Distribution Scheme Research Articles

Detecting bolt looseness of flanged joint based on multiple-mode conversion

This article detects the bolt looseness of a flanged joint based on the energy conversion between multiple wave modes. The mode conversion introduced by the flange carries abundant information referring to the bolt looseness, but it requires precise sensing technology to capture each mode. The mode transducer for cylindrical shells was first proposed based on the orthogonality of mode shapes. The distribution scheme and electrode function enable the piezo transducer array to selectively control multiple modes. Both the simulated and experimental validations were subsequently conducted to check the design scheme. We examined the shapes and mode signals of the excited wave field to ensure that the energy was concentrated in the target mode. Finally, two-stage detection was conducted for eight bolts: the first stage detected the initial looseness of a single bolt using two indicators; the second stage trained the machine learning classifiers to recognize the location of the loose bolts. The results exhibited a high sensitivity of mode conversion in detecting initial looseness, with an accuracy of [Formula: see text] in recognizing the loose location.

Asymmetric Charge Distribution in Atomically Precise Metal Nanoclusters for Boosted CO2 Reduction Catalysis.

Recently, atomically precise metal nanoclusters (NCs) have been widely applied in CO2 reduction reaction (CO2RR), achieving exciting activity and selectivity and revealing structure-performance correlation. However, at present, the efficiency of CO2RR is still unsatisfactory and cannot meet the requirements of practical applications. One of the main reasons is the difficulty in CO2 activation due to the chemical inertness of CO2. Constructing symmetry-breaking active sites is regarded as an effective strategy to promote CO2 activation by modulating electronic and geometric structure of CO2 molecule. In addition, in the subsequent CO2RR process, asymmetric charge distributed sites can break the charge balance in adjacent adsorbed C1 intermediates and suppress electrostatic repulsion between dipoles, benefiting for C-C coupling to generate C2+ products. Although compared to single atoms, metal nanoparticles, and inorganic materials the research on the construction of asymmetric catalytic sites in metal NCs is in a newly-developing stage, the precision, adjustability and diversity of metal NCs structure provide many possibilities to build asymmetric sites. This review summarizes several strategies of construction asymmetric charge distribution in metal NCs for boosting CO2RR, concludes the mechanism investigation paradigm of NCs-based catalysts, and proposes the challenges and opportunities of NCs catalysis.

Research on cross regional green certificate bilateral distributed robust trading strategy based on Bayesian theory.

To ensure the effective implementation of the compulsory green certificate transaction in China, a multi-participant cross-regional green certificate bilateral transaction model considering the relevant technical characteristics of the unit is proposed to solve the problems such as the mismatch between the supply and demand of green certificates and the reduction of the transaction plan by the grid operation constraints in the current green certificate transaction. To mobilize the initiative of participants, the model adopts the bilateral transaction of free negotiation. To balance the interests of transaction participants, the paper analyzes the complex interaction of multi participants in cross-regional bilateral transactions by using cooperative game theory. Simultaneously, the model considers multiple uncertainties among participants and takes into account the correlation of photovoltaic power in multiple periods. Based on the distributed robust theory, the multivariate nonlinear model and mixed-effect model of Bayesian are proposed to construct the fuzzy sets of the probability distribution for wind and photovoltaic power. On this basis, the paper analyzes the strategic behaviors of multi participants affected by uncertainty factors in the cross-regional green certificate bilateral trade through a numerical example, and quantitatively analyzes the sensitivity of each participant's transaction plan affected by uncertainty factors. The results show that the degree of competition will affect the strategic behavior and income of the participants, and the full cooperation mode can not only avoid the risk of fluctuations in the transaction price and transaction power within the alliance but also stabilize the supply and demand of green certificate transactions.

Gradient Distribution of Zincophilic Sites for Stable Aqueous Zinc-Based Flow Batteries with High Capacity.

