Steady-state System Research Articles

Radiolytic reduction of silver nanoparticles probed in situ via Cherenkov spectroscopy

Expanding on our previous efforts demonstrating the feasibility of extracting chemical information in steady-state systems of nanoparticles under irradiation, we demonstrate that the Cherenkov spectra can be used to observe chemical changes in real-time, in situ, and for non-equilibrium systems under irradiation. This is done by way of a dose-resolved measurement of the localized plasmon resonance absorption band associated with the formation and growth of silver nanoclusters and nanoparticles produced via radiolytic reduction of a solution of silver ion under exposure to an electron beam. Additionally, the method utilized in this study provided a significant resolution with a simplified optical system. This paves the way for a technique to determine similar changes in the material in medicinal, industrial (nuclear industry), and research applications—and indeed in any application involving ionizing radiation.

Variance sum rule: proofs and solvable models

We derive, in more general conditions, a recently introduced variance sum rule (VSR) (Di Terlizzi et al 2024 Science 383 971) involving variances of displacement and force impulse for overdamped Langevin systems in a nonequilibrium steady state (NESS). This formula allows visualising the effect of nonequilibrium as a deviation of the sum of variances from normal diffusion 2Dt, with D the diffusion constant and t the time. From the VSR, we also derive formulas for the entropy production rate σ that, differently from previous results, involve second-order time derivatives of position correlation functions. This novel feature gives a criterion for discriminating strong nonequilibrium regimes without measuring forces. We then apply and discuss our results to three analytically solved models: a stochastic switching trap, a Brownian vortex, and a Brownian gyrator. Finally, we compare the advantages and limitations of known and novel formulas for σ in an overdamped NESS.

Steady-state triad resonance between a surface gravity wave and two hydroacoustic waves based on the homotopy analysis method

Under water compressibility, resonant triads can occur within the family of acoustic-gravity waves. This work investigates steady-state triad resonance between a surface gravity wave and two hydroacoustic waves. The water-wave equations are solved as a nonlinear boundary value problem using the homotopy analysis method (HAM). Within the HAM framework, a potential singularity resulting from exact triad resonance is avoided by appropriately choosing the auxiliary linear operator. The resonant hydroacoustic wave component, along with the two primary waves (one hydroacoustic wave and one gravity wave), is considered to determine an initial guess for the velocity potential. Additionally, by selecting an optimal “convergence-control parameter,” the steady-state resonance between a surface gravity wave and two hydroacoustic waves is successfully obtained. It is found that steady-state resonant acoustic-gravity waves are ubiquitous under certain circumstances. The two primary wave components and the resonant hydroacoustic wave component take up most of the energy in the steady-state resonant acoustic-gravity wave system. The amplitude of the resonant hydroacoustic wave component is mainly determined by the primary hydroacoustic wave component, and the amplitudes of both hydroacoustic waves are approximately equal in all cases considered. In addition, when the overall amplitude of the wave system increases, both dimensionless angular frequencies decrease, indicating that the nonlinearity of the entire wave system becomes stronger with an increase in the wave system amplitude. The amplitude of the primary hydroacoustic wave has a relatively large effect on the system's nonlinearity. This work will enrich and deepen our understanding of acoustic-gravity waves.

Using Convex Optimization to Efficiently Apportion Tracer and Pollutant Sources From Point Concentration Observations

AbstractRivers transport elements, minerals, chemicals, and pollutants produced in their upstream basins. A sample from a river is a mixture of all of its upstream sources, making it challenging to pinpoint the contribution from each individual source. Here, we show how a nested sample design and convex optimization can be used to efficiently unmix downstream samples of a well‐mixed, conservative tracer in a steady state system into the contributions of their upstream sources. Our approach is significantly faster than previous methods. We represent the river's sub‐catchments, defined by sampling sites, using a directed acyclic graph. This graph is used to build a convex optimization problem which, thanks to its convexity, can be quickly solved to global optimality—in under a second on desktop hardware for data sets of ∼100 samples or fewer. Uncertainties in the upstream predictions can be generated using Monte Carlo resampling. We provide an open‐source implementation of this approach in Python. The inputs required are straightforward: a table containing sample locations and observed tracer concentrations, along with a D8 flow‐direction raster map. As a case study, we use this method to map the elemental geochemistry of sediment sources for rivers draining the Cairngorms mountains, UK. This method could be extended to non‐conservative and non‐steady state tracers. We also show, theoretically, how multiple tracers could be simultaneously inverted to recover upstream run‐off or erosion rates as well as source concentrations. Overall, this approach can provide valuable insights to researchers in various fields, including water quality, geochemical exploration, geochemistry, hydrology, and wastewater epidemiology.

