Transient Operation Research Articles

Development of a semi-empirical physical model for transient NOx emissions prediction from a high-speed diesel engine.

With emissions regulations becoming increasingly restrictive and the advent of real driving emissions limits, control of engine-out NOx emissions remains an important research topic for diesel engines. Progress in experimental engine development and computational modelling has led to the generation of a large amount of high-fidelity emissions and in-cylinder data, making it attractive to use data-driven emissions prediction and control models. While pure data-driven methods have shown robustness in NOx prediction during steady-state engine operation, deficiencies are found under transient operation and at engine conditions far outside the training range. Therefore, physics-based, mean value models that capture cyclic-level changes in in-cylinder thermo-chemical properties appear as an attractive option for transient NOx emissions modelling. Previous experimental studies have highlighted the existence of a very strong correlation between peak cylinder pressure and cyclic NOx emissions. In this study, a cyclic peak pressure-based semi-empirical NOx prediction model is developed. The model is calibrated using high-speed NO and NO2 emissions measurements during transient engine operation and then tested under different transient operating conditions. The transient performance of the physical model is compared to that of a previously developed data-driven (artificial neural network) model, and is found to be superior, with a better dynamic response and low (<10%) errors. The results shown in this study are encouraging for the use of such models as virtual sensors for real-time emissions monitoring and as complimentary models for future physics-guided neural network development.

Error analysis and improved method of time-domain distance protection for wind power transmission lines

Time-domain distance protection has a good transient performance and operation speed when applied to wind power transmission lines. In this paper, the error analysis and improved method of phase-to-phase time-domain distance protection are investigated. Firstly, based on the time-domain mathematical model, the location error caused by fault resistance is analyzed, and then the algorithmic convergence and accuracy of conventional phase-to-phase distance relay are evaluated. To solve the computational non-convergence problem, an improved phase-to-phase distance relay is proposed, in which the instantaneous negative-sequence current is used to improve the location accuracy, especially during the fault transient stage of wind farms. In order to avoid the protection overreach problem, the threshold selection method is designed based on the maximum allowable error of the calculated distance. According to the proposed method, the corresponding protection device is developed. And the hardware-in-the-loop (HIL) test system is built based on the real-time digital simulation (RTDS) platform. Extensive experimental tests are carried out, to verify the feasibility and superiority of the proposed time-domain distance protection.

A method for calculating the time‐varying equivalent circuit parameters of large‐capacity synchronous condenser considering field mutual leakage reactance

AbstractAccurate calculation of equivalent circuit parameters is a prerequisite for accurately calculating the large‐capacity synchronous condenser parameter model. Due to its special transient operating conditions, the high transient magnetic saturation effect during operation causes the non‐linearity and time‐varying of the equivalent circuit parameters. The field mutual leakage reactance is the critical factor affecting the field current, and the time‐varying of the equivalent circuit parameters is closely related to the field current. However, the existing equivalent circuit parameter calculation methods considering field leakage reactance cannot achieve time‐varying parameters. A calculation method of time‐varying equivalent circuit parameters based on a back propagation neural network algorithm is proposed, which solves the calculation problem of time‐varying equivalent circuit parameters considering field mutual leakage reactance. Then, a 300MVar condenser is taken as the research object, and the proposed method is used to simulate the different operating conditions of the condenser and verified by the finite element method and experiment. The results show that the method improves the calculation accuracy of the equivalent circuit parameter model, reduces the calculation time, and applies to different operating conditions.

Auto-Ignited Combustion Control in an Engine Equipped with Multiple Boosting Devices

&lt;div&gt;The combustion timing of auto-ignited combustion is determined by composition, temperature, and pressure of cylinder charge. Thus, for a successful auto-ignition, those key variables must be controlled within tight target ranges, which is challenging due to (i) nature of coupling between those variables, and (ii) complexity of managing multiple actuators in the engine. In this article, a control strategy that manages multiple actuators of a boosted homogeneous charge compression ignition (HCCI) engine is developed to maintain robust auto-ignited combustion. The HCCI engine being considered is equipped with multiple boosting devices including a supercharger and a turbocharger in addition to conventional actuators and sensors. Since each boosting device has its own pros and cons, harmonizing those boosting devices is crucial for successful transient operation. To address the multi-variable transient control problem, speed-gradient control methodology is applied to minimize coupling between boosting devices. Simulation results show that the control strategy overcomes turbo lag by utilizing the supercharger during transient. The controller developed is still appliable to manage multiple boosting devices with conventional engines as well as HCCI engine.&lt;/div&gt;

