Ziemowit Malecha
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Ziemowit Malecha has a total of 100 co-authors. Here are the top co-authors by number of shared publications:
- Jaroslaw Polinski
Wrocław University of Science and Technology
6 shared publications - Krystian Machaj
Institute of Power Engineering - National Research Institute
6 shared publications - Krzysztof Tomczuk
Faculty of Mechanical and Power Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
5 shared publications - Maciej Chorowski
Department of Cryogenics and Aerospace Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
5 shared publications - Zbigniew Rogala
Department of Cryogenics and Aerospace Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
5 shared publications Maciej Chorowski
Wroclaw University of Science and Technology
4 shared publications
Latest Publications
- Open Access
- 27 October 2026
- Published by Elsevier in SSRN Electronic Journal
Experimental and CFD investigation of discharge characteristics in 3D-printed non-standard perforated plates: The effect of hole edge rounding
This study investigates compressible gas flow through FDM 3D-printed perforated plates discharging directly into the atmosphere outside the scope of ISO 5167-2. The primary objectives are to eliminate numerical backflow in CFD models of free-discharge orifices, evaluate the impact of printing-induced hole edge rounding on pressure drop, and establish design relations for small flow restrictors ($10{,}000 \le Re \le 50{,}000$). Experimental tests were conducted with air at mass flow rates of 5–70 kg/h (pressures up to 800 mbar) using plates with 1, 5, and 9 holes (diameters 4.3 mm and 7.0 mm). Steady, compressible RANS simulations utilizing the lag elliptic blending $k-\varepsilon$ turbulence model were calibrated on the 9-hole configuration and validated against 1- and 5-hole plates. To prevent numerical backflow without computationally expensive external domain modeling, extending the downstream hole channels by at least 5 mm proved necessary and sufficient. Assuming perfectly sharp hole edges overpredicted upstream pressure by 15.2\% on average. Introducing an effective upstream edge fillet radius of merely 0.05 mm—approximately 1\% of the hole diameter—reduced the mean prediction error to 3.1\% for the calibration plate (5.3\% and 11.8\% for validation plates). Increasing the fillet radius to 0.60 mm further reduced pressure loss by up to 25\%. In conclusion, minor edge rounding inherent to additive manufacturing significantly alter flow characteristics, and incorporating real edge fillets alongside a short outlet extension is essential for accurate CFD flow metering and restrictor design.- 27 October 2026
- Published by Elsevier in SSRN Electronic Journal
Open AccessExperimental and CFD investigation of discharge characteristics in 3D-printed non-standard perforated plates: The effect of hole edge rounding
This study investigates compressible gas flow through FDM 3D-printed perforated plates discharging directly into the atmosphere outside the scope of ISO 5167-2. The primary objectives are to eliminate numerical backflow in CFD models of free-discharge orifices, evaluate the impact of printing-induced hole edge rounding on pressure drop, and establish design relations for small flow restrictors ($10{,}000 \le Re \le 50{,}000$). Experimental tests were conducted with air at mass flow rates of 5–70 kg/h (pressures up to 800 mbar) using plates with 1, 5, and 9 holes (diameters 4.3 mm and 7.0 mm). Steady, compressible RANS simulations utilizing the lag elliptic blending $k-\varepsilon$ turbulence model were calibrated on the 9-hole configuration and validated against 1- and 5-hole plates. To prevent numerical backflow without computationally expensive external domain modeling, extending the downstream hole channels by at least 5 mm proved necessary and sufficient. Assuming perfectly sharp hole edges overpredicted upstream pressure by 15.2\% on average. Introducing an effective upstream edge fillet radius of merely 0.05 mm—approximately 1\% of the hole diameter—reduced the mean prediction error to 3.1\% for the calibration plate (5.3\% and 11.8\% for validation plates). Increasing the fillet radius to 0.60 mm further reduced pressure loss by up to 25\%. In conclusion, minor edge rounding inherent to additive manufacturing significantly alter flow characteristics, and incorporating real edge fillets alongside a short outlet extension is essential for accurate CFD flow metering and restrictor design. - Open Access
- 25 June 2026
- Published by Elsevier in SSRN Electronic Journal
Systemic Rivalry and Operational Dynamics of Onshore and Offshore Wind Power: A Case Study of the German Power System (2020–2025)
