Operating Conditions Research Articles

Interleaving of screen printed PEDOT:PSS/ Ag nanowires ink via bottom-up approach into flexible polyurethane coats patterned on bi-stretch fabrics for multimodal sensing and IR heating

Textile-integrated wearable sensors have exhibited an immense potential to transform human life by fetching safety and comfort at the forefront. This work underscores a novel design approach to fabricate textile-integrated multimodal sensors using a conductive ink coated bi-stretch nylon fabric. Conductive ink has been formulated by dispersing high aspect ratio silver nanowires into water-dispersed PEDOT: PSS solution with polyvinylpyrrolidone that allows sensing of various stimuli, including mechanical strains, temperature, humidity, etc. The fabrication of sensing elements involves the interleaving of screen-printed conductive ink into flexible coats, using water-borne polyurethane, achieved by the facile pad-dry-cure technique on bi-stretch fabric like a sandwich. Remarkable mechanical strain sensing performance in terms of sensitivity, repeatability, and stability has been demonstrated along with flexibility, bendability, and compliant form factor, making them suitable for applications in wearable technology and smart textiles. Moreover, the temperature and humidity sensing exhibit rapid response and wide detection ranges, making the sensor adaptable to diverse environmental conditions. The sensing fabric responds well to different strain and compression conditions. The as-developed fabric can also operate as an IR heating element when biased at certain operating conditions. These attributes make such elements an ideal candidate for various applications, such as human motion tracking, environmental monitoring, and healthcare devices. The sensor's low-cost, solvent-free production and scalability make it a practical choice for mass adoption.

Research on control sequence of work mode switching for planetary row multi-mode hybrid powertrain system

According to the characteristics of the planetary row hybrid power system, it is an important issue for the system to adopt a reasonable working mode to realize the efficient operation of the hybrid power system in different operating conditions. Paper takes the single-row hybrid system as the research object. To address the complexity of the working model, a self-construction method using vehicle modeling is proposed. State-space equations are developed for each mode, and an objective function model integrating main and sub-mode switching rules is constructed using the DP algorithm. Learning Vector Quantization (LVQ) network clustering extracts mode-switching sequences. Urban and highway cycles (UDDS and HWFET) are chosen as global optimization parameters. The DP algorithm’s global optimization is applied, extracting sequence basis from UDDS and HWFET compound conditions. Utilizing LVQ input data, the main mode’s power demand is divided into 4 sub-mode regions, generating a switching sequence diagram. Joint simulations verify the effectiveness. ECMS control with regular mode switching yields 4.7458 L/100 km fuel consumption under UDDS & HWFET, 5.53% higher than DP optimization. WLTC conditions, with more medium and high-speed operation, consumption is 5.031 L/100 km, offering further optimization potential.

Energy consumption analysis of building air-conditioning systems using centrifugal compressor with gas bearing under annual operating conditions

The water chiller using centrifugal compressor with gas bearing is an important development direction for building energy saving, but the additional refrigerant cycle for bearing and motor colling leads to more complex performance especially under the various operating condition. Based on the heating and cooling loads of a building in Nanjing and the local climate conditions, this paper innovatively establishes an air conditioning system model using centrifugal compressor with gas bearings (CCGB), and analyzes the performance of air-cooling and water-cooling methods. Considering the heating and cooling loads, the annual energy consumption of two systems was compared consider the climate condition. The results show that the COP of water-cooling system is larger than that of air-cooling system due to the better heat transfer condition. The larger power consumption of compressor for air-cooling system leads to the larger motor cooling load. The cooling methods have obvious influence on the vapor-injected mass flow rate, but have small impact on the mass flow rate of motor cooling and bearing gas supply. The energy efficiency of air-cooling and water-cooling system in summer are 3.77 and 5.12 respectively, and the total energy consumption in the whole year are 32.4 tons and 33.5 tons of standard coal.

