Operating Conditions Research Articles

The effect of the electrodes shape in water electrolysis on the production of hydrogen

This article presents a comparative study of the production of hydrogen by the electrolysis of water using two forms of electrodes; cylindrical and flat. The objective of this comparison is to select the best type of electrolyzer to produce hydrogen for the same volume of water and the same operating voltage. An analytical study and experimental tests were carried out on a cylindrical electrolyser consisting of two concentric cylindrical electrodes separated by a variable thickness e (3.5 mm, 4 mm, 5.5 mm, 8.5 mm and 9 mm), then a flat electrolyser consisting of two flat electrodes spaced by a distance d (2 mm, 4 mm, 6 mm, 10 mm and 15 mm). For each cylindrical electrolyser there corresponds a single electrolyser with flat electrodes. The preliminary study revealed that the production of hydrogen in an electrolyzer is significantly affected by the distance separating the two electrodes and their shapes. Indeed, the comparative electrolysis results show that in each case the electrolyzer with cylindrical electrodes clearly prevails over its counterpart with flat electrodes for the same operating conditions and for the same quantity of water injected into the electrolysis.

Simulation data-driven attention fusion network with multi-similarity metric: A single-domain generalization diagnostic method for tie rod bolt loosening of a rod-fastening rotor system

The rod-fastening rotor system is the core rotating component of gas turbines and aero engines. The rod-disk joint structure of the rod-fastening rotor system is subjected to tie rod bolt loosening under complex operating conditions. Because of the uncertainty of fault occurrence, the model must possess diagnostic capability under unseen working conditions. Domain generalization methods are commonly employed to address such problems. They typically utilize multi-condition data or generate data from a single condition to train the model, enabling it to generalize to unknown operating conditions. However, difficulties in collecting complete multi-condition data and limitations in generating data, such as conflicting attributes and limited categories, make it challenging to ensure high-quality data for model training. To address these issues, this paper establishes a dynamic model of the rod-fastening rotor system to obtain high-fidelity simulation fault data, ensuring the physical accuracy, richness, and diversity of the source domain data. Meanwhile, a novel signal-domain generalization method called simulation data-driven attention fusion network with multi-similarity metric is proposed for tie rod bolt loosening in the rod-fastening rotor system. The attention feature fusion strategy is utilized to achieve the comprehensive representation of local and global features efficiently. Moreover, a multi-similarity loss based on deep metric learning is introduced to comprehensively consider the self-similarity and relative similarities between features, enabling class-level optimization of features. The proposed method is validated to have superior diagnostic performance in two cross-domain tasks compared with other methods. The proposed method can provide valuable insights into fault diagnosis in the rod-fastening rotor system.

Biofuel processing in a closed-loop geothermal system

Closed-loop geothermal systems (CLGS) provide a renewable heat source for district heating and electricity production. However, high capital costs can be a barrier to these applications. This study finds that the high pressures (>8MPa) and temperatures (>240°C) attained by working fluid inside the loop present additional opportunities: driving supercritical reactions that produce biodiesel from fatty acids. A new design for a subsurface geothermal reactor is proposed, enabling biofuel processing underground without the need for conventional processing equipment (pump, heater, reactors, and heat exchangers) or fossil fuel as energy source. To analyze the feasibility of this approach and its environmental and economic effects, a computational model that incorporates the simultaneous effects of flow dynamics, heat transfer, and reaction kinetics is developed. The application of CLGS is concluded to reduce the CO2 intensity of the resulting biodiesel by up to 33%. A geothermal-driven biofuel reactor could yield up to 95% fatty acid methyl esters. Additional sensible heat carried by the products can be redirected to electricity generation or district heating. The relevant requirements and optimal operating conditions for this approach to biofuel synthesis are identified. A technoeconomic analysis shows CLGS-based biofuel production cost can be comparable to natural gas-based production (∼840 $/ton). It also indicates return-on-investment after five years and highlights the most suitable locations that combine proximity to appropriate feedstocks and subsurface conditions. Integrated biodiesel production and energy extraction provides an alternative route to harnessing geothermal energy.

