vorticity, prevailed over the lower terrain in the warmer east regions, obstructing the transboundary dispersion of PM10 Base to Korea. Nevertheless, Korea experienced a transboundary contribution of PM10 Dust, reaching 35%. In addition, the source apportionments indicated a PM10 Anthro ratio of 55% in eastern China and a PM10 Biomass ratio of 35% in northeastern China, contributing 25% and 5% respectively to Korea. During April 14–30, 2021, the dipole pattern evolved further to the significantly colder west (Mongolia and China) and the moderately warmer east (the Yellow Sea and Korea). The transboundary contributions of PM10 Anthro, PM10 Dust, and PM10 Biomass were intensified as a composite mixture within Korea, an outcome attributed to the intermittent cyclones and subsequent anticyclones.

Abstract 12116: Identifying the Underlying Causes of Unexplained Dyspnea at High Altitude Using Normobaric Hypoxia With Transthoracic Echocardiography

Introduction: Exposure to high altitude results in hypobaric hypoxia, leading to physiological changes in the cardiovascular system that may result in dyspnea. Hypoxic simulation testing (HSTs) simulates changes in physiology that occur at a specific altitude. Hypothesis: The use of transthoracic echocardiography (TTE) during HST can detect the raise in right-sided pressures revealing underlying causes of high-altitude dyspnea. Methods: We performed a prospective observational study of consecutive patients with unexplained dyspnea at high altitude who were referred to an aerospace medicine program at a single institution between August 2021 and March 2023. Reduced FiO 2 was administered for 20 minutes to simulate altitude levels specific to patients’ history. TTE images were obtained at baseline and during peak hypoxia to evaluate right ventricular (RV) function, RV systolic pressure, and any possible shunts. Result: The study included 27 patients, with a mean age of 64.7 ±14.4 years and 14 (51.9%) females. No significant abnormalities were seen on baseline TTE and no significant differences in blood pressure and heart rate between baseline and hypoxia simulation were detected. Notably, RV systolic pressure increased at peak hypoxia, while RV systolic function declined as shown by a decrease in S’ wave and TAPSE ( Figure 1 ). Additionally, right to left shunts were present in 19 (70.4%) patients. Of these, four patients (21.1%) had shunts present at baseline with no changes at peak hypoxia, while the severity of shunts increased at peak hypoxia in eight cases (42.1%) and the shunts were only evident during hypoxia in seven patients (36.8%). Conclusion: TTE during HST provides valuable information by revealing the presence of symptomatic, sustained shunts and confirming the decline in RV hemodynamics explaining dyspnea at altitude. Further studies are necessary to establish the optimal clinical role of this physiologic method.

Financial Impact of Imaging Examination Site of Service in the Medicare Population.

The financial sustainability of the US healthcare system is a growing concern in an environment of declining reimbursement and rising costs. Variable Centers for Medicare and Medicaid (CMS) reimbursement and denial rates for specific imaging examinations exist across sites of service, adding complexity to financial planning for healthcare organizations. Understanding the financial implications of site of service in existing CMS reimbursement for imaging may be of strategic importance for organizations going forward. Current Procedural Terminology (CPT) codes were obtained for common cross-sectional imaging examinations using the 2022 CMS Medicare Physician Fee Schedule. Using reimbursement rates with historical volumes and denial rates, a simulation was created to estimate the overall reimbursement of paired hospital outpatient departments (HOPD) and free-standing office (FSO) sites. A baseline simulation was performed with random allocation of imaging examinations between sites of service, and an optimized simulation was performed to estimate the maximum financial impact of targeted allocation between sites. These simulations were performed for paired CT and MR scanners separately. For CT, the baseline simulation estimated annual average reimbursement for combined HOPD and FSO was $3.25M. Reimbursement increased to $3.51M after optimized reallocation of studies between sites of service, resulting in an expected gain of $260,162 for a set of paired HOPD and FSO scanners. For MR, the same approach resulted in baseline reimbursement of $2.51M, increasing to $2.60M upon reallocation between sites for an expected gain of $87,532. Assuming a stable cost of service delivery, this approach would result in improved margins of 8% for CT and 3.5% for MR. There were 28 CT and 19 MRI daily patient imaging appointments at each respective HOPD and FSO scanners, unchanged between baseline and optimized cases. Differences in reimbursement rates between sites were the dominant driver of increased margins at low denial rates, although denial rates became dominant at values greater than 50%. Given CMS payment and denial rate variability, optimally allocating imaging studies between sites of service may improve reimbursement for the same services delivered. Although financial incentives exist for site allocation, such decisions should require physician input to assess safety and appropriate level of care. This work contributes to an understanding of financial incentives of existing reimbursement policy and may guide future policy design towards high value care.