Effects of combustor wall cooling structure on combustor performances

For lean premixed low-emissions gas turbine combustors, the amount of air used for combustion chamber cooling is relatively low to maintain a “lean” state of the flame. However, the wall cooling structures may have significant effects on combustor performances such as pollutant emissions, outlet temperature distributions, and boundary temperatures since it can change the near-wall temperature distributions. Most of the existing studies focus on the influence of cooling hole structures on heat transfer characteristics, and few studies comprehensively investigate the comprehensive effects on the wall heat transfer, outlet temperature distribution, pollutant generation characteristics, flow field structure, and flame morphology. To explore the feasibility of enhancing combustor performance through the implementation of combustor wall cooling structures, this study conducted a series of experimental and numerical investigations using two combustion chamber liners. Results show that although the burner structure remains unchanged, using the improved liner can widen the low-emissions operation ranges, and will reduce CO emissions by 49.34 % under low-power operating conditions and NOx emissions by 7.95 % under high-power operation conditions. This is because it optimizes the distribution of the wall temperature gradient and the shape of the corner recirculation zone. Besides, the improved structure enhances the boundary wall heat transfer by about 10 %, which leads to a 14.31 K lower and more uniform temperature of the liner. This is because it eliminates the cooling air incident vortex and improves the heat transfer between different rows of cooling holes. Due to the change of flow-field and near-wall temperature distribution, the optimum fuel supply strategy is different for the two liner structures. In particular, the optimal fuel staging strategy for the original liner necessitates a higher inner stage equivalence ratio than that required for the improved liner configuration. The results of this study offer a solution strategy for effectively improving combustor performances by employing suitable liner cooling tactics.

Three-dimensional numerical simulation of coal and papermaking trash co-combustion in a circulating fluidized bed

Circulating fluidized bed (CFB) combustion is a important method of waste treatment with the benefits of harmlessness, recycling, and energy recovery. Using the existing co-firing CFB boiler to dispose of waste can reduce investment in boilers and environmental protection equipment. However, there is currently limited research available on the co-combustion of coal and waste (especially for papermaking trash), and the mechanism of pollutant emissions during unit operation remains unclear. In this work, the co-combustion of coal and papermaking trash in a three-dimensional industrial-scale CFB are investigated by using the Dense Discrete Phase Model (DDPM) method. Both homogeneous reactions (such as fuel particle pyrolysis, combustion, and surface reaction) and non-homogeneous reactions (such as gas combustion and pollutant generation) are considered. The co-combustion characteristics are comprehensively analyzed in terms of gas distribution, chemical reaction rates, and bed temperature under various operating conditions, including fuel mixing ratio, secondary air arrangement, and excess air coefficient. The results are obtained by comparing the distribution profile along the height. By reducing the mixing ratio of papermaking trash, the excess air coefficient, and adjusting the secondary air arrangement in the lower region, an expanding, reducing atmosphere is observed in the vicinity of the fuel feeding point. This, in turn, leads to an increase in CO concentration and a decrease in NO emissions, which is attributable to the interplay of gas distribution, chemical reaction rates, and bed temperature. A reduction in NO gas emissions was achieved by adjusting the secondary air arrangement and the excess air coefficient. Overall, this work provides valuable insights into the co-combustion characteristics of coal and papermaking trash in an industrial-scale circulating fluidized bed boilers.

Testing of an indirect ice detection methodology in the Horizon 2020 project SENS4ICE

AbstractSupercooled large droplets (SLD) icing conditions have been the cause of severe aircraft accidents over the last decades. Existing countermeasures, even on modern airplanes, are not necessarily effective against the resulting ice formations, which raises a demand for reliable detection of SLD in all conditions for safe operations. The EU-funded Horizon 2020 project SENS4ICE focused on new ice detection approaches and innovative sensor hybridization to target a fast and reliable (SLD-)ice detection. The performance-based (indirect) ice detection methodology is key to this approach and based on the changes of airplane flight characteristics under icing influence. This paper provides a short overview of the development and implementation of the indirect ice detection (IID) algorithms in SENS4ICE. Moreover, it gives and discusses first exemplary results from the IID tests in classical icing conditions during the SENS4ICE North America flight test campaign conducted in February/March 2023 out of St. Louis Regional Airport in Alton (Illinois, USA).