Numerical Analysis of Flood Invasion Path and Mass Flow Rate in Subway Stations under Heavy Rainfall Conditions

The occurrence of extreme weather, such as heavy rainfall and sudden increases in precipitation, has led to a notable rise in the frequency of flooding in subway stations. By conducting numerical simulations of flood disasters in subway stations under heavy rainfall conditions and gaining insights into the patterns of flood invasion inside the stations, it is possible to develop practical and feasible drainage designs for the stations. This paper employs the computational fluid dynamics (CFD) method, utilising the volume of fluid function (VOF) method and the renormalization k-ε group model within the vortex viscosity model. The complete process of flood invasion into subway stations with varying water levels (1500 mm, 2000 mm, and 2450 mm) is modelled, and the distribution of floods at different times under varying operational conditions is analysed to identify the evolutionary patterns of station flood history. The simulation calculations yielded the mass flow rate time history curve at the tunnel entrance and exit, which was then subjected to an analysis of its development trend over time. The total accumulated water in the subway station is calculated by integrating the difference in mass flow rate between the entrance and the tunnel exit, using the mass flow rate curve. In conclusion, the paper proposes drainage measures that provide valuable insights into pumping strategies when floodwaters infiltrate subway stations. The results indicate that the speed of flood spreading in subway stations increases with higher groundwater levels, and that the mass flow rate of floodwater entering the tunnels increases over time, eventually reaching a stable state. It was observed that, at certain times, the mass flow rate of floodwater into the tunnels exhibited a linear relationship with time.

Prediction and optimization of combustion performance and emissions for lean methane-hydrogen non-premixed flame using RSM-PSO methodology

To reduce carbon emissions, hydrogen is a clean energy as fuel to co-combustion with natural gas. This experimental work focuses on studying the effects of hydrogen addition to methane on combustion performance and emissions in non-premixed coaxial burners, establishing performance prediction models and optimizing operation conditions involving fuel velocity, hydrogen blending ratio and equivalence ratio. The global sensitivity and analysis of variance were adopted to quantify the contributions of independent variables on the output responses of temperature, O2, NO, CO and CO2 emissions. The hydrogen blending ratio is the most influential parameter, whose total effects of NO and CO2 emissions are 64.6% and 83.5%, respectively. The equivalence ratio is not significant to NO and CO2 emissions, and the adjusted determination coefficient of the reduced prediction model is 0.9707 and 0.9360, demonstrating a better goodness of fit. Based on the response surface model and particle swarm optimization algorithm, a Pareto was solved showing NO and CO2 emissions decrease by 42.0%–73.6% and 60.8%–89.5%, and peak temperature in the chamber centerline increases by 1.02%–3.61%. The research results provide a profound guide to predict and optimize combustion performance and emissions for methane-hydrogen non-premixed flame.

Unlocking thermochemical CO2/H2O splitting by understanding the solid-state enthalpy and entropy of material reduction process

Two-step redox thermochemical cycles, capable of directly converting CO2 and H2O respectively into CO and H2, offer a promising synthesis route towards green carbon-neutral fuels. The performance of such two-step cycles depends highly on the thermodynamic properties of splitting materials, particularly the solid-state enthalpy (Δhsolid) and entropy (Δssolid) changes during the reduction process. Here, we report an investigation into the roles of the Δhsolid and Δssolid. We shall show that a high Δssolid relaxes both reduction temperature and oxygen pressure, but increases oxidant consumption. Conversely, an increase in Δhsolid enhances reduction resistance while promotes oxidation reactions. There are therefore no perfect materials, and a trade-off is needed for an optimal solution. We also defined a thermodynamic region based on Δhsolid and Δssolid and typical operating conditions, and showed that higher values of both Δhsolid and Δssolid provided a larger reaction space. While lower Δhsolid and negative Δssolid may be more suitable for isothermal cycles. Our analyses also suggest future efforts in searching for splitting materials with a high Δssolid within an appropriate range of Δhsolid (280–460 kJ/mol).