Erosion characteristics of a centrifugal pump based on rosin-rammler distribution of particles

Abstract Centrifugal pumps are widely used in water conservancy, agricultural irrigation and petrochemical fields, but the transferred fluid usually contains solid particles. With prolonged operation in a solid-liquid flow, the components of centrifugal pump are easily eroded, resulting in damaged components, shortened life and reduced efficiency. It is crucial to study the erosion characteristics on centrifugal pump in solid-liquid flow. In this work, based on the particles with Rosin-Rammler distribution in a pump station, the transient operation and erosion process of the centrifugal pump under sandy flow are simulated by using the Eulerian-Lagrangian method and Oka model. Results show that the erosion of R-R distribution is characterized by both large and small particle sizes, but it is closer to the latter condition due to the higher proportion of small particle sizes. The distribution ratio of average erosion rate on hub, blade and shroud is 1.18: 1.68: 1.00, and the most eroded areas are in the leading edge of blade and the trailing edge of the blade suction surface.

Study on dynamic stress variation of head cover bolt during transient operation of pump turbine

Abstract The head cover bolt is an important connecting part of pump turbine, if it is damaged, it will affect the operation safety of the unit and the power station. During the transient process, such as start-up and load rejection in extreme case, the head cover bolt is subjected to complex load action, and its stress also changes, and extreme cases may occur. The dynamic stress level of the bolt will have a certain influence on its fatigue life, therefore, in order to further study the dynamic stress level and variation law of the bolt, this paper relies on a pumped-storage station, the dynamic stress of the head cover bolt during the start-up and load rejection is measured, and the pressure pulsation between the head cover and the runner and the bladeless zone are collected synchronously. Through the data analysis, the bolt dynamic stress level and variation law and the correlation between stress and pressure fluctuation under the power generation and pumping conditions are summarized.

Assessment of Fiber Bragg Grating sensors for monitoring induced strains on the draft tube cone of hydraulic turbines

Abstract In hydraulic turbines, several flow instabilities can take place inside the draft tube cone during off-design and transient operating conditions such as the rotating vortex rope which can severely damage the structure if sustained in time. In the frame of the AFC4Hydro H2020 research project, an extensive measurement campaign has been carried out to monitor and predict this rotating vortex rope phenomenon in a reduced scale Kaplan turbine model at the Vattenfall Research and Development facility in Älvkarleby, Sweden. The hydraulic turbine model has been operated in propeller mode with a fixed blade angle corresponding to its best efficiency point. Several sensors have been placed along the test stand to monitor vibrations, strains and pressures. Concretely, the present paper assesses the performance of using Fiber Bragg Grating sensors to measure the strains induced on the draft tube cone walls with high-spatial resolution in three different zones of influence: the upper and lower flanges and the vertical cone wall between the runner outlet and the elbow. To do so, a total of 3 arrays embedding a total of 48 Fiber Bragg Grating sensors were glued inside three grooves previously machined on these particular areas of the draft tube cone. Analysing the frequency response of the different Fiber Bragg Grating sensors, the strain patterns induced by the rotating and plunging components of the rotating vortex rope have been precisely determined. Moreover, their impacts at the different part load conditions tested have also been quantified.

An Adaptive Estimation Approach for Integrating Real-World Operation Dynamics in Engine-Out NOx Emission Modeling of a Wheel Loader

Accurately predicting engine-out nitrogen oxides (NOx) emissions on-board is crucial for effective emission control in heavy-duty engines. Real-world engine operating conditions, especially in non-road applications with frequent dynamic changes, can significantly affect NOx emission characteristics. However, these engine emission characteristics are conventionally measured on steady-state or regulated driving cycles, which may not fully reflect the emission levels under real-world operational dynamics. This highlights the necessity of integrating engine performance during transient operation into the NOx prediction model to enhance the accuracy of on-board predictions. This paper introduces a novel data-driven model to predict engine-out NOx emissions during the construction activities of a wheel loader. This paper begins by addressing discrepancies between steady-state map predictions and on-board NOx measurements. To bridge these gaps, the model identifies engine transient operating conditions by analyzing the time derivatives of engine speed and torque. The model structure integrates steady-state and transient emission maps, with the transient map being iteratively refined using the Kalman filter principle, thereby improving its accuracy and robustness in response to engine dynamics. The proposed method maintains a model structure that is easily implemented and similar to conventional steady-state emission maps, while also enabling online self-learning for model parameter updates. Model validation shows that the model has high prediction accuracy and the ability to differentiate between steady-state and transient engine working conditions during construction activities.

