In the current state of technology, gas turbines operate mainly with fossil fuels. In order to achieve a sustainable energy supply with gas turbines alternative fuels have to be used. In the past, many attempts have been made to use alternative fuels, such as solid biogenic fuels. The main problem with the use of these fuels has been the fouling of the turbine stages during operation. To counteract this problem, the use of film cooling as a protective mechanism to reduce deposits is considered in this work. In the past, studies have already shown that this application can reduce particle deposits. For this purpose, test blades with film cooling were created and exposed to an increased particle load in a test rig. Synthetically produced ash, which corresponds to the typical components of biogenic utilization, was used as test ash for the deposition tests. The deposits were analysed by means of a visual inspection paired with a REM-EDX examination. The deposition tests were additionally supported by CFD simulations and compared with the particle Stokes number. Overall, film cooling appears to be a suitable means of reducing deposits on a turbine blade. However, the geometric design needs to be modified compared to the classical film cooling setup. In addition, low momentum flux ratios and a delay of the flow seem to be favourable for film cooling as a protective mechanism. The blade deflection angle also plays an important role in the implementation of the new design.

Two-phase flow characterization in plate heat exchangers has traditionally relied on visual classification methods that lack objectivity and reproducibility, particularly for complex slug-like flow patterns. In this study, a novel quantitative framework using filter-based segmentation techniques to automatically analyse gas-liquid flow behaviour in corrugated channels with different chevron angles (/1 = 30 degrees, 45 degrees, 60 degrees) under realistic process conditions is developed. High-speed imaging combined with dual filtering approaches enabled reliable detection of gas structures and their classification into six clusters based on geometric and morphometric features (e.g., equivalent diameter, circularity, aspect ratio, and complexity score). Although the study encompasses Reynolds numbers up to 5000, quantitative investigations were restricted to the range 850 <= Re <= 2200 where the detection techniques are sufficiently robust for quantitative analysis. Experimental investigation reveals that chevron angle primarily controls spatial gas phase distribution: /1 = 60 degrees configurations concentrate gas flow centrally (up to 77.5%) promoting wavy longitudinal flow, while /1 = 30 degrees maintains balanced cross-flow distribution across channel width. Higher Reynolds numbers consistently drive transitions from slug-like flow to dispersed bubbly flow, with different cluster velocities beginning to homogenize at elevated Reynolds numbers. Gas fraction enhances coalescence processes, with irregular gas structures dominating 70.0-79.0% of the total gas area likely due to turbulent conditions. Volume-based gas fraction estimation systematically exceeds area-based methods by accounting for actual plate geometry where large structures occupy deep channel regions. The framework enables objective quantification of complex flow phenomena, providing enhanced understanding of bubble dynamics and morphological evolution in corrugated channels beyond traditional visual classification methods.

The ignition and combustion behavior of particle clouds is a key factor in designing and operating burners and combustion chambers. Because particle-particle interactions significantly impact these processes, research beyond single-particle studies is necessary. This study analyzes the influence of particle size, water content, and particle cloud density on ignition and combustion behavior using spectroscopic measurements. The results show that ignition delay time increases with increasing particle size and cloud density due to reduced heating rates. In contrast, combustion duration is primarily determined by water content and cloud density. Fragmentation induced by moisture shortens, whereas the high amount of volatiles obstructs oxygen diffusion in dense particle clouds prolongs burnout.

Biomass is a promising substitute fuel to reduce fossil CO2 emissions. However, the ignition and combustion behavior of these substitute fuels differ from fossil fuels. In this work the existing ignition oven method is extended using UVVIS and NIR spectroscopy. The measurement setup is used to investigate wood dust and olive cake regarding the ignition delay time, ignition temperature and occurring species during ignition and combustion. The ignition delay time is determined by using photodiode and spectrometer measurements. One important factor in the determination of the ignition delay time is the applied ignition criterion. A comparative analysis of five ignition criteria commonly applied in the literature reveals differences in the determined ignition delay times and the course of the ignition hyperbolas. In the spectral analysis of the ignition process of wood dust and olive cake the species potassium, sodium, lithium, rubidium, CaOH and CH are identified. The intensity of the molecular radiation is proportional to the content of the species in the fuel. For instance, the CaOH peaks in the spectra of wood dust are higher than those in the olive cake spectra while the CaO content is 2.21 wt% and 1.06 wt% in the ash respectively. The spectral measurements can be used in future work to predict slagging, fouling and corrosion. In subsequent research thermal radiation will be employed to calculate the temperature inside the particle cloud.