Model-based humidity observer for vehicle proton exchange membrane fuel cell based on a new sliding mode observer estimation technique

ABSTRACT The internal water content of the proton exchange membrane fuel cell (PEMFC) stack significantly affects its performance and duration. To achieve optimal humidity level, real-time monitoring of the humidity of the cathode and the anode is crucial for the PEMFC. As far as we know, the relative humidity of the PEMFC can be estimated via an observer based on the measurable output voltage. However, the rapid changes in vehicle operating conditions pose a challenge to the estimation accuracy. Different from the existing sliding mode observers (SMOs), this study pays more attention to the testing noise of voltage measuring sensor. To reduce the noise influence and improve the estimation accuracy, a new sliding mode observer (NSMO) combined with an online Kalman filter is presented based on the dimensionless model of the PEMFC in this study. The applicability of the proposed NSMO in estimating the cathode relative humidity and the anode relative humidity is illustrated by the simulation results. At the same time, comparisons between the NSMO and the online Kalman filter are provided which show a substantially higher estimation accuracy and lower cost for the NSMO. Furthermore, the final results indicate the mean absolute deviations of the cathode and the anode relative humidity for the NSMO are, respectively, reduced to 0.39% and 0.28%.

Transfer condition assessment of gas turbines via double multi-task Gaussian process

Predictive maintenance and health management are crucial means to enhance the operational reliability and safety of gas turbines, with time series condition assessment being a pivotal step in this process. However, the complexity of gas turbine and the harsh operating conditions raise the high cost and significant difficulty in acquiring data via sensors, which pose challenges for the conventional data-driven time series modeling methods. These result in the scarcity of available historical data and the deteriorate of condition assessment accuracy. To this end, this paper proposes an end-to-end, probabilistic transfer learning approach based on multi-task Gaussian process (MTGP) for the condition assessment of gas turbines with limited sensor data. For achieving effective transfer condition assessment, the proposed method, named double MTGP (dMTGP), employs (i) a recurrent neural networks (RNN) projector to extract the explicit evolution patterns, (ii) adopts the MTGP module to transfer knowledge from the source domain to the target domain, and (iii) presents a recursive MTGP strategy to have shared knowledge transferred cross outputs. Finally, the superiority and robustness of the proposed method are comprehensively validated against state-of-the-art competitors on four practical gas turbine units.

Morphological characteristics of activated sludge recovered from biological reactors impact on the energy requirement for centrifugal dewatering

The effect of sludge dewatering on handling and transport costs as well as the impact of various parameters like sludge characteristics, the used dewatering equipment and the adopted specific operating conditions, on the efficiency of this step, were considered in the present study. The influence of a laboratory-scale centrifugation for dewatering activated sludge (AS) was also investigated. Additionally, models to describe the relationships between independent variables like centrifugation speed, AS volume and fixed time and dependent variables like the percentage of solid cake (% GS). The used approach was based on the response surface methodology (RSM) along with a central composite design experiment, considering three distinct types of AS: healthy (BS), partially bulking (BF), and deflocculated (BD) sludges. The morphological characteristics of the sludge do have an effect on the operating conditions for the centrifugation, hence the energy consumption for a desired dewatering level. For the case of BS sludge, a centrifugation at 2600 rpm for 10 min, using a 10.3 mL plug, led to a solid cake of 12.65 %, whereas for BD sludge, with a higher centrifugation at 3000 rpm for 17.30 min and a smaller plug volume (8 mL) led to 6.82 %, roughly half of the previous percentage.

