Minimum Operating Research Articles

Research on the helium seepage mechanism in the strata surrounding bedded salt cavern helium storage and its tightness evaluation

To improve the safety and scale of helium storage, large-scale or strategic helium storage in underground salt caverns can be implemented. Salt caverns leached from bedded salt formations have been considered as potential underground storage sites for helium in China. However, the salt formations are typical lacustrine sedimentary rocks with an interbedded structure, thus the sealing feasibility of such salt caverns must be evaluated before they can be used. To this end, the helium seepage equation in deep strata and permeability evolution function are derived with the help of experimental results and theoretical analysis. Based on these, a helium seepage model considering slippage effect and threshold pressure gradient is proposed to evaluate the sealing properties of salt caverns for helium storage. The analysis results indicate that it is feasible to use bedded salt mine formations for helium storage when the formations permeability parameters and reservoir design parameters meet the long-term tightness evaluation criteria, including permeability, porosity, salt pillar width, salt roof thickness and operating pressure and duration. More importantly, specific recommendations are provided for the operation parameters, design parameters and formation permeability thresholds of salt caverns for ensuring helium storage safety while maintaining stable cavern operations. The threshold permeability of the target salt formations should be approximately 1 × 10−18 m2 to ensure tightness. The minimum and maximum operating pressure are recommended to be 10 MPa and 18 MPa respectively for 50 years of this helium storage. The salt pillar width should be 300 m, and the salt roof thickness should be 40 m.

Development and validation of a nomogram for predicting poor operative visibility during FESS in Chinese adult patients with CRS.

The purpose of this study is to develop and evaluate a nomogram that is capable of predicting poor operative visibility during functional endoscopic sinus surgery. To identify potential risk factors, patients with chronic rhinosinusitis who underwent functional endoscopic sinus surgery (FESS) between January 2019 and December 2022 were selected from our hospital's electronic medical record system. Data on general patient information, clinical manifestations, clotting-related test indices, Lund-Machay score of sinuses CT scanning, Lund-kennedy score of nasal endoscopies, anesthesia methods, intraoperative blood pressure and heart rate, and Boezaart bleeding score were collected. Minimum absolute convergence and selection operator (LASSO) regression, as well as multivariate logistic regression, were used to determine the risk factors. A nomogram was developed in order to predict poor operating visibility during FESS, and its performance was evaluated utilizing both the training and verification datasets via various measures including receiver operating characteristic (ROC) curve analysis, area under the curve (AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis. Of the 369 patients who met the inclusion criteria, 88 of them exhibited POV during FESS. By deploying LASSO and multivariate logistic regression analyses, six risk factors were identified and used to construct a nomogram for predicting POV during FESS. These factors include prothrombin time (PT), prothrombin activity (PTA), Lund-Mackay score (LMS), Lund-Kennedy score (LKS), anesthetic method, and intraoperative hypertension. The AUC of the training set was found to be 0.820 while that of the verification set was 0.852. The Hosmer-Lemeshow goodness-of-fit test and calibration curve analysis revealed good consistency between predicted and actual probabilities. Also, the decision curve demonstrated that the nomogram had a high degree of clinical usefulness and net benefit. The constructed nomogram has a strong ability to predict the poor intraoperative field in patients with chronic rhinosinusitis, which can help preoperative judgment of high-risk patients and provide evidence for perioperative management and preoperative plan formulation.

Optimizing DOCRs coordination when synergizing energy management with V2G using innovative optimization algorithms

This manuscript proposes an optimal coordination approach for directional overcurrent relays (DOCRs) within electric vehicle charging stations (EVCS), considering energy management and Vehicle-to-Grid (V2G) integration. The study focuses on maximizing efficiency and grid interaction in charging stations by addressing the challenge of DOCRs coordination. Three innovative optimization algorithms, namely the Hunger Games Search (HGS), Hunter Prey Optimization (HPO), and Sparrow Search algorithm (SSA), are employed to determine the optimal coordination for the DOCRs and get the time dial setting (TDS) and pickup current (IP) targeting minimum operating time for all DOCRs, considering factors such as load balancing, fault detection, and power quality in three different scenarios. EVSC has two types of charges DC and AC with two modes: V2G mode when EV is discharged into the grid, and G2V mode when EV is charged from the grid. Fast chargers are used to proceed as it can work in two modes V2G and G2V. Through extensive simulations and performance evaluations, the results demonstrate the effectiveness of the proposed approach. The three scenarios are simulated by using ETAP and MATLAB software according to fault location and to achieve voltage regulation and sufficient power for loads to achieved continuity of service for up to 1960.7 kW loads that kept fed. The proposed approach achieved the target of protecting the EVCS in minimum operating time of relays using the three algorithms and SSA introduces the best result more than other algorithms for the relay operating time with reduction up to 21 %.

