Inverse Modeling Method Research Articles

Magnetic and Gravity Surface Geometry Inverse Modeling of the TAG Active Mound

AbstractSeafloor massive sulfide deposits form in remote environments, and the assessment of deposit size and composition through drilling is technically challenging and expensive. To aid the evaluation of the resource potential of seafloor massive sulfide deposits, three‐dimensional inverse modeling of geophysical potential field data (magnetic and gravity) collected near the seafloor can be carried out to further enhance geologic models interpolated from sparse drilling. Here, we present inverse modeling results of magnetic and gravity data collected from the active mound at the Trans‐Atlantic Geotraverse hydrothermal vent field, located at 26°08′N on the Mid‐Atlantic Ridge, using autonomous underwater vehicle and submersible surveying. Both minimum‐structure and surface geometry inverse modeling methods were utilized. Through deposit‐scale magnetic modeling, the outer extent of a chloritized alteration zone within the basalt host rock below the mound was resolved, providing an indication of the angle of the rising hydrothermal fluid and the depth and volume of seawater/hydrothermal mixing zone. The thickness of the massive sulfide mound was determined by modeling the gravity data, enabling the tonnage of the mound to be estimated at 2.17 ± 0.44 Mt through this geophysics‐based, noninvasive approach.

Bayesian Inversion of Soil Hydraulic Properties from Simplified Evaporation Experiments: Use of DREAM(ZS) Algorithm

There is an increasing interest in identifying soil hydraulic properties from simplified evaporation experiments. However, the conventional simplified evaporation method includes a deficit due to using the linear assumption and not accounting for uncertainty in parameters. A suggested alternative method is assessing the parameter uncertainties through inverse modeling. We examined the combination of a Bayesian inverse method, namely, DREAM(ZS), and a numerical simulation model, namely, HYDRUS-1D, for parameter inversion with data in simplified evaporation experiments. The likelihood function could be conditioned only on pressure head observations (single-objective (SO)), or on both pressure head and evaporation rate observations (multi-objective (MO)), with different treatments on the top boundary condition. Three synthetic numerical experiments were generated in terms of the soil types of sand, loam and clay to verify the inverse modeling method. The MO approach performed better than the SO approach and linear assumption when the stage 1 evaporation rate was kept constant. However, the SO inversion was more robust when oscillations existed in the potential evaporation rate. Then, the SO inverse modeling was adopted to investigate two real experiments on loamy-sand soils and compared with the linear assumption. The linear assumption could be reliable for wet conditions with stage 1 evaporation but was not always useable for a relatively dry condition, such as that with stage 2 evaporation. The inverse modeling could be more successful in capturing the whole evaporation process of soils when both stage 1 and stage 2 were involved.

Robust Estimation of Absorbing Root Surface Distributions From Xylem Water Isotope Compositions With an Inverse Plant Hydraulic Model

The vertical distribution of absorbing roots is one of the most influential plant traits determining plant strategy to access below ground resources. Yet little is known of natural variability in root distribution since collecting field data is challenging and labor-intensive. Studying stable water isotope compositions in plants could offer a cost-effective and practical solution to estimate the absorbing root surfaces distribution. However, such an approach requires developing realistic inverse modeling techniques that enable robust estimation of rooting distributions and associated uncertainty from xylem water isotopic composition observations. This study introduces an inverse modeling method that supports the assessment of the root allocation parameter (β) that defines the exponential vertical decay of a plants’ absorbing root surfaces distribution with soil depth. The method requires measurements obtained from xylem and soil water isotope composition, soil water potentials, and sap flow velocities when plants’ xylem water is sampled at a certain height above the rooting point. In a simulation study, we show that the approach can provide unbiased estimates of β and its associated uncertainty due to measuring errors and unmeasured environmental factors that can impact the xylem water isotopic data. We also recommend improving the accuracy and power of β estimation, highlighting the need for considering accurate soil water potential and sap flow monitoring. Finally, we apply the inverse modeling method to xylem water isotope data of lianas and trees collected in French Guiana. Our work shows that the inverse modeling procedure provides a robust analytical and statistical framework to estimate β. The method accounts for potential bias due to extraction errors and unmeasured environmental factors, which improves the viability of using stable water isotope compositions to estimate the distribution of absorbing root surfaces complementary to the assessment of relative root water uptake profiles.