On the intrusiveness of phosphor thermometry for metal surface temperature measurements in reciprocating engines: an experimental and heat conduction modeling study

Abstract Lifetime-based phosphor thermometry has been applied in a wide variety of surface temperature measurement applications due to its relative ease of implementation and robustness to background interference when compared to other optical temperature measurement methods. It is often assumed that the technique is minimally intrusive if the thickness of the applied phosphor coating is < 20 µm. To evaluate this assumption, high-speed phosphor surface temperature and thermocouple measurements were performed on 4140 steel substrates installed in the cylinder head of an optically-accessible internal-combustion engine for four operating conditions. For phosphor thermometry measurements, four substrates were studied, each coated with a phosphor layer of different thickness ranging between 6 µm and 47 µm. The phosphor thermometry measured temperature swings during combustion were shown to be heavily impacted by the presence of the phosphor coatings, increasing by roughly a factor of 2 - 2.5 when increasing the thickness from 6 µm to 30 µm. A technique was implemented which utilizes the temperature data in combination with a heat conduction model to provide estimates of the temperature swing and heat transfer flux in the absence of the phosphor coating. It was shown that even the 6 µm phosphor coating could lead to an order of magnitude increase in the temperature swing relative to the uncoated 4140 steel substrate. Despite the intrusiveness of phosphor thermometry for the surface temperature measurements, reasonable agreement was demonstrated between heat flux estimates determined with the heat transfer modeling technique and those deduced from temperature-swing measurements using two different high-speed thermocouples. The results indicate that phosphor surface thermometry can be a reliable surface temperature and heat flux diagnostic for transient high heat flux environments, as long as proper care is taken to account for the impact of the phosphor layer on the measurement.

Challenging established norms: The unanticipated role of alcohols in UV/PDS radical quenching

UV/peroxydisulfate (UV/PDS) process is known to be highly efficient for degrading micropollutants from water by generating sulfate (SO4•−) and hydroxyl radicals (HO•). Reliable analyses of short-lived SO4•− and HO• are therefore critical for understanding reaction mechanisms and optimizing operating conditions. Currently, alcohols are commonly used as quenchers to distinguish radicals based on the assumption that they exclusively react with target radicals without other influences. However, this study for the first time reveals a series of unexpected effects that challenge this conventional wisdom because: 1) adding alcohols altered the decomposition rates of PDS by replacing the reactions between SO4•− and HO• with PDS by the reactions between secondary reactive species and PDS; and 2) SO4•− preferably reacted with alcohols to generate nonnegligible level of hydrogen peroxide (H2O2) under oxygen-rich conditions, which subsequently led to indirect formation of HO•. Additionally, the formation of H2O2 was substantially impacted by the types of alcohols, dosages, dissolved oxygen, and solution pH. Using probe tests as tools, we found that the actual SO4•− levels after dosing alcohols were only slightly different from assumed/expected levels, whereas the actually HO• levels were 43.7, 3364.9, and 12.5 times higher than assumed/expected conditions for samples dosed with methanol, iso-propanol, and tert-butanol, respectively. These unanticipated effects thus suggest that cautions are needed when using alcohols to qualitative and quantitative determine HO• and SO4•− in UV/PDS process.

A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions

Operating conditions reflect the mission evolution of rotating machinery in specific application scenarios. The monitoring data under different operating conditions exhibit disparate statistic distributions in terms of amplitude and frequency properties. In the modern operation and maintenance, domain adaptation approaches, which align the feature distributions of source and target domains, are commonly employed to address cross-domain diagnosis challenges. However, the availability of the target domain is serendipitous rather than guaranteed. To meet the engineering requirements, a domain feature decoupling network (DFDN) is proposed to achieve the fault diagnosis of rotating machinery under unseen operating conditions. The core concept of DFDN is to decouple the extracted features into domain-related features and fault-related features. These features are then adaptively integrated into domain-invariant features using a significance fusion method, thereby enabling the feature learning and knowledge transfer from seen source domains to unseen target domains. Furthermore, a self-learning joint optimization strategy is developed for DFDN to facilitate the efficient acquisition of generalized diagnosis knowledge. Two bearing datasets from different test rigs are considered to assess the domain generalization performance of DFDN. The experimental results demonstrate the superiority and potential of this method in coping with zero-shot fault diagnosis under unseen operating conditions.