Design and Visual Implementation of a Regional Energy Risk Superposition Model for Oil Tank Farms

Ensuring the safety of oil tank farms is essential to maintaining energy security and minimizing the impact of potential accidents. This paper develops a quantitative regional risk model designed to assess both individual and societal risks in oil tank farms, with particular attention to energy-related risks such as leaks, fires, and explosions. The model integrates factors like day–night operational variations, weather conditions, and risk superposition to provide a comprehensive and accurate evaluation of regional risks. By considering the cumulative effects of multiple hazards, including those tied to energy dynamics, and the stability and validity of the model are researched through Monte Carlo simulations and case application. The results show that the model enhances the reliability of traditional risk assessment methods, making it more applicable to oil tank farm safety concerns. Furthermore, this study introduces a practical tool that simplifies the risk assessment process, allowing operators and decision-makers to evaluate risks without requiring in-depth technical expertise. The methodology improves the ability to safeguard oil tank farms, ensuring the stability of energy supply chains and contributing to broader energy security efforts. This study provides a valuable method for researchers and engineers seeking to enhance regional risk calculation efficiency, with a specific focus on energy risks.

Harnessing waste palm-based activated carbon/difluoromethane pair for sustainable low-emission cooling systems

An adsorption cooling system (ACS) offers distinct advantages over conventional systems, including improved energy efficiency, waste heat power, low-emission refrigerant employment, and simplified mechanical design. The performance of ACS is greatly affected by the selection of the adsorbent/refrigerant pair. In this study, we experimentally investigate the adsorption capacity of difluoromethane (CH2F2) onto several highly porous waste palm-based activated carbons (PACs). Elemental analysis, SEM, XRD, and FT-IR spectroscopy are performed on raw, carbonized, and activated samples to gain deeper insights into their surface morphology and functional groups. Difluoromethane adsorption isotherms of the PACs are obtained using a high-precision thermogravimetric analyzer at typical operating adsorption and desorption temperatures of ACS. One of the studied PAC/difluoromethane pairs achieves a benchmark uptake of 2.741 kg difluoromethane per kg adsorbent at 30°C and 1893 kPa equilibrium pressure. The experimental data are correlated with substantial accuracy using the Dubinin-Astakhov (D–A) and Tóth isotherm models to predict adsorption performance under experimentally unexamined operating conditions. Furthermore, for this PAC/difluoromethane pair, key performance indicators such as specific cooling effect (SCE), coefficient of performance (COP), Clapeyron cycle (P-T-W) diagram, and heat of adsorption (Qst) are evaluated. The results demonstrate that the PAC/difluoromethane pairs have excellent potential for the development of high-performance, low-emission ACS.

Detecting Geothermal Operational Asset Anomalies Using the Locality-Sensitive Hashing (LSH) Algorithm

Geothermal power plants are crucial for sustainable energy generation, necessitating the reliable maintenance of their operating assets. This research proposes an approach for asset maintenance through anomaly detection using the Locality- Sensitive Hashing (LSH) algorithm. The accuracy and coverage of traditional anomaly detection approaches in geothermal power plants may be constrained by sensor monitoring systems. The LSH algorithm is used to improve detection skills and get a full understanding of the state of important assets. The proposed method utilizes historical sensor data collected during geothermal power plant operations. This data is transformed into hash codes using LSH, effectively capturing similarities between various operational states and asset conditions. By comparing the hash codes of the current operational state with a library of precomputed hash codes representing typical operating conditions, the LSH algorithm can identify deviations indicating potential irregularities. This facilitates early detection of anomalies, even in large-scale databases, enabling prompt maintenance interventions. The application of anomaly detection using the LSH algorithm provides benefits such as improved asset maintenance planning, reduced downtime, and increased operational safety. By leveraging data-driven analysis and the effectiveness of LSH, geothermal operators can detect faults early, enabling prompt interventions and optimizing reliability and efficiency. By leveraging historical sensor data and the efficient similarity approximation capabilities of LSH, the proposed approach enables early diagnosis of problems, improving maintenance planning and optimizing geothermal operations. Keywords: geothermal assets, locality-sensitive hashing, asset condition, fault detection, reliability

