Conservation Constraints Research Articles

Multi-objective ecological restoration priority in China: Cost-benefit optimization in different ecological performance regimes based on planetary boundaries

In the context of the “United Nations Decade on Ecosystem Restoration”, optimizing spatiotemporal arrangements for ecological restoration is an important approach to enhancing overall socioecological benefits for sustainable development. However, against the background of ecological degradation caused by the human use of most natural resources at levels that have approached or exceeded the safe and sustainable boundaries of ecosystems, it is key to explain how to optimize ecological restoration by classified management and optimal total benefits. In response to these issues, we combined spatial heterogeneity and temporal dynamics at the national scale in China to construct five ecological performance regimes defined by indicators that use planetary boundaries and ecological pressures which served as the basis for prioritizing ecological restoration areas and implementing zoning control. By integrating habitat conservation, biodiversity, water supply, and restoration cost constraints, seven ecological restoration scenarios were simulated to optimize the spatial layout of ecological restoration projects (ERPs). The results indicated that the provinces with unsustainable freshwater use, climate change, and land use accounted for more than 25%, 66.7%, and 25%, respectively, of the total area. Only 30% of the provinces experienced a decrease in environmental pressure. Based on the ecological performance regimes, ERP sites spanning the past 20 years were identified, and more than 50% of the priority areas were clustered in regime areas with increased ecological stress. As the restoration area targets doubled (40%) from the baseline (20%), a multi-objective scenario presents a trade-off between expanded ERPs in areas with highly beneficial effects and minimal restoration costs. In conclusion, a reasonable classification and management regime is the basis for targeted restoration. Coordinating multiple objectives and costs in ecological restoration is the key to maximizing socio-ecological benefits. Our study offered new perspectives on systematic and sustainable planning for ecological restoration.

OCEAN-MBRL: Offline Conservative Exploration for Model-Based Offline Reinforcement Learning

Model-based offline reinforcement learning (RL) algorithms have emerged as a promising paradigm for offline RL. These algorithms usually learn a dynamics model from a static dataset of transitions, use the model to generate synthetic trajectories, and perform conservative policy optimization within these trajectories. However, our observations indicate that policy optimization methods used in these model-based offline RL algorithms are not effective at exploring the learned model and induce biased exploration, which ultimately impairs the performance of the algorithm. To address this issue, we propose Offline Conservative ExplorAtioN (OCEAN), a novel rollout approach to model-based offline RL. In our method, we incorporate additional exploration techniques and introduce three conservative constraints based on uncertainty estimation to mitigate the potential impact of significant dynamic errors resulting from exploratory transitions. Our work is a plug-in method and can be combined with classical model-based RL algorithms, such as MOPO, COMBO, and RAMBO. Experiment results of our method on the D4RL MuJoCo benchmark show that OCEAN significantly improves the performance of existing algorithms.

Simulation of Human Movement in Zero Gravity.

In the era of expanding manned space missions, understanding the biomechanical impacts of zero gravity on human movement is pivotal. This study introduces a novel and cost-effective framework that demonstrates the application of Microsoft's Azure Kinect body tracking technology as a motion input generator for subsequent OpenSim simulations in weightlessness. Testing rotations, locomotion, coordination, and martial arts movements, we validate the results' realism under the constraints of angular and linear momentum conservation. While complex, full-body coordination tasks face limitations in a zero gravity environment, our findings suggest possible approaches to device-free exercise routines for astronauts and reveal insights into the feasibility of hand-to-hand combat in space. However, some challenges remain in distinguishing zero gravity effects in the simulations from discrepancies in the captured motion input or forward dynamics calculations, making a comprehensive validation difficult. The paper concludes by highlighting the framework's practical potential for the future of space mission planning and related research endeavors, while also providing recommendations for further refinement.

A Theory of Maximum Entropy Production and Its Application to Microwave Remote Sensing—Simultaneous Retrieval of Soil Moisture and Vegetation Water Content

AbstractA theory of maximum entropy production (MEP) for electromagnetic wave propagation in dielectric materials is proposed and applied to simultaneously retrieving soil moisture (SM) and vegetation water content (VWC) from L‐band microwave brightness temperature (TB). One representation of the MEP principle states that a non‐equilibrium system corresponds to such a configuration of energy fluxes that minimizes a dissipation function under the constraint of energy conservation. The dissipation function for radiative transfer is formulated as an analogy of that for heat transfer. A new physical parameter, radiative inertia as an analogy of thermal inertia, is introduced to characterize radiative attenuation in dielectric media. The radiative inertia is parameterized in terms of the penetration depth of electromagnetic waves as a function of the complex dielectric constant. The MEP based retrieval algorithm predicts SM and VWC by minimizing the dissipation function under the constraint of the conservation of radiative energy. The retrievals of SM and VWC based on the MEP theory were validated against field observations in tropical and temperate forested regions of the Amazon and North America. The proof‐of‐concept analysis demonstrates the capability of the MEP algorithm for simultaneous retrievals of SM and VWC even for dense canopy (e.g., VWC > 5 kg m−2). The MEP method is a new theoretical framework for developing innovative remote sensing algorithms of the Earth system not limited to microwave observations.

