Semi-physical Model Research Articles

Integrating an urban fire model into an operational wildland fire model to simulate one dimensional wildland–urban interface fires: a parametric study

Background Wildland fires that occur near communities, in the wildland–urban interface (WUI), can inflict significant damage to urban structures. Although computational models are vital in wildfires, they often focus solely on wildland landscapes. Aim We conducted a computational study to investigate WUI fire spread, encompassing both urban and wildland landscapes. Methods We developed a 1D landscape-scale semi-physical model by integrating a semi-physical urban fire spread model into an Eulerian level set model of wildfire. The model includes ignition and spread through radiation, direct flame contact and ember deposition. Key results Through a parametric study, we compare the relative change of spread rate from various structural properties and landscape layouts represented by model parameters, highlighting the significant impact of fire-resistant structure materials over surface treatments. Layout configurations play a pivotal role in fire spread, with isolated islands of combustibles effective in reducing spread rate, aligning with existing mitigation strategies. Conclusion Despite using a 1D domain and limitations on spatial and temporal variability, our model provides insights into underlying phenomena observed in WUI fires and their mitigation. It offers early-stage development of strategies for managing structure materials and landscape layouts. Implications Our model and findings provide insights into WUI fire dynamics, paving the way for advanced mitigation strategies.

Nutrient flows in biofloc-Nile tilapia culture: A semi-physical modelling approach

Biofloc culture systems potentially reduce the nutrient losses in aquaculture. However, knowledge of the nutrient flows in the system is not yet well-developed. This study deployed experimental data to develop a semi-physical model to understand the dynamics and flows of carbon (C), nitrogen (N), and phosphorus (P) in a biofloc-Nile tilapia-rearing system. The model involved eight process variables, which are pelleted feed A, C, N, P, fish, biofloc, periphyton, and water volume. Model calibration and validation were done under a Control-diet and High-NSP-diet, respectively. The diets differed by the type of starch in which the latter contains three times higher fibrous starch, called non-starch polysaccharides, than the former. Except for biofloc, the behaviour of the process variables fit the observations with a root mean square error (RMSE) of less than 30% of the corresponding average observations. The biofloc biomass was predicted using exponential growth model and results in a RMSE of 49% and 56% for the Control and High-NSP-diet, respectively. Scenario analyses, using the validated model, showed that the biofloc system generates less waste when the stocking density is doubled, which means double fish production and less nutrient losses. In terms of different diets, the high-NSP-diet resulted in more organic waste than the Control-diet. However, the amount of loss and unutilised C and P were similar which was mainly caused by the ability of biofloc and periphyton to assimilate more waste, especially C, in the High-NSP-diet.

Evaluation of Pedotransfer Functions to Estimate Soil Water Retention Curve: A Conceptual Review

The soil water retention curve (SWRC) is a vital soil property used to evaluate the soil’s water holding capacity, a critical factor in various applications such as determining soil water availability for plants, soil conservation and management, climate change adaptation, and mitigation of flood risks. Estimating SWRC directly in the field and laboratory is a time-consuming and laborious process and requires numerous instruments and measurements at a specific location. In this context, various estimation approaches have been developed, including pedotransfer functions (PTFs), over the past three decades to estimate soil water retention and its associated properties. Despite the efficiencies, PTFs and semi-physical approach-based models often have several limitations, particularly in the dry range of the SWRC. PTFs-based modeling has become a key research topic due to readily available soil data and cost-effective methods for deriving essential soil parameters, which enable more efficient decision-making in sustainable land-use management. Therefore, advancement and adjustment are necessary for reliable estimations of the SWRC from readily available data. This article reviews the evaluation of the current and past PTFs for estimating the SWRC. This study aims to evaluate PTF techniques and semi-physical approaches based on soil texture, bulk density, porosity, and other related factors. Additionally, it also assesses the performance and limitations of various common semi-physical models proposed and developed by Arya and Paris, Haverkamp and Parlange, the Modified Kovács model by Aubertin et al., Chang and Cheng, Meskini-Vishkaee et al., Vidler et al., and Zhai et al. This assessment will be effective for researchers in this field and provide valuable insight into the importance of new PTFs for modeling SWRC.