Knee kinematics are primarily determined by implant alignment but knee kinetics are mainly influenced by muscle coordination strategy

Implant malalignment has been reported to be a primary reason for revision total knee arthroplasty (TKA). In addition, altered muscle coordination patterns are commonly observed in TKA patients, which is thought to alter knee contact loads. A comprehensive understanding of the influence of surgical implantation and muscle recruitment strategies on joint contact mechanics is crucial to improve surgical techniques, increase implant longevity, and inform rehabilitation protocols. In this study, a detailed musculoskeletal model with a 12 degrees of freedom knee was developed to represent a TKA subject from the CAMS-Knee datasets. Using motion capture and ground reaction force data, a level walking cycle was simulated and the joint movement and loading patterns were estimated using a novel technique for concurrent optimization of muscle activations and joint kinematics. In addition, over 12′000 Monte Carlo simulations were performed to predict knee contact mechanics during walking, considering numerous combinations of implant alignment and muscle activation scenarios. Validation of our baseline simulation showed good agreement between the model kinematics and loading patterns against the in vivo data. Our analyses reveal a considerable impact of implant alignment on the joint kinematics, while variation in muscle activation strategies mainly affects knee contact loading. Moreover, our results indicate that high knee compressive forces do not necessarily originate from extreme kinematics and vice versa. This study provides an improved understanding of the complex inter-relationships between loading and movement patterns resulting from different surgical implantation and muscle coordination strategies and presents a validated framework towards population-based modelling in TKA.

Sovereign debt and post-war Ukrainian economic growth – system dynamics approach

The aim of the article is to examine the post-war national economy, notably overburdened with significant war expenditures and the effects of sovereign debt restructuring in a wartime period. The research uses system dynamics modelling methods, operates with S-shaped growth, overshoot and collapse dynamic patterns. The oscillation patterns of behaviour have been used to demonstrate the scenario options of possible external debt minimization.The dynamic hypothesis about non-linear behaviour of post-war debt trajectory has revealed the intrinsic growth rate in debt-dependent economy and the inflection point of no return to stable economic growth without radical decision of sovereign debt cancellation. The direct consequence of a negative solution for debt cancellation would be the unpredictable, even chaotic fluctuations of national economic growth rate. Baseline simulation to prove the results of research has been provided. The embedded “dependent-economy” type of macrostructure does not allow to overcome the critical debt overhang level and needs a new national model with appropriate policy to stabilize the economy. Performance of post-war debt repayment depends mostly on an innovative fund, which can be created by export abilities in the framework of post-war recovery plan. The results of the research may be applied by national authorities responsible for macroeconomic debt policy. The obtained results of the study allow us to draw a conclusion about the impossibility of developing the national economy within the framework of the existing economic structure of the developing country. The debt trap, which cannot be eliminated, does not allow to develop the national innovative economy and ensure economic growth and development.
 JEL classification: E37, F34, F35, G28, H63

SLA based Workflow Scheduling algorithm in Cloud Computing using Haris Hawks optimization

Task Scheduling is crucial facet in cloud paradigm as virtual resources need to be provisioned to the variable requests coming onto cloud console from various users and more over that tasks are depends on each other which creates a workflow which is a difficult task for cloud service provider to provision these tasks over appropriate VMs. Inefficient mapping of tasks to VMs increases makespan and lead to violation of SLA between users, cloud provider. In this paper, we modeled a SLA based workflow scheduling algorithm focuses on minimization of makespan and SLA violations. This algorithm developed using Harris hawks optimization. Experimentation carried out using workflowsim. Random workload fed as input to algorithm and it is evaluated against existing baseline approaches and simulation results revealed that our proposed approach minimizes makespan and SLA violations over existing approaches by 40% and 43% respectively.

Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model

Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.

Data-driven multi-objective optimization for electric vehicle charging infrastructure

This paper presents a data-driven methodology combining simulation and multi-objective optimization to efficiently implement transportation policy commitments, using as a case study the electric vehicle (EV) charging infrastructure in Newcastle upon Tyne, United Kingdom. The methodology leverages a baseline simulation model developed by our industry partner, Arup Group Limited, to estimate EV demand and quantities from 2020 to 2050. Four future energy scenarios are considered, and a multi-objective optimization approach is employed to determine the optimal types, locations, and quantities of charging points, along with the corresponding total capital and operational expenditures and charging point operating hours. Quantitatively, the variations of the portions of different types of charging points for the four scenarios are relatively small and within 3% range of the total number of charging points. The optimal solutions put priority on the slower charging points, with faster charging points having smaller portions each around 10%-13%.