A circle-based interface reconstruction algorithm based on the coupled volume-of-fluid and level set method

The interface tends to behave as a circular shape under the effect of surface tension in the two-dimensional two-phase fluid flow. In this study, based on the coupled volume-of-fluid and level set (VOSET) method, an accurate circle-based interface reconstruction (CIR) algorithm for structured meshes is proposed, which features a straightforward implementation procedure. A portion of the standard circle is employed to approximate the curve interface, without the need for complex classification, coordinate transformation, and equation transformation. The radius of this circle is computed simply by the curvature and signed distance at the mixed cell center, and the center of this circle is determined using a straightforward dichotomy method under the mass conservation constraints. Results indicate that the coupled VOSET and CIR (VOSET-CIR) method maintains superior computational accuracy in the signed distance, interface curvature, and dynamic interface reconstruction compared to the coupled VOSET and piecewise linear interface calculation (VOSET-PLIC) method, with accuracy improvements ranging from 34% to 1490% across different test cases. Furthermore, the VOSET-CIR method outperforms the efficient least squares volume-of-fluid interface reconstruction (ELVIRA) and PLIC algorithms in reconstructing random circles and surpasses the quadratic spline based interface reconstruction (QUASI) algorithm in reconstructing random squares. In terms of computational efficiency, except for the initial PLIC algorithm, the time costs of the VOSET-CIR method are substantially lower than those of the ELVIRA and QUASI algorithms, as well as the VOSET-PLIC method.

Experimental evaluation of phase-field-based load-specific shape optimization of nature-inspired porous structures

Triply periodic minimal surface (TPMS) structures excel in various research fields, ranging from bone support structures to heat exchangers. By implementing measures for shape alteration, the mechanical properties of the structure can be improved under certain load conditions. While interface-based methods such as the phase-field method have established themselves as powerful simulation techniques for the analysis of microstructure evolution and morphologically complex dynamic processes, they are not yet very well known and widely used for the application of shape optimization in mechanically loaded complex structures. In this study, an experimental procedure to validate shape-optimized samples is presented and applied to validate three computationally derived optimal candidates for sheet-based TPMS structures (Diamond, Gyroid, and Primitive) proposed by applying a mathematical model for shape optimization formulated in terms of the phase-field approach combined with linear elastic continuum mechanics and subject to the constraints of volume conservation. The present experimental study aims to validate recently obtained theoretical research results predict three different TPMS structures were shape-optimized under mechanical stress, using the phase-field method. In the following, the previous theoretical study is validated experimentally. The validation procedure creates a rare intersection between shape optimization phase-field simulations and experimental samples. The measurements show that the shape-optimized structures have a higher average stiffness, which leads to a shift in the plastic deformation range and thus confirms the computationally determined shape optimization.

Volume Conservation Constrained Multi-Material Reconstruction for Inconsistent Spectral CT Imaging

Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, the difficulty of decomposition also increases due to the nonlinearity nature of the measurements and ill condition of the problem, especially in the case of geometric inconsistency, which typically leads to low image qualities. Therefore, it is a crucial issue for inconsistent spectral CT imaging to improve the accuracy of material decomposition while suppressing the noise. This paper depends on a statistical reconstruction model with different priors to propose one-step multi-material algorithms. In these approaches, the gradient sparisty-based and convolutional neural network based methods are designed for the case of the consistent numbers of material and energies. And volume conservation constraint are further developed while the two numbers are not equal. An efficient Newton descent method is adopted based on the simple surrogate function. For simulation experiments with different noise levels, the largest peak signal-to-noise ratio (PSNR) obtained by the proposed method approximately increases by 20.924 dB and 18.283 dB compared with those of other algorithms. Magnified areas of real data also further demonstrated that the proposed methods has a better ability to suppress noise. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise compared with the state-of-the-art methods.