Enhanced removal of methyl orange and malachite green using mesoporous TO@CTAB nanocomposite: Synthesis, characterization, optimization and real wastewater treatment efficiency.

This study explores the synthesis of a novel titanium oxide-cetyltrimethylammonium bromide (TO@CTAB) nanocomposite for the effective removal of malachite green (MG) and methyl orange (MO) dyes. The optimization of the nanocomposite's performance was carried out using response surface methodology (RSM). The adsorption characteristics were further evaluated through isotherm models, kinetic studies and thermodynamic analyses. The mesoporous nature of TO@CTAB was confirmed through BET analysis, revealing a pore diameter of 4.625nm. The crystalline size of TO@CTAB is 54.78nm, and its crystalline index is 70.84%. The optimal operating conditions were established based on the results obtained from the ANOVA. The Langmuir isotherm model demonstrates superior adsorption performance compared to the Freundlich isotherm model, with adsorption efficiencies of 317.46mg/g for MO and 306.748mg/g for MG. The pseudo-second-order model, with an R2 value of 0.998 and 0.997 for MO and MG, respectively, provides a more accurate and reliable explanation of the adsorption process compared to the pseudo-first-order model. Furthermore, the high reusability and minimal deterioration of TO@CTAB were observed for up to 5 cycles. The analysis of the adsorption mechanism indicates that the adsorption of MG and MO occurs through H-bonding, electrostatic and π-π interactions. A comprehensive cost analysis of the process was conducted to evaluate the cost-effectiveness; total expenditure incurred during the process was determined to be within acceptable limits. TO@CTAB was assessed using real wastewater samples, demonstrating a decolourization efficiency of 82%. Additionally, it resulted in a reduction of COD, BOD, TSS and TDS.

Influence of Bulk Defect Density in CIGS on the Efficiency of Copper Indium Gallium Selenide Photocell

In CIGS-based thin films, bulk defects are believed to represent disturbances in a regular, periodic arrangement within the atoms or the crystalline media, which influence sheet resistivity. In this study, resistivity measurements were carried out on CIGS thin films using the Van Der Pauw technique. Then, a comparison between two Van Der Pauw four-point measurement configurations (aligned and square) was made, which showed that the square configuration was the most appropriate configuration that can be recommended to measure the thin film sheet resistance of CIGS films. Finally, a numerical simulation using SCAPS-1D software was used to study the influence of bulk defect density in CIGS films as an absorber layer in a model photocell. Using the simulated data, three operating zones for the model photocell were identified depending on its bulk defect density concentration. The influence of bulk defects on thickness, band gap, and doping were then analyzed. It was revealed that when the bulk defect density was less than 5 × 1013 cm−3 5.1013 cm−3 for an absorber of thickness in the order of over 3 μm, a band gap between 1.3 eV–1.4 eV and acceptor density of NA = 1016 cm-31016 cm−3 were the optimal operating conditions for the model photocell. It was concluded that the CIGS layer used as an absorber can be improved if its bulk defect density is tuned to optimal levels.

Opportunities and Challenges for Predicting the Service Status of SLM Metal Parts Under Big Data and Artificial Intelligence.

