Prediction Of Flow Rate Research Articles

Development of refrigerant flow rate correlation for electronic expansion valve using valve flow coefficient

The mass flow rate of R32 through two different designs of electronic expansion valves (EEVs) was experimentally measured. A total of 173 mass flow rate data were produced with EEV1 (D1 = 2.40 mm) and EEV2 (D2 = 1.65 mm). Although there are numerous empirical correlations available for prediction of mass flow rate through an electronic expansion valve (EEV), previous empirical correlations failed to predict these data with reasonable margins of error. This is because those correlations only incorporated geometry parameters related to the EEV orifice and needle, even though entire geometry of flow passage inside EEV affects flow rate. An empirical correlation was developed based on the experimental data. The entire geometry of the flow passage inside the EEV was represented by a single parameter known as the valve flow coefficient. The new empirical correlation is in good agreement with the experimental data of EEV1 and EEV2, with average deviations of 5.84% and 5.87%, respectively, even though only valve flow coefficient was used to represent EEV geometry.

Performance model evaluation of turbine flow meter in vertical gas-liquid two-phase flows

Aiming at the need for flow measurement of gas-liquid flows in domestic gas well production, this paper proposes a measurement method based on the combination of the turbine flow meter (TFM) and a rotating electric field conductance sensor (REFCS). In experiments, the REFCS is used for the measurement of the gas holdup. To verify the applicability of the TFM models investigated in the previous study, for the modeling part, the mass, momentum and torque models are evaluated in vertical upward gas-liquid two-phase flows. In our model test, the meter factor model of TFM considers the effects of the slip ratio between the gas and liquid phases and flow patterns. In particular, the gas holdup involved in calculating the slip ratio in the model evaluation is obtained from the REFCS measurements. Model test results show the torque model has better volumetric flow rate prediction accuracy than the mass and momentum models. In the present study, the ranges of the liquid and gas phases are Qw = 2–30 m3/d and Qg = 1–16 m3/d, it was found that the average absolute deviation (AAD) in the predicted volume flow rate is equal to 1.23 m3/d and the average absolute percentage deviation (AAPD) is equal to 7.69%. The evaluated results presented in this paper will allow better estimates of the volumetric flow rates of gas-liquid flows based on the combined TMF and REFCS measurements during the monitoring of gas well production.

Multiphase Flowrate Measurement With Multimodal Sensors and Temporal Convolutional Network

Accurate multiphase flow measurement is vital in monitoring and optimizing various production processes. Deep learning has as of late arose as a promising approach for assessing multiphase flowrate dependent on various customary flow meters. In this paper, we propose a multi-modal sensor and Temporal Convolution Network (TCN) based method to predict the volumetric flowrate of oil/gas two-phase flows. The volumetric flowrates of the liquid and gas phase vary from 0.96 - 6.13 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{{3}}$ </tex-math></inline-formula> /h and 5.5 - 121.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{{3}}$ </tex-math></inline-formula> /h, respectively. The multi-modal sequential sensing data are simultaneously collected from a Venturi tube and a dual-plane Electrical Capacitance Tomography (ECT) sensor in a pilot-scale multiphase phase flow facility. The reference data are derived from the single-phase flowmeters. Z-score and First-Difference (FD) data pre-processing methods are employed to manipulate the collected instantaneous time series multi-modal sensing data. The pre-processed data are utilized for training the TCN model. Experimental results reveal that the TCN model can effectively predict the multiphase flowrate based on the multi-modal sensing data. The results provide guidance on data pre-processing methods for multiphase flowrate estimation and demonstrate the effectiveness of combining multi-modal sensors and TCN for multiphase flowrate prediction under complex flow conditions.