Identifying effective parameters on capillary tube performance with HCFCs, HFCs blends, and hydrocarbon refrigerants: Numerical assessment

This work offers a comprehensive numerical evaluation of the performance of capillary tube utilizing different refrigerants, including HCFCs, HFCs blends (Zeotropic, Azeotropic), and hydrocarbon, such as such as R22, R404A, R407C, R410A, R507A, and R290 refrigerants to predict the critical length of the adiabatic capillary tube. Utilizing a comprehensive thermodynamic model is applied to simulate the behavior of different refrigerants. Moreover, the effective design parameters based on the internal diameter for the adiabatic capillary tube (ACT), inside surface tube roughness, degree of subcooled, metastable region, condenser diameter, design of evaporator pressure (at critical length), condenser operating pressure, and refrigeration capacity are considered. The numerical model is developed based on the governing equations for mass, momentum, and energy, while a specified empirical equations are employed to represent each of the friction factor for single-and-two phase flow and metastable region. The results showed that the longest length of the ACT of 10.5 m revealed with R410A at mass flow rate of 8 g/s compared with 0.25 m at mass flow rate of 16 g/s for R290. Also, the results showed that the minimum value of friction factor of 0.0189 for R410A compared with maximum value of 0.0208 for R407C. The developed model is validated with available experimental results and models under different operation conditions and is found expectable with a good agreement. Where the standard error for different refrigerant with range value between (2.774% to 3.677%). However, the model represents accurate prediction the flow behavior and the thermal performance of the ACT.

Adaptive Sensing Data Augmentation for Drones Using Attention-Based GAN.

Drones have become essential tools across various industries due to their ability to provide real-time data and perform automated tasks. However, integrating multiple sensors on a single drone poses challenges such as payload limitations and data management issues. This paper proposes a comprehensive system that leverages advanced deep learning techniques, specifically an attention-based generative adversarial network (GAN), to address data scarcity in drone-collected time-series sensor data. By adjusting sensing frequency based on operational conditions while maintaining data resolution, our system ensures consistent and high-quality data collection. The spatiotemporal The attention mechanism within the GAN enhances the generation of synthetic data, filling gaps caused by reduced sensing frequency with realistic data. This approach improves the efficiency and performance of various applications, such as precision agriculture, environmental monitoring, and surveillance. The experimental results demonstrated the effectiveness of our methodology in extending the operational range and duration of drones and providing reliable augmented data utilizing a variety of evaluation metrics. Furthermore, the superior performance of the proposed system was verified by comparing it with various comparative GAN models.

Techno-economic-environmental optimization of hybrid photovoltaic-thermoelectric generator systems based on data-driven approach

Hybrid photovoltaic-thermoelectric generator (PV-TEG) system combines two types of energy conversion which is an important innovation to advance the development of renewable energy technologies. Hybrid system in practice needs to track the best operating point in real-time with the help of maximum power point tracking (MPPT) technology to ensure the system outputs maximum energy and optimizes performance. To improve the energy conversion efficiency and utilization of hybrid PV-TEG systems, as well as to effectively combat the negative impacts under partial shading conditions (PSC) and non-uniform temperature distribution (NTD), this paper proposes a hybrid system MPPT technique based on the bi-directional long and short-term neural networks (Bi-LSTM). The Bi-LSTM can consider both past and future data information, which will enhance the training process of the network and the accuracy of the model, making the results more comprehensive. It approaches the global maximum power point by searching for the maximum power down in the forward layer and up in the backward layer. The algorithm can effectively prevent from falling into local optimum under PSC and NTD. The case study section tests and explores the feasibility of utilizing Bi-LSTM to obtain the maximum power from a hybrid PV-TEG system under five case study scenarios, namely, start-up test, step change in solar irradiance, stochastic variation, Hong Kong four-season real case, and uncertainty analysis. Comparative analysis with the other five algorithms are also conducted to thoroughly verify the superiority of the proposed technique for hybrid system MPPT applications. The simulation results indicate that the hybrid PV-TEG system with Bi-LSTM achieves the optimal MPPT performance stably and efficiently under different operating conditions. In particular, low irradiate condition in spring, the energy generated by Bi-LSTM exceeds the INC, SAO and P&O energy outputs by 80.51%, 61.22%, and 31.04%, respectively.

