Load Model Research Articles

Finite element modeling of concentrated impact loads on the masticatory muscles at the head

AbstractNumerical simulation has great potential to provide a more comprehensive understanding of human impact response to injury mechanisms. Finite Element (FE) models are used as a tool to study human injuries in greater detail, for example, the THUMS (Total Human Model Safety of TOYOTA) model, which is widely used as a reliable human model in different fields to predict human injuries such as fractures, internal organ damage, and brain tissue injuries. However, no available FE model can be used to simulate human‐robot collisions based on standards ISO/TS 15066 and the biomechanical characteristics of human soft tissues in vivo. The authors have developed a head model based on the structures (dimensions and anatomy) of the THUMS head model, specifically designed to simulate impact loads on the masticatory muscles. Based on medical imaging (MRI) data, the soft tissues at the location of the masticatory muscles in the THUMS head are transformed from monolayer to multilayer, that is, a composite geometry of skin‐fat‐muscle each with its own material model and parameters. The model was optimized and validated using the experimental data from the Fraunhofer IFF subjects study, which determined biomechanical thresholds for specific body locations in ISO/TS 15066 under dynamic collisions.

Walking load model considering damping and energy compensation strategy

Walking load model considering damping and energy compensation strategy

Data-driven modeling of low-frequency hydrodynamic loads

Abstract This paper presents a data-driven approach for estimating the linear and quadratic transfer functions relating incoming waves to hydrodynamic loads on floaters. The procedure relies on constructing a nonlinear auto-regressive (NARX) surrogate model for forecasting the hydrodynamic loads and a harmonic probing algorithm for extracting the transfer functions of the system. The implemented harmonic probing method is of numerical nature and avoids the use of computationally expensive symbolic coding tools. The method was verified against synthetic data generated from a potential-flow-based numerical model of the INO WINDMOOR 12 MW floater. The main advantage of NARX models is the fexibility of their structure, meaning that they do not presuppose that the nonlinearity of the hydrodynamic load is quadratic, as it is commonly done.

Cognitive impact of anticholinergic and sedative burden in people with HIV.

This study aims to estimate the extent to which anticholinergic and sedative burden is associated with cognitive ability and self-reported cognitive difficulties (SCD) in middle-aged and older adults living with HIV. This cross-sectional analysis examined data from the inaugural visit of participants enrolled in the Positive Brain Health Now (BHN) study. Cognitive ability was measured using the Brief Cognitive Ability Measure (B-CAM; higher is better) and SCD using the Perceived Deficits Questionnaire (PDQ; higher is worse). Medication burden was quantified using several scoring systems, including the Anticholinergic Cognitive Burden (ACB), Anticholinergic and Sedative Burden Catalog (ACSBC), Anticholinergic Drug Scale (ADS), Anticholinergic Risk Scale (ARS), and the Sedative Load Model (SLM). Multivariable Ordinary Least Squares and quantile regression were utilized to estimate average effects and distribution-specific impacts, respectively. Of 824 participants (mean age 53 years, 84.7% men), 41.4% used anticholinergics (ACSBC) and 39% used sedatives (SLM). High anticholinergic burden was linked to worse cognitive ability [ β = -3.81; 95% confidence interval (CI): -7.16, -0.46] and SCD ( β = 3.89; 95% CI: 1.08, 6.71). Using three or more anticholinergics worsened cognitive ability ( β = -4.45; 95% CI: -8.54, -0.35), and using three or more sedatives increased SCD ( β = 4.35; 95% CI: 0.92-7.78). Stronger negative associations were observed in participants with lower cognitive ability and more difficulties. These results suggest that anticholinergic and sedative burden may contribute to cognitive impairment in people with HIV. Personalized medication management and regular cognitive assessments could mitigate these adverse effects.