Evaluation of Mathematical Models for CO2 Frost Formation in a Cryogenic Moving Bed

Moving bed heat exchangers (MBHE)s are used in industrial applications including waste heat recovery and the drying of solids. As a result, energy balance models have been developed to simulate the heat transfer between a moving bed and the gas phase. Within these energy balance models, phase change of components within the gas phase has not been considered as the liquefaction or desublimation of the gas phase does not occur in typical industrial applications. However, available energy balance models for cryogenic CO2 capture (CCC) have only focused on fixed packed beds. The development of a suitable energy balance model to predict the energy duties for MBHEs that include phase change would be beneficial for CCC applications. This work investigated the development of moving bed energy balance models for the design of moving bed columns that involve phase change of CO2 into frost, using existing models for MBHEs and fixed-bed CCC capture. The models were evaluated by comparison with available moving bed experimental work and simulated data, predicted energy duty requirements and bed flow rates from the suggested moving bed CCC models to maintain thermal equilibrium. The comparisons showed a consistent prediction between the various methods and closely align with the available experimental and simulated data. Comparisons of energy duty and bed flow rate predictions from the developed energy balance models with simulated cases for an oil-fired boiler, combined cycle gas turbine (CCGT) and biogas upgrading showed energy duty requirements for the gas phase with a proximity of 0.1%, 20.8%, and 3.4%, respectively, and comparisons of gas energy duties from developed energy balance models with energy duties derived from experimental results were compared with a proximity of 1.1%, 1.1% and 0.6% to experimental results for CO2 % v/v concentrations of 18%, 8% and 4%.

Forecasting of volumetric flow rate of Ergene river using machine learning

Today, the significance of the estimation of physical parameters has considerably increased; for example, the prediction of water flow rate (WFR) is one of the types that will gain a substantial importance among the others. The predictions of WFR of rivers play a prominent role in the plans and constructions of new water dams, or to operate the ones that were formerly constructed. In this study, a variety of machine learning algorithms have been suggested in the estimations of one-ahead instantaneous measurement of river WFR. In this regard, four different prediction algorithms including long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM), ANFIS with subtractive clustering (SC), and the ANFIS with grid partition (GP) were initially trained and secondly tested. The study region was selected as Inanli measurement station, which is located on the Ergene River. A cumulative of 134 different models were formed using these algorithms. Instantaneous WFR predictions in terms of WFR data have shown that ANFIS-FCM and ANFIS-SC had given the best statistical error outcomes. Accordingly, the values of 0.34 m3/s, 0.72 m3/s, and 0.9728 have been found as a result of machine learning establishment corresponding to statistical accuracy results of MAE, RMSE, and R values, respectively. Namely, it has been concluded that error values of the ANFIS-FCM and ANFIS-SC computations have outcome to be sufficiently good. Also, it was concluded and shown in the current study that both tools of the ANFIS can be two efficacious methods to WFR forecasting.

Study of the effect of virtual mass force on two-phase critical flow

Abstract Critical (choked) flow is a highly concerning phenomenon in safety analysis for nuclear energy. The discharge mass flow rate prediction is crucial for engineering design and emergency response in case of nuclear accidents. Unfortunately, the critical flow is difficult to predict especially when the two-phase flow exists. The accuracy is based on a deeper understanding of the complex phenomenon of critical flow. The influence of virtual mass force on the two-phase critical flow was seldom concentrated on owing to the lack of suitable critical flow models for studies in detail. This study is based on a developed 6-equation two-phase critical flow model. It is confirmed that the virtual mass force contributes to the stability and convergence of the critical flow simulation and it will impact not only the critical mass flux but also the thermal hydraulic parameters along the discharge duct. The magnitude depends on the geometry of the discharge duct and the upstream condition. It is larger when the duct is longer and the pressure is lower. Furthermore, the virtual mass force for each flow regime was studied in detail with a sensitivity study. The results show that the most sensible condition for the virtual mass force is annular flow along a long tube under relatively low pressure. The future work is to develop a correlation of virtual mass force for critical flow specifically since the correlations in the literature were developed under general two-phase flow process conditions.

Using Machine Learning for Loss Prediction in a Hybrid Meanline Modeling Method to Deliver Improved Radial Turbine Performance Prediction

