Detailed Model Research Articles

Photochemical Mechanism and Control Strategy Optimization for Summertime Ozone Pollution in Yining City

To explore the formation mechanism of the ozone （O3） and emission reduction strategy in a northwestern city， an extensive field campaign was conducted in summertime in 2021 in Yining City， in which the 0-D box model incorporating the latest explicit chemical mechanism （MCMv3.3.1） was applied for the first time to quantify the O3-NOx-VOCs sensitivity and formulate a precise O3 control strategy in this city. The results showed that： ① the three indicators ［i.e.， O3 formation potential （OFP）， ·OH reaction rate （k·OH）， and relative incremental reactivity （RIR）］ jointly indicated that alkenes， oxygenated volatile organic compounds （OVOCs）， and aromatics were the highest contributors among anthropogenic volatile organic compounds （AVOC） to O3 formation， and the contribution of biogenic volatile organic compounds （BVOC） also could not be ignored. Additionally， the results based on RIR calculation implied that that the acetaldehyde， ethylene， and propylene were the most sensitive individual VOCs species in Yining City. ② The in-situ photochemical O3 variations were primarily influenced by the local photochemical production and export process horizontally to downwind areas or vertically to the upper layer， and the reaction pathways of HO2·+ NO and ·OH + NO2 contributed the most to the gross Ox photochemical production （60%） and photochemical destruction production （53%）， respectively. Hence， the reduction in local emissions for O3 precursors was more essential to alleviate O3 pollution in this city. ③ The outcome based on RIR（NOx） / RIR（AVOC） and EKMA jointly suggested that the photochemical regime in this city can be considered a transitional regime that was also nearly a VOCs-limited regime. Detailed mechanism modeling based on multiple scenarios further suggested that the NOx and VOCs synergic emission reduction strategies was helpful to alleviate O3 pollution. These results are useful to provide policy-related guidance for other cities facing similar O3 pollution in northwest China.

Staff Rides as Pedagogical Practice

ABSTRACT Political science instructors have started to make use of the “staff ride”—that is, the combined study of a military campaign, structured visits to related sites, and after-action analysis. Our study is the first examination of the potential utility of this pedagogical tool in civilian political science programs. Although the specific knowledge and skills that any given staff ride promotes depend on its content and structure, the potential benefits include four student learning outcomes: (1) staff rides show students how we might draw lessons from the past and apply them to present or future problems; (2) students may derive an enhanced ability to empathize with military and civilian decision makers; (3) staff rides can highlight the normative stakes in politics; and (4) they can foster connections among the participants. This article describes potential benefits of a staff ride, provides detailed instructional models based on the Battle of Gettysburg and the Tet Offensive, and considers ways to replicate these experiences in more accessible formats.

The application and modification of WRF-Hydro/Glacier to a cold-based Antarctic glacier

Abstract. The McMurdo Dry Valleys (MDV) are home to a unique microbial ecosystem that is dependent on the availability of freshwater. This is a polar desert and freshwater originates almost entirely from surface and near-surface melt of the cold-based glaciers. Understanding the future evolution of these environments requires the simulation of the full chain of physical processes from net radiative forcing, surface energy balance, melt, runoff and transport of meltwater in stream channels from the glaciers to the terminal lakes where the microbial community resides. To establish a new framework to do this, we present the first application of WRF-Hydro/Glacier in the MDV, which as a fully distributed hydrological model has the capability to resolve the streams from the glaciers to the bare land that surround them. Given that meltwater generation in the MDV is almost entirely dependent on small changes in the energy balance of the glaciers, the aim of this study is to optimize the multi-layer snowpack scheme that is embedded in WRF-Hydro/Glacier to ensure that the feedbacks between albedo, snowfall and melt are fully resolved. To achieve this, WRF-Hydro/Glacier is implemented at a point scale using automatic weather station data on Commonwealth Glacier to physically model the onset, duration and end of melt over a 7-month period (1 August 2021 to 28 February 2022). To resolve the limited energetics controlling melt, it was necessary to (1) limit the percolation of meltwater through the ice layers in the multi-layer snowpack scheme and (2) optimize the parameters controlling the albedo of both snow and ice over the melt season based on observed spectral signatures of albedo. These modifications enabled the variability of broadband albedo over the melt season to be accurately simulated and ensured that modelled surface and near-surface temperatures, surface height change and runoff were fully resolved. By establishing a new framework that couples a detailed snowpack model to a fully distributed hydrological model, this work provides a stepping stone to model the spatial and temporal variability of melt and streamflow in the future, which will enable some of the unknown questions about the hydrological connectivity of the MDV to be answered.