Probabilistic modeling of multivariate extreme non-Gaussian wind loads and its applications in envelope engineering

Probabilistic modeling for non-Gaussian wind load fields under extreme winds is crucial to the quantitative risk and reliability analysis of envelope structures. Based on the multi-point synchronous wind load data during typhoons, a probabilistic model is proposed to characterize multivariate non-Gaussian wind loads to improve the accuracy of the risk analysis considering data dimension reduction. In the proposed model, the Pareto distribution function is utilized to fit the marginal distribution, and the copula function is employed to construct the dependent structure of wind loads from different locations. In the marginal fitted results, the extreme wind load value in the edge region is about 0.10–0.15 higher than those from inner measurement points at quantile p = 95 %. In addition, the variation gradient from the different variable directions is approximately equal, and a sharp variation occurs at the point (0.10, 0.10) for bivariate joint distributions. Engineering applications given in this article are composed of three parts: calculating regional exceeding probability, estimating wind load vector under a specific regional exceeding probability, and determining the location of joint extreme wind loads using the joint gradient value. Finally, risk analysis of existing roof coverings is carried out, the edge corner region is weaker, and two engineering recommendations are given. For determining the regional design wind load, a comprehensive calculation framework considering the temporal dependence of different locations is established, and the reduction rates are in the range (5 %, 25 %).

Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges

This paper aims to extend a previously developed probabilistic model for simulating extreme response scenarios to include congested traffic flow on long-span bridges, addressing the challenge of accurately modeling traffic loads under changing conditions. While the model was initially designed for free-flow traffic, this study demonstrates how it can be adapted for congested conditions, with the objective of improving the accuracy of traffic load models. To overcome the limitation of traditional Weigh-in-Motion (WIM) systems in capturing congested traffic, congested flow characteristics were inferred from available free-flow data. The cellular automata (CA) method was applied to generate realistic congested traffic scenarios, which were used as input for the probabilistic model. Key simulation parameters, such as cell length and vehicle weight distribution, were adjusted to reflect congested conditions. The results validate the model’s flexibility, showing how, with the adaptation of some parameters, it can simulate both free-flow and congested traffic patterns effectively. This research provides a basis for improving traffic load models used in the design and assessment of long-span bridges, addressing the current limitations in existing codes and standards.

Modeling and analysis of a novel CPSC/CI combined profile for scroll compressors

The scroll profile design has become a research hotspot and plays a decisive role in scroll machines. However, the modeling process is complicated due to different expressions of peripheral and central profiles in most full meshing profile (FMPs). Moreover, systematic modeling and in-depth property investigation of scroll profile are still insufficient. Herein, this study simplifies scroll profile modeling by constructing a novel FMP that combines a cubic power series curve (CPSC) with a circular involute (CI) to improve the properties of scroll compressors. An analytical model of CPSC/CI scroll wraps is proposed using the orthogonal components approach and validated by numerical examples. Geometric models to calculate chamber volume and gas load action length, and gas load models, are established. The effects of the CPSC/CI on the scroll wrap tip, built-in volume ratio and gas load are investigated. The proposed CPSC/CI enhances scroll compressor's properties by providing favorable properties not only with wider range of variation in scroll wrap tip and built-in volume ratio but also smaller gas load vibrations due to its superior flexibility, compared to circular arc/CI. This study provides new possibilities for enhancing scroll compressor performance through the design of scroll wrap profile.

An optimization dispatching strategy for integrated electricity and natural gas systems with fast charging stations

With the increasing number of electric vehicles (EVs), a large amount of charging load will affect the safety and economic operation of the distribution network (DN). In this paper, an optimization dispatching strategy for integrated electricity and natural gas systems (IEGS) with fast charging stations (FCSs) is proposed to address the impact of fast charging loads. Firstly, the FCS load model is established based on the simulation of vehicle traveling and vehicle charging. Secondly, a natural gas supply model is established, which includes constraints such as the adjustment range, direction, and total number of supply flow adjustments. Then, an optimization dispatching strategy for IEGS based on energy storage and gas storage is proposed. Finally, the effectiveness of the proposed strategy is verified. The simulation results show that when the proposed strategy is applied, the voltage of FCS node will be stabilized within a good range. And the energy purchase cost will be reduced by more than 50%. The proposed method improves the carrying capacity of DN for FCSs.