Improvement in phenol adsorption capacity on eco-friendly biosorbent derived from waste Palm-oil shells using optimized parametric modelling of isotherms and kinetics by differential evolution

Phenolic pollution is very common, and toxic and water-soluble compounds and their derivatives can have significant harmful effects on humans, aquatic life, and the environment. The adsorption method is the most efficient way of handling, but the high cost of biosorbents obstructs the feasibility of this approach. In this study, biomass waste derived from Palm-oil shells is synthesized as an eco-friendly biosorbent for phenol adsorption. A novel inverse modelling method based on differential evolution optimization (DEO) is used to estimate the isotherm and kinetic model parameters, which facilitates to identify the inherent mechanisms in the adsorption process for removing phenol from wastewater. The DEO based model parameters provides an accurate prediction that is very close to experimental data, thus resulting in higher regression coefficient, R2, and relatively low Pearson’s Chi-square, χ2& root mean square error (RMSE). Phenol adsorption found to be following Langmuir isotherm (R2 = 0.995; χ2 = 0.429; RMSE = 4.420) and Pseudo 2nd order kinetic model (R2 = 0.992; χ2 = 0.346; RMSE = 15.58). With a biosorbent size of 0.85 mm resulted in phenol removal efficiency of 98 %. For large scale industrial process, a design methodology is developed to estimate the amount of biosorbent (Palm-oil shell-based GAC) required to meet the desired phenol removal concentration.

Deep‐Learning‐Based Inverse Modeling Approaches: A Subsurface Flow Example

AbstractDeep‐learning has achieved good performance and demonstrated great potential for solving forward and inverse problems. In this work, two categories of innovative deep‐learning‐based inverse modeling methods are proposed and compared. The first category is deep‐learning surrogate‐based inversion methods, in which the Theory‐guided Neural Network (TgNN) is constructed as a deep‐learning surrogate for problems with uncertain model parameters. By incorporating physical laws and other constraints, the TgNN surrogate can be constructed with limited simulation runs and accelerate the inversion process significantly. Three TgNN surrogate‐based inversion methods are proposed, including the gradient method, the Iterative Ensemble Smoother method, and the training method. The second category is direct‐deep‐learning‐inversion methods, in which TgNN constrained with geostatistical information, named TgNN‐geo, is proposed as the deep‐learning framework for direct inverse modeling. In TgNN‐geo, two neural networks are introduced to approximate the random model parameters and the solution, respectively. In order to honor prior geostatistical information of the random model parameters, the neural network for approximating the random model parameters is first trained by using observed or generated realizations. Then, by minimizing the loss function of TgNN‐geo, the estimation of model parameters and the approximation of the model solution can be simultaneously obtained. Since the prior geostatistical information can be incorporated, the direct‐inversion method based on TgNN‐geo works well, even in cases with sparse spatial measurements or imprecise prior statistics. Although the proposed deep‐learning‐based inverse modeling methods are general in nature, and thus applicable to a wide variety of problems, they are tested with several subsurface flow problems. It is found that satisfactory results are obtained with high efficiency. Moreover, both the advantages and disadvantages are further analyzed for the proposed two categories of deep‐learning‐based inversion methods.

Inverse modelling of PV power prediction based on GA method

Traditional modelling methods are generally forward modelling, that is, the basic parameters of photovoltaic cells provided by manufacturers need to be known before in order to calculate the power output of photovoltaic cells. However, the traditional modelling method is no longer applicable to complex situations, such as if manufacturer parameters are not available, or if the original manufacturer parameters are inaccurate due to prolonged usage. In this study, a new inverse modelling method is proposed, which uses genetic algorithm (GA) to identify the internal parameters of photovoltaic cell equation and solve the equation by Gauss-Seidel iterative method. The experimental test was conducted in Wuhan, China. The results show the root mean square error is only 1.34V and 2.66V for two-day tests, which demonstrated the effectiveness of the proposed method.

Inverse Infiltration Modeling of Dike Covers Made of Dredged Material Using PEST and AMALGAM

A variety of studies recently proved the applicability of different dried, fine-grained dredged materials as replacement material for erosion-resistant sea dike covers. In Rostock, Germany, a large-scale field experiment was conducted, in which different dredged materials were tested with regard to installation technology, stability, turf development, infiltration, and erosion resistance. The infiltration experiments to study the development of a seepage line in the dike body showed unexpected measurement results. Due to the high complexity of the problem, standard geo-hydraulic models proved to be unable to analyze these results. Therefore, different methods of inverse infiltration modeling were applied, such as the parameter estimation tool (PEST) and the AMALGAM algorithm. In the paper, the two approaches are compared and discussed. A sensitivity analysis proved the presumption of a non-linear model behavior for the infiltration problem and the Eigenvalue ratio indicates that the dike infiltration is an ill-posed problem. Although this complicates the inverse modeling (e.g., termination in local minima), parameter sets close to an optimum were found with both the PEST and the AMALGAM algorithms. Together with the field measurement data, this information supports the rating of the effective material properties of the applied dredged materials used as dike cover material.