Optimization of Integrated Energy System Considering Electricity and Hydrogen Coordination in the Context of Carbon Trading

In order to improve the consumption of renewable energy and reduce the carbon emissions of integrated energy systems (IESs), this paper proposes an optimal operation strategy for an integrated energy system considering the coordination of electricity and hydrogen in the context of carbon trading. The strategy makes full use of the traditional power-to-gas hydrogen production process and establishes a coupling model comprising cogeneration and carbon capture equipment, an electrolytic cell, a methane reactor, and a hydrogen fuel cell. Taking a minimum daily operating cost and minimal carbon emissions from the system as objective functions, a mixed-integer nonlinear optimal scheduling model is established. This paper designs examples based on MATLAB R2021b and uses the GUROBI solver to solve them. The results show that compared with the traditional two-stage operation process, the optimization method can reduce the daily operation cost of an IES by 26.01% and its carbon emissions by 90.32%. The results show that the operation mode of electro-hydrogen synergy can significantly reduce the carbon emissions of the system and realize a two-way flow of electro-hydrogen energy. At the same time, the addition of carbon capture equipment and the realization of carbon recycling prove the scheduling strategy’s ability to achieve a low-carbon economy of the scheduling strategy.

Robust predictive minmax fault‐tolerant control for multi‐phase batch processes under partial actuator faults and asynchronous switching

AbstractIn view of the uncertainties, partial actuator faults, asynchronous switching as well as unknown external interference in multi‐phase batch processes, a robust predictive minmax fault‐tolerant control approach is presented. First, considering that a controller and a system state cannot switch synchronously during switching, a match‐and‐mismatch switching model is constructed. Then, an output tracking error is introduced to establish an equivalent extended switching model. In this extended model, the robust control issue is transformed into the minmax optimization issue by establishing the minmax performance index. On this basis, the control laws with control input and interference input are designed. Second, sufficient conditions are given to assure the system separately has asymptotic and exponential stabilities in each phase and batch. By calculating the given sufficient conditions online, the gains of the control laws, and the minimum and maximum operating time under match and mismatch cases are obtained. With the maximum operating time, the controller receives the switching signal beforehand to eliminate the mismatch case of switching. Additionally, a robust criterion is supplied to confirm the system's robustness. Finally, a simulation study of an injection molding process shows that the approach is valid and feasible.

The Effect of Swirl Number on Lean Blow Out Limits of Lean Direct Injection Combustors

Abstract This is the first study where a single variable sweep of SN is conducted to assess its impact on lean blowout limits (LBO) in a liquid-fueled lean direct injection (LDI) combustor. This study uses a scaled NASA SV-LDI (Swirl Venturi—Lean Direct Injection) hardware and is concerned with the impact of swirl number on the lean blow-out limit of a single-element LDI system at atmospheric pressure. The swirl numbers (SN) were varied from 0.31 to 0.66 using continuously variable active swirl number control system that was developed in-house. It is shown that the minimum operating equivalence ratio is a linearly increasing function of swirl number. While previous literature agrees with the positive slope for this correlation, past work has suggested that the LBO limit is proportional to the swirler vane angle which is shown to be untrue for LDI systems. By actively varying the swirl number, it is proven that LBO is proportional to SN, and it is well known that SN is not proportional to swirler vane angle. Increased SN reduces LBO margin because the better-mixed, high swirl dilutes locally rich pockets of fuel–air mixture in a globally lean flow. In addition to a baseline venturi, which was scaled from NASA's geometry, two other venturis were tested. A low-pressure loss venturi with a large throat diameter showed poor blow-out performance whereas a parabolically profiled venturi improved LBO over the baseline for the same swirl number.