How uncertainty in calibration data affects the modeling of non-point source pollutant loads in baseflow

Baseflow is a major transport pathway for non-point source (NPS) pollutants. Watershed water quality (WWQ) models calibrated by low-quality data may produce misleading predictions of baseflow NPS pollutant loads, resulting in poor management decisions. We evaluated how models of the baseflow nitrate loads in the Huron River basin, southwest of Lake Erie, were affected by uncertainty in the calibration data. Based on a five-year time series of daily streamflow, nitrate concentration, and specific conductance, two sets of “observed” baseflow nitrate load data that include uncertainty were estimated using various tracer-based and non-tracer-based hydrograph separation methods, in conjunction with assumptions regarding baseflow nitrate concentrations. We calibrated the Soil and Water Assessment Tool plus (SWAT+) model with the two “observed” data sets and used the Generalized Likelihood Uncertainty Estimation (GLUE) approach to quantify parameter and predictive uncertainties. The results showed that baseflow accounted for 26 %–34 % of the mean annual total streamflow (11.8 m3/s) and 8 %–37 % of the mean annual total nitrate load (14.3 kg·ha−1·year−1) in the Huron River basin. The baseflow and nitrate load estimates from the non-tracer-based methods resembled those from the tracer-based method but had greater uncertainty. The posterior parameter distributions, as well as the weighted means and 90 % prediction intervals of the simulated baseflow nitrate loads, exhibited minimal variation when different calibration data sets for SWAT+ and different threshold likelihood values for GLUE were used. Our analysis emphasizes the necessity of calibrating WWQ models with baseflow pollutant loads/concentrations when addressing water quality issues related to baseflow. It also demonstrates the feasibility of utilizing multiple non-tracer-based hydrograph separation methods to estimate baseflow NPS pollutant loads. These non-tracer-based methods offer a simplicity and broader applicability compared to tracer-based methods. This study has provided insights into how calibration data uncertainty impacts the modeling of NPS pollution in baseflow and highlights the practical value of non-tracer-based hydrograph separation methods.

Can combined wind and solar power meet the increased electricity load on heatwave days in China after the carbon emission peak? A case study in southern Hebei

Wind and photovoltaic (PV) power are the fastest-growing renewable energy sources; however, they are vulnerable to weather extremes. In the context of global warming, the frequency of extreme weather events, particularly heatwaves (HW), is anticipated to increase. Compared to normal summer days, HW days not only cause a rise in electricity demand but also exhibit a complementary growth in wind/PV generation. Therefore, determining whether the increase in wind/PV generation on HW days can meet the corresponding surge in electricity load is pivotal for replacing fossil fuel-based electricity. To address this question, this study conducts a case study for southern Hebei Province, which has the highest combined wind/PV installations in China, for the period 2031–2040 under the scenario that China will achieve its carbon peak (2030) and carbon neutrality goals as planned. We use recently developed load and wind power models and calibrate a boosting ensemble learning model to simulate PV generation. The results reveal that the total increase in wind/PV generation on HW days can fully meet the total increase in electricity consumption starting in 2039. However, due to the uneven distribution of wind/PV generation and load changes throughout the HW days, 9 GWh of energy storage capacity is required to ensure electricity supply in the early morning hours. Under China's climate change adaptation strategy, Hebei Province can harness wind/PV energy to address peak load demands during HW conditions and initiate climate-resilient urban development pilot projects.