Current collectors, as reaction sites, play a crucial role ininfluencing various electrochemical performances in emerging cost-effective zinc-based flow batteries (Zn-based FBs). 3D carbon felts (CF) are commonly used but lack effectiveness in improving Zn metal plating/stripping. Here, a current collector with gravity-induced gradient copper nanoparticles (CF-G-Cu NPs) is developed, integrating gradient conductivity and zincophilicity to regulate Zn deposition and suppress side reactions. The CF-G-Cu NPs electrode modulates Zn nucleation and growth via the zincophilic Cu/CuZn5 alloy has been confirmed by density functional theory (DFT) calculations. Finite element simulation demonstrates the gradient internal structure effectively optimizes the local electric/current field distribution to regulate the Zn2+ flux, improving bottom-up plating behavior for Zn metal and mitigating top-surface dendrite growth. As a result, Zn-based asymmetrical FBs with CF-G-Cu NPs electrodes achieve an areal capacity of 30 mAhcm-2 over 640h with Coulombic efficiency of 99.5% at 40mAcm-2. The integrated Zn-Iodide FBs exhibit a competitive long-term lifespan of 2910h (5800 cycles) with low energy efficiency decay of 0.062% per cycle and high cumulative capacity of 112800 mAhcm-2 at a high current density of 100mAcm-2. This gradient distribution strategy offers a simple mode for developing Zn-based FB systems.

Impact of the innovation promotion strategy on digital technology diffusion in regional innovation ecosystems

ABSTRACT From the external perspective of digital technology diffusion in regional innovation ecosystems, this study divides innovation promotion into information dissemination and precision subsidy promotion strategies, and uses multi-agent modelling and simulation method to analyse the impact of innovation promotion strategies on digital technology diffusion in regional innovation ecosystems. The results are as follows. (1) The diffusion rate and depth of digital technology in regional innovation ecosystems with an information dissemination promotion strategy are better than those without this strategy. Early, high-strength centralised promotion strategy has the highest rate and depth of digital technology diffusion, and implementing distributed information promotion strategy in the late stage can maximise the rate and depth of digital technology diffusion. (2) When the number of target enterprises is insufficient, the precision subsidy promotion strategy cannot achieve the desired effect. With an increase in the number of target enterprises, the diffusion rate and depth of digital technology in regional innovation ecosystems gradually increased and showed a classic ‘S’-type diffusion curve. By focusing on the overall trend, the average rate and depth of digital technology diffusion in regional innovation ecosystems with a relationship embeddedness strategy are the best.

Coordinated distributed model predictive control for multi energy carrier systems.

Introducing Energy hubs (EHs) is a beneficial strategy for incorporating quickly expanding renewable energies. However, the stochastic nature of renewable energy sources (RESs) and fluctuating energy demand have produced a number of difficulties, including unstable voltage/frequency, challenging energy management, and difficult network interaction. Additionally, the changing in response time of electrical and heat demands will make control challenging. This paper proposes a distributed control system for use with dynamic EHs. The RESs and loads present in the multi-carrier system cause the dynamics considering here. In order to optimise system performance, this research suggests a distributed model predictive control strategy that considers expected behaviour and operational restrictions. The strategy's potential is demonstrated via simulations in which the proposed scheme is applied to a benchmark system.

Research on intelligent optimal allocation control strategy for stability and braking of heavy tyre blowout vehicle

The traditional control strategy mainly compensates and corrects the driving state of heavy vehicles with a tyre blowout, and rarely considers the need for emergency braking. It is also difficult to effectively coordinate the problems of lateral yaw, slip and tailspin, and longitudinal braking performance degradation. For this, an intelligent optimization allocation control strategy combining stability control and braking control is innovatively designed in this article. The joint simulation model for heavy tyre blowout vehicles is built based on the Dugoff tyre blowout model and the TruckSim vehicle model. A linear quadratic form regulator stable control based on the particle swarm optimization algorithm optimization is used to dynamic equilibrium of the additional yaw moment generated by the vehicle tyre blowout. A logic threshold braking control based on adaptive adjustment is adopted to achieve an ideal braking effect for the vehicle. In order to simultaneously consider the stability and braking performance of vehicles with blowout tyre, a braking force distribution strategy and a fuzzy optimization allocation algorithm are proposed to achieve coordination of lateral and longitudinal control strategies. The results indicate that intelligent optimized allocation control can effectively correct the deviation trajectory of a heavy tyre blowout vehicle, enabling it to achieve rapid braking while maintaining stability.