Методи та інформаційні системи ідентифікації джерел радіоактивних атмосферних за-бруднень шляхом оберненого моделювання

The paper reviews the methods for identifying an unknown source of pollution by inverse mod-eling and information systems for air pollution forecasting and analysis. Several different for-eign and Ukrainian air pollution forecasting systems, such as the European Union's Nuclear Emergency Response System RODOS, have been developed on the basis of atmospheric transport models. However, the key data that determine the quality of forecasting in such sys-tems are the characteristics of the emission sources. In the case of detection of pollution from an unknown emission source, there should be performed inverse simulation. The use of the RODOS system, as well as other existing forecasting systems for such a task is possible but it requires multiple manual start of calculations of atmospheric transfer models in the reverse mode. Presented in the paper results of the application of inverse modeling methods during ra-diation incidents of the last decade demonstrate that modern methods of inverse modeling are sufficiently developed to set the task of automating inverse modeling in information systems for air pollution analysis and forecasting. Even though these methods not always can exactly identify the source of emissions due to the lack of measurements and poor conditioning of the inverse atmospheric transport problem, their application always leads to a significant reduction (by an order of magnitude or more) in the search for unknown sources compared to the detec-tion of pollutants. At present, in the existing forecasting systems the methods of inverse model-ing are only partially automated, namely for the case of known location and unknown emissions of the source of pollution. Therefore, this paper proposes the architecture of the future system for identifying unknown sources of emissions by inverse modeling.

Methodologies for Measuring Microbial Methane Production and Emission from Soils—A Review

Methane is an important greenhouse gas. Prediction of its long-term effect on climate changes requires understanding of the sources of its release into the atmosphere. The present work aimed to review the most widely used modern methods for measuring microbial production and emission of methane in soils, and to assess their applicability to various microbial ecosystems. A classification of methods for measuring methane production in microbial soil ecosystems is proposed, based on the basic physical and mathematical approaches to data processing for each method. Under laboratory conditions, methane production is determined by soil samples incubation and layered mass balance methods, which makes it possible to investigate the methanogenic potential of a microbial community for subsequent simulation of microbial processes; these methods, however, cannot be directly extrapolated to natural ecosystems. To determine methane production in field experiments, gradient methods are used, as well as a deep soil chamber technique developed for wetlands. Methods for measuring methane emission are described, as well as their advantages and limitations. While traditional chamber techniques are simple and therefore widely used, their implementation is limited by such factors as changes in environmental conditions caused by using the chamber, small spatial scales, and difficulties in chamber installation in some ecosystems. More recently developed micrometeorological methods are free of these limitations and provide for proper estimations of gas emissions from ecosystems even if these emissions are heterogeneous in space and time. To estimate emissions from such specific objects as landfills and livestock complexes or at large scales (regional or even global), the inverse modeling method is used.

A Novel Versatile Decoupling Structure and Expedited Inverse-Model-Based Re-Design Procedure for Compact Single-and Dual-Band MIMO Antennas

