Conservation Constraints Research Articles

Nonthermal eigenstates and slow relaxation in quantum Fredkin spin chains

We study the dynamics and thermalization of the Fredkin spin chain, a system with local three-body interactions, particle conservation, and explicit kinetic constraints. We consider deformations away from its stochastic point in order to tune between regimes where kinetic energy dominates and those where potential energy does. By means of exact diagonalization, perturbation theory, and variational matrix product states, we show there is a sudden change of behavior in the dynamics that occurs, from one of fast thermalization to one of slow metastable (prethermal) dynamics near the stochastic point. This change in relaxation is connected to the emergence of additional kinetic constraints, which lead to the fragmentation of Hilbert space in the limit of a large potential energy. We also show that this change can lead to thermalization being evaded for special initial conditions because of nonthermal eigenstates (akin to quantum many-body scars). We provide clear evidence for the existence of these nonthermal states for large system sizes even when far from the large-potential-energy limit, and explain their connection to the emergent kinetic constraints. Published by the American Physical Society 2024

Detection prospects of gravitational waves from SU(2) axion inflation

We study detection prospects of a gravitational-wave background (GWB) sourced by SU(2) gauge fields considering all possible observational constraints. More precisely, we consider bounds set by cosmic microwave background measurements, primordial black hole overproduction, as well as backreaction of the gauge fields on the background evolution. Gravitational-waves data from the first three observing runs of the LIGO-Virgo-KAGRA Collaboration show no evidence for a GWB contribution from axion inflation. However, we are able to place conservative constraints on the parameters of the SU(2) inflation with current data. We investigate conditions on the inflationary potential that would lead to a detectable signal that evades astrophysical and cosmological constraints and discuss detection prospects for third generation networks. Published by the American Physical Society 2024

Obstacle avoidance control of UAV formation based on distributed model prediction

Aiming at the formation and maintenance problem of UAVs in an obstacle environment, a distributed model predictive control (DMPC) algorithm with no reference trajectory considering system constraints is proposed. In order to deal with the constraint coupling and cost coupling existing in model predictive control (MPC), the assumed trajectory is introduced to design a low conservative compatibility constraint and a cost function of no reference trajectory, so that the algorithm can be executed in a distributed and synchronous manner. Secondly, the terminal constraint is designed based on the speed barrier method to ensure the safety of the terminal domain, and a feasible terminal control input is given. The cost function is taken as a Lyapunov function, combined with the constructed stability constraint, and the iterative feasibility and system stability of the algorithm are analyzed. In addition, in order to take into account the real-time performance, a non-strictly stable DMPC algorithm that can better meet the requirements of formation obstacle avoidance is given on the basis of the proposed algorithm. The effectiveness and superiority of the proposed algorithm are verified by numerical simulation.

A three-dimensional curve interface reconstruction algorithm for two-phase fluid flow

The curve interface reconstruction algorithm has received significant attention in the context of two-dimensional two-phase flow. However, it remains absent in the three-dimensional scenario. This paper proposes a novel three-dimensional curve interface reconstruction (CIR) algorithm to address this challenge within structured meshes for the first time. Specifically, a portion of the spherical surface is employed to reconstruct the three-dimensional curve interface segment, with the radius and center coordinates determined by curvature and mass conservation constraints, respectively. To enhance curvature accuracy, a sphere-based iterative reconstruction (SIR) algorithm is proposed to calculate the reconstructed distance function (RDF) for the three-dimensional curve interface. Various tests involving the interface reconstruction of spherical, ellipsoidal, and cubic objects demonstrate that the coupled SIR and CIR (SIR-CIR, simplified by SCIR) method achieves higher accuracy than many popular methods, particularly with coarse mesh resolutions. Additionally, the SCIR method offers the advantages of straightforward implementation and coding for interface reconstruction in two-phase flow research. This advantage results in reduced computational costs compared to the coupled volume-of-fluid and level set (VOSET) method, which also utilizes an iterative method to solve RDF.

Preventing mass loss in the standard level set method: New insights from variational analyses

For decades, the computational multiphase flow community has grappled with mass loss in the level set method. Numerous solutions have been proposed, from fixing the reinitialization step to combining the level set method with other conservative schemes. However, our work reveals a more fundamental culprit: the smooth Heaviside and delta functions inherent to the standard formulation. Even if reinitialization is done exactly, i.e., the zero contour interface remains stationary, the use of smooth functions lead to violation of mass conservation. We propose a novel approach using variational analysis to incorporate a mass conservation constraint. This introduces a Lagrange multiplier that enforces overall mass balance. Notably, as the delta function sharpens, i.e., approaches the Dirac delta limit, the Lagrange multiplier approaches zero. However, the exact Lagrange multiplier method disrupts the signed distance property of the level set function. This motivates us to develop an approximate version of the Lagrange multiplier that preserves both overall mass and signed distance property of the level set function. Our framework even recovers existing mass-conserving level set methods, revealing some inconsistencies in prior analyses. We extend this approach to three-phase flows for fluid-structure interaction (FSI) simulations. We present variational equations in both immersed and non-immersed forms, demonstrating the convergence of the former formulation to the latter when the body delta function sharpens. Rigorous test problems confirm that the FSI dynamics produced by our simple, easy-to-implement immersed formulation with the approximate Lagrange multiplier method are accurate and match state-of-the-art solvers.