Addressing Disparities Between Physician Rated and Self-Reported Dyspnea Using Machine Learning

Nearly 50% of intensive care and 25% of tertiary care patients experience dyspnea, or the sensation of unsatisfied breathing. Dyspnea decreases quality of life, impacts clinical outcomes, and can have long standing psychological consequences for some patients. Current methods of identifying dyspnea severity (such as the respiratory distress observation scale or RDOS) lack suffcient quantifiable measurements of dyspnea to accurately predict its severity. In our testing, while the RDOS does have predictive power, it frequently produces underestimates compared to self-reported dyspnea severity (r = 0.32; p = 0.0081). Furthermore, dyspnea severity can change throughout the day, requiring healthcare professionals to conduct RDOS evaluations multiple times to attempt to meet patient needs. Therefore, there is an urgent need for continuous, accurate monitoring of dyspnea severity in the hospital setting. To address this need, we are using non-invasive data collected during induced bouts of dyspnea to train a machine learning (ML) algorithm in dyspnea prediction. We employ a steady-state end-tidal forcing breathing system and adjust end-tidal gas tensions manually while collecting PO­­­­2, PCO2, SpO2, and ventilation data. Preliminary findings show several physiological and demographic factors independently correlate to dyspnea severity, including BMI (r = 0.49; p = 0.007), end-tidal PCO­­2 (r = 0.33; p = 0.008), and inspired PO2 (r = -0.25; p = 0.043). We achieved an accuracy of 84%, 83.3%, and 82% in-dataset predictions using k-nearest neighbor, random forest, and logistic regression approaches respectively. Several physiological biomarkers and demographic factors were used as inputs and self-reported dyspnea was used as the predicted output. These findings show promise for the use of ML in dyspnea prediction. With further testing and use of a broader participant population, it may be possible to use ML models such as these to improve patient outcomes and overall quality of life. University of California, Riverside. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Denoising and Extension of Response Functions in the Time Domain.

Response functions of quantum systems, such as electron Green's functions, magnetic, or charge susceptibilities, describe the response of a system to an external perturbation. They are the central objects of interest in field theories and quantum computing and measured directly in experiment. Response functions are intrinsically causal. In equilibrium and steady-state systems, they correspond to a positive spectral function in the frequency domain. Since response functions define an inner product on a Hilbert space and thereby induce a positive definite function, the properties of this function can be used to reduce noise in measured data and, in equilibrium and steady state, to construct positive definite extensions for data known on finite time intervals, which are then guaranteed to correspond to positive spectra.

On the existence, regularity and uniqueness of $$L^p$$-solutions to the steady-state 3D Boussinesq system in the whole space and with gravity acceleration

On the existence, regularity and uniqueness of $$L^p$$-solutions to the steady-state 3D Boussinesq system in the whole space and with gravity acceleration

Cost optimal analysis for a differentiated vacation machining system with discouragement under the two threshold control policies

The present study explores the cost analysis for machine repair problem of queueing system using two-threshold control policies with discouragement and differentiated vacation. The way repairmen offer service has been established by two-threshold control policies based on queue size, with randomly distributed vacations of type A1 (full vacation) and type A2 (working vacation). Prior to the queue length being less than the lower threshold, both repairmen are on A1 type vacations. One repairman remains on A1 type vacation while the second repairman takes A2 type vacation if the queue length is in the intermediate range of the two thresholds. If the queue length is at or exceeds the upper threshold, both repairmen serve the queue to the best of their abilities. Meanwhile, during A1 and A2 types of vacations, failing machines exhibit impatience, which is a common occurrence in getting the service. It is supposed that vacation and repair time are exponentially distributed. The recursive method is utilized to calculate steady-state system probabilities. Furthermore, several metrics have been defined to measure the effectiveness of the system. A cost model is developed using system performance data for cost analysis. The Fibonacci search algorithm (FSA) and an artificial bee colony (ABC) are used to find the optimal value for the choice of variables with the lowest expected cost. A real-world instance demonstrating the implementation of the proposed model in practice will be shown at the end.