This paper investigates the prediction of two-phase gas-liquid flow regimes in both horizontal and slightly inclined pipes. For this purpose, the mechanistic model of Taitel et al. (1976) and the machine learning approach have been adopted. First, the mechanistic model was implemented, tested and optimised by introducing factors in the transition equations to determine the configuration that gives the highest prediction accuracy for a specific two-phase system for which experimental data points are available. Second, several machine learning models are trained, tested and additionally validated. This is done by splitting the experimental data set corresponding to the pipe inclination range (-10 degrees degrees to 10 degrees) degrees ) into training, test and validation sets. The best classifier achieved an accuracy of 95.5% after the test step and up to 98.9% after the validation step. Finally, the Taitel et al. model with the optimal configuration and the best machine learning classifier (XGB classifier) are used to generate the two-dimensional flow regime map.

It is well known that the pressure drop estimation is crucial for optimizing flow systems in various industrial applications. Although several available conventional models, such as the semi-empirical model, have been used for estimating pressure drop for a two-phase flow, it is challenging to achieve accurate results owing to the complexity of two-phase flow. In the present work, we are considering the problem of predicting pressure drop in two-phase flow in pipes using fully connected neural networks (FCNNs). Motivated by experimental data published in the literature, the FCNN model has been trained and then the results have been predicted. It is worth noting that the used experimental dataset was collected for a horizontal flow loop with a length of 9.15 m and diameter of 0.0254m, conveying a two-phase flow system. A critical comparison between the performance of the present FCNN model and mechanistic model is discussed, where results demonstrate how the present model is pre-eminent to handle the estimation of pressure drop in two-phase flow over the mechanistic models. However, results show that the FCNN model accuracy is estimated at 76 %, where the model is superior for the high pressure drop case over the low pressure drop case. The present FCNN model might be applicable to similar systems, without the need for further experimental measurements. By online uploading out the Python FCNN codes, we hope to fast-track the readers in applying FCNN algorithm to their own problems.

This paper investigates and assesses the potential applicability of global mass transfer coefficients derived from large-scale experiments to the bubble growth of a single bubble in a super-saturated flow (σ=9). Therefore, it presents, for a specific flow velocity (u=1ms, Re=10,678), a comparison between correlation-based modelling and 3D Large Eddy Simulation–Volume of Fluid (LES-VOF) Computational Fluid Dynamics (CFD) simulations (minimum cell size of 10 µm, Δt = 10 µs). After the verification of the CFD with pool nucleation bubbles, two cases are regarded: (1) the bubble flowing in the bulk and (2) a bubble on a wall with a crossflow. The correlation-based modelling results in a nearly linear relationship between bubble radius and time; meanwhile, theoretically, the self-similarity rule offers r ~ Bt0.5. The Avdeev correlation gives the best agreement with the CFD simulation for a bubble in the flow bulk (case 1), while the laminar approach for calculation of the exposure time of the penetration theory shows good agreement with the CFD simulation for the bubble growth at the wall (case 2). This preliminary study provides the first quantitative validation of global mass transfer coefficient correlations at the single-bubble scale, suggesting that computationally intensive CFD simulations may be omitted for rapid estimations. Future work will extend the analysis to a wider range of flow velocities and bubble diameters to further validate these findings.

The EU Horizon 2020 project "FlowEnhancer" aimed to reduce crude oil fouling in the tube side of shell and tube heat exchangers by investigating maldistribution of tubeside velocity. As part of the project, a test sequence was performed in a pilot-plant fouling rig to investigate how crude oil fouling is affected by pulsating flow. The results of these tests, carried out here in two parallel test sections of 3 m length and consisting of double pipe heat exchangers with 19 mm ID tubes, are presented here. The pulsation (periodically fluctuating flow velocity) was induced by opening and closing control valves to these two parallel test sections. The test conditions were 200 degrees C inlet temperature on the tube side (crude) and 320 degrees C on the shell side (hot heat transfer fluid), with velocity fluctuations around a mean of 1.2 m/s in one test-section and 0.8 m/s in the second test section. The tests showed lower fouling rates with pulsation, but also lower average heat transfer coefficients.