Optimizing industrial tunnel kiln operations for ceramic roof tile production: A bi-objective approach

The ceramic firing process constitutes a highly energy-intensive production step that significantly affects the quality and final properties of the ceramic product. This work presents a model-based, bi-objective optimization approach for an industrial-scale tunnel kiln used in ceramic roof tile production, focusing on the exploration of trade-offs between productivity and thermal energy consumption. Initially, sintering kinetics are established based on lab experiments and integrated within a novel physics-based tunnel kiln model, thus allowing sintering evolution to be considered for the derivation of the optimal kiln operation. Two bi-objective optimization studies are then conducted to explore trade-offs under steady-state and transient-state kiln operations, thus generating a set of non-dominated operating policies. The attained Pareto optimal solutions are then evaluated with respect to the values of Specific Energy Consumption (SEC), which is distinctly minimized. This results in optimal SEC values of 0.283 for the steady state and 0.277 kWh/kg for the transient state kiln operation.

On the prediction of the induced damage by the start-up sequence of Francis turbines: On operational resilience framework

The number of transient operations in hydraulic machinery connected to power grid, notably start-ups and shut-downs, has observed a substantial increase in recent decades, primarily driven by the global shift towards intermittent renewable energy sources. Simultaneously, advancements in MW power converter technology and its cost-effectiveness have altered the operational paradigms of hydropower plants. These transformations offer novel opportunities for asset management within the industry. This study introduces a new method aimed at forecasting stress during start-up procedures, leveraging steady-state measurements acquired from a reduced scale model of the hydraulic turbine. Through a dedicated modular telemetry measurement system and the integration of strain gauges onto the reduced-scale runner blades, a tool is devised to predict dynamic loads characteristic of these transient operations. Utilizing steady-state measurements and a Voronoi cell tessellation, the transient predicted stress signal is constructed by concatenating the information from each encountered cell, thus providing an estimated projection of the stress likely to occur during the sequence. The proposed methodology demonstrates a robust forecast of the stresses on the runner blades during steady-state and transient operations of Francis turbines. Its immediate value lies in the ability to correctly estimate the lifetime of the runner as well as the possible integration into an optimization framework for stress reduction, thereby potentially extending the operational lifespan of the unit, from the runner structure perspectives.

Exploring the potential and challenges of phase change materials in future heat storage systems via computations and experiments

The current work describes computational and experimental efforts to enable new heat storage technologies. Approximately 50% of the energy consumed globally is used to generate and manage heat. This is still predominantly acquired from the combustion of fossil fuels, consequently generating just over 40% of the total global CO2 emissions. To achieve a sustainable future, there is a need for sustainable, decarbonised and dependable energy sources. Heat storage is a key technology that could help achieving this goal. Recently published work by the current authors has shown the importance of heat storage in decarbonising heating (and cooling) of buildings using phase change materials (PCMs). The black-box approach followed evaluated the energetic, cost, and environmental impacts of a novel concept PCM storage scheme with microgeneration. The results obtained showed that this system had advantages over current and future competing heating technologies. However, PCM technology remains at low technological readiness levels because of the lack of understanding of the complex heat transfer physics associated with their phase change transition. For example, understanding and characterizing the propagation of the solid-liquid front during charging and discharging in such heat storage modules will be important in predicting and optimising their performance. In the current work, computations have been performed to understand and optimise the effects of container geometry and size on the charging and discharging heat flux profiles of a simple heat storage module. Furthermore, fundamental experiments have also been implemented to identify the complex governing mechanisms of heat transfer and storage in such materials during transient operation.