The Integration of 3D Virtual Reality and 3D Printing Technology as Innovative Approaches to Preoperative Planning in Neuro-Oncology.

Our study explores the integration of three-dimensional (3D) virtual reality (VR) and 3D printing in neurosurgical preoperative planning. Traditionally, surgeons relied on two-dimensional (2D) imaging for complex neuroanatomy analyses, requiring significant mental visualization. Fortunately, nowadays advanced technology enables the creation of detailed 3D models from patient scans, utilizing different software. Afterwards, these models can be experienced through VR systems, offering comprehensive preoperative rehearsal opportunities. Additionally, 3D models can be 3D printed for hands-on training, therefore enhancing surgical preparedness. This technological integration transforms the paradigm of neurosurgical planning, ensuring safer procedures.

Probabilistic Assessment of Global Damage Index for Steel Moment-Resisting Frames Based on Local Responses

ABSTRACT A new scenario-based method is proposed to determine the performance range of structural systems. The advantage of this method is using inter-storey drift as a damage index, while exploiting local damage indices of the main structural members without the need for detailed analytical models and computationally expensive non-linear dynamic analyses. A new damage function is proposed by linking the local (plastic hinge rotations) and global (inter-storey drifts) damage levels. The accuracy of the proposed function is evaluated and compared with the indices in ASCE41–06, ATC-13, and HAZUS for a set of steel moment-resisting frames subjected to earthquakes of varying intensities.

Detailed modeling of odd–even staggering in fission-fragment charge distributions

During the last 10 years or so the Brownian shape-motion (BSM) model has been used in numerous calculations of fission-fragment mass and charge distributions with encouraging agreement with experimental measurements. In this model the structure obtained in the fission-fragment distributions is entirely a consequence of the structures in the calculated five-dimensional (5D) potential-energy surfaces. The potential-energy model until recently did not accommodate the influence of the emerging fragment properties on the calculated potential energy. Therefore there were no odd-even effects in the calculated fission-fragment distributions. Recent extensions of the potential-energy model allow properties of the nascent fragments to be included in the potential-energy model. Application of the BSM model to execute random walks on these more detailed potential-energy surfaces led to calculated fission-fragment yields that exhibited odd-even effects, which “by eye” indicated reasonable agreement with experimental data. The present work goes a step further with a quantitative comparison between experimental and theoretical results based on the global and local odd-even staggering observables. Theoretical calculations and experimental observations both show that pairing effects and enhancement of two-nucleon relative to one-nucleon transfer in heavy-ion collisions decrease with excitation energy and implementing a damping of these quantities with excitation energy leads to improved agreement with experiment. Characteristic variations of the local staggering with charge split seen in the experimental data are also present in the calculated results.

Optimizing Returns: A Holistic Financial Model for Off-Grid Glamping Ventures

Glamping is becoming increasingly popular and may yield better financial returns than traditional hotel developments in some situations, especially in unspoiled locations which are inaccessible for traditional hotel/resort construction because it disturbs the site too much. This paper presents a detailed financial model for analysing the performance of glamping properties to inform decision-making about investing in them. It focuses particularly on properties that are “off the beaten track” for singles, couples, small families and others who want to “unwind” and “commune with nature” in relatively isolated off-grid locations, such as nature reserves, vineyard/wineries, golf resorts or wellness and spa hideaways. In the authors’ view, glamping is particularly well suited to this kind of hotel. In the normal way, the financial model summarizes the development of these properties in detail and itemizes the specific capital investments needed to establish an off-grid glamping property, in contrast to a similar traditional grid-connected hotel.