Useful properties of phenomenological-based models

This work compiles the conceptual and practical proposals of various research groups regarding interesting properties of mathematical models. Although different types of mathematical models are mentioned, phenomenological-based semi-physical models (PBSM) are the main focus of this paper. The partitioning of the process to be modeled, the scalability of the model, and the interpretability of its parameters are presented as important properties of the model that help to understand and use the mathematical model. The use of these properties is illustrated with examples, providing the respective bibliographical reference where an expanded presentation of each example can be reviewed. The importance of each model property presented is explained from the inherent condition of the model as a mathematical object and from the expectation of the final users of the model. The confidence in the model is increased when the user knows how the model is obtained. These properties provide valuable information to the end use of the model. Therefore, the modeler must understand and apply this knowledge during model deduction and write, in the model report, how the mentioned properties were tested.

Extended metric validation of a semi-physical Space Weather Modeling Framework conductance model on field-aligned current estimations

In this study, a detailed metric survey on the “Galaxy 15” (April 2010) space weather event is conducted to validate MAGNetosphere–Ionosphere–Thermosphere (MAGNIT), a semi-physical auroral ionospheric conductance model characterizing four precipitation sources, against AMPERE measurements via field-aligned current (FAC) characteristics. As part of this study, the comparative performance of three ionosphere electrodynamic specifications involving auroral conductance models, MAGNIT, Ridley Legacy Model (RLM) (empirical), and Conductance Model for Extreme Events (CMEE) (empirical), within the Space Weather Modeling Framework (SWMF), is demonstrated. Overall, MAGNIT exhibits marginally improved predictions; root mean square error values in upward and downward FACs of MAGNIT predictions compared to AMPERE data are smaller than those of CMEE and Ridley Ionosphere Model (RIM) by ∼12.7% and ∼6.24% before the storm, ∼4.52% and ∼2.13% better during the main phase, ∼1.98% and ∼1.27% worse during the second minimum, and better by ∼1.84% and ∼1.49% by the beginning of the recovery, respectively. In all three model configurations, the dusk and night magnetic local time (MLT) sectors over-predict throughout the storm, while the day and dawn MLT sectors under-predict in response to interplanetary magnetic field (IMF) conditions. In addition to accuracy and bias, similar results and conclusions are drawn from additional metrics, including in the categories of correlation, precision, extremes, and skill, and recommendations are made for the best-performing model configuration in each metric category. Visual data–model comparisons conducted by studying the FAC location and latitude/MLT spread throughout various phases of the storm suggest that the spatial extent of the FACs is captured relatively well in the night-side auroral oval, unlike in the day-side oval. The spread in latitude of the FACs matches that in the previous literature on other model performances. This information on auroral precipitation sources and their weight on FACs, along with metrics from model–data comparisons, can be used to modify MAGNIT settings to optimize SWMF model performance.

General relativistic effects and the near-infrared variability of Sgr A*

A systematic study, based on the third-moment structure function, of Sgr A*’s variability finds an exponential rise time, τ1,obs = 14.8−1.5+0.4 minutes, and decay time, τ2,obs = 13.1−1.4+1.3 minutes. This symmetry of the flux-density variability is consistent with earlier work, and we interpret it as being caused by the dominance of Doppler boosting, as opposed to gravitational lensing, in Sgr A*’s light curve. A relativistic, semi-physical model of Sgr A* confirms an inclination angle of i ≲ 45°. The model also shows that the emission of the intrinsic radiative process can have some asymmetry even though the observed emission does not. The third-moment structure function, which is a measure of the skewness of the light-curve increments, may be a useful summary statistic in other contexts of astronomy because it senses only temporal asymmetry; that is, it averages to zero for any temporally symmetric signal.