A Novel Nonlinear Adaptive Control Method for Longitudinal Speed Control for Four-Independent-Wheel Autonomous Vehicles

As autonomous driving technology and four-independent-wheel chassis systems advance, four-independent-wheel autonomous vehicles have increasingly become a focal area of modern research. The longitudinal control problem for four-independent-wheel autonomous vehicles presents challenges such as complex models, high nonlinearity, and strong system uncertainties. This paper proposes a novel hierarchical control algorithm to address these challenges, innovatively combining the advantages of adaptive backstepping and dynamic sliding mode control algorithms in the upper controller, allowing it to effectively overcome the impact of uncertain system parameters and suppress the common chattering phenomenon in the output of typical sliding mode control methods. Based on the design of the upper controller, an innovative optimized longitudinal force distribution strategy and the construction of a tire reverse longitudinal slip model are proposed, followed by the design of a fuzzy PID controller as the lower slip ratio controller to achieve precise whole-vehicle longitudinal speed tracking and improve overall control performance. This method not only improves the accuracy of speed tracking but also enhances the robustness and adaptability of the control system. Finally, the effectiveness and superiority of the proposed hierarchical control method are verified through CarSim simulations.

Privacy-enhanced distributed revocable identity management scheme based self-sovereign identity

In recent years, the rapid proliferation of digital services and resources on Industrial Internet has imposed higher demands on universality and privacy of identity management. Particularly with the advent of the digital economy, prudent users are urged to maintain control over their digital identity credentials. However, traditional identity management methods have failed to meet this requirement and thus have been prone to raise users' concerns about potential financial loss. Specifically, conventional identity management systems(IDMS) have been plagued by imperceptible privacy disclosure, which derives from the flaws in single points of failure, excessive disclosure, correlation analysis, traceability, and revocation. The emerging Self-Sovereign Identity (SSI) architecture aims to tackle these issues and is propelling the evolution of privacy-enhanced distributed identity management. To this end, we proposed a privacy-enhanced distributed identity management scheme with sequential aggregate issuance, threshold traceability and revocability in the setting of multiple credential issuers and regulators. We adopted the Decentralized identifiers(DIDs) and verifiable credentials(VCs) based on the SSI architecture to ensure the hierarchical identity authentication. The security and performance analysis shows that our proposal achieves the desired design goals and is feasible for distributed Industrial Internet.

Optimizing Multi-Channel Green Supply Chain Dynamics with Renewable Energy Integration and Emissions Reduction

In response to the global imperative of mitigating greenhouse gas emissions (GHGs) and the shifting landscape of business models toward multi-channel structures, this study delves into the intricacies of a green supply chain. Operating through both online and traditional channels with uncertain demands, the producer’s distribution strategy prompts an exploration of supply chain dynamics. Utilizing an integer programming model, this study calculates optimal prices, optimizes total profit, and minimizes transportation costs to curtail carbon dioxide emissions, depending on the transportation mode. Additionally, this study incorporates renewable energy sources into the production and transportation processes to further minimize carbon dioxide emissions. The integration of renewable energy not only supports environmental goals, but also contributes to the overall profitability of the supply chain by reducing energy costs. Employing a theoretical technique for linearization, the model, resolved through the Jimenez and TH methods, demonstrates efficacy in reconciling economic and environmental goals. The Jimenez method enables the transformation of fuzzy parameters into deterministic equivalents, allowing for a more reliable optimization during uncertainty, while the TH method provides an interactive fuzzy multi-objective approach, aligning the model’s dual objectives for both economic and environmental goals. Notably, when transportation costs to both markets are equal, the model prioritizes devices with a lower environmental impact, showcasing adaptability. Furthermore, the proposed solution empowers decision makers to influence pricing and enhance the entire supply chain’s profitability. In conclusion, this research offers nuanced insights, strategically aligning economic viability with environmental sustainability in the discourse on green supply chains.

Distributed model predictive control for asynchronous multiagent systems with self‐triggered coordinator

AbstractThis paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self‐triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade‐off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed‐loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.

Parasitic taxa are key to the vertical stratification and community variation of pelagic ciliates from the surface to the abyssopelagic zone.