Coupled Activation of the Hyperdirect and Cerebellothalamic Pathways with Zona Incerta Deep Brain Stimulation.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) or ventral intermediate nucleus (VIM) are established targets for the treatment of Parkinson's disease (PD) or essential tremor (ET), respectively. However, DBS of the zona incerta (ZI) can be effective for both disorders. VIM DBS is assumed to achieve its therapeutic effect via activation of the cerebellothalamic (CBT) pathway, whereas the activation of the hyperdirect (HD) pathway likely plays a role in the mechanisms of STN DBS. Interestingly, HD pathway axons also emit collaterals to the ZI and red nucleus (RN) and the CBT pathway courses nearby to the ZI. The aim was to examine the ability of ZI DBS to mutually activate the HD and CBT pathways in a detailed computational model of human DBS. We extended a previous model of the human HD pathway to incorporate axon collaterals to the ZI and RN. The anatomical framework of the model system also included representations of the CBT pathway and internal capsule (IC) fibers of passage. We then performed detailed biophysical simulations to quantify DBS activation of the HD, CBT, and IC pathways with electrodes located in either the STN or ZI. STN DBS and ZI DBS both robustly activated the HD pathway. However, STN DBS was limited by IC activation at higher stimulus amplitudes. Alternatively, ZI DBS avoided IC activation while simultaneously activating the HD and CBT pathways. From both neuroanatomical and biophysical perspectives, ZI DBS represents an advantageous target for coupled activation of the HD and CBT pathways. © 2024 International Parkinson and Movement Disorder Society.

Impact factor analysis affecting the operation of asset lifecycle by using asset information modeling and blockchain technology

Purpose Currently, owners find it difficult to manage their assets throughout their project life cycle. The fact that asset information models (AIMs) are mandatory as deliverables for building information modeling-driven projects makes it a key requirement for the client to understand in detail those factors affecting asset operation. Hence, because the Kingdom of Saudi Arabia is the most significant market in the Middle East, this study aims to investigate those factors where blockchain and AIMs could impact the asset management (AM) life cycle. Design/methodology/approach Researchers used a hypothesis-based approach over a systematic literature review and a workshop (descriptive statistics) to understand the current challenges in AM. Later, a second workshop was run to understand the impact factor analysis affecting the operation of the asset life cycle by using asset information modeling and blockchain technology over a multiquantitative method. Findings Results found that factors affecting the operation of assets could be the improvement of trust and stakeholder’s influences; the availability of handover process products’ accurate data; manufacturers providing detailed product models; increasing the speed of preparing holistic and integrated AM systems; improving collaboration between stakeholders; and returning clients’ investments faster. Originality/value Understanding the factors affecting AM life cycle based on the utilization of AIMs and blockchain then allows investors and their team members to work in a secure and collaborative environment that helps them to pre-identify certain risks and improve decision-making in a more effective way, as is required by ISO55000.

A current injection method for converter power loss representation in electromagnetic transient simulation

The power-electronics converters are increasingly applied in modern power system, which contains various sub-systems, such as renewable energy power plants and high voltage direct current transmission systems, etc. The electromagnetic transient simulation has been one of the important tools for the study of complex system containing large numbers of power converters. The coordination control of the power converters and corresponding power generation and load units is one of the challenges ensuring stability and obtaining optimal efficiency. However, the detailed modeling of the power converters considering the conduction and switching losses can cost a great number of computation resources, which makes it difficult to simulate a large system. This work proposed a current injection method for converter power loss representation, which can accurately consider the conduction and switching losses of the power-electronics switches with low computation complexity. A look-up table (LUT) of the power loss ratio is calculated before the simulation, and a current source representing the power loss is connected in parallel with the converter to track the reference loss generated by the look-up table. The proposed method can be combined with multiple converter modeling schemes, such as the two-resistance model, the switching-function model, and the average value model, etc. The topologies of the two-level converter and the modular multi-level converter (MMC) were used in the case studies to demonstrate the effectiveness of the proposed method. Compared to the device-level model, the simulation power loss deviation is 0.64% and 4.13% applying the current injection method model in the case studies of the two-level converter and the MMC.