This paper presents a comprehensive comparative analysis of the generation profiles of onshore and offshore wind farms using high-resolution (15-minute) time-series data for the German power sector over the 2020--2025 temporal horizon. The widespread hypothesis regarding the geographical complementarity of these two generation technologies was rigorously verified. Using classical Pearson correlation and non-parametric Spearman rank correlation, it was demonstrated that the global coupling between both subsystems remains consistently high over the analyzed multi-year period ($\bar{r} = 0.630$, $\bar{\rho} = 0.697$, $\bar{R}^2 = 0.398$). Based on a detailed analysis of monthly trajectories from 2020 to 2025, it is proven that periodic drops in linear indices (e.g., $R^2$ falling below $0.20$ in specific winter months) do not indicate weather autonomy, but rather result from phase shifts and amplitude asymmetries of weather fronts, as confirmed by high Spearman rank correlation values. Furthermore, the deconstruction of percentile distributions and the analysis of quarter-hourly power ramps revealed that the offshore subsystem is characterized by a permanently higher dynamic aggressiveness ($\bar{\text{CV}}_{\Delta P} = 0.050$), generating sudden power steps of up to 31.9\% of maximum capacity within 15 minutes. Advanced detection of continuous power loss blocks demonstrated that the onshore subsystem exhibits an extreme capacity for generating power drops, with a multi-year average maximum block value of $19,310.52$~MW, imposing critical requirements on the volume of spinning reserve and the flexibility of balancing units.- 25 June 2026
- Published by Elsevier in SSRN Electronic Journal
Open AccessSystemic Rivalry and Operational Dynamics of Onshore and Offshore Wind Power: A Case Study of the German Power System (2020–2025)
This paper presents a comprehensive comparative analysis of the generation profiles of onshore and offshore wind farms using high-resolution (15-minute) time-series data for the German power sector over the 2020--2025 temporal horizon. The widespread hypothesis regarding the geographical complementarity of these two generation technologies was rigorously verified. Using classical Pearson correlation and non-parametric Spearman rank correlation, it was demonstrated that the global coupling between both subsystems remains consistently high over the analyzed multi-year period ($\bar{r} = 0.630$, $\bar{\rho} = 0.697$, $\bar{R}^2 = 0.398$). Based on a detailed analysis of monthly trajectories from 2020 to 2025, it is proven that periodic drops in linear indices (e.g., $R^2$ falling below $0.20$ in specific winter months) do not indicate weather autonomy, but rather result from phase shifts and amplitude asymmetries of weather fronts, as confirmed by high Spearman rank correlation values. Furthermore, the deconstruction of percentile distributions and the analysis of quarter-hourly power ramps revealed that the offshore subsystem is characterized by a permanently higher dynamic aggressiveness ($\bar{\text{CV}}_{\Delta P} = 0.050$), generating sudden power steps of up to 31.9\% of maximum capacity within 15 minutes. Advanced detection of continuous power loss blocks demonstrated that the onshore subsystem exhibits an extreme capacity for generating power drops, with a multi-year average maximum block value of $19,310.52$~MW, imposing critical requirements on the volume of spinning reserve and the flexibility of balancing units. - Open Access
- 22 May 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (11) , 5174
Gaussian Process Surrogate Model with Uncertainty Quantification for PWR Pin-Cell Criticality Prediction
Surrogate models for nuclear reactor calculations typically provide point predictions without quantifying uncertainty, limiting their use in risk-informed applications. While several studies have applied machine learning to reactor physics, systematic evaluation of prediction interval calibration against Monte Carlo statistical uncertainty remains underexplored. This study develops a Gaussian Process regression (GPR) surrogate model that provides both accurate predictions and calibrated uncertainty estimates for the infinite multiplication factor () of a pressurized water reactor pin-cell. A dataset of 400 OpenMC Monte Carlo simulations was generated using Latin Hypercube Sampling across boron concentration (0–2000 ppm), fuel temperature (600–1200 K), and moderator temperature (500–600 K). The GPR model achieves with prediction errors below the Monte Carlo statistical uncertainty (), indicating that model accuracy is limited only by inherent training data noise. The key contribution is demonstrating that GPR prediction intervals are well-calibrated, achieving 92.5% coverage for 95% confidence bounds (bootstrap 95% CI: [87.5%, 97.5%], containing the nominal level; binomial test p = 0.297), with mean prediction uncertainty closely matching the Monte Carlo statistical uncertainty ( vs. ). This near-perfect match suggests the surrogate has captured essentially all deterministic variation, with residual uncertainty attributable to Monte Carlo noise alone. Variance-based sensitivity analysis confirms boron concentration accounts for 99% of output variance. The surrogate preserves physically meaningful reactivity coefficients (Doppler: pcm/K; boron worth: pcm/ppm) while providing -fold computational speedup. The framework is restricted to fresh fuel with fixed enrichment; extension to burnup-dependent scenarios is left for future work.- 22 May 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (11) , 5174