Distributed robust optimal configuration of multi-microgrid interconnected systems based on multi-objective bee colony algorithm

The distributed robust optimal allocation method for multi-microgrid interconnected systems usually involves a large number of variables and constraints, and the computational complexity is high in practical applications, which makes it difficult to solve the problem. Therefore, a distributed robust optimal allocation method for multi-microgrid interconnection systems based on multi-objective swarm algorithm is proposed. A distributed robust optimization configuration constraint index model for multi-microgrid interconnection system is established. Considering the influence of energy storage technology operation characteristics on its service life, a micro-grid hybrid energy storage capacity optimization configuration model with the minimum annual comprehensive energy storage cost as the objective function is established with charge and discharge power and residual power as the constraint conditions. The multi-objective swarm algorithm is used to realize the optimization model of distributed robust configuration microgrid interconnection system. By determining the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points, the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points are determined. The hybrid energy storage configuration model of multi-microgrid interconnection system is established with the minimum alternative operating cost as the objective function, so as to realize the distributed robust optimal configuration of multi-microgrid interconnection system. The simulation results show that the distributed configuration of multi-microgrid interconnection system with the proposed method has good robustness and strong optimization control ability.

Enhanced reaction extraction distillation via multi-objective optimization and 4E analysis for sustainable recovery of organic compounds from wastewater

Resource utilization and sustainable treatment of wastewater, which can achieve efficient energy utilization and environmental protection, have received hot social attention. Tetrahydrofuran and methanol are the two most common substances in pharmaceutical wastewater. It is very necessary to explore the wastewater treatment process containing these two substances. In this study, extractive distillation and reactive distillation were proposed to effectively separate the tetrahydrofuran-methanol-water component of pharmaceutical wastewater. To further reduce energy consumption, two strengthening processes, double-column reactive-extractive distillation and reactive-extractive dividing wall column distillation were designed. Taking the minimum operating and equipment cost as the objective function, the processes were optimized by using the multi-objective genetic optimization algorithm to obtain the best process parameters. Four processes were evaluated in terms of total annual cost, energy consumption, gas emissions and thermodynamic efficiency. Compared to the traditional three-column extractive distillation process, the above-mentioned four aspects of the reactive-extractive dividing wall column process was reduced by 40.63%, 43.13%, 43.14% and 17.71%, respectively. The reactive-extractive dividing wall column distillation process is of great significance for the efficient purification of valuable organic matter in wastewater and environmental protection.

Multiparameter optimization of the acid gas removal process using self-heat recuperation technology in IGCC system based on genetic algorithm

The acid gas removal (AGR) process in the integrated gasification combined cycle (IGCC) system consumes a large amount of heat and reduces the generation efficiency. Process modification of self-heat recuperation (SHR) technology based on vapor compression heat pumps is expected to reduce the energy consumption of the AGR process. On this basis, this paper conducted a comprehensive and parametric analysis of the AGR process integrated with a vapor compression heat pump. Wherein, two key parameters, the stripper pressure (P1) and the heat pump heat exchanger inlet pressure (P2), were analyzed in detail to understand the energy consumption characteristics of each module of the system. By establishing the optimization objective functions of minimizing system operating cost and exergy loss respectively, the system multi-parameter optimization was conducted based on the genetic algorithm by coding and solving in HYSYS and MATLAB. The corresponding parameter variables for the optimal cases of the two optimization objectives are also comparatively derived. The results showed that P1 mainly changes the SHR capacity of the system by changing the allowable flow rate of the SHR mass under different working conditions, while P2 mainly affects the SHR capacity of the system by changing the heat of the fraction at the top of the stripper. The minimum operating cost of the system decreased to 0.0655 $/kgH2S, which is 1.50 % lower compared to the original case. The minimum exergy loss of the system is reduced from 7788.89 kW to 7398.59 MW, which is about 5.01 % lower than that of the original case.

Minimal operation region prediction for networked control robotic manipulators subject to time‐varying delays and disturbances

AbstractDue to the disturbances and varying latency, a teleoperated robotic manipulator might not comply with the master control commands. Although prior studies on minimising the impact of network latency and disturbances on teleoperated robots were conducted, there has been very little research on the prediction of minimal operation regions of robotic arms, especially in the worst‐case scenarios when the disturbances and time delays still prevail even after impact minimisation. This study investigates the problem and proposes a novel solution to predicting minimal operation regions of networked control robotic manipulators. The proposed method can be used to forecast safe operation regions in which the manipulators will certainly enter and exclude regions that the robots will never penetrate. Leveraging on a Lyanonov Krasovskii criterion, the method performs region prediction by establishing minimal reachable bounding sets of the nonlinear, perturbed robotic arm's state vectors guided via a time‐varying delay‐dominant network. Though predominantly nonlinear, the entire prediction process is formulated as a tractable Linear Matrix Inequality (LMI) optimisation problem, which can be solved efficiently and effectively. Efficacy of the proposed method is validated with simulations where a simulated robotic arm is distorted with time‐varying delays and disturbances.