Numerical investigation on the effect of extra-feedback target position on the fringe visibility in a dual optical feedback interferometry system.

Optical feedback interferometry (OFI) exhibits good potential in laboratory and engineering applications as an interferometric measurement technology with unique structure. One challenge of this technology is that the OFI signals may be feeble, and the OFI fringe visibility is low when the optical feedback strength is weak. It has been demonstrated that the OFI fringe amplitude can be enhanced by introducing an extra-feedback into an OFI system. At the same time, it has been confirmed that the position of the extra-feedback target must be strictly controlled as it will directly affect the fringe amplitude. However, the details of how the extra-feedback positions affect the OFI fringe amplitude, and its underpinning mechanism still needs to be unveiled. In this paper, we aim to theoretically investigate the influence of the extra-feedback target position on the OFI fringe amplitude and explore the underpinning mechanism. Firstly, a simplified analytical model for characterizing a dual-channel optical feedback interferometry (DOFI) system in steady state was derived from the Lang-Kobayashi equations. A method of solving the analytical model was developed to further explore the nature of a DOFI system. On top of that, the influence of the extra-feedback target position on the OFI fringe amplitude and its underpinning mechanism was explored, based on which the criteria for how to achieve large fringe amplitudes were summarized. The obtained results provide helpful guidance in constructing a DOFI system with enhanced fringe visibility, and further promote the practical applications of OFI technology.

A fuzzy adaptive complementary filter for attitude estimation based on norm judgment

Unmanned aerial vehicle (UAV) has great application prospect because of its capability of three-dimensional space operation. The reliability of attitude and heading reference system (AHRS) for UAV attitude estimation is crucial for the application of UAV. When a UAV is subjected to unknown electromagnetic interference and force interference in flight, its attitude detection system can suffer from reduced accuracy or even failure. In this paper, a fuzzy adaptive complementary filter (CF) for attitude estimation based on norm judgment is proposed to solve the problem that the sensor is easily disturbed by the complex flight environment and the fixed filter parameters are difficult to obtain the UAV attitude accurately under different flight states. Firstly, the correction model of the gyroscope, which includes four filter parameters, namely accelerometer weight, magnetometer weight, proportional gain P and integral gain I, is established. Secondly, the influence of the four parameters on the estimation accuracy is analyzed. Finally, the adaptive adjustment rules are designed to adjust the filter parameters online and thus achieve the accurate and reliable measurement of UAV attitude. The feasibility of the proposed algorithm is verified through static, dynamic and interference experiments with the specially designed AHRS test platform. And the results show that the attitude estimation algorithm designed in this paper can ensure the high accuracy of the system in both steady state and high-speed rotation, shield the pitch and roll angles from the acceleration of motion, and keep the yaw angle from the external magnetic field.

Thermal performance of forced convection of water- NEPCM nanofluid over a semi-cylinder heat source

1) BackgroundPhase change materials (PCMs) have been used statically, which has caused the use of these materials to face challenges. Encapsulating PCMs and combining them with the base fluid can significantly solve the problem of using PCMs in BTM systems. In the present study, based on computational fluid dynamics, forced convection heat transfer of nano-encapsulated phase change materials (NEPCM) in a BTM system are simulated. The main aim of the present research is to reduce the temperature at the surface of the hot cylinder. 2) MethodsIn this research, we simulated lithium battery thermal management systems in both steady and transient states. The effects of using NEPCM particles to water were investigated. Modeling is implemented using the finite volume method and the PIMPLE and SIMPLE algorithms in OpenFoam. Furthermore, the effects of battery heat flux, Reynolds number, and the presence of nanoparticles (NPs) were analyzed. We intend to evaluate the optimal state of the system by studying the mentioned parameters. 3) Significant FindingsOur study shows that adding 3.5% NEPCM to water can reduce the length of the vortex by 22% and in unsteady-state simulation, it is observed that the presence of NEPCM particles in water reduces battery temperature up to 0.66 K.

EPR-Net: constructing a non-equilibrium potential landscape via a variational force projection formulation.