Probabilistic surrogate modeling of damage equivalent loads on onshore and offshore wind turbines using mixture density networks

Abstract. The use of load surrogates in offshore wind turbine site assessment has gained attention as a way to speed up the lengthy and costly siting process. We propose a novel probabilistic approach using mixture density networks to map 10 min average site conditions to the corresponding load statistics. The probabilistic framework allows for the modeling of the uncertainty in the loads as a response to the stochastic inflow conditions. We train the data-driven model on the OpenFAST simulations of the IEA 10 MW reference wind turbine (IEA-10MW-RWT) and compare the predictions to the widely used Gaussian process regression. We show that mixture density networks can recover the accurate mean response in all load channels with values for the coefficient of determination (R2) greater than 0.95 on the test dataset. Mixture density networks completely outperform Gaussian process regression in predicting the quantiles, showing an excellent agreement with the reference. We compare onshore and offshore sites for training to conclude the need for a more extensive training dataset in offshore cases due to the larger feature space and more noise in the data.

Computational Models of Dynamic Load Sources for Modeling of Construction Structures Operation Used in Monitoring of Technical Condition of Buildings and Structures

This research aims to develop principles for assessing the impact of mega-cyclic vibrodynamic loads on the reliability of construction structures. The relevance of the reasons for the development and improvement of algorithms for numerical modeling of multicycle dynamic loads on building structures is due to the steadily increasing intensity of such loads on buildings and structures in megacities, as well as the acute practical problem of significant differences in the dynamic characteristics of buildings and structures obtained as a result of mathematical modeling and determined by experimental methods. The article presents research materials on computational equivalent models of dynamic load sources for numerical modeling of the behavior of construction structures under their influence, using the method of vibroacoustic analogies. The article examines models of sources of dynamic impact on construction sites. Algorithms and final formulas for computational modeling of the simplest sources of dynamic load are developed using the method of vibroacoustic analogies. The dynamic properties of the simplest dynamic load sources were analyzed. A significant difference between the computational models of real and ideal dynamic load sources. The article presents research and development results intended for calculating the distribution of dynamic loads on elements of construction structures in industrial and civil engineering projects located in areas with high levels of transport vibrodynamic impacts. An important property of the proposed computational equivalent models of sources of dynamic impact on building structures is the possibility of computational verification of critical elements, points, or nodes of load-bearing structures of buildings and structures under dynamic overloads. The position of these critical elements, points, and nodes of load-bearing structures under dynamic loads can differ significantly from their position determined using static and quasistatic computational modeling methods.

Probabilistic risk analysis of local verification of Load Model 1 in Eurocode for Soil-Steel Composite bridges in Sweden

Bridges must be designed to ensure safety for all users. At the same time, the design should be performed with an appropriate risk level. In Sweden, Soil-Steel Composite bridges (SSCB) are the most common bridge type. For SSCB, local verification of Load Model 1 in Eurocode is most often governing the design. The objective of this study was to investigate whether local verification of LM1 load case could be modified without decreasing the agreed risk level in Eurocode. Weight in motion measurements from real traffic were extrapolated with Rice formula. Monte Carlo simulations were used to simulate the 1000-year return period event to obtain the acceptable risk level as prescribed in Eurocode. The results show that the local verification of LM1 is conservative, considering the acceptable risk level in Eurocode. With a modified implementation of local verification, this paper shows that a potential saving of up to 14% in terms of economic cost and CO2-equivalents is possible. A modified implementation of local verification of LM1 in Eurocode for SSCB is proposed, which could reduce the climate impact by up to 14% associated to the construction of new SSCB in Sweden.

The Organization of Academic General Internal Medicine Practice at the Top Primary Care Schools.