Abstract Low fidelity modeling approaches remain attractive due to an unrivaled ability to predict full turbine performance maps quickly compared to high-fidelity approaches such as computational fluid dynamics (CFD), especially in the preliminary design process. As improvements in performance on a component level approach a point of diminishing returns, the ability to efficiently optimize the complete charging system for a given duty is a topic attracting significant research interest. In the case of turbocharging applications, existing engine and powertrain simulations require turbine maps to calculate the turbine performance, which are usually obtained from experimental testing. Unfortunately, the need for extrapolation is unavoidable because of the limited range of testing data available, leading to inaccuracies especially at off-design conditions. To enable intensive modeling and optimization of complete vehicle powertrains for different drive cycles, the current piece of work seeks to combine the advantages of machine learning techniques and physical meanline modeling to facilitate faster, more accurate predictions of complete turbocharger maps. This paper presents a novel methodology for turbocharger turbine rotor and nozzle performance prediction based on hybrid modeling. The turbine rotor and nozzle were parameterized to conduct CFD simulations for a wide variety of turbine geometries, which were used to form a database to train an artificial neural network (ANN). The predicted losses provided by the ANN were then utilized in the meanline code, substituting for the conventional empirical loss models. As well as removing the need for empirical loss models, modifications were undertaken to the meanline approach to further enhance modeling accuracy. First, in order to accurately characterize the stage mass flow capacity, the losses occurring in the nozzle and rotor were subdivided into those occurring before and after the throat. A second novel aspect is that the aerodynamic blockage level at the rotor throat was implemented as a variable rather than a constant value. By training the ANN to predict the variation of blockage with geometry and operating condition, a more accurate depiction of the changing secondary flow fields could be achieved. The capabilities of the hybrid meanline modeling method were evaluated on several unseen test cases. The resulting predictions of efficiency and mass flowrate demonstrated strong correlation with CFD results and experimental test results. The hybrid meanline modeling method therefore displays great potential in wide range radial turbine performance prediction with enhanced accuracy in comparison to traditional approaches.

Mass flow characteristics of CO2 operating in a transcritical cycle flowing through a needle expansion valve in a direct-expansion solar assisted heat pump

An expansion device is one of the key components of heat pump systems. Although in the literature there are several studies that analyze the effect of opening the expansion valve on heat pumps, none of them reported this effect for heat pumps that operate with solar energy. In this study, the mass flow rate characteristics through an expansion device in a direct-expansion solar assisted heat pump (DX-SAHP) operating with CO2 is investigated. With experimental data, a new correlation is proposed. A statistical method is used to determine the dimensionless Π-groups that most influences the mass flow rate. The parameters selected to compose the new correlation are: outlet and inlet pressures of the expansion device, geometric (cross-section area) and fluid viscosity. The proposed correlation shows good agreement with the measured data with average and standard deviations of 0.77% and 10.4%, respectively. The maximum relative deviation of all predictions of mass flow rate is less than ±30% and approximately 90% of mass flow rate experimental data are within ±20% of the predictions. In addition, for a same needle expansion valve opening, the mass flow rate increases with the augment in the solar radiation flux.

Measurement of Liquid–Liquid Flows Using Turbine Flowmeter and Conductance Sensor With Multiheight Electrodes in Vertical Pipes

Liquid–liquid two-phase flows are common industrial processes in chemical, petroleum, and other related fields. It is a great challenge for the flow measurement using the turbine flowmeter (TFM) in low-velocity liquid–liquid flows due to the serious slippage effect between the oil and water phases. The complex physical discrepancies between the two phases make the response of TFM vastly different from that in single-phase flow. This study proposes a system combining TFM and conductance sensors with multiheight electrodes (CSMHEs) to determine liquid–liquid two-phase flows. This study investigates the effects of phase distribution and slippage on the wheel hub drag torque. The model of wheel hub drag torque in different flow patterns is established, and a new variable meter factor model of TFM in liquid–liquid flows is constructed. Experiments in 20-mm simulated well reveal that the TFM can make accurate predictions of total flow rate and enable online measurements of individual phase superficial velocities. The absolute average percentage deviations (AAPDs) of superficial water and oil velocities are 2.01% and 7.24%, respectively.

How to accurately predict nanoscale flow: Theory of single-phase or two-phase?