Devising a technique for assessing the accuracy of measuring electric motor torque

The object of this study is a technique for assessing the accuracy of measuring the torques of electric motors. This technique is based on the improvement of dynamic torque measurement processes under transient operating modes. Special attention was paid to devising adaptive methods to normalize the output signal, which take into account vibrations and nonlinearities of the measuring channel. In order to improve the accuracy of measurements under conditions when the controlled parameter has different deviations from its average value, a technique for integrating time measurement intervals was proposed. That has made it possible to generalize parameter fluctuations, providing more stable measurement results. This paper reports the simulation of the process of measuring torques of electric motors under transient operating modes and under vibration conditions. That has made it possible to test the proposed technique for estimating the variance of the methodological error, which includes the integration of measurement time intervals and makes it possible to increase the accuracy of the measurement under the conditions of transient modes in electric motor operation. An algorithm for correcting the error of the dynamic torque of the electric motor has been proposed, which allows it to be corrected. This becomes possible owing to the use of a reference measure and the proposed technique for determining the deviations of the controlled parameter of the measured quantity, as well as the automatic calibration of measuring transducers. The algorithm allows for flexible settings of the corrective action, expanding the potential of electric motor control systems. It can complement soft measurement methods, as well as adjust the threshold values of deviations under different operating modes of electric motors. This allows the measured systems to better adapt to changes in operating conditions, to adjust the specified measurement accuracy under conditions of uncertainty

Torque Calculation and Dynamical Response in Halbach Array Coaxial Magnetic Gears through a Novel Analytical 2D Model

Coaxial magnetic gears have piqued the interest of researchers due to their numerous benefits over mechanical gears. These include reduced noise and vibration, enhanced efficiency, lower maintenance costs, and improved backdrivability. However, their adoption in industry has been limited by drawbacks like lower torque density and slippage at high torque levels. This work presents an analytical 2D model to compute the magnetic potential in Halbach array coaxial magnetic gears for every rotational angle, geometry configuration, and magnet specifications. This model calculates the induced torques and torque ripple in both rotors using the Maxwell Stress Tensor. The results were confirmed through Finite Element Analysis (FEA). Unlike FEA, this analytical model directly produces harmonics values, leading to faster computational times as it avoids torque calculations at each time step. In a case study, a standard coaxial magnetic gear was compared to one with a Halbach array, revealing a 14.3% improvement in torque density and a minor reduction in harmonics that cause torque ripple. Additionally, a case study was conducted to examine slippage in both standard and Halbach array gears during transient operations. The Halbach array coaxial magnetic gear demonstrated a 13.5% lower transmission error than its standard counterpart.

Characterizing the Unsteady Flow Field in Low-Flow Turbine Operation

Abstract Current operational considerations require steam turbines to operate in a more flexible way, with more frequent and faster start-up and an increasing part-load operation. For very low mass flowrates, the interaction of highly separated flow with the high-speed rotor blades causes windage flow. This type of flow is characterized by increased temperature and highly unsteady flow, which forms vortex structures that rotate at a fraction of the rotor speed. If their magnitude is sufficiently high and the frequency is close to the blade eigenfrequency, non-synchronous vibration (NSV) can be induced. In this paper, low-flow turbine operation is investigated using a three-stage turbine rig that features an instrumentation concept focused on capturing aerodynamic and aeroelastic phenomena. Extensive steady probe, unsteady pressure, and tip-timing measurements are utilized. The experimental scope covers a wide range of operating points in terms of rotational speed and mass flowrates. Low-flow regimes are detected by a reversal in torque and increase in temperature. Unsteady measurements during transient operation identified large-scale vortical flow structures rotating along the circumference, so-called rotating instabilities (RIs). The onset, growth, and breakdown regimes of RI are characterized for different low-flow conditions. The quantitative characteristics of RI with regard to nodal diameter and rotational speed are derived by a cross-correlation of multiple unsteady sensors. The blade vibration measurements show a moderate structural response from unsteady aerodynamic excitation, indicating no significant NSV occurring in the present experimental setup. Later in the study, an acoustic excitation system has been applied to trigger a locked-in NSV without interrupting the coherent flow structures. From that, significant blade response has been observed, revealing a high degree of mistuning and damping of the rotor blading.