Estimation of potato yield using a semi-mechanistic model developed by proximal remote sensing and environmental variables

Timely monitoring of potato yield status is crucial for guiding field management and ensuring the sustainability of food trade. In recent years, non-destructive remote sensing techniques have emerged as one of the most promising methods for crop yield monitoring. However, most existing potato yield estimation models have primarily been tested at specific growth stages within a single ecosystem, highlighting the urgent need for a model that can be applied across different ecological environments. In this study, we developed a semi-physical model based on hierarchical linear modeling (HLM) to automatically correct yield estimation based on remotely sensed vegetation indices using environmentally relevant variables such as net solar radiation at the surface (ssr), air temperature at 2 m above the surface (t2m), and soil water content in the root zone (swvl). The accuracy and transferability of the model were further evaluated by comparing it with aboveground destructive sampling methods, conventional empirical models, and machine learning approaches. The results demonstrated that site-specific relationships exist between aboveground agronomic traits parameters like biomass and leaf area index (LAI) with respect to yield estimation. Conventional linear statistical and machine learning methods faced challenges in transferring their constructed yield estimation models across regions and years. However, the HLM approach exhibited excellent generalization ability across all trial samples. In the middle and later stages of potato growth, the standardized LAI determining index proved to be the most effective vegetation for predicting yield, while ssr, sktmax, and shallow swvl were identified as key environmental variables affecting yield. By integrating these variables using HLM modeling techniques, we achieved optimal generalization performance (R2 = 0.57, RMSE = 7.65 t/ha, NRMSE = 25.02 % during tuber growth stage; R2 = 0.60, RMSE = 7.37 t/ha, NRMSE = 24.13 % during starch accumulation stage). This study underscores the importance of considering environmental factors when constructing a reliable model for estimating potato yield at different spatial and temporal scales using remote sensing technology.

Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime

Land surface temperature (LST) is a fundamental parameter in global climate, environmental, and geophysical studies. Remote sensing is an essential approach for obtaining large-scale and frequently updated LST data. However, due to the wide field of view of remote sensing sensors, the observed LST with diverse view geometries suffers from inconsistency caused by the thermal radiation directionality (TRD) effect, which results in LST products being incomparable, especially during daytime. To address this issue and correct current off-nadir LSTs to nadir LSTs, a semi-physical time-evolved kernel-driven model (TEKDM) is proposed, which depicts multitemporal TRD patterns during the daytime. In addition, we employ a Bayesian optimization method to calibrate seven unknown parameters in the TEKDM. Validation results using the U.S. Climate Reference Network (USCRN) sites show that the RMSE (MBE) for GOES-16 and MODIS off-nadir LST products is reduced from 3.29 K (−2.0 K) to 2.34 K (−0.02 K), with an RMSE reduction of 0.95 K (29%) and a significant reduction in systematic bias. Moreover, the proposed method successfully eliminates the angular and temporal dependence of the LST difference between the satellite off-nadir LST and in situ nadir LST. In summary, this study presents a feasible approach for estimating the high-accuracy nadir LST, which can enhance the applicability of LST products in various domains.

Energy efficient coverage path planning for USV-assisted inland bathymetry under current effects: An analysis on sweep direction

Effective coverage path planning for USVs is essential to optimize the performance of bathymetric surveys. Traditionally, it is well recognized that the best sweep direction for coverage path planning is the parallel direction that finds the minimum span of the region of interest (ROI). However, the results in this paper first reveals that it is not always the case when the water current is involved. In this paper, we first formulate an energy consumption estimation model for USV and introduce a novel coverage path planning strategy. Then, we investigate the relationship between sweep direction and USV energy consumption under the influence of water currents, referred to as the E-β curve. We then systematically analyze the variations of the E-β curve under different shapes of ROIs, various water current attacking angles, and different coverage strategies. Finally, the model is extended to more realistic scenarios involving non-convex ROIs. The practicality of the model is validated USV semi-physical model. The results indicate that, under water currents, our proposed model allows for an optimal sweep direction that minimizes the energy consumption of USVs. This contrasts with traditional methods, which fail to achieve the optimal energy cost. The results hold significant implications for the design of USV mapping strategies in inland river.