Review Work on Modelling & Simulation of Car Cruise Control System using PID Controller

This work focuses on the modeling and simulation of car cruise control systems with the implementation of a Proportional-Integral-Derivative (PID) controller. The primary objective is to develop a comprehensive model that integrates a PID controller for maintaining car speed under varying conditions. The methodology involves detailed modeling techniques to simulate car dynamics and the tuning of PID parameters for optimal control

Exploration of optimal control based surrogate modeling as a basis for fuel efficient 4D aircraft routing on graphs

AbstractFuel efficient coordination of aircraft operations in the Terminal Maneuvering Area of large airports can contribute to the reduction of the environmental impact of air traffic. To exploit the full potential of the air traffic system, coordinated routing in the Terminal Maneuvering Area, runway assignment and scheduling need to be optimized considering detailed models of the aircraft dynamics and performance. Due to the inhomogeneous nature and level of detail of these combined discrete and continuous decision problems, the optimization of the overall operations poses significant challenges. As part of an integrated approach to the solution of this hybrid problem, this work explores the generation of surrogate models for the fuel consumption of individual aircraft using optimal control methods to exploit the physical capability of the aircraft within an operationally permissible envelope. The surrogate models approximate the predicted fuel consumption of a given point-mass aircraft model on short generic trajectory segments. The trade-off between flight duration and fuel consumption on such segments is analyzed, focusing on the influences of initial aircraft mass, altitude, distance, mean climb angle, along-track wind velocity and linear wind shear. An extensive description of the optimal control-based data generation and surrogate modeling methodology is followed by a discussion of the effects of parameter variation. Based on an illustrative case study, the applicability of the approach is critically analyzed.

Completing the picture of field-grown cereal crops: a new method for detailed leaf surface models in wheat.

The leaf angle distribution (LAD) is an important structural parameter of agricultural crops that influences light interception, radiation fluxes and consequently plant performance. Therefore, LAD and its parametrized form, the Beta distribution, is used in many photosynthesis models. However, in field cultivations, these parameters are difficult to assess and cereal crops in particular pose challenges since their leaves are thin, flexible, and often bent and twisted around their own axis. To our knowledge, there is only a very limited set of methods currently available to calculate LADs of field-grown cereal crops that explicitly takes these special morphological properties into account. In this study, a new processing pipeline is introduced that allows for the generation of realistic leaf surface models and the analysis of LADs of field-grown cereal crops from 3D point clouds. The data acquisition is based on a convenient stereo imaging setup. The approach was validated with different artificial targets and results on the accuracy of the 3D reconstruction, leaf surface modeling and calculated LAD are given. The mean error of the 3D reconstruction was below 1mm for an inclination angle range between 0° and 75° and the leaf surface could be quantified with an average accuracy of 90%. The concordance correlation coefficient (CCC) of 99.6% (p-value = [Formula: see text]) indicated a high correlation between the reconstructed inclination angle and the identity line. The LADs for bent leaves were reconstructed with a mean error of 0.21° and a standard deviation of 1.55°. As an additional parameter, the insertion angle was reconstructed for the artificial leaf model with an average error < 5°. Finally, the method was tested with images of field-grown cereal crops and Beta functions were approximated from the calculated LADs. The mean CCC between reconstructed LAD and calculated Beta function was 0.66. According to Cohen, this indicates a high correlation. This study shows that our image processing pipeline can reconstruct the complex leaf shape of cereal crops from stereo images. The high accuracy of the approach was demonstrated with several validation experiments including artificial leaf targets. The derived leaf models were used to calculate LADs for artificial leaves and naturally grown cereal crops. This helps to better understand the influence of the canopy structure on light absorption and plant performance and allows for a more precise parametrization of photosynthesis models via the derived Beta distributions.