An increase in upper-ocean thermal stratification is being observed worldwide due to global warming. However, how ocean stratification affects the vertical profile of plankton communities remains unclear. Understanding this is crucial for assessing the broader implications of ocean stratification. Pelagic ciliates cover multiple functional groups, and thus can serve as a model for studying the vertical distribution and functional strategies of plankton in stratified oceans. We hypothesize that pelagic ciliate communities exhibit vertical stratification caused by shifts in functional strategies, from free-living groups in the photic zone to parasitic groups in deeper waters. 306 samples from the surface to the abyssopelagic zone were collected from 31 stations in the western Pacific and analyzed with environmental DNA (the V4 region of 18S rDNA) metabarcoding of pelagic ciliates. We found a distinct vertical stratification of the entire ciliate communities, with a boundary at a depth of 200m. Significant distance-decay patterns were found in the photic layers of 5m to the deep chlorophyll maximum and in the 2,000m, 3000m and bottom layers, while no significant pattern occurred in the mesopelagic layers of 200m - 1,000m. Below 200m, parasitic Oligohymenophorea and Colpodea became more prevalent. A linear model showed that parasitic taxa were the main groups causing community variation along the water column. With increasing depth below 200m, the ASV and sequence proportions of parasitic taxa increased. Statistical analyses indicated that water temperature shaped the photic communities, while parasitic taxa had a significant influence on the aphotic communities below 200m. This study provides new insights into oceanic vertical distribution, connectivity and stratification from a biological perspective. The observed shift of functional strategies from free-living to parasitic groups at a 200m transition layer improves our understanding of ocean ecosystems in the context of global warming.

A study on the spatial distribution and optimization strategies of day care centers for the elderly

Background: At present, the degree of ageing in China is becoming severe, especially in Shanghai, which is the largest city with the highest degree of aging in China. How to adjust the community to effectively meet the needs of the elderly groups has become the focus of social attention. Objectives: In this study, spatial analysis was used to explore the matching degree of the elderly population with the distribution of day care centers in Xuhui District, Shanghai, China, to explore the problems, and to propose optimization strategies with a view to providing feasible suggestions for the construction of old-age daycare. Methods: This research adopts the methods of literature study, map analysis, questionnaire survey, especially tries to use the spatial analysis software of Arc GIS in order to set up a spatial database of Xuhui District's elderly population based on street administrative territorial entity. Results: The results show that the quantity and quality of the day care centers in Xuhui District can not meet the actual needs of the elderly. Secondly, the services provided in daycare centers deviate from the actual needs of older population. Thirdly, the day care service content lists are quite homogeneous, which neglects the diversified and personalized needs . Main Contribution to Evidence-Based Practice: From the perspective of spatial distribution study, this article enriches the theory of the construction and management of the elderly care facilities in urban areas, and provides reference for the formulation of aging policy. Meanwhile, it gives some suggestions on optimizing the spatial distribution of community facilities to fulfil the actual needs for optimal matching of the elderly care resources.

Integrated multi-manned disassembly line balancing problem with reverse supply chain design strategies by considering lot sizing

ABSTRACT Due to strict governmental regulations, manufacturers have increasingly focused on recovering end-of-life (EoL) products through disassembly lines, aiming to achieve economic benefits while addressing the environmental aspects of supply chain sustainability. Disassembly line balancing (DLB) has become a key challenge for enterprises seeking to perform environmentally conscious manufacturing as part of a closed-loop supply chain policy. Therefore, designing a sustainable closed-loop supply chain requires analyzing interrelated problems by focusing on strategic-, tactical-, and operational-level decisions simultaneously. This study introduces an integrated problem that addresses multi-manned DLB, lot sizing, and reverse supply chain (RSC) design problems. To mathematically represent the problem, a generic optimization model is developed to minimize overall costs, including station opening, inventory holding, shortage, transportation, and collection center/facility opening costs. This study addresses several research questions to explore the impact of distribution strategies (centralized and decentralized) on multi-manned DLB and lot size decisions within the context of reverse supply chain design.

Cutting-Edge Strategies for Overcoming Therapeutic Barriers in Alzheimer's Disease.

