Principle Of Conservation Of Energy Research Articles

Sparse Estimation for Hamiltonian Mechanics

Estimating governing equations from observed time-series data is crucial for understanding dynamical systems. From the perspective of system comprehension, the demand for accurate estimation and interpretable results has been particularly emphasized. Herein, we propose a novel data-driven method for estimating the governing equations of dynamical systems based on machine learning with high accuracy and interpretability. The proposed method enhances the estimation accuracy for dynamical systems using sparse modeling by incorporating physical constraints derived from Hamiltonian mechanics. Unlike conventional approaches used for estimating governing equations for dynamical systems, we employ a sparse representation of Hamiltonian, allowing for the estimation. Using noisy observational data, the proposed method demonstrates a capability to achieve accurate parameter estimation and extraction of essential nonlinear terms. In addition, it is shown that estimations based on energy conservation principles exhibit superior accuracy in long-term predictions. These results collectively indicate that the proposed method accurately estimates dynamical systems while maintaining interpretability.

Sound escapes any gravity well

The principle of mechanical energy conservation limits the height at which a body of mass m and initial total mechanical energy Ei can get in a uniform gravitational field g, namely hmax=Ei/mg . However, the same conservation principle does not pose any limit to the height reachable by a sound wave in a vertical column of (ideal) fluid in the same uniform gravitational field. Here, we provide two accessible proofs of a counter-intuitive result that can represent an amusing digression from the usual presentation of the topic.

Theoretical investigation of MHD peristalsis of non‐Newtonian nanofluid flow under the impacts of temperature‐dependent thermal conductivity: Application to biomedical engineering

AbstractNanofluids have the tendency to improve thermal characteristics in a broader context, such as in medical processes, industrial cooling applications, the transportation industry, hybrid engines, gas temperature reduction, microelectromechanical systems, refrigerators, nuclear reactors, vehicle temperature control, pharmaceutical processes, thermal management of vehicles, microelectronics, and chillers, etc. Therefore, present study investigates the effects of a two‐dimensional magnetohydrodynamics (MHD) peristalsis of the Carreau–Yasuda nanofluid through conservation principles of energy, concentration, mass, and momentum, which is motivated by recent advances in biological engineering. The entire system is coupled through viscous dissipation, Joule heating, a heat sink/source, Brownian diffusion, a radially magnetic field, thermophoresis motion, and slip conditions in a curved geometry. Lubrication theory is employed in order to simplify governing equations. Resulting systems are nonlinear coupled differential equations whose exact solutions are difficult to obtain, therefore, numerical solutions are obtained through NDSolve in Mathematica. The key features of peristaltic movement and rates of heat transfer are disseminated in detail via graphical representations for variation of various physical parameters. According to the findings, for greater values of the Hartman number, temperature increases, and the velocity decreases. The validity of these results has been verified using the existing published articles of Hayat et al. (2018).

An approach to hydraulic design of Conical Central Baffle Flumes

Design methodology for providing a Conical Central Baffle flume (CCBF) has not yet been found in the literature, though it is required by the hydraulic engineers considering the increased popularity of the baffled flumes, now-a-days. Some guidance for discharge prediction using CCBF is only found in the literature. The main objective of the present research is to address the issue of hydraulic design of CCBFs. A measuring flume serves as a device for quantifying the flow rate in an open channel. These flumes operate on the basis of principles of critical flow and energy conservation. Conventionally, it is assumed that the critical flow occurs at location of the minimum flow cross-section or throat. In the current investigation, a CCBF is employed to gauge the flow discharge. The CCBF configuration involves placing one conical obstruction in a trapezoidal channel, thus creating a throat. One of the objectives of the study is to scrutinize the prevailing location of the critical section and introduce a refining approach to discharge prediction. Through a series of experiments and numerical simulations, the present study endeavors to ascertain the true location of the critical section in CCBF. Contrary to the traditional belief that the critical section is always at the throat, the investigation revealed that the critical section tends to shift upstream with increased discharge. The theoretical analysis, employing energy expression and critical flow concepts, unveiled that despite this shifting nature, the energy-critical depth ratio of the flume remains constant. Based on these findings, a discharge prediction model is proposed. The model demonstrates a mean relative error of ±1.42 % and a maximum relative error of 3.5 % in discharge prediction. The theoretical investigation also highlighted that the critical section occurring at the throat signifies the minimum discharge value, and induced a threshold choking flow condition in the flume. The present study also proposes the expressions for minimum as well as maximum discharge measuring capacity of a CCBF which leads to its proper design methodology, which has never been presented so far in the literature. Additionally, a design example is presented, elucidating the process of designing a conical CCBF for a trapezoidal channel based on the understanding of the limiting discharge.