Pulsation Stability Analysis of a Prototype Pump-Turbine during Pump Mode Startup: Field Test Observations and Insights

Pump-turbines experience complex flow phenomena and fluid–structure interactions during transient operations, which can significantly impact their stability and performance. This paper presents a comprehensive field test study of the pump mode startup process for a 150 MW prototype pump-turbine. By analyzing pressure fluctuations, structural vibrations, and their short-time Fourier transform (STFT) results, multiple stages were identified, each exhibiting distinct characteristics. These characteristics were influenced by factors such as runner rotation, free surface sloshing in the draft tube, and rotor–stator interactions. The natural frequencies of the metallic components varied during the speed-up and water-filling stages, potentially due to gyroscopic effects or stress-stiffening phenomena. The opening of the guide vanes and dewatering valve inside the guide vanes significantly altered the amplitude of the rotor–stator interaction frequency, transitioning the vibration behavior from forced to self-excited regimes. Interestingly, the draft tube pressure fluctuations exhibited sloshing frequencies that deviated from existing prediction methods. The substantial phenomena observed in this study can help researchers in the field to deepen the understanding of the complex behavior of pump-turbines during transient operations and identify more meaningful research directions.

Energy consumption during transient operation of coal-fired generating units with cold end system back pressure regulation

The dynamic characteristics of key parameters were investigated in this study, which reflected the operational flexibility and efficiency of thermal power units during load regulation. A refined model of the cold end of the unit was proposed. Simultaneously, an innovative exploration was conducted into the coordinated achievement of flexible load regulation and the promotion of energy-efficient operations. Two variables, the variable load rate and cooling water flow, were selected, and the continuous dynamic characteristics of unit energy consumption during load adjustment were determined. The ratio of total variable load coal consumption to load regulation and variable load time was defined as the average regulated coal consumption BZ, which was introduced to enhance the intuitive evaluation of flexibility and efficiency. It was shown that with the increase in variable load rate, there was a decrease observed in the total coal consumption during the variable load process. This trend was consistent with earlier publications. Concurrently, a reduction in cooling water flow had resulted in an increase in power generation coal consumption and a decrease in coal consumption for circulating cooling water pumps. The elevated rate of load variation was attributed to the increased flow of cooling water. A decrease of 14.9 % in back pressure was noted with the increase in the flow of the cooling water. As a result of this reduction in back pressure, the coal consumption rate for power generation was decreased by 1.334 g/kWh, reflecting a decrease of 0.43 %. Additionally, a reduction in total coal consumption of 0.16 t during variable load operation was witnessed, while a 1.97 % increase in the variable load rate was recorded. The optimal flexibility and coal-saving effect were achieved under the conditions of 30 MW/min and 60000 t/h, with ΔBZ = −50 g/(MW·s). Finally, the feasibility of the proposed scheme was verified through the case of a 350 MW unit.

Novel virtual sensors development based on machine learning combined with convolutional neural-network image processing-translation for feedback control systems of internal combustion engines

Physical sensors are commonly used to record performance data of internal combustion engines (ICEs) for online feedback control and calibration, but they are prone to diagnostic and increased development costs. Lookup tables are commonly used in conventional calibration and feedback control; however, the table parameters increase with the advancement of ICE technologies under transient operations. Consequently, the calibration and control systems are time-consuming. This work proposes novel virtual sensors to address these issues by predicting the combustion, performance, and emission of ICEs using neural networks and image processing/translation. The novel sensors are targeted for onboard feedback control systems under transient driving. Firstly, a virtual diesel engine (VDE) was developed and calibrated against experimental data taken from a production 2.2 L turbocharged diesel engine. The VDE was calibrated under WLTC, JC08, and NEDC transient operations and was used to generate teaching data. Next, the virtual sensors are developed using five machine learning (ML) regressors. The result shows that the coefficient of determination R2 from all ML regressors exceeded 0.94, and the XG-Boost outperforms other ML techniques with R2 > 0.977. XG-Boost parameter estimations were 8 times faster than that on a desktop simulation. Then, an image classification model using a deep convolutional neural network (D-CNN) is constructed, and the dependency of performance parameters and exhaust emissions with the rate of heat release (R.H.R) and in-cylinder pressure profile is confirmed. The performance parameters and emissions dependency was compared individually with R.H.R. and the in-cylinder pressure profile. As a result, a strong correlation between the performance and R.H.R. was observed. Finally, a generative adversarial network (GAN) model was constructed to translate the in-cylinder pressure profile to R.H.R. profile. A novel method to develop virtual sensors for advanced feedback control of any type of ICEs is proposed for the first time.