Open AccessGaussian Process Surrogate Model with Uncertainty Quantification for PWR Pin-Cell Criticality Prediction
Surrogate models for nuclear reactor calculations typically provide point predictions without quantifying uncertainty, limiting their use in risk-informed applications. While several studies have applied machine learning to reactor physics, systematic evaluation of prediction interval calibration against Monte Carlo statistical uncertainty remains underexplored. This study develops a Gaussian Process regression (GPR) surrogate model that provides both accurate predictions and calibrated uncertainty estimates for the infinite multiplication factor () of a pressurized water reactor pin-cell. A dataset of 400 OpenMC Monte Carlo simulations was generated using Latin Hypercube Sampling across boron concentration (0–2000 ppm), fuel temperature (600–1200 K), and moderator temperature (500–600 K). The GPR model achieves with prediction errors below the Monte Carlo statistical uncertainty (), indicating that model accuracy is limited only by inherent training data noise. The key contribution is demonstrating that GPR prediction intervals are well-calibrated, achieving 92.5% coverage for 95% confidence bounds (bootstrap 95% CI: [87.5%, 97.5%], containing the nominal level; binomial test p = 0.297), with mean prediction uncertainty closely matching the Monte Carlo statistical uncertainty ( vs. ). This near-perfect match suggests the surrogate has captured essentially all deterministic variation, with residual uncertainty attributable to Monte Carlo noise alone. Variance-based sensitivity analysis confirms boron concentration accounts for 99% of output variance. The surrogate preserves physically meaningful reactivity coefficients (Doppler: pcm/K; boron worth: pcm/ppm) while providing -fold computational speedup. The framework is restricted to fresh fuel with fixed enrichment; extension to burnup-dependent scenarios is left for future work. - Open Access
Experimental Analysis of LNG Cold Exergy Recovery Using Thermoelectric Generators
A dedicated experimental setup was designed and constructed to investigate the performance of thermoelectric generators (TEGs) operating under cryogenic temperature conditions, representative of the heat recovery process during liquefied natural gas (LNG) regasification. The test bench enables precise control of both hot- and cold-side temperatures, simulating real operating conditions of mobile LNG systems. Six types of commercial TEG modules were tested at two hot-side temperatures (283 K and 340 K) and variable cold-side temperatures (150–260 K). The results showed that higher temperature differences significantly improved electrical power generation and conversion efficiency. For one of the TEGs, the electrical power and efficiency nearly doubled compared to the case with a hot-side temperature of 283 K. As the cold-side temperature increased, both performance indicators decreased almost linearly, as expected, with the lowest gradients observed for one of the TEGs, indicating the highest potential for total exergy recovery. These findings indicate that TEGs can effectively recover cryogenic heat during LNG vaporization. Scaling the system to practical applications shows that electric power exceeding 1 kW can be achieved in railway transport and up to 10 kW in marine applications, making this approach technically and economically viable for large-scale mobile LNG systems.
Open AccessExperimental Analysis of LNG Cold Exergy Recovery Using Thermoelectric Generators
A dedicated experimental setup was designed and constructed to investigate the performance of thermoelectric generators (TEGs) operating under cryogenic temperature conditions, representative of the heat recovery process during liquefied natural gas (LNG) regasification. The test bench enables precise control of both hot- and cold-side temperatures, simulating real operating conditions of mobile LNG systems. Six types of commercial TEG modules were tested at two hot-side temperatures (283 K and 340 K) and variable cold-side temperatures (150–260 K). The results showed that higher temperature differences significantly improved electrical power generation and conversion efficiency. For one of the TEGs, the electrical power and efficiency nearly doubled compared to the case with a hot-side temperature of 283 K. As the cold-side temperature increased, both performance indicators decreased almost linearly, as expected, with the lowest gradients observed for one of the TEGs, indicating the highest potential for total exergy recovery. These findings indicate that TEGs can effectively recover cryogenic heat during LNG vaporization. Scaling the system to practical applications shows that electric power exceeding 1 kW can be achieved in railway transport and up to 10 kW in marine applications, making this approach technically and economically viable for large-scale mobile LNG systems.