Effects of Fuel Cell Size and Dynamic Limitations on the Durability and Efficiency of Fuel Cell Hybrid Electric Vehicles under Driving Conditions

In order to enhance the durability of fuel cell systems in fuel cell hybrid electric vehicles (FCHEVs), researchers have been dedicated to studying the degradation monitoring models of fuel cells under driving conditions. To predict the actual degradation factors and lifespan of fuel cell systems, a semi-empirical and semi-physical degradation model suitable for automotive was proposed and developed. This degradation model is based on reference degradation rates obtained from experiments under known conditions, which are then adjusted using coefficients based on the electrochemical model. By integrating the degradation model into the vehicle simulation model of FCHEVs, the impact of different fuel cell sizes and dynamic limitations on the efficiency and durability of FCHEVs was analyzed. The results indicate that increasing the fuel cell stack power improves durability while reducing hydrogen consumption, but this effect plateaus after a certain point. Increasing the dynamic limitations of the fuel cell leads to higher hydrogen consumption but also improves durability. When considering only the rated power of the fuel cell, a comparison between 160 kW and 100 kW resulted in a 6% reduction in hydrogen consumption and a 10% increase in durability. However, when considering dynamic limitation factors, comparing the maximum and minimum limitations of a 160 kW fuel cell, hydrogen consumption increased by 10%, while durability increased by 83%.

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

An FCN-based transfer-learning method for spatial infrared moving-target recognition

Spatial infrared (IR) moving-target sample data are difficult to collect; thus, they cannot be used for mass-training, and limit development and application of deep learning methods in spatial IR target recognition. This study proposes a transfer-learning method based on fully convolutional network (FCN) for spatial IR moving-target recognition. First, we built a set of semi-physical scale models of spatial IR moving targets and obtained IR radiation characteristics as target-domain data using actual shot data and image inversion. Simulation data of the spatial target were obtained using the IR radiation intensity model, and used as the source-domain data together with the public UCR dataset. We used the dynamic time warping distance to measure the similarity between datasets to select appropriate source-domain datasets. We used an FCN framework as the main classification framework with a large number of source-domain samples for training. Knowledge transfer and model fine-tuning facilitate target-domain learning using only a few samples to improve classification accuracy. The experimental results show that the proposed model achieved an accuracy rate of 78%, which increases by the 20% improvement compared to direct training on the target domain. This study verifies the effectiveness of transfer learning between spatial targets and related domains and provides a reference for application of deep learning in spatial situational awareness.

Feedforward PID Controller Design For a Parabolic Trough Solar Collector

Distributed parameter systems (DPS) are widely recognised in the process industry. An innovative feedforward control strategy for the DPS of a parabolic trough collector (PTC) has been developed with the aim of rejecting disturbances. This feedforward is designed on a transfer function based on a semi-physical model. This transfer function includes an irrational term that models the resonance dynamics in the system response. For validation, this approach is compared with a proportional-integral (PI) design and other existing feedforward designs in literature. The results show that the proposed approach significantly improves the system performance in the presence of disturbances addressing system dynamics at medium and high frequencies.

Comparison of Semi-Physical and Empirical Models in the Estimation of Boreal Forest Leaf Area Index and Clumping With Airborne Laser Scanning Data

Comparison of Semi-Physical and Empirical Models in the Estimation of Boreal Forest Leaf Area Index and Clumping With Airborne Laser Scanning Data

Phenomenological-based semi-physical model characterizing the central metabolic response of Escherichia coli to changes in batch culture temperature

This work presents a phenomenological-based semi-physical model characterizing the E. coli response to environmental temperature changes in batch cultures. The proposed model is based on a general description of the mass balance of the central metabolism and an energy balance that focuses on key parameters. This model can represent changes in specific growth rate and alteration to the mass flows of the central metabolism. Moreover, the model can make predictions regarding microorganism growth, respiratory rate, glucose consumption, acetate accumulation, and heat exchange between E. coli and its environment at several different temperatures. The fit and validation of the model was assessed against published data obtained under aerobic growth conditions in agitated tank bioreactor cultures. Our assessment of this model indicates that mass and energy balances can predict global changes in E. coli metabolism that are associated with different temperature conditions and differences in heat flow between bacteria and the environment. This model can be used to correlate environmental conditions with experimental bioreactor parameters and simulate microorganismal response during the design of temperature control systems for bioreactors and can even be used as a base model to represent heat-inducible E. coli strains used in recombinant protein production.