Helmholtz and the Conservation of Energy: Contexts of Creation and Reception

Helmholtz and the Conservation of Energy: Contexts of Creation and Reception

Helmholtz and the Conservation of Energy: Contexts of Creation and Reception

Helmholtz and the Conservation of Energy: Contexts of Creation and Reception

Is the Local Energy Conservation Law Valid inside the Electronic Device of Charged Capacitors?

The energy conservation is one of the most fundamental and well established principles in physics. Emmy Noether extended the energy conservation principle to the quantum field theoretical domain in empty space by relating the time translation invariance of the universe with energy conservation. While this is the case in open empty space, it seems that the local space enclosed by conducting metallic plates has unexpected property suggesting that the energy conservation principle does not necessarily apply to localized bound system of capacitors in electrodynamics. This point of view was raised by noticing the fact that the spherical capacitor has calculable electrostatic self-potential energy in both the inner and outer shells respectively which was not taken into account in the conventional consideration of the total energy stored in the capacitors. It seems like the concept of moving charges one by one into the capacitor plates has blindsided the investigators to bypass the necessary steps to account for the additional repulsive self-potential energy that accumulates simultaneously in both of the capacitor plates in the process of charging the capacitor. The investigation shows that the repulsive self-potential energies from both of the capacitor plates have indeed been omitted in the conventional calculation of the stored energy in the capacitors. We present the dynamics of the stored potential energy in capacitors and discuss its physical consequences in relation to the anomalous energy devices reported in the past.

Global existence of the strong solution to the climate dynamics model with topography effects and phase transformation of water vapor

This study investigates a climate dynamics model that incorporates topographical effects and the phase transformation of water vapor. The system comprises the Navier–Stokes equations, the temperature equation, the specific humidity equation, and the water content equation, all adhering to principles of energy conservation. Applying energy estimation methods, the Helmholtz–Weyl decomposition theorem, and the Brezis–Wainger inequality, we derive high-order a priori estimates for state functions. Subsequently, based on the initial data assumptions V0 ∈ H4(Ω), T0′,q0,mw0∈H2(Ω), we can prove that a strong solution to this system exists globally in time and establish the uniqueness of the global strong solution.

Advanced Cascaded Recompression Absorption System Equipped with Ejector and Vapor-Injection Enhanced Vapor Compression Refrigeration System: ANN based Multi-Objective Optimization

Although traditional compression absorption refrigeration cycle (CARC) addresses the respective limitations of the Absorption Refrigeration Cycle (ARC) and Vapor Compression Refrigeration (VCR) systems while harnessing their individual strengths. But this arrangement faces challenges related to power wastage, insufficient thermal energy absorption, and high compressor power requirements. To address these issues, in this study, an advanced RAC (Recompression Absorption System) is integrated with enhanced VCR incorporated with ejector to develop advanced proposed Ejector-Compression Recompression Absorption cycle (E-CRAC) and Ejector enhanced Vapor-Injection Compression Recompression Absorption Cycle (EI-CRAC). A computational model is developed in the Engineering Equation Solver (EES) employing energy, mass, and exergy conservation principles to conduct a thorough 1st and 2nd law analysis. An Artificial Neural Network (ANN) model, combined with a Genetic algorithm, enabled multi-objective optimization to pinpoint optimal operational conditions. The COP of the proposed systems is nearly three times higher than the traditional CARC system, showing an improved efficiency in cooling operations. Additionally, EI-CRAC and E-CRAC demonstrate near 10% and 20% COP enhancement, as well as 15% and 25% increase of Exergy efficiency over benchmark basic CRAC respectively. Furthermore, the research highlights the sensitivity of these systems to various operating conditions, such as pressure drop across ejector nozzle, evaporator temperature, condenser pressure, absorber temperature etc., emphasizing the need for precise control to maximize efficiency. The results of this detailed theoretical thermodynamic analysis with optimization provide a comprehensive understanding of the proposed systems while offering valuable insights for further scope of improvements.