Lateral impact resistance of CFST cantilever members stiffened with encased I-shaped CFRP

The lateral impact resistance of cantilever concrete-filled steel tubular (CFST) members stiffened with encased I-shaped carbon fiber reinforced plastic (CFRP) profiles subjected to lateral impact is investigated in this paper. The impact tests are performed on four specimens to examine the dynamic response. Finite element analysis models are established and verified against tested results. Baseline simulations are also created using the validated models to further analyze the damage pattern and energy dissipation mechanism of each component in the impact events. It is found that compared with traditional CFST members, the new composite members have higher peak impact force, lower dynamic displacement, and larger energy dissipation capacity. Additionally, the effects of design parameters on the impact performance are evaluated by parametric analyses, and the results provide valuable suggestions for developing design guidance.

Simulations of winter ozone in the Upper Green River basin, Wyoming, using WRF-Chem

Abstract. In the Upper Green River basin (UGRB) of Wyoming and the Uintah Basin of Utah, strong wintertime ozone (O3) formation episodes leading to O3 mixing ratios occasionally exceeding 70 parts per billion (ppb) have been observed over the last 2 decades. Wintertime O3 events in the UGRB were first observed in 2005 and since then have continued to be observed intermittently when meteorological conditions are favorable, despite significant efforts to reduce emissions from oil and natural gas extraction and production. While O3 formation has been successfully simulated using observed volatile organic compound (VOC) and nitrogen oxide (NOx) mixing ratios, successful simulation of these wintertime episodes using emission inventories in a 3-D photochemical model has remained elusive. An accurate 3-D photochemical model driven by an emission inventory is critical to understanding the spatial extent of high-O3 events and which emission sources have the most impact on O3 formation. In the winter of 2016/17 (December 2016–March 2017) several high-O3 events were observed with 1 h mixing ratios exceeding 70 ppb. This study uses the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate one of the high-O3 events observed in the UGRB during March 2017. The WRF-Chem simulations were carried out using the 2014 edition of the Environmental Protection Agency National Emissions Inventory (EPA NEI2014v2), which, unlike previous versions, includes estimates of emissions from non-point oil and gas production sources. Simulations were carried out with two different chemical mechanisms: the Model for Ozone and Related Chemical Tracers (MOZART) and the Regional Atmospheric Chemistry Mechanism (RACM), and the results were compared with data from seven weather and air quality monitoring stations in the UGRB operated by the Wyoming Department of Environmental Quality (WYDEQ). The simulated meteorology compared favorably to observations with regard to temperature inversions, surface temperature, and wind speeds. Notably, because of snow cover present in the basin, the photolysis surface albedo had to be modified to predict O3 in excess of 70 ppb, although the models were relatively insensitive to the exact photolysis albedo if it was over 0.65. O3 precursors, i.e., NOx and VOCs, are predicted similarly in simulations with both chemical mechanisms, but simulated VOC mixing ratios are a factor of 6 or more lower than the observations, while NOx is also underpredicted but to a lesser degree. Sensitivity simulations revealed that increasing NOx and VOC emissions to match observations produced slightly more O3 compared to baseline simulations, but an additional sensitivity simulation with doubled NOx emissions resulted in a considerable increase in O3 formation. These results suggest that O3 formation in the basin is most sensitive to NOx emissions.

A kinetic Monte Carlo study of positive streamer interaction with complex dielectric surfaces

We present a computational study of positive streamers in air propagating over dielectric plates with square channels running orthogonal to the propagation direction. The study uses a newly developed non-kinetic Particle-In-Cell model based on Îto diffusion and kinetic Monte Carlo, which does not introduce artificial smoothing of the plasma density or photo-electron distributions. These capabilities permit the computational study to use high-resolution grids with large time steps, and also incorporates geometric shielding for particle and photon transport processes. We perform Cartesian 2D simulations for channel dimensions ranging from 250 µm to 2 mm, and track streamers over a distance of 4 cm and times ranging up to 300 ns, for various voltages ranging from 15 kV to 30 kV. These baseline simulations are supplemented by additional studies on the effects of transparency to ionizing radiation, streamer reignition, dielectric permittivity, and 3D effects. The computer simulations show: 1) Larger channels restrict streamer propagation more efficiently than narrow channels, and can lead to arrested surface streamers. 2) Geometric shielding of ionizing radiation substantially reduces the number of starting electrons in neighboring channels, and thus also reduces the onset point of streamer reignition. 3) Decreasing the streamer channel separation leads to slower streamers. 4) Increasing the dielectric permittivity increases the discharge velocity. The results are of generic value to fields of research involving streamer-dielectric interactions, and in particular for high-voltage technology where streamer termination is desirable.