Unveiling the roles of turbulence characteristics on the degradation of methylene blue by the activation of H2O2 through vortex-based hydrodynamic cavitation under neutral condition

Presently, fast degradation of organic wastewater has been achieved by the synergetic method of hydrodynamic cavitation and H2O2 at a laboratory scale, while the physico-chemical effect, especially the turbulence characteristics on the degradation process remains lacking. This paper presents investigation on the degradation of dyeing wastewater containing methylene blue (MB) under neutral condition, within a vortex-based hydrodynamic cavitation (VBHC) reactor. Operating conditions are firstly optimized and then used to collaborate with H2O2 to study the synergetic effects of this hybrid treatment method. The turbulence characteristics are subsequently obtained by a computational fluid dynamics (CFD) simulation, and the correlation between them and the degradation process of MB is revealed. The results indicate that the highest degradation rate of 91.95 % is achieved under optimal conditions. Higher inlet pressure (4.5 bar) owns the thickest vapor volume fraction, greatest vapor transfer rate, and largest size of cloud cavity in the vortex core region, indicating the strongest cavitation intensity; meanwhile, moderate solution temperature (30 ℃) also favors for the greatest cavitation intensity inside the reactor. The optimal cavitation intensity activates the considerable production of reactive oxygen species, and results in good consistency with the MB degradation performance, which predominately occurs in the annular region around the vortex rope. This research unveils the rationale behind the physical essences of the VBHC process and its influence on the degradation efficacy of MB, which can provide reliable guidance for further development of cavitational reactors to degrade organic pollutants by the VBHC/H2O2 hybrid treatment method.

Experimental and thermodynamic study of amyl acetate, amyl acetoacetate, and isoamyl acetoacetate in CO2 solvent

Acetate ester compounds find widespread use in various applications such as surface coatings, ink formulations, pharmaceuticals, and adhesives. It is essential to investigate the phase transition behavior of amyl acetate, amyl acetoacetate, and isoamyl acetoacetate in high-pressure supercritical CO2 (SC-CO2). The vapor-liquid equilibria (VLE) of the SC-CO2 + amyl acetate, SC-CO2 + amyl acetoacetate, and SC-CO2 + isoamyl acetoacetate systems were examined at different temperatures (313.2 ≤ T ≤ 393.2 K) and pressures (1.67 ≤ P ≤ 20.76 MPa). The solubility curve of these systems shows Type-I phase behavior, and the critical points of these binary mixtures were observed between the critical properties of the pure components involved in the systems. The solubility of amyl acetate, amyl acetoacetate, and isoamyl acetoacetate in the SC-CO2 + amyl acetate, SC-CO2 + amyl acetoacetate, and SC-CO2 + isoamyl acetoacetate systems increases with increasing temperature at constant pressure. The two-parameter model of Peng-Robinson equation of state along with a mixing rule, accurately correlated the phase transition behavior and critical mixtures curves for all three systems. The binary interaction parameters (BIPs) were adjusted, and the minimum root mean square deviation percentage was identified for all three systems. The calculated error% was found to be within reasonable limits, with values of 4.95 %, 3.93 %, and 4.18 % for SC-CO2 + amyl acetate, SC-CO2 + amyl acetoacetate, and SC-CO2 + isoamyl acetoacetate systems, respectively. Furthermore, the interaction parameters for the SC-CO2 + amyl acetoacetate mixture was found to be temperature-dependent, and the tested linear regression correlation coefficient for the BIPs parameters of (kij) and (ηij) are 0.98533 and 0.99083, respectively. This is the first research study on the phase behavior of acetate ester compounds in SC-CO2 solvents, and the results have a significant impact on industries at different operating conditions.