Aggregation and breakage dynamics of alumina particles under shear by coupled Computational Fluid Dynamics – Discrete Element Method

HypothesisShear affects simultaneous aggregation and fragmentation of fine particles. Understanding the effect of shear on the dynamics of particle aggregation and break-up is important to predict aggregate size and structure. It is hypothesized that there is a transition from pure breakage of large aggregates to regimes where restructuring and aggregation also play a role as aggregates become smaller. SimulationsHere, aggregation and fragmentation dynamics of alumina particles are investigated under laminar shear flow using Discrete Element Method (DEM) coupled with Computational Fluid Dynamics (CFD). The effect of the shear rate on the aggregation and breakage rates is quantified accounting for particle–particle and particle–fluid interactions. FindingsHigh shear rates promote the formation of small, compact aggregates. The collision efficiency decreases with increasing shear rate following a power law for shear rates higher than 1250 s−1. The transition from the pure breakage limit to the region dominated by breakage and restructuring has been observed for the first time. The breakage rate decreases significantly as the transition occurs upon decreasing aggregate size. CFD-DEM-derived collision efficiency and breakage rate equations are proposed that can be readily employed in detailed population balance equation models for industrial particle process design.

Metallic melt transport across castellated tiles

In future fusion reactors, extended melt pools in combination with strong plasma-induced accelerations, suggest that the metallic melt could reach the gaps between castellated plasma-facing components, potentially accompanied by profound changes in their mechanical response. The first results of a combined experimental and modelling effort to elucidate the physics of melt transport across gaps are presented. Transient melting of specially designed tungsten samples featuring toroidal gaps has been achieved in ASDEX Upgrade providing direct evidence of gap bridging. Detailed modelling with the MEMENTO melt dynamics code is reported. Empirical evidence and simulations reveal that the presence of gaps can be safely ignored in macroscopic melt motion predictions as well as that the re-solidification limited melt spreading facilitates gap bridging and leads to poor melt attachment. The findings are discussed in the context of ITER and DEMO.

Discovering optimal features for neuron-type identification from extracellular recordings.

Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.

A Simple Model of Flood Peak Attenuation

AbstractA simple analytical model was developed for evaluating the attenuation of flood wave peak discharge. The physically‐based model represents the flood wave along its trajectory, based on the diffusive model. Relative peak discharge decreases along the downstream distance according to a power function. The distance is scaled by the attenuation factor related to river hydrodynamics (flow rating, hydraulic diffusivity, celerity, and floodplain storage) and input hydrograph (initial peak discharge, hydrograph volume, and its relative curvature). It also informs the attenuation length, which is a practical indicator of the river distance in which discharge decreases by a given factor. Sensitivity analyses indicate that initial peak discharge, volume, floodplain storage, and slope are the governing factors of attenuation. Model's validity and accuracy were demonstrated by reproducing data from (a) numerical solutions of the Saint‐Venant equations covering a wide range of conditions, (b) 29 observations from 11 historical dam‐breaks, (c) 15 observations of natural floods in seven rivers and (d) a detailed hydrodynamic model. The model errors were generally lower than 10% and not larger than the typical uncertainty of flood observations. The accuracy is higher than simplified empirical models and analogous to a detailed hydrodynamic model that is representative of current practice. The proposed flood attenuation model can be easily applied using a few common parameters and a simple equation in a basic spreadsheet. It is suitable for practical applications such as first assessments of natural and dam‐break floods, engineering design, and analyses of large river networks supported by remote sensing data.