Multiple-input multiple-output (MIMO) antennas are considered to be the key components of fifth generation (5G) mobile communications. One of the challenges pertinent to the design of highly integrated MIMO structures is to minimize the mutual coupling among the antenna elements. The latter arises from two sources, the coupling in the free space and the coupling currents propagating on a ground plane. In this paper, an array of H-shaped parasitic patches is proposed as a decoupling structure for compact MIMO antennas to reduce propagation of the coupling currents on a shared ground plane. The proposed decoupling structure is generic, and it can be applied to different antenna configurations as demonstrated in the work. Furthermore, it is employed to develop a new high-performance compact dual-band MIMO structure featuring acceptable level of element coupling at both operating frequencies. The design is validated both numerically and experimentally. The mutual coupling levels are less than -17 dB and -20 dB, with the total efficiency of 89% and 90%, and the realized gain of 6.6 dB and 7 dB at the two resonant frequencies of 5 GHz and 6 GHz, respectively. Topological complexity of the compact MIMO systems featuring elaborated decoupling structures, a large number of geometry parameters, as well as the necessity of handling multiple performance figures, constitute the major challenges of antenna design, in particular, its re-design for various specifications. To alleviate these difficulties, the paper also provides a procedure for rapid geometry scaling of the dual-band MIMO antennas. Our approach is based on inverse surrogate modeling methods, and results in numerically-derived expressions that enable a precise control over the operating antenna bands within broad ranges thereof (from 4 GHz to 8 GHz for the lower band, and from 1.1 to 1.3 ratio of the upper to lower operating frequency). The aforementioned procedure is accompanied by an optimization-based design refinement scheme. A practical utility of the procedure is corroborated using multiple verification case studies as well as physical measurements of the antenna designed for the exemplary set of performance specifications.

Analysis of Spray Uniformity of Sprayers Based on Deep Belief Network

Improving spray quality has a important practical significance for controlling the drift of harmful substances, increasing the effective utilization rate of pesticides and reducing environmental pollution. The influence of factors on spray quality is analyzed, and the nozzle wear is regarded as one of the influencing factors for the first time in this paper. It also constructs the relationship between spray distribution uniformity and influencing factors Model by proposes a soft measurement method based on deep belief network. Besides, when the distribution coefficient of variation and another influencing factors is given, the pressure value corresponding to them can get by neural network inverse modeling method, And the pressure can be used as the controlled variable in the spraying process. Compared to BP, the DBN model has stronger nonlinear mapping capabilities, higher prediction accuracy, and high practical value.

Effect of Proton-Pump Inhibitors on Glucose and Insulin Metabolism on Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Some studies have revealed an improvement in glucose metabolism after proton-pump inhibitors (PPI) therapy; however, this evidence is inconclusive and limited. The study aimed to examine the effect of PPI on glucose and insulin metabolism in patients with type 2 diabetes through a systematic review and meta-analysis. Only randomized controlled trials evaluating the impact of PPI on glucose or insulin concentrations in type 2 diabetes were searched in PubMed-Medline, SCOPUS, Web of Science and Google Scholar databases. A meta-analysis was conducted using a random-effects model and generic inverse variance method. Sensitivity analysis was performed using the leave-one-out method. Meta-analysis revealed no significant effect of PPI intervention on fasting glucose (mean difference [MD] -11.42 [95% CI, -29.68 to 6.83], I2 = 80%, p = 0.22), fasting insulin (MD 1.51 [95% CI, -0.36 to 3.37], I2 = 32%, p = 0.11), HOMA-IR (MD -0.16 [-0.98 to 0.65], I2 = 0%, p = 0.70), HOMA-β (MD 19.97 [-21.59 to 61.52], I2 = 71%, p = 0.35), and HbA1c concentrations (MD -0.34 [-0.99 to 0.31], I2 = 89%, p = 0.30). The treatment with PPI, in the short term, had no significant effects on glucose and insulin metabolism in patients with type 2 diabetes.

Ultrasonic monitoring of pipe wall interior surface temperature

Accurate temperature measurement is a crucial aspect of structural health monitoring and prognosis. Conventional temperature measurement devices are either incapable of measuring subsurface temperatures in solids or need to be invasively installed. This study investigates the use of an ultrasonic technique for non-invasive measurement of subsurface temperatures in steel components; the temperature of a point on an inaccessible surface is inferred using a time-of-flight measurement from a transducer placed on an opposing accessible surface. Two different inversion approaches are presented, one named the assumed distribution method and the other named the inverse thermal modelling method. The robustness and accuracy of the two ultrasonic temperature inversion methods are quantitatively assessed via simulations and controlled experiments. It was found that both the assumed distribution and inverse thermal modelling methods demonstrate short thermal response times and are able to track the temperature evolution of inaccessible surfaces. A series of experimental studies show that in the presence of a 15°C difference between the accessible and inaccessible surfaces, the inaccessible surface temperature is typically measured to within better than 2°C with respect to a resistance temperature detector reference measurement. Additionally, the article compares the measurement performance achieved using a deployable electromagnetic acoustic transducer and a permanently installed piezo-electric PZT transducer. The time-of-flight measurements taken using the electromagnetic acoustic transducer system had higher random noise than the PZT system (standard deviations of 0.42 and 0.016 ns, respectively), subsequently leading to higher random noise in the temperature estimates.