Accurate evaluation and recognition of nanoscale flow is the premise of the extension of classical theories of fluid mechanics to nanoscales. Despite the widely reported nonuniform characteristics of nanoconfined fluids, nanoscale flow is still considered as a single-phase flow in general, resulting in large deviations in theoretical predictions of velocity profile and flow rate. Considering the significant characteristics of a two-phase flow in nanoscales and the similarity between nanoscale flow and gas–liquid two-phase annular flow, we put forward a novel viewpoint that nanoscale flows should be described based on the theory of a two-phase flow. To support this idea, nanoscale flows under different fluid types, densities, temperatures, fluid–solid interactions, and driving pressures are extensively tested using molecular dynamics simulations. The results demonstrate that nanoscale flows can be divided into an adsorption phase and a bulk phase, and the characteristics of a two-phase flow are especially obvious under low fluid density, strong fluid–solid interaction, and low fluid temperature. The reasonability is further demonstrated by systematically analyzing the interphase density difference, interphase velocity difference, interphase mass exchange, and interfacial fluctuation, which are typical characteristics of a two-phase flow at macroscales. Finally, we present a series of theoretical descriptions of nanoscale flow from the perspective of a two-phase flow. By adopting different viscosity and density in the adsorption phase and bulk phase, the new model can better capture the physical details of nanoscale flow, such as velocity distribution and flow rate.

A general design procedure for gas–liquid Taylor flow T-junction microreactors

A gas flow rate prediction model under constant pressure operation and a cross-device energy efficiency prediction model are proposed. A Taylor flow T-junction microreactor design procedure is proposed.

Modeling subcritical multi-phase flow through surface chokes with new production parameters

The produced hydrocarbons from underground reservoirs must eventually pass through surface chokes installed to control the surface flow rate at an optimum value, which should regularly be checked against the recommendations of the production engineers to prevent problems such as water coning. Accurate prediction of the surface flow rate is, therefore, crucial as it will lead to fulfilling the development plan goals of the reservoir and production optimization. In this regard, many correlations have been developed to predict the flow rate through surface choke and most of them being developed from only one dataset gathered from a single reservoir, hence with limited prediction capability and high error. Furthermore, these correlations predict the oil flow rate only as a function of wellhead pressure, gas-oil ratio, and choke size. In this study, two machine learning techniques are used to develop models for better prediction of the multi-phase flow rate for the oil wells using two new parameters of basic sediment and water (BS&W) and fluid temperature which were overlooked previously. A total of 182 production tests were utilized in developing these models which are covering a wide range of data. Graphical and statistical approaches are utilized to compare the forecasted values against the field data. Furthermore, absolute error is used as a statistical approach to assess the developed models based on machine learning in comparison to conventional correlations available in the published literature. The findings illustrate that an acceptable relation exists between the field data and predicted values with coefficients of determination equal to 0.9840 and 0.9706 for artificial neural network (ANN) and least squares support vector machine coupled simulated annealing (LSSVM-CSA), respectively, based on total datapoints. The results from this study will greatly assist petroleum engineers to have particular estimations of liquid flow rates from wellhead chokes.

Application of GA-BPNN on estimating the flow rate of a centrifugal pump

Pumps consume nearly 8% of global electricity as the essential equipment for liquid transportation. A practical method for improving centrifugal pump energy efficiency is accurately predicting and controlling the pump operation status. However, current estimation methods for sensorless flow rate prediction have a significant error at low flow rate conditions. This study adds valve opening as the estimation model input variable, including motor shaft power and speed, to form a back-propagation neural network (BPNN) on an asynchronous motor-driven multistage centrifugal pump. By optimizing the initial weights and thresholds of BPNN, a GA-BPNN model was proposed to improve the prediction accuracy by using a genetic algorithm (GA). The results indicate that, with the addition of the valve opening as an input variable, the accuracy of BPNN-VO and GA-BPNN prediction improves significantly more than BPNN-NVO. Furthermore, the GA-BPNN model produces a significantly lower mean square error (MSE) and root mean square error (RMSE) than the original BPNN model. According to the experimental comparison and analysis, the absolute error of GA-BPNN between predicted flow rate and measured flow rate is less than 0.3 m3/h, the average relative error is less than 2%, and the relative error of low flow rate is less than 5%. This GA-BPNN estimation model significantly improves the accuracy of flow rate prediction, especially at small flow rates, and extends the scope of centrifugal pumps’ monitoring and control technology without flow sensors.