Signalized and Unsignalized Road Traffic Intersection Models: A Comprehensive Benchmark Analysis

Road traffic flow models allow the development and testing of intelligent transportation solutions. Macroscopic intersection models are especially relevant for the simulation of large traffic networks. In this article, we study four first-order signalized and unsignalized intersection models. The two unsignalized approaches are the first-in-first-out (FIFO) model (roundabout-type intersection) and an optimal non-FIFO model (highway-type intersection). The optimal control operates upstream for the first signalized intersection model. It occurs downstream for the second signalized model. All four models satisfy the expected physical constraints of vehicle conservation, traffic demand, and assignment. The models are minimal and allow a comprehensible analysis of the results. We determine mathematical relationships between the intersection models and empirically analyze the performances using Monte Carlo simulations. The numerical simulations assume random demand, supply, and assignment. Besides average performances, the approach accounts for the flow ranges of variation. A benchmark analysis compares the intersection models. We observe that the optimal signalized intersection models overcome the performances of the FIFO model in congested states. They may even reach the performances of the idealistic non-FIFO model. Further applications for the four intersection models are discussed.

A novel air balancing method based on an improved perceptron under multiple constraints for the energy conservation of ventilation system

Air balancing is a key technology for improving indoor environmental quality and reducing ventilation system energy consumption. Existing air balancing methods just considered damper angles and fan control voltages, combined as single constraint strategy to balance air. Fan power, as the direct parameter affecting system electricity consumption, has not been included in air balancing modelling. On the other hand, single constraint strategy makes current methods inflexible to maintain airflow accuracy and energy efficiency together. This paper proposes a novel air balancing energy-saving model with multiple constraint control strategy. Damper angle, fan control voltage, and fan power are combined by an improved perceptron model. It is aimed at minimizing ventilation system energy consumption, by achieving optimal airflow accuracy and energy conservation. The proposed improved perceptron under multiple constraints (IPMC) method is validated on the experimental system with 5 terminals, air balancing relative errors within 6.0 %. Compared with the current high-precision and energy-saving methods in references, IPMC reduces energy consumption by 12.0 % and 7.6 %, respectively, with improved airflow prediction accuracy. The proposed air balancing method has three innovation: 1) Constrained by the fan power, the IPMC method improves ventilation energy efficiency; 2) Independent and flexible constraints enable more accurate airflow control; 3) Integrated by an improved perceptron, multiple constraints consider airflow prediction accuracy and energy conservation at the same time. This method is expected to better satisfy airflow demand and reduce effectively energy consumption of ventilation system, thus improving building ventilation energy utilization efficiency without losing thermal comfort and indoor air quality.

Generalized Carter constant for quadrupolar test bodies in Kerr spacetime

We establish the existence of a deformation of the usual Carter constant which is conserved along the motion in a fixed Kerr background of a spinning test body possessing the spin-induced quadrupole coupling of a black hole. The conservation holds perturbatively up to second order in the test body’s spin. This constant of motion is obtained through the explicit resolution of the conservation constraint equations, employing covariant algebraic and differential relations amongst covariant building blocks of the Kerr background. For generic spin-induced quadrupole couplings, which describe compact objects such as neutron stars, we obtain a no-go result on the existence of such a conserved quantity.

The maximum mass of a black hole which can tidally disrupt a star: measuring black hole spins with tidal disruption events

ABSTRACT The tidal acceleration experienced by an object at the event horizon of a black hole decreases as one over the square of the black hole’s mass. As such there is a maximum mass at which a black hole can tidally disrupt an object outside of its event horizon and potentially produce observable emission. This maximum mass is known as the ‘Hills mass’, and in full general relativity is a function of both the black hole’s spin a• and the inclination angle of the incoming object’s orbit with respect to the black hole’s spin axis ψ. In this paper, we demonstrate that the Hills mass can be represented by a simple analytical function of a• and ψ, the first general solution of this problem. This general solution is found by utilizing the symmetries of a class of critical Kerr metric orbits known as the innermost bound spherical orbits. Interestingly, at fixed black hole spin the maximum Hills mass can lie at incoming orbital inclinations outside of the black hole’s equatorial plane ψ ≠ π/2. When compared to previous results in the literature this effect can lead to an increase in the maximum Hills mass (at fixed spin) by as much as a factor of $\sqrt{11/5} \simeq 1.48$ for a maximally rotating black hole. We then demonstrate how Bayesian inference, coupled with an estimate of the mass of a black hole in a tidal disruption event, can be used to place conservative constraints on that black hole’s spin. We provide a publicly available code tidalspin which computes these spin distributions.