- Open Access
- 21 January 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (2) , 1075
Deep Learning for Real-Time Detection of Brassicogethes aeneus in Oilseed Rape Using the YOLOv4 Architecture
The growing global population and increasing food demand highlight the need for sustainable agricultural practices that balance productivity with environmental protection. Traditional blanket pesticide spraying leads to overuse of chemicals, environmental pollution, and biodiversity loss. This study aims to develop an innovative approach to precision pest management using mobile computing, computer vision, and deep learning techniques. A mobile measurement platform equipped with cameras and an onboard computer was designed to collect real-time field data and detect pest infestations. The system uses an advanced object detection algorithm based on the YOLOv4 architecture, trained on a custom dataset of rapeseed pest images. Modifications were made to enhance detection accuracy, especially for small objects. Field tests demonstrated the system’s ability to identify and count pests, such as the pollen beetle (Brassicogethes aeneus), in rapeseed crops. The collected data, combined with GPS information, generated pest density maps, which can guide site-specific pesticide applications. The results show that the proposed method achieved a mean average precision (mAP) of 83.7% on the test dataset. Field measurements conducted during the traversal of rapeseed fields enabled the creation of density maps illustrating the distribution of pollen beetles. Based on these maps, the potential for pesticide savings was demonstrated, and the migration dynamics of pollen beetle were discussed.
- 21 January 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (2) , 1075
Open AccessDeep Learning for Real-Time Detection of Brassicogethes aeneus in Oilseed Rape Using the YOLOv4 Architecture
The growing global population and increasing food demand highlight the need for sustainable agricultural practices that balance productivity with environmental protection. Traditional blanket pesticide spraying leads to overuse of chemicals, environmental pollution, and biodiversity loss. This study aims to develop an innovative approach to precision pest management using mobile computing, computer vision, and deep learning techniques. A mobile measurement platform equipped with cameras and an onboard computer was designed to collect real-time field data and detect pest infestations. The system uses an advanced object detection algorithm based on the YOLOv4 architecture, trained on a custom dataset of rapeseed pest images. Modifications were made to enhance detection accuracy, especially for small objects. Field tests demonstrated the system’s ability to identify and count pests, such as the pollen beetle (Brassicogethes aeneus), in rapeseed crops. The collected data, combined with GPS information, generated pest density maps, which can guide site-specific pesticide applications. The results show that the proposed method achieved a mean average precision (mAP) of 83.7% on the test dataset. Field measurements conducted during the traversal of rapeseed fields enabled the creation of density maps illustrating the distribution of pollen beetles. Based on these maps, the potential for pesticide savings was demonstrated, and the migration dynamics of pollen beetle were discussed.
- Open Access
Wake Losses, Productivity, and Cost Analysis of a Polish Offshore Wind Farm in the Baltic Sea
This study presents a comprehensive analysis of the long-term energy performance and economic viability of offshore wind farms planned for locations within the Polish Exclusive Economic Zone of the Baltic Sea. It focuses on the impact of wind farm layout, aerodynamic wake effects, and rotor blade surface degradation. Using the Jensen wake model, modified Weibull wind speed distributions are computed for various turbine spacing configurations (5D, 8D, and 10D) and wake decay constants . The results reveal a trade-off between turbine density and individual turbine efficiency: tighter spacing increases the total annual energy production (AEP) but also intensifies wake-induced losses. The study shows that cumulative losses due to wake effects can range from 16.5% to 38%, depending on the scenario considered. This corresponds to capacity factors ranging from 33.4% to 45.2%. Finally, lifetime productivity scenarios over 20 and 25 years are analyzed, and the levelized cost of electricity (LCOE) is calculated to assess the economic implications of design choices. The analysis reveals that, depending on the values of the considered parameters, the LCOE can range from USD 116.3 to 175.7 per MWh produced. The study highlights the importance of early stage optimization in maximizing both the energy yield and cost-efficiency in offshore wind farm developments.Open AccessWake Losses, Productivity, and Cost Analysis of a Polish Offshore Wind Farm in the Baltic Sea