While prior studies have explored staffing infrastructure for primary care practices in general, little is known about the range of academic primary care practice models and supports available for academic general internists. To characterize the range of practice arrangements and expectations for attending academic physicians in general internal medicine (GIM) practices at the top 22 medical schools across the USA. Cross-sectional survey administered electronically between October 30, 2022, and December 28, 2022. Clinical leaders in GIM at the top 22 primary care medical schools, as identified by the 2023 US News and World Report Rankings. Clinical load, productivity expectations, cross-coverage, and team-based care models. Twenty-two leaders responded, representing 68% (15/22) of medical schools surveyed. The practices were mostly in urban locations (18/22, 82%) and 86% (19/22) included residents. Practices ranged from 7 to 200 PCPs and from 3 to 112 clinical FTEs. A full-time (1.0 FTE) clinical role for academic attending GIM physicians entailed a median of 9 (IQR 8, 10) weekly half-day clinic sessions, with a median panel size expectation of 1600 (IQR 1450, 1850) patients and a median yearly RVU expectation of 5200 (IQR 4161, 5891) yearly RVUs generated. Staff support was most commonly present for prescription refills and patient portal message checks. It was less commonly available for time sensitive form completion. Occasional clinical coverage for other physicians was an expectation at all practices. In this study, we characterize the organization of and supports available in academic GIM practices affiliated with the top primary care medical schools. Our findings provide comparative information for leaders of academic GIM practices seeking to enhance primary care delivery for their faculty and trainees. They also highlight areas where standardization may be beneficial across academic GIM.

Exploiting the determinant factors on the available flexibility area of ADNs at TSO‐DSO interface

AbstractActive distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil‐fuel‐based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two‐step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69‐bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs.

Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: Insights from real-world data in Sweden

Accurately estimating the charging behaviours of electric vehicles (EVs) is crucial for various applications, such as charging station planning and grid impact estimation. However, the analysis of EV charging behaviours using real-world data remains limited due to (confidential) data availability constraints. Furthermore, while existing modelling studies have demonstrated EVs as effective tools for electricity balancing and delivery between locations, their potential remains unexplored empirically. This study aims to bridge the research gap by studying EV charging behaviours and their capacity for electricity balancing and delivery. Using data from 179,665 real-world charging sessions in Sweden, we employed statistical and clustering analysis to scrutinize charging behaviours comprehensively. Synthetic weekly charging load profiles are generated for both residential areas and workplaces, considering varying charging power levels, which can be used as inputs for large-scale EV charging load modelling. Furthermore, performance indicators are proposed to quantify the potential of EVs for electricity balancing and delivery. Results show that EVs exhibit significant potential for electricity balancing (up to 51.5 kWh daily). Many EV owners underutilize their EV battery capacity, providing an opportunity for active electricity delivery across locations. This study can help understand EV charging behaviours and recognize their significant potentials for electricity regulation and integrating more renewables in the future power system.

Effect of Design Parameters on Buckling Tendency of an Eccentric Drag Link Used in a Truck Steering Linkage: A DoE/RSM-Based Design Optimisation Study

The steering system, deemed one of the most critical subsystems for ensuring the safety of a vehicle, is characterised by its pivotal role in manoeuvrability and control. The primary function of transmitting the steering input from the steering wheel to the vehicle's wheels is carried out by the drag link within the steering system. Therefore, the durability and proper functioning of the drag link in vehicles, especially carrying heavy loads, are of critical importance for both safety and efficiency. The main purpose of this study is to determine the optimal geometry of the drag link utilised in the steering system of a 4x2 truck using the Design of Experiments-Response Surface Methodology (DoE-RSM). Firstly, an idealised worst-case load model was used to determine the maximum force applied by the Pitman arm on the drag link. The stress distribution and deformation on the drag link caused by the maximum design load were deter-mined using Finite Element Analysis (FEA). Based on the results of the analyses, eccentricity, rod thickness, and fillet radius were defined as design parameters. The parameter ranges were determined by considering the volume swept by the wheel and buckling criteria under maximum load conditions. According to the DoE-RSM results, concerning the local sensitivity per-centages of the parameters on total deformation, it was observed that the eccentricity (e) parameter has the most significant impact, with an approximate positive rate of 74%.