Investigation of the energy dissipation mechanism for centrifugal air compressor used for fuel cell based on CFD coupling with CIT

Effective evaluation indicator is the focus of air compressor structural optimization and development of control strategies, leaving to be further investigated from physical occurrence mechanism. Considering air expansion and compression, on the basis of coupling computational fluid dynamics (CFD) with classical irreversible thermodynamics (CIT), an extended entropy generation rate was established. Accordingly, various entropy generation rates for air compressor under wide operating conditions was investigated, aiming to what, where and how much the energy was dissipated. Results show that entropy generation rate related to air expansion and compression is always the largest contributor to the total entropy generation of the system under all operating conditions. A new dimensionless number R is defined to distinguish the fluid-related entropy generation rate and mechanical-related entropy generation rate. Furthermore, physical-based empirical models concerning the relationship of entropy generation rates, motor speeds and loads are proposed in this study, whose usage for guiding structural design was discussed. The work done in this paper can not only provide a new method for guiding the structural design of air compressors, but also providing a new idea for air compressor-fuel cell system’s optimal control.

CRF release from a unique subpopulation of accumbal neurons constrains action-outcome acquisition in reward learning

BackgroundThe nucleus accumbens (NAc) mediates reward learning and motivation. Despite an abundance of neuropeptides, peptidergic neurotransmission from the NAc has not been integrated into current models of reward learning. The existence of a sparse population of neurons containing corticotropin releasing factor (CRF) has been previously documented. Here we provide a comprehensive analysis of their identity and functional role in shaping reward learning. MethodsTo do this, we took a multidisciplinary approach that included florescent in situ hybridization (Nmice ≥ 3), tract tracing (Nmice = 5), ex vivo electrophysiology (Ncells ≥ 30), in vivo calcium imaging with fiber photometry (Nmice ≥ 4) and use of viral strategies in transgenic lines to selectively delete CRF peptide from NAc neurons (Nmice ≥ 4). Behaviors used were instrumental learning, sucrose preference and spontaneous exploration in an open field. ResultsHere we show that the vast majority of NAc CRF-containing (NAcCRF) neurons are spiny projection neurons (SPNs) comprised of dopamine D1-, D2- or D1/D2-containing SPNs that primarily project and connect to the ventral pallidum and to a lesser extent the ventral midbrain. As a population, they display mature and immature SPN firing properties. We demonstrate that NAcCRF neurons track reward outcomes during operant reward learning and that CRF release from these neurons acts to constrain initial acquisition of action-outcome learning, and at the same time facilitates flexibility in the face of changing contingencies. ConclusionWe conclude that CRF release from this sparse population of SPNs is critical for reward learning under normal conditions.

An inquiry into transport phenomena and artificial intelligence-based optimization of a novel bio-inspired flow field for proton exchange membrane fuel cells

Achieving an effective flow field configuration is requisite to ensure the optimal performance of a proton exchange membrane fuel cell (PEMFC). Herein, an unprecedented bio-inspired flow field is offered to improve species transport and enhance the power density of PEMFCs in order to provide a pathway for effective utilization of them. The flow field consists of two twin parts, resembling the lung structure, and a repeating converging-diverging pattern, so-called “converging-diverging lung-inspired serpentine (CDLIS).” Utilizing multiphase computational fluid dynamics, this study delves into the transport phenomena and evaluates the comprehensive efficacy of a three-dimensional PEMFC. The CDLIS flow field considerably enhances both crosswise and lengthwise mass transfer by inducing several favorable flow mixing phenomena and improving reactant's velocity distribution. The output power of CDLIS exhibits a substantial increase of 10.87%, 43.63%, 12.22%, 12.83%, 12.52%, and 13.8% compared with the simple serpentine, parallel, two-path serpentine, four-path serpentine, lung-inspired serpentine, and combined parallel-serpentine flow fields, respectively. Due to its highly efficient mass transfer capabilities, enhanced convective mass flow, and outstanding current density, CDLIS possesses the most effective water management and the lowermost flooding risk. To further improve the performance of the PEMFC, artificial intelligence codes based on the non-sorting genetic algorithm II (NSGA II) and a surrogate analytical model are developed to optimize the operating conditions of CDLIS at the limiting current density situation where concentration losses impede the mass transfer of reactants. The target parameters for optimization are current density, water saturation (WS), and oxygen transport resistance (OTR). Under optimum conditions, a remarkable current density of 6.35 A cm−2, a low WS of 0.043, and a reduced OTR of 5.06 s m−1 are obtained.