We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state systems. EPR-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product space. Remarkably, our loss function has an intimate connection with the steady entropy production rate (EPR), enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and the state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an eight-dimensional (8D) limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics.

Computational Assessment of Control Strategy for PMSG Wind Turbines aiming at Voltage Regulation on Connection Point

The growing deployment of wind energy conversion systems experienced in recent years has been reflected in increasing requirements for connection of such complexes to electric networks. In this way, in a near future, wind farms could be required to behave as conventional power plants, which, besides the characteristics of energy supply, promote support to the electrical system in steady state and transient conditions. In this context, this work is focused in a computational assessment of a control strategy directed to wind energy conversion system with permanent magnet synchronous generator (PMSG) aiming to regulate the voltage at the Point of Common Coupling. Therefore, after the mathematical description of the main physical components of such generating units, it proceeds to the computational implementation and performance evaluation of the control method using the ATP-EMTP simulator.

Low-velocity impact experiments of porous ice balls simulating Saturn's ring particles: Porosity dependence of restitution coefficients and the mechanism of inelastic collision

We investigated the porosity dependence of the restitution coefficient (ε) of porous ice balls with porosities of 49.6%, 53.8%, and 60.8% colliding with granite, ice and porous ice plates at a low impact velocity (vi = 0.93–96.9 cm s−1) and temperature of −13.8 °C. The relationship between the impact velocity and the restitution coefficient was divided into two regions by the critical velocity vc: In the quasi-elastic region (vi≤vc), the restitution coefficient had a constant value of εqe regardless of the impact velocity and could be explained by using Dilley's viscous dissipation model. In the inelastic region (vi≥vc), the restitution coefficient decreased with the impact velocity and could be explained by using the improved Andrews' plastic deformation model. We then determined the porosity dependence of the εqe and vc empirically for collisions between the porous ice ball and the porous ice plate from our results. Then we extrapolated the porosity dependence of the restitution coefficient by substituting the calculated εqe and vc for the porosity of 10 to 70%. The restitution coefficient was found to decrease with increasing porosity of the porous ice, and to be strongly dependent on the porosity of the porous ice. Considering the energy budget in the steady-state dense ring system which is controlled by the restitution coefficient of the ring particles, the velocity dispersion of the ring particles was estimated to be ∼10−2 mm s−1 to cm s−1 for the ring particles with the porosity from 45% to 70% and < 10–90 cm s−1 for those with the porosity <45%.

A Decomposition-Based Approach for Deriving Positive Steady States of a Class of Chemical Reaction Networks with Non-Mass-Action Kinetics

Steady states are frequently used to investigate the long-term behaviors of (bio)chemical systems. Recently, there has been a growing interest in network-based approaches due to their efficiency in deriving parametrizations of positive steady states in systems with mass-action kinetics. In this study, we extend this approach to derive positive steady states in networks under non-mass-action kinetics, specifically mixed kinetics. In a system with mixed kinetics, some reactions {may follow} mass-action kinetics, while others in the same network follow different rate laws, such as quotient rate laws. An example of such complexity is evident in a mathematical model of the insulin signaling pathway in type 2 diabetes. To compute its positive {steady states}, we adapt our existing network decomposition approach, originally designed for mass-action kinetics, to handle networks with non-mass-action kinetics. This approach involves breaking down a given network into smaller, independent subnetworks to derive the positive steady states of each subnetwork separately. These individual steady states are then combined to obtain the positive steady states of the entire network. This strategy makes computations more manageable for complex and large networks. More importantly, this method could separate reactions with purely mass-action kinetics into certain subnetworks from those that follow different rate laws. We also present an illustrative example that provides insights into methods for transforming networks with mixed kinetics into their associated mass-action systems.