This study presents a comprehensive analysis of the long-term energy performance and economic viability of offshore wind farms planned for locations within the Polish Exclusive Economic Zone of the Baltic Sea. It focuses on the impact of wind farm layout, aerodynamic wake effects, and rotor blade surface degradation. Using the Jensen wake model, modified Weibull wind speed distributions are computed for various turbine spacing configurations (5D, 8D, and 10D) and wake decay constants . The results reveal a trade-off between turbine density and individual turbine efficiency: tighter spacing increases the total annual energy production (AEP) but also intensifies wake-induced losses. The study shows that cumulative losses due to wake effects can range from 16.5% to 38%, depending on the scenario considered. This corresponds to capacity factors ranging from 33.4% to 45.2%. Finally, lifetime productivity scenarios over 20 and 25 years are analyzed, and the levelized cost of electricity (LCOE) is calculated to assess the economic implications of design choices. The analysis reveals that, depending on the values of the considered parameters, the LCOE can range from USD 116.3 to 175.7 per MWh produced. The study highlights the importance of early stage optimization in maximizing both the energy yield and cost-efficiency in offshore wind farm developments. - Open Access
Energy Mix Constraints Imposed by Minimum EROI for Societal Sustainability
This study analyzes the feasibility of energy mixes composed of different shares of various types of power generation units, including photovoltaic (PV) and wind farms, hydropower, fossil fuel-based plants, and nuclear power. The analysis uses the concept of Energy Return on Investment (EROI), which is considered the most reliable indicator for comparing different technologies as it measures the energy required rather than monetary costs needed to build and operate each technology. Literature-based EROI values for individual generation technologies were used, along with the minimum EROI thresholds for the entire energy mix that are necessary to sustain developed societies and a high quality of life. The results show that, depending on the assumed minimum EROI value, which ranges from 10 to 30, the maximum share of intermittent renewable energy sources (IRESs), such as PV and wind farms, in the system cannot exceed 90% or 60%, respectively. It is important to emphasize that this EROI-based analysis does not account for power grid stability, which currently can only be maintained by the inertia of large synchronous generators. Therefore, the scenario with a 90% IRES share should be regarded as purely theoretical.
Open AccessEnergy Mix Constraints Imposed by Minimum EROI for Societal Sustainability
This study analyzes the feasibility of energy mixes composed of different shares of various types of power generation units, including photovoltaic (PV) and wind farms, hydropower, fossil fuel-based plants, and nuclear power. The analysis uses the concept of Energy Return on Investment (EROI), which is considered the most reliable indicator for comparing different technologies as it measures the energy required rather than monetary costs needed to build and operate each technology. Literature-based EROI values for individual generation technologies were used, along with the minimum EROI thresholds for the entire energy mix that are necessary to sustain developed societies and a high quality of life. The results show that, depending on the assumed minimum EROI value, which ranges from 10 to 30, the maximum share of intermittent renewable energy sources (IRESs), such as PV and wind farms, in the system cannot exceed 90% or 60%, respectively. It is important to emphasize that this EROI-based analysis does not account for power grid stability, which currently can only be maintained by the inertia of large synchronous generators. Therefore, the scenario with a 90% IRES share should be regarded as purely theoretical.
- Open Access
- 10 February 2025
- Published by Polish Academy of Sciences Chancellery in Archives of Electrical Engineering
- p. 227
Evaluation of the influence of temperaturę on the parameters of the Jiles–Atherton model and magnetization curves of a 50% Fe–Ni type alloy
This paper presents an original method for defining the magnetization characteristics of a 50% iron-nickel alloy as functions of ambient temperature. The presented method, based on Jiles–Atherton theory, reduces the cost and time needed to build a multifactor theoretical model of a ferromagnetic material in relation to temperature. The determination of the J-A equation parameters, which are crucial to obtain theoretical magnetization characteristics that are consistent with the real ones, is always challenging due to the imperfection of the J-A model. The authors focused on determining the temperature dependent magnetization characteristics DC in the range of low magnetic field strength. Based on the results of the experiment, the relationships between temperature and parameters of the basic J-Amodelwere determined and discussed. The study was carried out for a wide temperature range often specified for high-performance electrical or electromechanical devices. The data presented and the method described can be successfully used to build Multiphysics models of magnetic phenomena. Based on the available knowledge, material data for the 50% Fe–Ni alloy, treated without a H2 reducing atmosphere, has not yet been published in the universal form presented by the authors. The presented data and relationships between physical quantities were verified and confirmed experimentally. The presented measurement method is consistent with the industry standard IEC 60404-4.