Suitability Study of Biofuel Blend for Light Commercial Vehicle Application under Real-World Transient Operating Conditions

&lt;div&gt;Driving schedule of every vehicle involves transient operation in the form of changing engine speed and load conditions, which are relatively unchanged during steady-state conditions. As well, the results from transient conditions are more likely to reflect the reality. So, the current research article is focused on analyzing the biofuel-like lemon peel oil (LPO) behavior under real-world transient conditions with fuel injection parameter MAP developed from steady-state experiments. At first, engine parameters and response MAPs are developed by using a response surface methodology (RSM)-based multi-objective optimization technique. Then, the vehicle model has been developed by incorporating real-world transient operating conditions. Finally, the developed injection parameters and response MAPs are embedded in the vehicle model to analyze the biofuel behavior under transient operating conditions. The results obtained for diesel-fueled light commercial vehicle (LCV) have shown better fuel economy than LPO biofuel with their developed fuel injection parameter MAP. The maximum BTE obtained was 29.7% for diesel and 29.5% for LPO at 2100 rpm and 20 Nm torque. The mean HC emissions were identified as 0.02046 g/km for diesel and 0.03488 g/km for LPO fuel over the modified Indian driving cycle (MIDC). Except for NOx emission, LPO biofuel exhibited diesel-like performance and emission characteristics under the MIDC.&lt;/div&gt;

Estimation of Piston Surface Temperature During Engine Transient Operation for Emissions Reduction

Abstract Piston surface temperature is an important factor in the reduction of harmful emissions in modern gasoline direct injection (GDI) engines. In transient operation, the piston surface temperature can change rapidly, increasing the risk of fuel puddling. The prediction of the piston surface temperature can provide the means to significantly improve multiple-pulse fuel injection control strategies through the avoidance of fuel puddling. It could also be used to intelligently control the piston cooling jet (PCJ), which is common in modern engines. Considerable research has been undertaken to identify generalized engine heat transfer correlations and to predict piston and cylinder wall surface temperatures during operation. Most of these correlations require in-cylinder combustion pressure as an input, as well as the identification of numerous model parameters. These requirements render such an approach impractical. In this study, the authors have developed a thermodynamic model of piston surface temperature based on the global energy balance (GEB) methodology, which includes the effect of PCJ activation. The advantages are a simple structure and no requirement for in-cylinder pressure data, and only limited experimental tests are needed for model parameter identification. Moreover, the proposed model works well during engine transient operation, with maximum average error of 6.68% during rapid transients. A detailed identification procedure is given. This and the model performance have been demonstrated using experimental piston crown surface temperature data from a prototype 1-liter 3-cylinder turbocharged GDI engine, operated in both engine steady-state and transient conditions with an oil jet used for piston cooling turned both on and off.

A method to partition boiling heat transfer mechanisms using synchronous through-substrate high-speed visual and infrared measurements

Heat transfer during boiling involves a variety of transport mechanisms. Available experimental techniques cannot yet fully delineate these mechanisms which has contributed to a long-standing, persistent challenge of constructing accurate mechanistic models for boiling. In this work, we develop a method to identify and distinguish between the individual heat transfer mechanisms that occur during boiling using synchronous, through-substrate, high-speed visual and infrared measurements. Local heat fluxes are deduced from temperature measurements and a synchronized set of binarized phase maps are obtained from processing high-speed visual measurements. Experimental pool boiling investigation of HFE-7100 fluid on an indium tin oxide surface revealed four distinct heat transfer signatures in the heat flux maps corresponding to liquid convection, contact line evaporation, vapor convection, and local microconvection due to rewetting during boiling. To classify these regions, pixel-wise binary and morphological operations are performed on the phase maps of the entire surface. In contrast with prior partitioning techniques which use standalone heat flux measurements, our synchronous high-resolution visualization enables the region classification around fine bubble footprints that otherwise would not be detected with heat flux maps alone. The heat fluxes and superheats are then partitioned into their underlying mechanisms using classified regions from the phase maps. Analysis of the nucleate boiling regime data shows that different mechanisms contribute in varying degrees to the overall heat transfer at different points along the boiling curve: for the surface-fluid combination studied, single-phase liquid heat transfer is the primary contributor at low heat fluxes while at high heat fluxes, contact line evaporation contributed the most. We further employ the experimental approach to partitioning dynamic processes resulting from step increases in heat input demonstrating its potential for investigating transient phenomena such as dryout. The experimental and post-processing method introduced in this study is the first to delineate partitioning between different heat transfer mechanism regions in the presence of multiple interacting bubbles over the entire surface throughout the boiling curve in both steady and transient operating conditions.