Experimental study of single-phase change material thermal diode based on calcium chloride hexahydrate

Phase change material thermal diodes designed on the basis of different heat transfer forms and coefficients caused by different phase transition degrees in opposite heat transfer directions are considered as potential thermal management devices. However, the use of a variety of materials or only relying on numerical simulation research makes its structure complex or idealized, which reduces the possibility of practical application. Therefore, in this work, a simple thermal diode structure containing only CaCl&lt;sub&gt;2&lt;/sub&gt;·6H&lt;sub&gt;2&lt;/sub&gt;O single-phase variable material is proposed in combination with changes in heat transfer form and heat transfer coefficient in solid-liquid phase change and natural convection process. The corresponding device is prepared, and a steady-state heat flux test system is set up for experimental study, the measured results are close to those recorded in the literature with good accuracy. The influence of the temperature difference between hot end and cold end and the direction of positive heat transfer and negative heat transfer on the thermal rectification effect of the thermal diode are studied experimentally. The results show that the heat flux of the thermal diode decreases with the decrease of the difference in temperature between the cold source and hot source, and the thermal rectification ratio reaches to 1.58 when the forward and reverse along the antigravity direction and gravity direction, respectively. The optimal cold source temperature range is 20–25 ℃, which is close to room temperature. The proposed phase change material thermal diode structure has a certain application potential in energy saving and thermal management of building.

Performance Evaluation of Distribution System Interconnected with Solar PV Plants Using DIgSILENT

Many African countries, such as Botswana, receive high insolation that can be used to generate electricity. However, they still rely on thermal power generation and imports from neighbouring countries through various African power pools. Thermal generation methods result in an increase in carbon footprint. Therefore, in pursuance of the global climate change goals, the Government of Botswana has subscribed to allow all end users with the capacity to install solar rooftops and sell extra electricity to the power utility. These interconnections of solar PV plants to the grid are bound to bring challenges to the steady state system performance. This research, therefore, evaluates the performance of the integrated system focusing on the greater Gaborone distribution system (GGDS), where five government projects are being piloted. The study has adopted two scenarios by providing a performance comparative analysis between the system baselines to a solar integrated system using DIgSILENT Power Factory software. The system studies conducted include power flow, effects on voltage and line loading. This research found out that integrating PV plants to GGDS distribution network improves voltage profiles and a decrease in overall percentage loading of the feeders and interconnectors. However, some notes could only permit a limited loading. In conclusion, the implications of the effects of solar PV on the distribution system differ from one circuit to another; therefore, it calls for utilities to conduct an intensive network assessment for each particular feeder before interconnecting with solar.

Estimation-free Prediction Algorithms

For Time-varying, Time-invariant, and steady-state systems, Kalman Filter can be implemented as a prediction algorithm, since it produces the state prediction and the corresponding prediction error covariance matrix via the state estimation and the corresponding estimation error covariance matrix. Lainiotis Filter is equivalent to Kalman Filter and can be used to compute the prediction. In this paper, for Time-varying, Time-invariant and steady state systems, estimation-free Prediction Algorithms are derived via Kalman and Lainiotis filters; they are equivalent and compute iteratively the prediction and the corresponding prediction error covariance matrix. The estimation and the corresponding estimation error covariance matrix are not needed and are not computed. The proposed estimation-free prediction algorithms are faster than the Kalman filter.

Reliability Assessment of Power System based on Load Flow Analysis of the IEEE 57 Bus used in Micro Grid Applications

The complex power system consists of more interconnections; therefore, modelling such a type of network is a very difficult task in the commercialization area cycle. MATPOWER has all steady-state power system models, which are extensively used in academic research for power flow (PF) modeling. The PF architecture designed in this is extensible; it is easy to add or modify variables and constraints in the standard case structure. This paper presents the details of the mathematically scaled loads in IEEE 57 bus power system network modelling by using the standard test case data. Internally, all the bus voltages are expressed in p.u., and phase angles are expressed in radians, but the generators and loads are expressed in terms of power ratings. The scaling of these is defined based on the scaled voltage of the corresponding bus voltage. The scaling load procedure used in this paper is very useful for designing a low-voltage power system network for practical analysis purposes. Two simulations are performed in this paper for the analysis of the actual load flow (ALF) and scaled load flow (SLF) power system models. The voltage, phase angle, and power flow through the lines are compared to analyze the accuracy of both simulations. When compared with ALF, it has shown good accuracy, computational efficiency, and convergence properties.

Control Policy, Manageable Arrival Rates, and Reverse Balking in Mutually Dependent Queueing Model

A single server has a limited amount of storage and processing power dependent queueing model with examination of a control strategy, manageable rates of arrival and balking in reverse is examined in this paper. Using this model, a steady-state solution and system characteristics may be found. To aid comprehension, numerical findings are provided, as well as a pertinent conclusion.