Double diffusive on powell eyring fluid flow by mixed convection from an exponential stretching surface with variable viscosity/thermal conductivity

This study delves into heat and mass transference in fluids with variable thermo-physical features, focusing on the Powell-Eyring fluid, a non-Newtonian substance with unique characteristics. Our aim is to understand the behaviour of this shear-thinning fluid when interacting with an exponentially stretchable surface, considering factors like mixed convection and thermal radiation. Our research endeavours to uncover novel findings in this intricate domain, with the primary objective of comprehending how this shear-thinning fluid behaves when it encounters an exponentially stretchable surface. To address the complexities, we employ the BVP4C technique in MATLAB, transforming governing equations into nonlinear partial differential equations (PDEs) based on mass, linear momentum, and energy conservation principles. The Homotopy method aids in navigating these equations, and numerical solutions are calculated using the powerful BVP4C technique for ordinary differential equations (odes). The research stands out for its comprehensive exploration of the interplay among diverse factors, including radiation, variable viscosity, mixed convection, and activation energy constraints. Findings are presented through tables and graphs, offering valuable insights into the intricate physical phenomena within this multifaceted field.

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis.

At present, the reconfiguration, maintenance, and review of power lines play a pivotal role in maintaining the stability of electrical grid operations and ensuring the accuracy of electrical energy measurements. These essential tasks not only guarantee the uninterrupted functioning of the power system, thereby improving the reliability of the electricity supply but also facilitate precise electricity consumption measurement. In view of these considerations, this article endeavors to address the challenges posed by power line restructuring, maintenance, and inspections on the stability of power grid operations and the accuracy of energy metering. To accomplish this goal, this article introduces an enhanced methodology based on the hidden Markov model (HMM) for identifying the topology of distribution substations. This approach involves a thorough analysis of the characteristic topology structures found in low-voltage distribution network (LVDN) substations. A topology identification model is also developed for LVDN substations by leveraging time series data of electricity consumption measurements and adhering to the principles of energy conservation. The HMM is employed to streamline the dimensionality of the electricity consumption data matrix, thereby transforming the topology identification challenge of LVDN substations into a solvable convex optimization problem. Experimental results substantiate the effectiveness of the proposed model, with convergence to minimal error achieved after a mere 50 iterations for long time series data. Notably, the method attains an impressive discriminative accuracy of 0.9 while incurring only a modest increase in computational time, requiring a mere 35.1 milliseconds. By comparison, the full-day data analysis method exhibits the shortest computational time at 16.1 milliseconds but falls short of achieving the desired accuracy level of 0.9. Meanwhile, the sliding time window analysis method achieves the highest accuracy of 0.95 but at the cost of a 50-fold increase in computational time compared to the proposed method. Furthermore, the algorithm reported here excels in terms of energy efficiency (0.89) and load balancing (0.85). In summary, the proposed methodology outperforms alternative approaches across a spectrum of performance metrics. This article delivers valuable insights to the industry by fortifying the stability of power grid operations and elevating the precision of energy metering. The proposed approach serves as an effective solution to the challenges entailed by power line restructuring, maintenance, and inspections.

Coupled influences of magnetic field and several thermal loads on vibration of thermoelastic nano-ceramic (Si3N4) beam

Our study investigates the vibration of a thermoelastic nano-ceramic (Si3N4) beam exposed to a magnetic field under the effect of a moving heat source and different types of thermal loads. These nanobeams are modeled as non-Fourier heat conduction. In our formulation, the equation of motion and the principle of energy conservation from two coupled partial differential equations have been driven. A direct approach has been used to find the solutions to the governing equations using the Laplace transform definition. Numerical Laplace transform inversion was calculated using Hoing's approximation approach. Solutions have been applied to silicon nitride (Si3N4) nanobeam ceramics. Figures show the distribution of temperature increment, lateral deflection, and stress with different values of magnetic field and several thermal load parameters. In all the studied nanobeam functions, the magnetic field and several thermal loads’s parameters have significant effects. They could be used as tuners to control the vibration and temperature increment. In the present study, a moving heat source, and different thermal loads have been applied to a non-Fourier nanobeam exposed to a magnetic field. The results have been compared with those obtained in the thermoelastic cases.