Alzheimer's disease (AD) remains one of the hardest neurodegenerative diseases to treat due to its enduring cognitive deterioration and memory loss. Despite extensive research, few viable treatment approaches have been found; these are mostly due to several barriers, such as the disease's complex biology, limited pharmaceutical efficacy, and the BBB. This presentation discusses current strategies for addressing these therapeutic barriers to enhance AD treatment. Innovative drug delivery methods including liposomes, exosomes, and nanoparticles may be able to pass the blood-brain barrier and allow medicine to enter specific brain regions. These innovative strategies of medicine distribution reduce systemic side effects by improving absorption. Moreover, the development of disease-modifying treatments that target tau protein tangles, amyloid-beta plaques, and neuroinflammation offers the chance to influence the course of the illness rather than only treat its symptoms. Furthermore, gene therapy and CRISPR-Cas9 technologies have surfaced as potentially groundbreaking methods for addressing the underlying genetic defects associated with AD. Furthermore, novel approaches to patient care may involve the utilization of existing medications having neuroprotective properties, such as those for diabetes and cardiovascular conditions. Furthermore, biomarker research and personalized medicine have made individualized therapy approaches possible, ensuring that patients receive the best care possible based on their unique genetic and molecular profiles.

Load forecasting based on multi-core learning Support Vector Machine (SVM)

Abstract The development of smart grids requires enhanced data integration, robust risk assessment, and dynamic response optimization. In this paper, a multi-core learning Support Vector Machine (SVM) model is presented to improve the accuracy and efficiency of load and photovoltaic output forecasting. The model leverages kernel function optimization and parallel computing frameworks to handle large-scale data efficiently. Additionally, a comprehensive risk assessment system is developed to quantify risks such as overvoltage, undervoltage, line overload, and load loss in distribution networks. An adaptive genetic algorithm-based risk control model is also proposed, optimized in two stages—day-ahead and intra-day—to achieve minimal comprehensive risk through real-time adjustments in distributed power output and electric vehicle charging strategies. Furthermore, an integrated virtual synchronous control online verification method for source-network-load-storage is introduced, enhancing system response speed and control accuracy. These innovations collectively provide a solid theoretical foundation and technical support for the efficient and safe operation of smart grids, addressing the increasing demands of modern energy systems.

Research on energy interactive operation optimization strategy of multi-energy network with integrated energy based on cooperative game

Abstract Integrated energy includes multi-energy networks such as electricity, gas, cold, and heat. Horizontal multi-energy coordination and vertical source network storage load interaction can effectively support large-scale new energy consumption. In this paper, a multi-energy flow model of the integrated energy multi-energy network is constructed, an optimization model of energy sharing and mutual benefit of the multi-energy network is established based on Nash negotiation, and then the model is solved to obtain the optimal distribution scheme of cooperation benefits. Finally, a numerical example is given to verify the proposed energy sharing and mutual benefit method. The results show that the total power generation cost of each park is reduced by 27%, and the profit distribution of each park is more reasonable.

V2X Application Server and Vehicle Centric Distribution of Commitments for V2V Message Authentication

Safety applications, such as intersection collision warnings and emergency brake warnings, enhance road safety and traffic efficiency through periodic broadcast messages by vehicles and roadside infrastructure. While the Elliptic Curve Digital Signature Algorithm (ECDSA) is a widely used security approach, its performance limitations make it unsuitable for time-critical safety applications. As such, a symmetric cryptography-based technique called Timed Efficient Stream Loss-tolerant Authentication (TESLA) offers a viable alternative. However, applying standard TESLA in the context of vehicle-to-vehicle (V2V) communications has its own challenges. One challenge is the difficulty of distributing authentication information called commitments in the highly dynamic V2V environment. In this paper, we propose two novel solutions to this problem, namely, V2X Application Server (VAS)-centric and vehicle-centric. The former is an application-level solution that involves selective unicasting of commitments to vehicles by a central server, the VAS, and the latter is a reactive scheme that involves the periodic broadcast of commitments by the vehicles themselves. Extensive simulations are conducted using representatives of the real V2V environment to evaluate the performance of these approaches under different traffic situations; as well as performance comparison with a state-of-the-art distribution solution. The simulation results indicate that the VAS-centric solution is preferable for use in a TESLA-like V2V security scheme. It demonstrates desirable features, including timely delivery of commitments and high distribution efficiency, with over 95% of commitments sent by the VAS are associated with relevant safety messages when compared with the vehicle-centric and state-of-the-art solutions. Formal security analysis, conducted using the Random Oracle Model (ROM), proves the correctness of our proposed distribution schemes. Additionally, an informal security analysis shows the resilience of the proposed schemes against various attacks, including impersonation, replay, and bogus commitment messages.