Selective laser melting (SLM) technology is a high-end dual-use technology that is implemented in aerospace and medical equipment, as well as the automotive industry and other military and civilian industries, and is urgently needed for major equipment manufacturing and national defense industries. This paper examines the challenges of uncontrollable service states and the inability to ensure service safety of SLM metal parts under nonlinear and complex operating conditions. An overview of the prediction of the service status of SLM metal parts was introduced, and an effective approach solving the problem was provided in this paper. In this approach, the cross-scale coupling mechanism between mesoscopic damage evolution and macroscopic service state evolution is clarified by tracking the mesoscopic damage evolution process of SLM metal parts based on ultrasonic nonlinear responses. The failure mechanism is organically integrated with hidden information from monitoring big data, and a "chimeric" model to accurately evaluate the service status of SLM metal parts is constructed. Combining nonlinear ultrasound technology with big data and artificial intelligence to construct a "chimeric" model and consummate the corresponding methods and theories for evaluating the service status of SLM metal parts is an effective way to reveal the mesoscopic damage evolution and service status evolution mechanisms of SLM metal parts under complex factor coupling, and to accurately describe and characterize the service status of parts under complex operating conditions. The proposed approach will provide a theoretical basis and technical guarantee for the precise management of SLM parts' service safety in key equipment fields such as aerospace, medical equipment, and the automotive industry.

Design approach for tilt propellers of UAM/eVTOLs for cruise and hover considering aerodynamic and aeroacoustic characteristics via a multi-fidelity model

The design of quiet and efficient propellers/rotors is driven by the rapid development of Vertical Takeoff and Landing (VTOL) vehicles in urban areas. However, this task is challenging due to the expensive computational cost of propeller aerodynamic and aeroacoustic optimization based on accurate high-fidelity (HF) models. In contrast, many low-fidelity (LF) methods are less costly but also less accurate for complex propeller blade geometries, such as those involving sweep. This work aims to use a multi-fidelity (MF) surrogate model (SM) that combines a small HF data set for accuracy and a larger LF data set to speed up modeling. The MF approach inherits the accuracy of the HF method while retaining the computational efficiency of the LF model. The HF and LF aerodynamic data sets for training the MF model are obtained from a RANS solver and a meshless Large Eddy Simulation (LES) method, respectively. The obtained surface pressure of the blade is then used to calculate the noise signals radiating from the propeller using a Farassat's Formulation 1A (F1A) code. For practical design, we developed an optimization framework that combines these aerodynamic and aeroacoustic solvers, the MF model, design of experiment (DoE), and a multi-objective optimizer. The framework aims to optimize the blade aerodynamic shape in terms of chord, twist, and sweep distributions along the span from a baseline propeller, with objectives of efficiency and noise reduction under thrust constraints for both cruise and hover operating conditions. The obtained optimal designs, in the form of three-dimensional Pareto fronts, increased the aerodynamic efficiency by approximately 2% for cruise and 5% for hover and decreased the overall sound pressure level (OASPL) for hover by about 4 dB from the baseline propeller. The MF surrogate model, which utilizes both HF and LF data, doubled the optimization efficiency compared to the surrogate model based solely on HF data. Furthermore, the model based only on LF data fails to capture the three-dimensional flow effects induced by propeller sweep, which is crucial for reducing propeller noise. The aerodynamic benefits include reduced flow separation near the blade root during hover and more ideal loading distribution during cruise, while the aeroacoustic improvements are demonstrated through the “dephase” effect on noise signals emitted from different parts of the swept blade.

Grain boundary self-diffusion and point defect interactions in α-U via molecular dynamics

Though metallic U-Zr fuel has been used in nuclear reactors since the 1960s, many of its fundamental and thermodynamic properties are still unknown. The α-U phase, which has a highly anisotropic crystal structure and physical properties, is present in U-Zr fuel. The character and behavior of α-U grain boundaries will strongly impact fuel thermophysical performance under irradiation. We study the interaction of point defects with grain boundaries, diffusion along grain boundaries, and the predicted diffusional creep behavior of α-U via molecular dynamics. We calculate the segregation energy of vacancies and interstitials to grain boundaries and quantify the biased sink strength of the grain boundaries, and observe that this sink strength is not strongly dependent on the grain boundary orientation. We also find that grain boundary diffusivity is strongly dependent on the grain boundary energy and grain boundary orientation. The presence of point defects within the grain boundary can induce diffusion in grain boundaries with low formation energies and can enhance diffusion in high-energy grain boundaries. We also find that diffusional creep of α-U at prototypical metallic fuel operation conditions is extremely high and could help explain observed metallic fuel swelling behaviors.