In this article, an open-source expansion planning tool for energy hubs, based on the Python library PyPSA, is presented. The expansion planning optimization uses an operational optimization based on hourly timeseries. The energy hub model contains detailed energy generation, conversion, conditioning, and storage components for electricity, heat, and various chemicals, including CH4, H-2, and CO2. For heat, multiple temperature levels are considered. As an example, the tool is used to optimize the expansion planning for a Waste-to-Energy CHP plant and district heating (DH) network in Germany. Techno-economical conditions for 2023 and projected conditions for 2050 are used as inputs. Results are analysed using timeseries and frequency analysis. This provides insight in how each planned component should be used to optimize revenues. The analyses show the importance of using hourly timeseries as input, as certain processes are operated in close alignment with periodic fluctuations in electricity prices or heat demand. Parameters are varied to evaluate the robustness of the investment decisions. The optimization results show that the studied CHP plant could transform to an energy hub, benefiting from synergetic effects when coupling multiple energy sectors using Power-to-X technologies. The tool can be used to assist operators in their investment planning to shape the future of CHP sites in Europe. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Two correlations have been considered in the literature (Martin et al., 1971 and Kim et al., 2001) for the computation of heat transfer in liquid-gas bubbly flow in horizontal pipes. Motivated by the benefits of these two correlations, we investigate the horizontal two-phase bubbly flow in a double-pipe heat exchanger with inner diameter of d=21.6 mm and length L=16⋅d using the mechanistic one-dimensional cell model. The results of the model have been compared with the correlations to assess the two approaches considered. Firstly, the first approach was assumed, whereby the properties of water-oxygen mixture can be considered as those of a single pseudo-fluid. This was achieved by appropriately averaging the thermodynamic properties of the two involved phases. Secondly, the second approach was considered, whereby the properties of the liquid phase were used directly. This was done after it was realised that it had not been commonly addressed in the literature. The results demonstrate that this approach yields satisfactory heat transfer coefficients in the case of small gas fractions (up to 6%). Secondly, a similar study utilising the computational fluid dynamics (CFD) method based on the discrete phase method (DPM) is conducted, which has not been addressed in previous works for the heat transfer investigation. The findings indicate that the CFD method successfully recovers a highly accurate heat transfer estimation, with a slight improvement in heat transfer (up to 5%) with increasing gas fraction. The mechanistic model, being numerically inexpensive compared to CFD, can predict heat transfer and pressure drop with good agreement, proving its competitiveness. The results presented here are useful for understanding and optimizing heat loss in the cooling circuit of PEM electrolysis.

For a sustainable future, it is essential to prepare gas turbines for the use with biogenic residual and waste materials. One problem that needs to be solved when using these fuels is the problem of deposits, mainly due to their ash composition with low melting temperature. For this reason, the deposition behavior caused by the ashes of biogenic residual and waste materials must first be understood before suitable countermeasures are taken in the second step. At first, an exemplary fuel composition was created in this work. In the next step, a thermodynamic calculation typical for the utilization of these materials was carried out with this composition. As part of this work, physical processes that mainly lead to the deposition of fly ash particles were also discussed. Finally, typical adhesion models were listed in the literature and applied to the calculated particle composition. A viscosity and energy model was used to carry out the calculations. The results show a high influence of phosphor on the deposits. Overall, the calculated ash showed a uniform proportion of oxides, phosphates, sulphides, slag and salts. It could also be shown that salt condensation can have a significant influence.

This paper aims to determine the impact of load flexible operations on heating surface depositions. Therefore, measurements along the flue gas channel during full and partial load in a lignite fired power plant with an electrical output of 600 megawatts were made. For this purpose, the particle wire mesh method and the temperature-range-probe were used along the flue gas path in the burner area, the radiation section, the superheater area and the air preheater. With the aid of the particle wire mesh method, the fly ash particles contained in the flue gas were characterized with respect to particle morphology and chemical composition. As result, no impact of the load-flexible mode on the fly ash particles could be detected. The chemical composition of the particles found corresponded to the fuel ash composition. The temperature-range-probes were used over short and long term periods within the flue gas to examine the deposition amount and composition near the burner area. Results showed more depositions and the deposition of ferrous sulphides during full load in the burner area. Long term temperature-range-probes showed different layers of deposition and a limited growth possibly due to load flexible operation. The results of the practical investigations in real power plant operation indicate that the changes in the fouling behaviour are caused by thermochemical processes in the area close to the wall or directly on the heating surfaces. In addition, however, changes in the mill operation also play an important role, which affect the flame shape and position, temperature and flow velocity distribution, etc., which are not part of this work. In addition to the short term and long term measurements and calculations, an online deposit identification method is presented, which allows the in-process monitoring of the local cleanliness of the heating surfaces in the radiation section and the burner area of the steam generator and the cleaning efficiency of existing water blowers. With the help of an algorithm, the two key figures "local cleanliness" and "cleaning efficiency" are determined from the measurement signals, which are used to evaluate the local fouling situation and the cleaning efficiency of the water blowers. Through the combination of so called heat flux sensors and the evaluation of large amounts of signal data, it is possible to monitor the fouling of the heating surfaces by means of a non-invasive, simple and cheap technology. This allows the online optimization of the local heating surface cleaning which is demonstrated for a lignite-fired power plant in Germany.