Thermoeconomic, environmental and uncertainty assessments and optimization of a novel large-scale/low carbon hydrogen liquefaction plant integrated with liquefied natural gas cold energy

Abstract Presently, the liquefaction of hydrogen represents a promising solution to alleviate challenges associated with its storage and transportation. It is crucial to formulate methodological frameworks for scrutinizing hydrogen liquefaction routes to enhance energy efficiency. This paper endeavors to establish, assess feasibility, and refine a novel approach for a high-capacity hydrogen liquefaction facility, leveraging the cold energy from liquefied natural gas (LNG). This new route utilizes four hybrid refrigeration systems, each designed to handle 50 × 103 kg daily. Significant energy savings are achievable through the primary utilization of LNG’s energy in the precooling stage and the generation of electrical power during the vaporization phase. The architecture of this novel route is crafted around the principles of energy conservation, incorporating thermodynamic assessments alongside economic and environmental viability studies. Furthermore, the performance of this innovative hydrogen liquefaction method is thoroughly evaluated across both non-optimized and optimized scenarios. Advanced techniques such as composite curve and uncertainty analyses are employed to provide a detailed examination of heat cascades and cost differentials. The findings indicate that managing LNG’s cold energy is crucial for refining the hydrogen liquefaction route, potentially reducing the specific power requirement of the optimum route by 27.4% compared to its non-optimum counterpart. Moreover, in the optimized scenario, there is a decrease of ~4.72% in unit production expenses, 26.26% in CO2 emissions, and 21.85% in specific power usage for avoided CO2 emissions.

A Mathematical Model for Dynamic Electric Vehicles: Analysis and Optimization

In this article, we introduce a flexible and reliable technique to simulate and optimize the characteristics of a Dynamic Electrical Vehicle (DEV). The DEV model is a discrete event-based modeling technique used in electrical vehicle research to improve the effectiveness and performance of various electrical vehicles (EVs) components. Here, the DEVS model is applied to EV research in several ways, including battery management optimization, evaluation of power train design and control strategy, and driver behavior analysis. The essential power train elements, including the battery, motor, generator, internal combustion engine, and power electronics are included in the mathematical model for the dynamic electric vehicle. The model is derived using the conservation of energy principle. The model includes mathematical equations for the electrical power output, battery charge level, motor torque, motor power output, generator power output, internal combustion engine torque, mechanical power delivered to the generator, and the efficiencies of the power electronics, motor, generator, and engine. The model is examined by using a numerical method called the Runge–Kutta Method of order 4 for dynamic electric vehicle’s performance under various driving states for maximum effectiveness and performance. It is revealed that the DEV model provides a systematic method to simulate and optimize the behavior of complex EV systems.

A novel energy optimization framework to enhance the performance of sensor nodes in Industry 4.0

AbstractIndustry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings.

Experimental study on the strain energy evolution mechanism of thick and hard sandstone roof in Xinjiang Mining Area

For understanding the mechanical performance and strain energy evolution mechanism of thick hard roof sandstone samples, a sequence of uniaxial compression trials with acoustic emission (AE) monitoring were carried out. The results indicate: (1) The stress-strain curve of the thick hard roof sandstone specimens exhibits distinct stage characteristics. Based on the evolvement of instantaneous axial stiffness, it is separated into Fracture Closure Phase, Elastic Deformation Phase, Steady Fracture Expansion Phase, Unsteady Fracture Expansion Phase, and Post-Peak Phase. (2) The AE energy and cumulative count curves of the thick hard roof sandstone specimens also exhibit significant stage characteristics and can be mutually corroborated with the stage division of the stress-strain curve. (3) Based on the energy conservation principle, the evolution of strain energy density in the thick hard roof sandstone specimens under uniaxial compression loading was analyzed, and plastic strain energy increment was employed to study the stage characteristics of strain energy dissipation. (4) A damage constitutive model for the thick hard roof sandstone specimens was constructed, considering the characteristics of strain energy dissipation. This model effectively describes the stress-strain relationship among the samples, which undergo strain hardening, strain softening, and sudden destruction.