Optimal planning of mobile energy storage in active distribution network

AbstractMobile energy storage (MES) has the flexibility to temporally and spatially shift energy, and the optimal configuration of MES shall significantly improve the active distribution network (ADN) operation economy and renewables consumption. In this study, an optimal planning model of MES is established for ADN with a goal of minimising the annual cost of a distribution system. Firstly, the annual cost of a distribution system is set up with consideration of the investment cost and operation cost of MES, wind and PV curtailment cost, network loss cost and the peak‐valley arbitrage income of MES. Then, the distributed photovoltaic and wind power access constraints, power conservation constraints of ADN, power generation constraint, system security constraint, energy coupling and displacement constraints of MES are further tailored to establish the MES planning model. Afterwards, the proposed model is solved by the second‐order cone relaxation combined with the large M algorithm. Finally, the simulation results of the modified IEEE 33‐bus distribution network validate the effectiveness of the proposed model.

Tourism eco-efficiency and its influencing factors under the constraint of energy conservation and emissions reduction in China

Tourism eco-efficiency and its influencing factors under the constraint of energy conservation and emissions reduction in China

DRL based low carbon economic dispatch by considering power transmission safety limitations in internet of energy

Economic dispatch, as a crucial method for ensuring the normal operation of power systems, is typically modeled as an optimization problem and solved using solvers. The introduction of low-carbon requirements has increased the complexity of the optimization problem, as economic dispatch now needs to consider effectively reducing the emission of CO2. The utilization of renewable energy can mitigate carbon emissions during power system operation, but its inherent uncertainty poses safety risks to transmission lines between microgrids and the main grid. Hence, this paper explores the issue of low-carbon economic dispatch to ensure the reliability of power transmission. We introduce a two-stage low-carbon economic dispatch model grounded in deep reinforcement learning. In the first stage, we incorporate the transmission power limits between microgrids and the main grid as constraints, generate day-ahead dispatch strategies, and determine the weights of factors in the reward function. In the second stage, we employ two methods: adjusting the weight of the penalty for exceeding the transmission limits and adding conservative safety limit constraints, to solve the problem of transmission power exceeding limits caused by uncertainties of renewable energy and prediction errors. Three deep reinforcement learning algorithms, all rooted in the Actor–Critic structure, are employed to implement the two-stage economic dispatch model. Experimental results affirm the efficacy of the proposed model in mitigating the risk of transmission power exceeding its limit, reducing carbon emissions, and minimizing operational costs.

Learning physical models that can respect conservation laws

Recent work in scientific machine learning (SciML) has focused on incorporating partial differential equation (PDE) information into the learning process. Much of this work has focused on relatively “easy” PDE operators (e.g., elliptic and parabolic), with less emphasis on relatively “hard” PDE operators (e.g., hyperbolic). Within numerical PDEs, the latter problem class requires control of a type of volume element or conservation constraint, which is known to be challenging. Delivering on the promise of SciML requires seamlessly incorporating both types of problems into the learning process. To address this issue, we propose ProbConserv, a framework for incorporating conservation constraints into a generic SciML architecture. To do so, ProbConserv combines the integral form of a conservation law with a Bayesian update. We provide a detailed analysis of ProbConserv on learning with the Generalized Porous Medium Equation (GPME), a widely-applicable parameterized family of PDEs that illustrates the qualitative properties of both easier and harder PDEs. ProbConserv is effective for easy GPME variants, performing well with state-of-the-art competitors; and for harder GPME variants it outperforms other approaches that do not guarantee volume conservation. ProbConserv seamlessly enforces physical conservation constraints, maintains probabilistic uncertainty quantification (UQ), and deals well with shocks and heteroscedasticities. In each case, it achieves superior predictive performance on downstream tasks.

Mode-resolved phonon transmittance using lattice dynamics: Robust algorithm and statistical characteristics

Lattice dynamics (LD) enables the calculation of mode-resolved transmittance of phonons passing through an interface, which is essential for understanding and controlling the thermal boundary conductance (TBC). However, the original LD method may yield unphysical transmittance over 100% due to the absence of the constraint of energy conservation. Here, we present a robust LD algorithm that utilizes linear algebra transformations and projection gradient descent iterations to ensure energy conservation. Our approach demonstrates consistency with the original LD method on the atomically smooth Si/Ge interface and exhibits robustness on rough Si/Ge interfaces. The evanescent modes and localized effects at the interface are revealed. In addition, bottom-up analysis of the phonon transmittance shows that the anisotropy in the azimuth angle can be ignored, while the dependency on the frequency and polar angle can be decoupled. The decoupled expression reproduces the TBC precisely. This work provides comprehensive insights into the mode-resolved phonon transmittance across interfaces and paves the way for further research into the mechanism of TBC and its relation to atomic structures.