Analysis of high frequency oscillations based on MMC-HVDC system

Modular multilevel converter is widely used in new energy grid-connection and power grid asynchronous interconnection by virtue of its advantages of flexible and easy to expand structure and no commutation failure problem. In recent years, high-frequency oscillations have been observed in several flexible DC transmission projects at home and abroad, while the internal harmonic dynamic characteristics and control system of the MMC system are complex and the mechanism of high-frequency oscillations has not been sufficiently analysed at present. In this paper, the detailed DQ impedance model and the equivalent circuit impedance model on the AC side are firstly derived based on the state-space model of the MMC system, and the impedance sweeping method with superimposed small perturbations is proposed. Meanwhile, the dynamic characteristics of the detailed DQ impedance model are compared with those of the electromagnetic transient model. The amplitude-frequency characteristics of the system impedance under different system delays are further compared, and the mechanism of the high-frequency oscillation of the MMC-HVDC system is revealed.

Thermodynamic limitations on brain oxygen metabolism: physiological implications.

Recent thermodynamic modelling indicates that maintaining the brain tissue ratio of O2 to CO2 (abbreviated tissue O2 /CO2 ) is critical for preserving the entropy increase available from oxidative metabolism of glucose, with a fall of that available entropy leading to a reduction of the phosphorylation potential and impairment of brain energy metabolism. This provides a novel perspective for understanding physiological responses under different conditions in terms of preserving tissue O2 /CO2 . To enable estimation of tissue O2 /CO2 in the human brain, a detailed mathematical model of O2 and CO2 transport was developed, and applied to reported physiological responses to different challenges, asking: how well is tissue O2 /CO2 preserved? Reported experimental results for increased neural activity, hypercapnia and hypoxia due to high altitude are consistent with preserving tissue O2 /CO2 . The results highlight two physiological mechanisms that control tissue O2 /CO2 : cerebral blood flow, which modulates tissue O2 ; and ventilation rate, which modulates tissue CO2 . The hypoxia modelling focused on humans at high altitude, including acclimatized lowlanders and Tibetan and Andean adapted populations, with a primary finding that decreasing CO2 by increasing ventilation rate is more effective for preserving tissue O2 /CO2 than increasing blood haemoglobin content to maintain O2 delivery to tissue. This work focused on the function served by particular physiological responses, and the underlying mechanisms require further investigation. The modelling provides a new framework and perspective for understanding how blood flow and other physiological factors support energy metabolism in the brain under a wide range of conditions. KEY POINTS: Thermodynamic modelling indicates that preserving the O2 /CO2 ratio in brain tissue is critical for preserving the entropy change available from oxidative metabolism of glucose and the phosphorylation potential underlying energy metabolism. A detailed model of O2 and CO2 transport was developed to allow estimation of the tissue O2 /CO2 ratio in the human brain in different physiological states. Reported experimental results during hypoxia, hypercapnia and increased oxygen metabolic rate in response to increased neural activity are consistent with maintaining brain tissue O2 /CO2 ratio. The hypoxia modelling of high-altitude acclimatization and adaptation in humans demonstrates the critical role of reducing CO2 with increased ventilation for preserving tissue O2 /CO2 . Preservation of tissue O2 /CO2 provides a novel perspective for understanding the function of observed physiological responses under different conditions in terms of preserving brain energy metabolism, although the mechanisms underlying these functions are not well understood.

Impedance driven collective effects in CEPC

The Circular Electron Positron Collider is a double ring lepton collider covering beam energy from 45 GeV (Z) to 180 GeV (tt-bar). Beam coupling impedance and the collective effects that are triggered by the impedance are important subjects when targeting high machine performance of an electron positron collider. A robust impedance model is required for the instabilities evaluations as well as to investigate their possible mitigations. Meanwhile, a thorough investigation on the collective effects is essential to identify the critical issues on the beam instabilities. In this paper, a detailed impedance model has been built for the collider ring and dominant impedance contributors have been identified by evaluating the effective impedances. The potential collective instabilities driven by the impedance are also discussed.