Low-temperature CO2 Hydrogenation to Olefins on Anorthic NaCoFe Alloy Carbides.

The hydrogenation of carbon dioxide to olefins (CTO) represents an ideal pathway towards carbon neutrality. However, most current CTO catalysts require a high-temperature condition of 300-450°C, resulting in high energy consumption and possible aggregation among active sites. Herein, we developed an efficient iron-based catalyst modified with high-sodium content (7%) and low-cobalt content (2%), achieving a CO2 conversion of 22.0% and an olefin selectivity of 55.9% at 240°C and 1000 mL/g/h, and it is even active at 180°C and 4000 mL/g/h with more than 25% olefins in hydrocarbons. The catalyst was kept stable under continuous operating conditions of 500 hours. Numerous characterizations and calculations reveal high content of sodium as an electronic promoter enhances the stability of the active anorthic Fe5C2 phase at low temperatures. Further incorporating the above catalyst with cobalt, as a structural promoter, causes Fe species to form a FexCoy alloying phase, which in turn facilitates the formation of higher active anorthic (FexCoy)5C2 phase, different from the conventional carbides and alloy carbides. An in-depth investigation of the synergistic effects of structural and electronic promoters can improve catalyst performance, increase reaction efficiency and cost-effectiveness, and provide profound insights for understanding and optimizing CO2 hydrogenation reactions.

Fusion State-of-Health Estimation of Lithium-Ion Batteries Based on Improved XGBoost Algorithm and Adaptive Kalman Filter

Abstract Accurately predicting the state-of-health of lithium-ion batteries (LIBs) is of paramount significance for safety and stability of battery systems. This paper introduces a fusion model, which integrates the characteristic of data-driven model and equivalent circuit model to enhance precision. The first step is to preprocess data, including extracting health features, correlation screening, and compressing data. Subsequently, the hyperparameters of XGBoost algorithm are optimized using a weighted artificial bee colony algorithm, resulting in an improved XGBoost (IXGB) data-driven model. Finally, the observed values from the data-driven model and the prior values based on the equivalent circuit model are combined through adaptive Kalman filter (AKF), developing an improved XGBoost and adaptive Kalman filter (IXGB-AKF) fusion model, which makes the most of historical experience and the current state of LIBs. Validation is conducted using publicly available NASA Li-ion Battery Aging Datasets, with different datasets under various operating conditions, including different battery cells, different discharge depths and rates of LIBs. The resulting root mean square error values of the former three operating conditions are 1.834%, 2.570%, and 3.456%, respectively. The results indicate that the IXGB-AKF fusion model exhibits good accuracy and robustness under different operating conditions.

Adaptive Joint Sigma-Point Kalman Filtering for Lithium-Ion Battery Parameters and State-of-Charge Estimation

Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic, nonlinear, and time-varying behavior of LIBs under different operating conditions. In this paper, an advanced joint estimation approach of the model parameters and SoC is proposed utilizing an enhanced Sigma Point Kalman Filter (SPKF). Based on the second-order equivalent circuit model (2RC-ECM), the proposed approach was compared to the two most widely used methods for simultaneously estimating the model parameters and SoC, including a hybrid recursive least square (RLS)-extended Kalman filter (EKF) method, and simple joint SPKF. The proposed adaptive joint SPKF (ASPKF) method addresses the limitations of both the RLS+EKF and simple joint SPKF, especially under dynamic operating conditions. By dynamically adjusting to changes in the battery’s characteristics, the method significantly enhances model accuracy and performance. The results demonstrate the robustness, computational efficiency, and reliability of the proposed ASPKF approach compared to traditional methods, making it an ideal solution for battery management systems (BMS) in modern EVs.