Can end-to-end data-driven models outperform traditional semi-physical models in separating 1-min irradiance?

As a crucial component of the model chain, which facilitates irradiance-to-power conversion during solar resource assessment and forecasting, separation modeling continues to draw attention in both academia and industry. However, when evaluating even the best separation model today, one can quickly recognize its limited accuracy compared to other energy meteorology models such as transposition models. The task of separating global horizontal irradiance into diffuse and beam components does not seem soluble by any derivative effort aimed at tweaking the existing semi-physical models. As a result, an appealing alternative is to consider end-to-end data-driven models, which have demonstrated predictive capability in scenarios where the volume of data is substantial and the interaction among variables is complex. This work discusses the separation of 1-min irradiance from a data-driven perspective. In this preliminary study, a total of 10 representative data-driven separation models are developed and compared to the state-of-the-art semi-physical models, using a comprehensive 1-min irradiance database that spans five years and covers numerous climate types. The average error of the data-driven models is found to be 15.2% to 22.6% lower than that of the semi-physical models for training locations and 7.9% to 17.6% lower for completely unseen locations. Data-driven models also have significantly lower standard deviations (up to 87.2% even for completely unseen locations), highlighting their robustness. In addition, this work provides a guideline for choosing between data-driven and semi-physical models based on data availability, application needs, computational resources, interpretability, and model adaptability. Furthermore, the study underscores the challenges in accurately predicting the diffuse fraction using available input features and indicates that the incorporation of additional weather-related variables and domain knowledge could enhance the performance of data-driven separation models.

A semi-physical thermodynamic transient rolling resistance model with nonlinear viscoelasticity

Rolling resistance dictates a large part of the energy consumption of trucks. Therefore, it is necessary to have a sound understanding of the parameters affecting rolling resistance. This article proposes a semi-physical thermodynamic tyre rolling resistance model, which captures the essential properties of rolling resistance, such as transient changes due to temperature effects and the strain-amplitude dependency of the viscous properties. In addition, the model includes cooling effects from the surroundings. Both tyre temperature and rolling resistance are obtained simultaneously in the simulation model for each time step. The nonlinear viscoelasticity in rubber is modelled using the Bergström–Boyce model, where the viscous creep function is scaled with temperature changes. The cooling of the tyre is considered with both convective and radiative cooling. Moreover, the article explains different material parameters and their physical meaning. Additionally, examples of how the model could be used in parameter studies are presented.

Semi-Physical Modeling of Soft Pneumatic Actuators With Stiffness Tuning

Abstract The inherent low stiffness in soft robots makes them preferable for working in close proximity to humans. However, having this low stiffness creates challenges when operating in terms of control and sensitivity to disturbances. To alleviate this issue, soft robots often have built-in stiffness tuning mechanisms that allow for controlled increases in stiffness. Additionally, redundant pneumatic manipulators can utilize antagonistic pressure to achieve identical positions under increased stiffness. In this paper, we develop a model to predict the stiffness and configuration of a pneumatic soft manipulator under different pressure inputs and external forces. The model is developed based on the physical characteristics of a soft manipulator while enabling efficient parameter estimation and computation. The efficacy of the modeling approach is supported via experimental results.

Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System

In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using the recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using real-time data from a hydropower turbine testing system. This led to semi-physical modeling, in which first principles and data-driven modeling are integrated to produce dynamic models for DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.

Advancements in remote sensing based crop yield modelling in India

Crop yield prediction at regional levels is an essential task for the decision-makers for rapid decision making. Pre-harvest prediction of a crop yield can prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. With the advent in digital world, various advanced techniques are employed for crop yield prediction. Remote Sensing (RS) data with its capability to provide the synoptic view of the Earth’s surface, has numerous returns in the area of crop monitoring and yield prediction. This study provides as a review for the advanced techniques for crop yield prediction in India with RS data as a base. The advanced techniques like RS based statistical yield modelling, machine learning based yield modelling, semi-physical yield modelling are described in the current study. The assessment of the studies related to integration of RS data in crop simulation model is also described in a section. All the techniques involved in the current study show significant improvements in crop yield prediction, enabling the development of new agricultural applications in India.