Generalizing minimum safe operating altitudes for fixed‐wing UAVs in real‐time

AbstractThis paper discusses a method of determining the minimum safe altitude of an uncrewed aerial vehicle (UAV) at any point within a designated airspace by conducting a glide reachability analysis. Recently, fixed‐wing UAVs are more regularly deployed near population centers and in extreme environments, requiring increasingly robust emergency systems and planning. The long‐ranges and adverse terrain associated with monitoring the Volcán de Fuego in Guatemala by a team from the University of Bristol (UoB) increases the likelihood that motor failure would result in the aircraft being unable to Return To Home (RTH) and impossible to retrieve. A method for delineating a boundary representing the minimum safe altitude required for the aircraft to safely glide to the airfield in the event of a motor failure was developed within MATLAB, defined by the UAV's minimum glide angle in wind. This model was subsequently compared with flight data from UoB missions around Fuego to better improve its accuracy and analyze the limitations of the missions.

Stochastic Flow Analysis for Optimization of the Operationality in Run-of-River Hydroelectric Plants in Mountain Areas

The highly temporal variability of the hydrological response in Mediterranean areas affects the operation of hydropower systems, especially in run-of-river (RoR) plants located in mountainous areas. Here, the water flow regime strongly determines failure, defined as no operating days due to inflows below the minimum operating flow. A Bayesian dynamics stochastic model was developed with statistical modeling of both rainfall as the forcing agent and water inflows to the plants as the dependent variable using two approaches—parametric adjustments and non-parametric methods. Failure frequency analysis and its related operationality, along with their uncertainty associated with different time scales, were performed through 250 Monte Carlo stochastic replications of a 20-year period of daily rainfall. Finally, a scenario analysis was performed, including the effects of 3 and 30 days of water storage in a plant loading chamber to minimize the plant’s dependence on the river’s flow. The approach was applied to a mini-hydropower RoR plant in Poqueira (Southern Spain), located in a semi-arid Mediterranean alpine area. The results reveal that the influence of snow had greater operationality in the spring months when snowmelt was outstanding, with a 25% probability of having fewer than 2 days of failure in May and April, as opposed to 12 days in the winter months. Moreover, the effect of water storage was greater between June and November, when rainfall events are scarce, and snowmelt has almost finished with operationality levels of 0.04–0.74 for 15 days of failure without storage, which increased to 0.1–0.87 with 3 days of storage. The methodology proposed constitutes a simple and useful tool to assess uncertainty in the operationality of RoR plants in Mediterranean mountainous areas where rainfall constitutes the main source of uncertainty in river flows.

Spurious Local Minima are Common for Deep Neural Networks With Piecewise Linear Activations.

In this article, theoretically, it is shown that spurious local minima are common for deep fully connected networks and average-pooling convolutional neural networks (CNNs) with piecewise linear activations and datasets that cannot be fit by linear models. Motivating examples are given to explain why spurious local minima exist: each output neuron of deep fully connected networks and CNNs with piecewise linear activations produces a continuous piecewise linear (CPWL) function, and different pieces of the CPWL output can optimally fit disjoint groups of data samples when minimizing the empirical risk. Fitting data samples with different CPWL functions usually results in different levels of empirical risk, leading to the prevalence of spurious local minima. The results are proved in general settings with arbitrary continuous loss functions and general piecewise linear activations. The main proof technique is to represent a CPWL function as maximization over minimization of linear pieces. Deep networks with piecewise linear activations are then constructed to produce these linear pieces and implement the maximization over minimization operation.

Collaborative hierarchical scheduling model of interconnected multi-microgrid and ADN considering DR with different strategies

The demand response (DR) resources of multiple entities have different operation strategies, which makes it difficult to realize the co-optimization. This paper proposes a cooperative hierarchical scheduling optimization model of interconnected multi-microgrid and an active distribution network (ADN) considering DR with different operation strategies. First, the collaborative hierarchical scheduling framework is proposed and the interaction characteristics and cooperative scheduling mode of multi-interconnected microgrid and ADN are analyzed. Second, the two-layer cooperative hierarchical scheduling model is established, considering DR with different strategies. The upper layer model takes the minimum operating cost of the ADN as the objective to optimize the trading tariff between the ADN and microgrids, and its own DR, while the lower layer model takes the operating cost of multi-microgrid as the objective to optimize the purchasing and selling of electrical energy for the ADN, the DR, and the interaction power. Finally, the case studies with three microgrids and an ADN are used to demonstrate the validity and effectiveness of the proposed model.