Acceleration of Modeling Capability for GDI Spray by Machine-Learning Algorithms

Cold start causes a high amount of unburned hydrocarbon and particulate matter emissions in gasoline direct injection (GDI) engines. Therefore, it is necessary to understand the dynamics of spray during a cold start and develop a predictive model to form a better air-fuel mixture in the combustion chamber. In this study, an Artificial Neural Network (ANN) was designed to predict quantitative 3D liquid volume fraction, liquid penetration, and liquid width under different operating conditions. The model was trained with data derived from high-speed and Schlieren imaging experiments with a gasoline surrogate fuel, conducted in a constant volume spray vessel. A coolant circulator was used to simulate the low-temperature conditions (−7 °C) typical of cold starts. The results showed good agreement between machine learning predictions and experimental data, with an overall accuracy R2 of 0.99 for predicting liquid penetration and liquid width. In addition, the developed ANN model was able to predict detailed dynamics of spray plumes. This confirms the robustness of the ANN in predicting spray characteristics and offers a promising tool to enhance GDI engine technologies.

Research on Support Structure of Rectangular Cryogenic Infrared Lens with Large Aperture

This paper presents the design and optimization of a composite flexible support structure aimed at addressing the challenges associated with maintaining the positional accuracy and surface integrity of large-aperture cryogenic infrared lenses with long focal lengths. The primary objective of the structure is to maintain precise lens alignment while preserving the surface shape under operational conditions. The design complexities and underlying principles of the flexible support structure are systematically explored. A mechanical model of the flexible support structure was derived based on its structural characteristics, and the equilibrium equation was established to ensure the lens meets thermal deformation requirements in various directions. Optimization of key design parameters was conducted for a lens operating at 200 K, measuring 304 mm × 230 mm. The gravitational deformation of the optimized lens exhibited a root mean square (RMS) surface accuracy of 7.72 nm in the X direction, 7.08 nm in the Y direction, and 9.60 nm in the Z direction for lens surface 1. For lens surface 2, RMS values were 8.62 nm in the X direction, 8.41 nm in the Y direction, and 9.64 nm in the Z direction. At 200 K and lower temperatures, the RMS values of lens surfaces 1 and 2 were 2.41 nm and 2.74 nm, respectively, with a first-order mode frequency of 143.37 Hz.

Adjoint shape optimization for enhanced heat transfer in sweeping jet impingement on concave surface

The sweeping jet has gained increasing attention in the field of impingement heat transfer due to its unique advantages. Current research primarily focuses on impinging on flat walls, with less attention given to curved wall scenarios. Recent studies have shown that the trapped vortex ring generated by a sweeping jet impinging on a curved surface can limit the effective cooling range. Therefore, modifying the structure of the fluidic oscillator offers considerable potential for enhancing the impingement heat transfer. In this paper, the shape optimization of the conventional fluidic oscillator is performed using an adjoint optimization method. Numerical simulations were first conducted with a jet Reynolds number of 10,308, an impingement distance of four times the jet hydraulic diameter, and an impingement wall radius of ten times the jet hydraulic diameter as the operating conditions. To accurately reproduce the jet dissipation characteristics and the trapped vortex ring structure, the turbulent dissipation rate was modeled with a well calibrated Generalized k-ω (GEKO) model. The optimized structure aimed to minimize the surface-averaged temperature. The results indicated that the improved structure reduced the jet's oscillation angle, resulting in a more concentrated jet velocity and less dissipation. This intensified the strength of the wall jet in the non-oscillation plane and pushed the trapped vortex ring farther outward, thus increasing the effective cooling range. Time- and surface-averaged results on the impingement wall revealed that the Nusselt number of the improved structure increased by 11.6%, and the temperature decreased by 2.4 K compared to the baseline structure.