Data-Driven Relationship for Reduction Factor of Ballasted Railway Bridge Deflections Due to Load Distribution Within Track

Increasing the operating speed of the trains on modern networks necessitates performing dynamic analyses to assess the performance of bridges under passage of trains. The detailed investigation of their responses requires constructing complex computational models capable to take the train–track–bridge interaction effects into account. Such models have successfully been developed; however, employing those elaborated models for practical engineering applications, or to perform studies that require a large number of analyses may become infeasible. Among such situations are conducting probabilistic investigations, screening of entire networks, or sensitivity analyses. These concerns have been addressed by employing simplified models mostly relying on moving load modeling strategy which disregards the train–track–bridge interaction effects. Those neglected contributions can be compensated by implementing additional correction factors. The distribution of loads within track is one of those disregarded effects where a reduction factor is recommended by design guidelines to take its contribution into account. It has been shown that the existing relationship for these reduction factors delivers an acceptable performance for vertical accelerations, while showing a less favorable performance for displacements. Then, a data-driven strategy is adopted in this study to propose easy-to-apply relationships for reduction factors of deflections, due to load distribution within the track. In this context, three different distributive lengths of triangular load footprints have been considered, namely 2.0, 2.5 and 3.0[Formula: see text]m. The procedure employed has trained and tested for more than 1[Formula: see text]200 train configurations, comprising conventional, articulated and regular vehicles, and including several tens of thousand data points for each distributive length. The performance observed in the new models revealed a considerable improvement with respect to the existing relationship.

Collaborative design of atmospheric cutterhead opening and disc cutter arrangement

As the core components of the shield machine, the cutterhead body and the cutter directly interact with the soil, which plays a vital role in tunneling efficiency and performance. For the atmospheric cutterhead, the cutterhead opening and the cutter arrangement are mutually restricted, which brings great challenges to the design of the cutterhead. To solve this problem, an innovative collaborative strategy for cutterhead structure and cutter arrangement is proposed in this paper. By constructing a parametric model, we deeply analyzed the relationship between the cutterhead parameters and the cutter size and defined the feasible range of parameter changes. On this basis, we have developed a set of cutter arrangement specifications to ensure the coordinated operation between the cutter holders and between the cutter holder and the gate, avoiding potential interference problems, and calculating the minimum cutter spacing. In addition, the study also takes into account the various loads on the cutter and constructs a load model specifically for the atmospheric cutterhead. By taking an atmospheric cutterhead with a diameter of 15 m as an example, we elaborate on the implementation process of this method, which not only maximizes the cutterhead opening but also effectively controls the unbalanced load. The methodology of this study provides a new perspective and solution for the design of other types of cutterhead.

Economic management of microgrid using flexible non-linear load models based on price-based demand response strategies

Demand response programmes are used in microgrid research without considering the different price elasticity of distinct load types. To evaluate the impacts of demand response efforts, it is necessary to utilise a mix of nonlinear and linear models for creating load-responsive models in a manner that is realistic. With an aim to address this gap in research, an exhaustive technoeconomic investigation is being conducted to determine the effect that price-dependent demand response programmes have on the optimal scheduling of microgrids when linear and nonlinear load models are present. The flexible elasticity model is used to accurately describe how customers respond to changes in electricity price. Four load models, including exponential, hyperbolic, linear, and logarithmic, were constructed for each demand response programme. Rime is a newly created physics-based algorithm that has been deployed to handle the anticipated energy management problem that the microgrid may be experiencing. To evaluate the efficacy of the proposed strategy, four case studies based on grid price and participation scenarios have been carried out. The findings from our research are notably impactful, revealing an 8 % reduction in energy consumption when applying the critical peak price (CPP) load demand model. This efficiency gain translates into a notable enhancement in the load factor, increasing from 0.83 to 0.86, and a significant drop in overall generation costs from $25,463 to $21,823 under optimal conditions. These results vividly illustrate the practical value of our proposed research work, showcasing their ability to generate substantial energy savings and cost efficiencies in microgrid operations. The improvements in operational efficiency and cost-effectiveness underscore the potential of these models to refine microgrid management, delivering both economic and performance gains that are crucial for advancing sustainable energy solutions.