MuST: Robust Image Watermarking for Multi-Source Tracing

In recent years, with the popularity of social media applications, massive digital images are available online, which brings great convenience to image recreation. However, the use of unauthorized image materials in multi-source composite images is still inadequately regulated, which may cause significant loss and discouragement to the copyright owners of the source image materials. Ideally, deep watermarking techniques could provide a solution for protecting these copyrights based on their encoder-noise-decoder training strategy. Yet existing image watermarking schemes, which are mostly designed for single images, cannot well address the copyright protection requirements in this scenario, since the multi-source image composing process commonly includes distortions that are not well investigated in previous methods, e.g., the extreme downsizing. To meet such demands, we propose MuST, a multi-source tracing robust watermarking scheme, whose architecture includes a multi-source image detector and minimum external rectangle operation for multiple watermark resynchronization and extraction. Furthermore, we constructed an image material dataset covering common image categories and designed the simulation model of the multi-source image composing process as the noise layer. Experiments demonstrate the excellent performance of MuST in tracing sources of image materials from the composite images compared with SOTA watermarking methods, which could maintain the extraction accuracy above 98% to trace the sources of at least 3 different image materials while keeping the average PSNR of watermarked image materials higher than 42.51 dB. We released our code on https://github.com/MrCrims/MuST

Abstract 5023: Analytical validation of the Oncomine™ Dx Express Test using liquid biopsy samples as an IUO assay in a CAP/CLIA lab

Abstract Introduction: The Oncomine™ Dx Express Test (ODxET) is a next-generation sequencing (NGS) test. It can be used as an investigational use only (IUO**) test to detect somatic DNA and RNA alterations in human cell-free total nucleic acid (cfTNA) isolated from plasma samples. This study outlines the analytical validation of the ODxET for relevant single nucleotide variants (SNV) and insertion/deletions (INDEL) in liquid biopsy specimens. The Ion Torrent Genexus™ platform has a short turnaround time with minimal operator input. This study was performed in a College of American Pathologists (CAP™) accredited and Clinical Laboratory Improvement Act (CLIA) licensed laboratory. Methods: Forty-seven unique samples (39 clinical, 3 derived, 2 reference and 3 commercially available DNA controls) were analyzed. All experiments began with sample extraction, except the lower input limit of detection (LLOD). We evaluated limit of detection, variant calling accuracy, and precision. Two control NIST™ samples were sequenced with 3 technical replicates each for INDEL and SNVs analytical accuracy. The LOD was evaluated at both the target input of 2ng/µl (40ng), and the minimum input,0.33ng/µl (6.6ng) with allele frequency (AF) levels at 1%, 0.5%, and 0.25%. The 39 clinical plasma samples were used for variant calling accuracy. Inter-assay reproducibility was assessed by sequencing DNA controls across four runs by different operators over multiple days/systems. Intra-assay repeatability was assessed by sequencing 4 samples in replicate on the same run by one operator on the same day/system. Results: The analytical accuracy and NPV was 100% for SNV/INDEL variants. The 40ng input LOD sensitivity was 96% for SNVs at 0.5% AF and 100% for INDELs at 0.25% Minimum AF. The lower input LOD sensitivity was 89% for SNVs and 100% for INDEL at 1% AF. Variant calling accuracy on samples with pre-characterized data was 100% for the 13 positive samples. Specificity was >99% for all samples including both healthy donor and positive samples. Repeatability was 100% for INDELs and >96% for SNVs, reproducibility was 100% for both variant classes. Conclusion: This validation study to estimate analytical accuracy, limit of detection, variant calling accuracy and precision has demonstrated that the ODxET assay is highly robust in detecting SNV and INDEL mutations in plasma samples and is suitable for use in a CAP/CLIA laboratory as an IUO assay.**=In Vitro Diagnostic use only. Not available in all regions including North America. Citation Format: Juan C. Espinoza, Rajendra Ramsamooj, Akemi Yuki, John Williamson, Diarra Hassell, Madhu Jasti, Suzanne Salazar, David Ginzinger. Analytical validation of the Oncomine™ Dx Express Test using liquid biopsy samples as an IUO assay in a CAP/CLIA lab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5023.