Evaluation and quantification of compressor model predictive capabilities under modulation and extrapolation scenarios

Testing and evaluation of select semi-empirical and black-box compressor models is carried out to quantify performance in modulation (variable speed), extrapolation, and additionally, variable superheat scenarios. Three representative literature models and an artificial neural network (ANN) model are benchmarked against the industry standard AHRI model. A methodology quantifying model performance, compared against experimental data, in said scenarios is presented. High-fidelity test data taken from either a hot-gas bypass load stand or compressor calorimeter. Scroll, screw, reciprocating, and spool compressor technologies were collected with R410A, R1234ze(E), R134a, and R32 refrigerants totaling 434 experimental points. Data is divided into training, extrapolation, variable speed, and variable superheat data splits to examine model performance. Mean Absolute Percentage Error (MAPE) is computed for mass flow rate and power after training models with training data and evaluating them against the other data splits. Two literature models are true semi-empirical formulations while the other, the ANN, and AHRI model are more empirical in nature. Neither semi-empirical model predicted all compressors. When the compressor type is predicted, the semi-empirical models yield MAPE’s less than 8%, 5%, and 4% for mass flow rate and power prediction in extrapolation, modulation, and variable superheat scenarios, respectively. The exception is the Popovic and Shapiro model performing at 21% MAPE in variable superheat power prediction for the spool compressor with R1234ze(E). The ANN showed highest errors of 9.3%, 12%, and 17% in extrapolation, modulation, and variable superheat scenarios, respectively. All models outperformed the AHRI model by several orders of magnitude in these scenarios.

An Efficient Quantification Method Based on Feature Selection for High-Dimensional Uncertainties of Multistage Compressors

Abstract Multistage compressors are widely used in aero-engines, gas turbines, and industrial compressors. However, many inevitable geometric/condition uncertainties will lead to low manufacturing qualified rate and severe performance degradation of multistage compressors. There might be hundreds of uncertainty variables simultaneously, so the “curse of dimensionality” problem has seriously constrained the uncertainty quantification (UQ) study in multistage compressors. In this paper, a feature selection method based on interpretable machine learning and bidirectional search is developed to reduce the dimensionality in a multistage compressor UQ study. The method is applied to a three-stage compressor and reduces the uncertainties dimensionality from 292 to 41 and 66 for mass flow rate and efficiency prediction. Consequently, the required sample size is reduced from 1674 to 160 with the model accuracy almost unchanged. For the first time, this study realizes the UQ modeling study on the high dimensional problem of a multistage compressor. In addition, significant stage-by-stage uncertainty propagation is found in the compressor. The last stage has the most significant efficiency deviation, which is significantly affected by the geometric uncertainty of the first two stages. Therefore, traditional studies, which usually simplify the multistage compressor UQ problem to a single cascade/row level, may seriously underestimate the uncertainty effect due to the neglect of stage-by-stage propagation. This study not only reveals the necessity for direct UQ study of multistage compressors but also reduces the computational cost, which lays a foundation for the uncertainty control of multistage compressors in engineering practice.

A new statistical approach to identify critical mass flow rate in CO2 nozzles near saturation conditions

The pressing needs to increase efficiency in heat pumps require refined design of various system components. In common reverse cycles, the throttling valve performs pure energy dissipation, and therefore it is well suited to be replaced by technologies such as ejectors and turbines, to augment system performance. In both two-phase turbines and two-phase ejectors a supersonic convergent-divergent nozzle is commonly required, which is responsible for accelerating the refrigerant fluid from the high-pressure part (e.g. condenser) to the low pressure part (e.g. evaporator). In the design phase of these nozzles, the accurate prediction of the maximum mass flow rate, also known as the critical mass flow rate, is particularly complex due to the two-phase nature of the expansion. In this paper, a new statistical approach to determine the value of the critical mass flow rate for CO2, as refrigerant fluid, is presented and assessed. This approach is based on the MF (Massflow Factor) parameter, which well correlates the value of mass flow rate in sonic conditions. The relationship shown here is based on open experimental information and it is validated on data from the open literature and the industry. The relative error on critical flow rate is less than 15% in the validation range, placing this statistical approach at a level of accuracy comparable to other physical models. An example of design is provided in this paper to demonstrate the potential of the relationship found. This approach provides the designer with a straightforward and validated basis for a reliable preliminary design of expanding two-phase nozzles.