Using search-constrained inverse distance weight modeling for near real-time riverine flood modeling: Harris County, Texas, USA before, during, and after Hurricane Harvey.

Flooding poses a serious public health hazard throughout the world. Flood modeling is an important tool for emergency preparedness and response, but some common methods require a high degree of expertise or may be unworkable due to poor data quality or data availability issues. The conceptually simple method of inverse distance weight modeling offers an alternative. Using stream gauges as inputs, this study interpolated stream elevation via inverse distance weight modeling under 15 different model input parameter scenarios for Harris County, Texas, USA, from August 25th to September 15th, 2017 (before, during, and after Hurricane Harvey inundated the county). A digital elevation model was used to identify areas where modeled stream elevation exceeded ground elevation, indicating flooding. Imagery and observed high water marks were used to validate the models' outputs. There was a high degree of agreement (between 79 and 88%) between imagery and model outputs of parameterizations visually validated. Quantitative validations based on high water marks were also positive, with a Nash-Sutcliffe efficiency of in excess of .6 for all parameterizations relative to a Nash-Sutcliffe efficiency of the benchmark of 0.56. Inverse distance weight modeling offers a simple, accurate method for first-order estimations of riverine flooding in near real-time using readily available data, and outputs are robust to some alterations to input parameters.

Electromagnetic evidence that benign epileptiform transients of sleep are traveling, rotating hippocampal spikes

ObjectiveBenign epileptiform transients of sleep (BETS) have a unique voltage topography and a posteriorly propagating, inferiorly rotating diphasic EEG pattern. The source generators of BETS have not been definitively identified. We aimed to clarify the cerebral localization of BETS using MEG and electromagnetic source imaging (EMSI). MethodsWe analyzed BETS recorded with simultaneous MEG and EEG in four patients with epilepsy. Magnetic source imaging (MSI) and EMSI using equivalent current, single moving and rotating dipole inverse models was performed on averaged BETS potentials. MEG beamforming was performed in one case with abundant BETS. ResultsMSI and EMSI revealed hippocampal dipole source maxima in all cases, with current flow direction rotating from inferomedial to superomedial or superolateral between the first and second BETS peaks. Moving dipole analyses revealed spatiotemporal propagation along the anterior-posterior hippocampal axis and concomitant electromagnetic field rotation. Beamformer source reconstruction revealed an identical hippocampal localization. ConclusionsConverging evidence from different electromagnetic inverse modeling methods indicates that BETS are traveling, rotating hippocampal spikes, whose diphasic waveform is due to back and forth propagation along the anterior-posterior axis of the hippocampus. SignificanceThe hippocampal localization and longitudinal, rotating propagation pattern of BETS raises the possibility of a sleep-related functional role for these hippocampal spikes.

IDENTIFIKASI PATAHAN DI DESA TAMAN AYU KECAMATAN GERUNG KABUPATEN LOMBOK BARAT MENGGUNAKAN METODE GEOMAGNET

This research aimed to identify the presence of faults in Taman Ayu Village, Gerung District, West Lombok Regency by mapping the magnetic field. The fault outcrops found near this location becomes the reason to suspected the fault existence that reaching residents, farming area and access roads, so it is necessary to study the type, depth and direction of the fault. Measurements were made on 11 lines with a length of each line is 3 km. Each line of measurement is 300 m apart from each other and the distance between measurement points is 50 m, 100 m, and 200 m depending on the distance from the target location. Separation between regional anomalies and residual anomalies using the upward continuation method at an altitude of 1000 m. Qualitative interpretation of residual anomalies results in low anomalies with values from -890.9 nT to 897.3 nT. Quantitative interpretation of the five cross sections by the inverse modeling method with Oasis Montaj 6.4.2 software resulting subsurface geological structures in the form of normal fault. This normal fault is at a depth of 30 m to 480 m which is composed of layers of silt, sandy silt, sand, clay and breccia. The fault passes through Gapuk Village, Taman Ayu and Kebon Ayu and passes through the highway in Northeast – Southwest direction

Machine learning enhanced inverse modeling method for variable speed air conditioning systems

Abstract Various faults may occur in the air conditioning systems due to improper installation and poor maintenance. Various fault detection and diagnosis methods have been developed, which need lots of data to evaluate the protocols. However, experimental data is usually not sufficient. The FDD protocols especially machine learning based can easily overfit the limited experiment data. It may be not satisfied for the real applications because of wider range of operation. The machine learning enhanced inverse modeling method is presented to generate the simulation data under various conditions of different scenarios. The clustering algorithm is used to classify the training data reasonably balancing the weights of different conditions. The particle swarm optimization (PSO) is developed to obtain the global optimal estimation of model parameters under wider operation conditions. The experimental data of both variable and constant speed systems are used to validate clustering-PSO enhanced algorithm, which shows acceptable capability and accuracy of prediction.