Zonotope approximation based flexible cluster division method for load‐side resource scheduling

AbstractIn view of the insufficient flexibility and adjustment capabilities caused by the high proportion of renewable energy connected to the distribution network and the limitations of the source‐side adjustment method, a load‐side resource flexibility cluster partitioning method based on zonotope approximation is proposed to cope with the flexibility adjustment requirements brought by uncertain resources. First, the constraint space of load‐side resources such as energy storage, multi‐source mobile energy storage, electric vehicles, and temperature‐controlled loads is described as a convex polyhedral form general model based on the virtual battery framework, and the zonotope approximation is used to integrate its constraint space with the Minkowski sum. Secondly, modularity and flexibility supply and demand balance index are proposed to perform flexibility clustering of load‐side resources. Then, the minimum comprehensive operating cost of the cluster aggregator is set as the optimization goal, the cluster constraint space is equivalent to the cost function, and the optimal charging and discharging power and flexibility adjustment capacity of the load‐side resources are obtained. Finally, the method proposed here is verified through numerical example simulation to effectively balance aggregation accuracy and calculation speed.

Bi-Layer Planning of Integrated Energy System by Incorporating Power-to-Gas and Ground Source Heat Pump for Curtailed Wind Power and Economic Cost Reduction

The popularization of renewable energy is limited by wasteful problems such as curtailed wind power and high economic costs. To tackle these problems, we propose a bi-layer optimal planning model with the integration of power to gas and a ground source heat pump for the existing integrated energy system. Firstly, the inner layer optimizes the daily dispatch of the system, with the minimum daily operation cost including the penalty cost of curtailed wind power. Then, the enumeration method of outer-layer optimization determines the device capacity of various schemes. After that, optimal planning can be achieved with the minimum daily comprehensive cost. The result of this example shows that the improved system can reduce curtailed wind power and system costs, thus improving the overall economy. Finally, the influences of algorithms and gas prices on planning optimization are studied.

Investigation on the thermodynamic performance and machinery mass in the N2O-based mixtures recompression Brayton cycle for a nuclear-powered spacecraft

As a reliable energy system with a long service life, nuclear energy can produce massive power for spacecraft in deep space exploration. Due to the compact layout and excellent performance, the supercritical recompression Brayton cycle has received extensive attention and has become a hot research topic in thermoelectric conversion technology. The critical properties of the working medium are a constraint on the cycle performance. Adding additional gas into a pure S-N2O working medium can effectively change the properties of the critical point and improve the cycle performance. However, there is no standard for selecting appropriate binary mixture components to determine which additive gas can improve cycle efficiency. In this work, several gases are chosen as the potential N2O-based mixtures (N2O-O2, N2O-He, N2O-Kr, N2O-Ar, N2O-H2S, N2O-CO, N2O-SO2, N2O-CH4, N2O-C2H6, N2O-C3H8, N2O-C4H8, N2O-C4H10, N2O-C5H10, N2O-C5H12, N2O-C6H6) as the working medium in the system. The detailed sensitivity analysis reveals the influence of essential parameters (the split ratio, pressure ratio, selection minimum operating temperature, maximum operating temperature, and minimum operating pressure) on the thermodynamic efficiency and Brayton machinery unit mass. According to the analysis results, the thermal efficiency improves with an increase in the split ratio, pressure ratio, and maximum operating temperature and decreases in the selection minimum operating temperature and minimum operating pressure for all mixtures. The exergy efficiency also showed a similar variation trend. However, the influence of the maximum working temperature on it is different from that of the thermal efficiency, which decreases with the increase of the maximum working temperature. The cycles operated close to the critical area can provide better cycle performance, and N2O-He has the best thermal performance among all proposed N2O-based mixtures, which can be used as a potential choice as the working medium for future nuclear-powered spacecraft.

On subordination conditions for systems of minimal di erential operators

In this paper, we provide a review of results on apriori estimates for systems of minimal differential operators in the scale of spaces \(L^p(\Omega),\) where \(p\in[1,\infty].\) We present results on the characterization of elliptic and \(l\)-quasielliptic systems using apriori estimates in isotropic and anisotropic Sobolev spaces \(W_{p,0}^l(\mathbb
 R^n),\)\(p\in[1,\infty].\) For a given set \(l=(l_1,\dots,l_n)\in\mathbb
 N^n\) we prove criteria for the existence of \(l\)-quasielliptic and weakly coercive systems and indicate wide classes of weakly coercive in \(W_{p,0}^l(\mathbb
 R^n),\) \(p\in[1,\infty],\) nonelliptic, and nonquasielliptic systems. In addition, we describe linear spaces of operators that are subordinate in the \(L^\infty(\mathbb R^n)\)-norm to the tensor product of two elliptic differential polynomials.