Utility-Aware Payment Channel Network Rebalance

The payment channel network (PCN) is a promising solution to increase the throughput of blockchains. However, unidirectional transactions can deplete a user's deposits in a payment channel (PC), reducing the success ratio of transactions (SRoT). To address this depletion issue, rebalance protocols are used to shift tokens from well-deposited PCs to under-deposited PCs. To improve SRoT, it is beneficial to increase the balance of a PC with a lower balance and a higher weight (i.e., more transaction executions rely on the PC). In this paper, we define the utility of a transaction and the utility-aware rebalance (UAR) problem. The utility of a transaction is proportional to the weight of the PC and the amount of the transaction, and inversely proportional to the balance of the receiver. To maximize the effect of improving SRoT, UAR aims to find a set of transactions with maximized utilities, satisfying the budget and conservation constraints. The budget constraint limits the number of tokens shifted in a PC. The conservation constraint requires that the number of tokens each user sends equals the number of tokens received. We prove that UAR is NP-hard and cannot be approximately solved with a constant ratio. Thus, we propose two heuristic algorithms, namely Circuit Greedy and UAR_DC. Extensive experiments show that our approaches outperform the existing approach by at least 3.16 times in terms of utilities.

Automatic tuning of robust model predictive control in iterative tasks using efficient Bayesian optimization

Robust model predictive control (RMPC) is an effective technology for controlling uncertain systems while robustly handling constraints, and its closed-loop performance heavily relies on the selection of objective functions. However, the objective functions are typically chosen to be close to the real control objectives, despite an objective function that leads to less conservative constraints often provides better closed-loop performance. In this paper, we propose an automatic tuning framework for RMPC in iterative tasks. In particular, we parameterize RMPC and develop a Bayesian optimization (BO) method to tune it by solving a black-box optimization problem. We then introduce an efficient transfer learning framework within BO, which speeds up the searching process and enhances the controller performance. The effectiveness of the proposed tuning framework is illustrated on numerical examples.

Bi-layer sizing and design optimization method of propulsion system for electric vertical takeoff and landing aircraft

Electric vertical takeoff and landing (eVTOL) aircraft is a promising solution for future urban air mobility, but it suffers from insufficient energy and power density as opposed to fossil fuel-based aircraft. This paper proposes a bi-layer design optimization method for electric propulsion systems. To tackle the issue of high-dimension design parameters and multiple performance constraints, the design process is divided into two parameter optimization problems. In the upper layer, only scalar parameters of components are considered to determine a feasible sizing result subject to energy-flow limits. The outcome of this layer further serves as the initial guess of the lower design layer. The constraints of power conservation and conversion among components are taken into account. The optimization is scheduled in a cascaded paradigm that maximizes the total energy efficiency with minimized total weight. The blade-element momentum method is employed to estimate propeller thrust and torque coefficients. The proposed method is applied to a tilt-wing eVTOL. The sizing results shows that the eVTOL mass is reduced by 11.66% as opposed to the benchmark method. The proposed method provides a generic framework for sizing and design optimization independent of configuration and mission profiles.

Extended load flexibility of utility-scale P2H plants: Optimal production scheduling considering dynamic thermal and HTO impurity effects

Flexible power-to-hydrogen (P2H) production enables the admittance of renewable energies on a utility scale and provides the connected electrical power system with ancillary regulatory services. To extend the flexibility and thus improve the profitability of green hydrogen production, this paper presents an optimal production scheduling approach for utility-scale P2H plants composed of multiple alkaline electrolyzers. Unlike existing works, this work discards the conservative constant steady-state constraints and first leverages the dynamic thermal and hydrogen-to-oxygen (HTO) impurity crossover processes of the electrolyzers to improve flexibility. Doing this optimizes their effects on the loading range and energy conversion efficiency. The proposed multiphysics-aware scheduling model is formulated as mixed-integer linear programming (MILP). It coordinates the electrolyzers’ operation state transitions and load allocation subject to comprehensive thermodynamic and mass transfer constraints. A decomposition-based solution method, SDM-GS-ALM, is adopted to address the scalability issue for scheduling large-scale P2H plants composed of tens of electrolyzers. With an experiment-verified dynamic electrolyzer model, case studies show that the proposed method remarkably improves the hydrogen output and profit of P2H production powered by either solar or wind energy compared to the existing scheduling approach.