ПОДХОДЫ К ОСУЩЕСТВЛЕНИЮ РИСК-МЕНЕДЖМЕНТА В УСЛОВИЯХ НЕОПРЕДЕЛЕННОЙ И ТУРБУЛЕНТНОЙ ВНЕШНЕЙ СРЕДЫ (НА ПРИМЕРЕ ЗЕРНОВОГО ПРОИЗВОДСТВА)

In the modern Russian socio-economic system, there is an objective increase in uncertainty and turbulence, which requires adequate management responses. In connection with the sanctions pressure, in agriculture the strategy of reducing the risks of import dependence is becoming a stimulating factor for development. The study was conducted on the example of grain production development, which has some specifics from the standpoint of management and, in particular, risk management. The purpose of the study is to generalize approaches to risk management in an uncertain and turbulent external environment and to develop on this basis a risk distribution scheme between management entities in the risk management system adapted to the specifics of grain production. It is shown that the increase in the values of economic indicators of grain production is influenced by natural and climatic conditions, they determine the quality of grain products, costs and the complex of applied agrotechnological measures. In addition to the observed "standard" variability of natural and climatic conditions, the study also identifies extreme risk events that require forecasting and consideration when managing grain production. Also, risks in the considered sphere are caused by diversity and interdependence of such factors as natural and climatic, soil, technological, economic, social, legal, etc. In their mutual interweaving, these factors determine annual fluctuations of economic indicators (yield, gross harvest, sown area) in grain production in Russia. As a result of the study, it was established that risk management procedures in the sphere of grain production should be carried out according to the hierarchical principle with the allocation of at least two levels: the coordinating level - the executive level. Based on this idea, an industry structural and functional model of risk management at various levels of decision-making has been developed. This model has the property of dualism: some levels are simultaneously coordinating, offering strategic solutions to lower levels, and executive - for higher structures. Implementation of the developed model in the practice of risk management in grain production will increase its sustainability and predictability.

Discretely Nonlinearly Stable Weight-Adjusted Flux Reconstruction High-Order Method for Compressible Flows on Curvilinear Grids

To achieve genuine predictive capability, an algorithm must consistently deliver accurate results over prolonged temporal integration periods, avoiding the unwarranted growth of aliasing errors that compromise the discrete solution. Provable nonlinear stability bounds the discrete approximation and ensures that the discretization does not diverge. Nonlinear stability is accomplished by satisfying a secondary conservation law, namely for compressible flows; the second law of thermodynamics. For high-order methods, discrete nonlinear stability and entropy stability, have been successfully implemented for discontinuous Galerkin (DG) and residual distribution schemes, where the stability proofs depend on properties of L2-norms. In this paper, nonlinearly stable flux reconstruction (NSFR) schemes are developed for three-dimensional compressible flow in curvilinear coordinates. NSFR is derived by merging the energy stable flux reconstruction (ESFR) framework with entropy stable DG schemes. NSFR is demonstrated to use larger time-steps than DG due to the ESFR correction functions, at the cost of larger error levels at design order convergence for equivalent degrees of freedom, while preserving discrete nonlinear stability. NSFR differs from ESFR schemes in the literature since it incorporates the FR correction functions on the volume terms through the use of a modified mass matrix. We also prove that discrete kinetic energy stability cannot be preserved to machine precision for quadrature rules where the surface quadrature is not a subset of the volume quadrature. This result stems from the inverse mapping from the kinetic energy variables to the conservative variables not existing for the kinetic energy projected variables. This paper also presents the NSFR modified mass matrix in a weight-adjusted form. This form reduces the computational cost in curvilinear coordinates because the dense matrix inversion is approximated by a pre-computed projection operator and the inverse of a diagonal matrix on-the-fly and exploits the tensor product basis functions to utilize sum-factorization. The nonlinear stability properties of the scheme are verified on a nonsymmetric curvilinear grid for the inviscid Taylor-Green vortex problem and the correct orders of convergence were obtained on a curvilinear mesh for a manufactured solution. Lastly, we perform a computational cost comparison between conservative DG, overintegrated DG, and our proposed entropy conserving NSFR scheme, and find that our proposed entropy conserving NSFR scheme is computationally competitive with the conservative DG scheme.