In this study the behaviour of the trace components caesium (Cs) and strontium (Sr) in a fluidized bed municipal waste incineration is investigated. Doped RDF (refuse derived fuel) was combusted and bed and fly ash concentration were measured for varied fuel injection temperatures and doping amounts. The distribution of the trace components was calculated and shows that Cs is mostly transferred to the fly ash fraction. The same was found to a lesser extend for Sr. The influence of both, injection temperature and doping amounts, was mostly inconclusive. The comparison of the distribution with data from grate combustion experiments shows a significantly higher transfer of Cs to the fly ash. This was even more pronounced for Sr and indicates a transfer of Sr mostly by entrainment of coarse particles in the flue gas. The experimental results give an indication for the release behaviour of Cs and Sr in fluidized bed municipal waste incineration and relevant influencing factors.

Shell-and-tube heat exchangers (STHE) are widely used in the process and energy industry. Maldistribution and the often resulting fouling in these STHE cause additional energy consumption and lower production throughput. Increased average wall shear stress, compared to that of the maldistributed case inside the tubes is expected to mitigate fouling. This can be achieved by a uniform distribution into the tubes. Field and laboratory data suggest that common crude oil fouling is profoundly mitigated above 10 Pa and is significantly reduced above a wall shear stress of 15 Pa [ 1 ]. Many STHE already have lower design shear stresses than those mentioned. Therefore, if maldistribution takes place, tubes with less flow velocity will have even more fouling. To investigate tubeside flow maldistribution, a parametric STHE model is studied with computational fluid dynamics (CFD). At first, a comparison between the standard k- ϵ -model and the new standard SST-model is performed to check if SST could provide improved simulation results. Afterward, a range of geometrical parameters will be investigated to find influencing quantities of maldistribution. The resulting velocity distributions are visualized and evaluated by using different statistical approaches. At least, a sensitivity analysis will be done to show how each parameter influences the tubeside flow distribution in STHE.

The heat and mass transfer to solid particles in one-dimensional oscillating flows are investigated in this work. A meta-correlation for the calculation of the Nusselt number (Sherwood number) is derived by comparing 33 correlations and data point sets from experiments and simulations. These models are all unified by their dependencies on the amplitude parameter 10−3≤ϵ≤103 and the Reynolds number 10−1≤Re≤106, while the ϵ-Re plane is applied as a framework in order to graphically display the various models. This is the first study to consider this problem in the entire ϵ-Re plane quantitatively while taking preexisting asymptotic models for various areas of the ϵ-Re plane into account.

Heat exchangers can be coated with functional layers to reduce the negative effects of established fouling films. In the process, long-term layer stability is a requirement for the fouling-inhibiting properties of the surface coatings. Various atomic layer deposition (ALD), with layer thicknesses between 10 and 50nm, and Parylene (polymeric) coatings, with layer thicknesses < 50 mu m, are applied to stainless steel (1.4571) and structural steel (1.0038) substrates. The stability against critical thermal process conditions and cleaning media (acids, alkalis, solvents) is examined. The surface free energy of the substrates is determined before and after durability tests using a contact angle measuring device. In this study the targeted water contact angle was between 80 degrees and 110 degrees and the related surface free energy < 45mN/m. The change in the surface free energy is a measure of the coating durability. The tested Parylene-coated substrates show no significant changes and no layer defects. In the case of the ALD-coated substrates, only the coating with TiO2 deposition using a chlorine-free precursor shows promising results. Non-systematical layer defects and corrosion can sometimes be optically observed on the TiO2 and Al2O3 ALD-coated substrates. A strong increase in the polar part of the surface free energy is observed. This may be due to the physisorption and chemisorption of water with the coating, increasing polar interactions. In further research, fouling experiments must be performed to investigate the influence of the coatings on the induction time.