The ARM Radar Network: At the Leading Edge of Cloud and Precipitation Observations

AbstractImproving our ability to predict future weather and climate conditions is strongly linked to achieving significant advancements in our understanding of cloud and precipitation processes. Observations are critical to making these advancements because they both improve our understanding of these processes and provide constraints on numerical models. Historically, instruments for observing cloud properties have limited cloud–aerosol investigations to a small subset of cloud-process interactions. To address these challenges, the last decade has seen the U.S. DOE ARM facility significantly upgrade and expand its surveillance radar capabilities toward providing holistic and multiscale observations of clouds and precipitation. These upgrades include radars that operate at four frequency bands covering a wide range of scattering regimes, improving upon the information contained in earlier ARM observations. The traditional ARM emphasis on the vertical column is maintained, providing more comprehensive, calibrated, and multiparametric measurements of clouds and precipitation. In addition, the ARM radar network now features multiple scanning dual-polarization Doppler radars to exploit polarimetric and multi-Doppler capabilities that provide a wealth of information on storm microphysics and dynamics under a wide range of conditions. Although the diversity in wavelengths and detection capabilities are unprecedented, there is still considerable work ahead before the full potential of these radar advancements is realized. This includes synergy with other observations, improved forward and inverse modeling methods, and well-designed data–model integration methods. The overarching goal is to provide a comprehensive characterization of a complete volume of the cloudy atmosphere and to act as a natural laboratory for the study of cloud processes.

Determining Position and Orientation of a 3-Wheel Robot on a Pipe Using an Accelerometer

Accurate positioning of robots on pipes is a challenge in automated industrial inspection. It is typically achieved using expensive and cumbersome external measurement equipment. This paper presents an Inverse Model method for determining the orientation angle $(\alpha $ ) and circumferential position angle $(\omega $ ) of a 3 point of contact robot on a pipe where measurements are taken from a 3-axis accelerometer sensor. The advantage of this system is that it provides absolute positional measurements using only a robot mounted sensor. Two methods are presented which follow an analytical approximation to correct the estimated values. First, a correction factor found though a parametric study between the robot geometry and a given pipe radius, followed by an optimization solution which calculates the desired angles based on the system configuration, robot geometry and the output of a 3-axis accelerometer. The method is experimentally validated using photogrammetry measurements from a Vicon T160 positioning system to record the position of a three point of contact test rig in relation to a test pipe in a global reference frame. An accelerometer is attached to the 3 point of contact test rig which is placed at different orientation $(\alpha $ ) and circumferential position $(\omega $ ) angles. This work uses a new method of processing data from an accelerometer sensor to obtain the $\alpha $ and $\omega $ angles. The experimental results show a maximum error of 3.40° in $\alpha $ and 4.17° in $\omega $ , where the $\omega $ circumferential positional error corresponds to ±18mm for the test pipe radius of 253mm.

Continuous Casting Secondary Cooling: Laboratory Heat Transfer Measurements and Accuracy Considerations by Comparison with the Real Situation

In secondary cooling of continuous casting, it is very important to know the cooling heat flux for the actual spray cooling situation with respect to various parameters like the local position, the nozzle types, distances, and the water and air flow rates, to be able to control the cooling conditions precisely. As heat flux measurements on a casting machine are too challenging, experimental laboratory test rigs are designed and used for measurements by different research groups. Therefore, metal probes of different dimensions and materials are heated up to the desired temperature and then exposed to spray nozzles. The heat flux is usually measured by temperature sensors immersed in the probe body, and then determined from the measured temperature using inverse modelling methods. Herein, the differences between the real and laboratory conditions are focused on using a mathematical heat transfer simulation model. The influence of strand surface temperature, nozzle spray water flow conditions, and Leidenfrost effect are pointed out. A procedure to use heatflux data measured on a test rig for cooling control on a real caster despite the different conditions is proposed.