Exploring pressure, temperature, and flow patterns in ciliated microfluidic systems

The integration of cilia-induced flow, nanofluids, and the inclusion of cobalt ferrite particles holds significant promise in fluid dynamics, heat transfer, and nanotechnology, offering potential breakthroughs in various technological and material applications. We explore the behavior of cilia-induced flow in a nanofluid confined within an annular domain, employing the Williamson fluid model to characterize the behavior of cobalt ferrite (CoFe2O4) nanoparticles. Our analysis is based on a mathematical treatment rooted in fundamental mass, momentum, and energy conservation principles while considering physical constraints (low Reynolds number and long wavelength) and adopting a dimensionless approach. By applying regular perturbation techniques, we derive series solutions for velocity and temperature profiles, providing insight into the complex interplay among cilia-generated flow, nanofluid properties, and the influence of Cobalt ferrite nanoparticles within the annular configuration. In particular, we uncovered clear correlations among cilia length, amplitude ratio, flow rate, and the Prandtl number with temperature distribution. Also, we observed substantial reductions in temperature trends under Weissenberg numbers and particle volume fractions.

The Physical Vacuum Models with Intrinsic Angular Momentum: From Vorticity of Maxwell’s Luminiferous Ether to Pseudomagnetism

In this work, the main milestones of the development of physical vacuum model with intrinsic angular momentum were considered. J. C. Maxwell was the first researcher who introduced this property in the physical vacuum for the mathematical description of electromagnetic oscillations and results of Faraday’s experiments with magnetic fields. The milestones of the development of Maxwell’s model are the following: the introduction by Planck, Einstein and Stern of atomic oscillators with so-called zero-point energy; the introduction by Feynman of virtual particles created by quantum objects; the introduction of pseudomagnetic interaction between spins by Abragam et al. The enriching of our knowledge about the physical vacuum characterized by intrinsic angular momentum lets us to explain the wave and inertial properties of quantum objects, to create a theory alternative to Special Relativity, to determine the conditions of generation of a process having superluminal speed (spin supercurrent), to determine the conditions of obtaining energy from the spin system of physical vacuum. In some cases, without taking into account this energy (based only on Newton’s laws) the violation of the principles of energy conservation takes place.

A novel efficient energy absorber with free inversion of a metal foam-filled circular tube

In this paper, a novel efficient energy absorber with free inversion of a metal foam-filled circular tube (MFFCT) is designed, and the axial compressive behavior of the MFFCT under free inversion is studied analytically and numerically. The theoretical analysis reveals that the energy is mainly dissipated through the radial bending of the metal circular tube, the circumferential expansion of the metal circular tube, and the metal filled-foam compression. The principle of energy conservation is used to derive the theoretical formula for the minimum compressive force of the MFFCT over free inversion under axial loading. Furthermore, the free inversion deformation characteristics of the MFFCT are analyzed numerically. The theoretical steady values are found to be in good agreement with results of the finite element (FE) analysis. The effects of the average diameter of the metal tube, the wall thickness of the metal tube, and the filled-foam strength on the free inversion deformation of the MFFCT are considered. It is observed that in the steady deformation stage, the load-carrying and energy-absorbing capacities of the MFFCT increase with the increase in the average diameter of the metal tube, the wall thickness of the metal tube, or the filled-foam strength. The specific energy absorption (SEA) of free inversion of the MFFCT is significantly higher than that of the metal tube alone.

Droplet jumping by modulated electrowetting

We investigate jumping of sessile droplets from a solid surface in ambient oil using modulated electrowetting actuation. We focus on the case in which the electrowetting effect is activated to cause droplet spreading and then deactivated exactly at the moment the droplet reaches its maximum deformation. By systematically varying the control parameters such as the droplet radius, liquid viscosity and applied voltage, we provide detailed characterisation of the resulting behaviours including a comprehensive phase diagram separating detachment from non-detachment behaviours, as well as how the detach velocity and detach time, i.e. duration leading to detachment, depend on the control parameters. We then construct a theoretical model predicting the detachment condition using energy conservation principles. We finally validate our theoretical analysis by experimental data obtained in the explored ranges of the control parameters.