- 10 February 2025
- Published by Polish Academy of Sciences Chancellery in Archives of Electrical Engineering
- p. 227
Open AccessEvaluation of the influence of temperaturę on the parameters of the Jiles–Atherton model and magnetization curves of a 50% Fe–Ni type alloy
This paper presents an original method for defining the magnetization characteristics of a 50% iron-nickel alloy as functions of ambient temperature. The presented method, based on Jiles–Atherton theory, reduces the cost and time needed to build a multifactor theoretical model of a ferromagnetic material in relation to temperature. The determination of the J-A equation parameters, which are crucial to obtain theoretical magnetization characteristics that are consistent with the real ones, is always challenging due to the imperfection of the J-A model. The authors focused on determining the temperature dependent magnetization characteristics DC in the range of low magnetic field strength. Based on the results of the experiment, the relationships between temperature and parameters of the basic J-Amodelwere determined and discussed. The study was carried out for a wide temperature range often specified for high-performance electrical or electromechanical devices. The data presented and the method described can be successfully used to build Multiphysics models of magnetic phenomena. Based on the available knowledge, material data for the 50% Fe–Ni alloy, treated without a H2 reducing atmosphere, has not yet been published in the universal form presented by the authors. The presented data and relationships between physical quantities were verified and confirmed experimentally. The presented measurement method is consistent with the industry standard IEC 60404-4.
- Open Access
- 20 January 2025
- Published by MDPI AG in Computation
- Vol. 13 (1) , 20
Estimation of Wind Farm Losses Using a Jensen Model Based on Actual Wind Turbine Characteristics for an Offshore Wind Farm in the Baltic Sea
This study investigates the effects of velocity deficits on the performance of wind turbines in multi-row wind farms, focusing on two types of turbines: Gamesa G132 and Gamesa SG8. The analysis examines the impact of turbine spacing on key performance metrics, including Annual Energy Production, energy production losses, and the capacity factor. Two models are used: the classical Jensen model, assuming a constant thrust coefficient (), and an updated model that incorporates the actual turbine-specific characteristics. The results demonstrate that as turbine spacing decreases, the velocity deficit behind the turbines increases, leading to significant reductions in AEP and higher energy losses. These effects are particularly pronounced for spacings of 5D and 3D, raising concerns about the economic feasibility of such wind farms. This study also highlights that the proposed updated Jensen model, which accounts for the specific turbine characteristics, provides results that are closer to real-world observations. This study showed that for a Baltic Sea wind farm location, the capacity factor for the wind farm is in the range of 0.366 to 0.476, depending on the turbine spacing.- 20 January 2025
- Published by MDPI AG in Computation
- Vol. 13 (1) , 20
Open AccessEstimation of Wind Farm Losses Using a Jensen Model Based on Actual Wind Turbine Characteristics for an Offshore Wind Farm in the Baltic Sea
This study investigates the effects of velocity deficits on the performance of wind turbines in multi-row wind farms, focusing on two types of turbines: Gamesa G132 and Gamesa SG8. The analysis examines the impact of turbine spacing on key performance metrics, including Annual Energy Production, energy production losses, and the capacity factor. Two models are used: the classical Jensen model, assuming a constant thrust coefficient (), and an updated model that incorporates the actual turbine-specific characteristics. The results demonstrate that as turbine spacing decreases, the velocity deficit behind the turbines increases, leading to significant reductions in AEP and higher energy losses. These effects are particularly pronounced for spacings of 5D and 3D, raising concerns about the economic feasibility of such wind farms. This study also highlights that the proposed updated Jensen model, which accounts for the specific turbine characteristics, provides results that are closer to real-world observations. This study showed that for a Baltic Sea wind farm location, the capacity factor for the wind farm is in the range of 0.366 to 0.476, depending on the turbine spacing. - Open Access
- 21 September 2024
- Published by Elsevier in Applied Thermal Engineering
- Vol. 257
Comprehensive experimental and modeling analysis of helically-wound coiled vaporizer
- 21 September 2024
- Published by Elsevier in Applied Thermal Engineering
- Vol. 257
Open AccessComprehensive experimental and modeling analysis of helically-wound coiled vaporizer