Analytical predictions of concrete pumping: Extending the Khatib–Khayat model to Herschel–Bulkley and modified Bingham fluids

It has been widely reported that self-compacting concrete and other cement-based materials are shear-thickening materials. However, there is a lack of an analytical model on the prediction of flow rate–pressure drop relation in the pumping of concrete that takes into account both the presence of the lubrication layer and the shear-thickening behavior of fresh concrete. This work fills this gap by extending analytical predictions of concrete pumping in the presence of a lubrication layer to nonlinear rheological models including the Herschel–Bulkley model and the modified Bingham model. In doing so, a new method is developed to obtain the flow rate–pressure drop relation for the steady Hagen–Poiseuille coaxial flow of two immiscible, incompressible fluids. Analytical expressions are presented for the flow rate–pressure drop relation, shear rate distribution, and velocity distribution based on the two-fluid Herschel–Bulkley model and separately based on the two-fluid modified Bingham model. With those analytical expressions, volumetric flow rate vs. pressure loss curves can be readily constructed, given values for the other eight quantities: three for the rheological properties of the bulk concrete, three for the rheological properties of the fluid in the lubrication layer, pipe radius, and thickness of the lubrication layer.

Experimental and numerical investigation of flow distribution pattern at a T-shape roadway crossing under extreme storms

ABSTRACT A major drainage systemwhich uses urban roadways as flood passages during extreme storm events has been proven to be effective and low-cost. However, to date, the amount of flow diverted at a bifurcating road crossing still could not be accurately assessed. Previous studies mainly focused on diversion flat bottom channels with no turning radius. Results from these studies are not applicable to the road crossing due to the significant discrepancies between the two flow fields. In this study, the flow distribution pattern at a roadway crossing with a T-shape was studied experimentally and numerically. The three-dimensional computational fluid dynamics (CFD) model was constructed using FLUENT. The numerical model was validated using the experimental results and showed satisfying agreements. Results of the 2k factorial design experiments show that the flow distribution ratio is sensitive to turning radius, longitude slope, flowrate and downstream boundary conditions. Detailed numerical studies were conducted to reveal the relationship between the flow distribution ratio and the individual impacting factor. Findings from this research could provide more accurate flowrate predictions to guide the design of the road crossings to improve the performance of the major drainage system during extreme storms.

A capillary bundle model for the forced imbibition in the shale matrix with dual‐wettability

AbstractThe forced imbibition during hydraulic fracturing is one of the main mechanisms of oil production in shale oil reservoirs. However, the shale matrix has complex structures of nanopores and is rich in organic matters. The wettability of nanopores in organics matters is different from the nanopores in inorganic matters, and the characteristic of dual‐wettability leads to complex mechanisms of forced imbibition. This paper proposes a model for describing the imbibition in a dual‐wettability shale oil reservoir based on the capillary tube model. The external displacement pressure gradient is also considered to study the forced and spontaneous imbibition of hydraulic fracturing liquid into oil‐wet organic nanopores and water‐wet inorganic nanopores. The slip effect in organic nanopores and the boundary‐layer effect in inorganic nanopores are considered in this model. The analytical expressions for describing the location of the oil–water imbibition front in an oil‐wet organic nanopore and a water‐wet inorganic nanopore are derived, respectively. The semi‐analytical solution for predicting flow rate in a shale core with an external pressure gradient is given and demonstrated with a case study.

Predictive Modeling of Local Film-Cooling Flow on a Turbine Rotor Blade

Abstract In the turbine section of a modern gas turbine engine, components exposed to the main gas path flow rely on cooling air to maintain hardware durability targets. Therefore, monitoring turbine cooling flow is essential to the diagnostic and prognostic efficacy of a condition-based operation and maintenance (CBOM) approach. This study supports CBOM goals by leveraging supervised machine learning to estimate relative changes to local film-cooling flowrate using surface temperature measured on the pressure side of a rotating turbine blade operating at engine-relevant aerothermal conditions. Throughout the lifetime of a film-cooled turbine component, characteristics of the film-cooling flow—such as film trajectory and cooling effectiveness—vary as degradation-driven geometry distortions occur, which ultimately affects the relationship between the model input and the model output—film-cooling flowrate predictions. The present study addresses this complication by testing a data-driven model on multiple turbine blades of the same nominal design, but with each blade exhibiting different localized film-cooling flow characteristics. By testing the model in this manner, strategies for mitigating the detrimental effects of film-cooling flow characteristic variations on model performance were investigated, and the corresponding flowrate prediction accuracy was quantified.