Modeling of nonequilibrium effects in a compressible plasma based on the lattice Boltzmann method

A magnetohydrodynamic lattice Boltzmann method (MHD-LBM) model for a 2D compressible plasma based on the finite volume scheme is established. The double distribution D2Q17 discrete velocities are used to simulate the fluid field. The hyperbolic Maxwell equations, which satisfy the elliptic constraints of Maxwell's equations and the constraint of charge conservation, are used to simulate the electromagnetic field. The flow field and electromagnetic field are coupled to simulate a compressible plasma through the electromagnetic force and magnetic induction equations. Four typical cases, the Taylor vortex flow, strong blast, Orszag–Tang vortex, and one-dimensional Riemann problems, are simulated to validate the MHD-LBM model for a compressible plasma. It is found that shock waves widely exist in a compressible plasma, and strong nonequilibrium effects exist around each shock wave. The quantitative simulation for the Brio–Wu problem demonstrates that this model can easily obtain the physical characteristics of nonequilibrium effects at sharp interfaces (shock waves and detonation waves). The magnetic fields can affect the magnitudes to which the system deviates from its equilibrium state. The viscosity can increase the magnitudes to which the system deviates from its equilibrium state. Compared with existing compressible MHD, these results for nonequilibrium effects can provide mesoscopic physical insights into the flow mechanism of a shock wave in a supersonic plasma.

Evolution of Proton Radiation Therapy Brainstem Constraints on the Pediatric Proton/Photon Consortium Registry

Increasing concern that brainstem toxicity incidence after proton radiation therapy might be higher than with photons led to a 2014 University of Florida (UF) landmark paper identifying its risk factors and proposing more conservative dose constraints. We evaluated how practice patterns changed among the Pediatric Proton/Photon Consortium Registry (PPCR). This prospective multicenter cohort study gathered data from patients under the age of 22 years enrolled on the PPCR, treated between 2002 and 2019 for primary posterior fossa brain tumors. After standardizing brainstem contours, we garnered dosimetry data and correlated those meeting the 2014 proton-specific brainstem constraint guidelines by treatment era, histology, and extent of surgical resection. A total of 467 patients with evaluable proton radiation therapy plans were reviewed. Median age was 7.1 years (range: <1-21.9), 63.0% (n = 296) were men, 76.0% (n = 357) were White, and predominant histology was medulloblastoma (55.0%, n = 256), followed by ependymoma (27.0%, n = 125). Extent of resection was mainly gross total resection (GTR) (67.0%, n = 312), followed by subtotal resection (STR) or biopsy (20.0%, n = 92), and near total resection (NTR) (9.2%, n = 43). The UF brainstem constraint metrics most often exceeded were the goal D50% of 52.4 gray relative biological equivalents (43.3%, n = 202) and maximal D50% of 54 gray relative biological equivalents (12.6%, n = 59). The compliance rate increased after the new guidelines (2002-2014: 64.0% vs 2015-2019: 74.6%, P = .02), except for ependymoma (46.3% pre- vs 50.0% post-guidelines, P = .86), presenting lower compliance (48.8%) in comparison to medulloblastoma/ primitive neuroectodermal tumors/pineoblastoma (77.7%), glioma (89.1%), and atypical teratoid/rhabdoid tumors (90.9%) (P < .001). Degree of surgical resection did not affect compliance rates (GTR/NTR 71.0% vs STR/biopsy 72.8%, P = .45), even within the ependymoma subset (GTR/NTR 50.5% vs STR/biopsy 38.1%, P = .82). Since the publication of the UF guidelines, the pediatric proton community has implemented more conservative brainstem constraints in all patients except those with ependymoma, irrespective of residual disease after surgery. Future work will evaluate if this change in practice is associated with decreased rates of brainstem toxicity.

Investigation of a new method for direct chemistry integration in Conditional Source-term Estimation

This paper examines a new Conditional Source-term Estimation (CSE) implementation in which an integral inversion is performed for species mass fractions without any chemistry tabulation. To tackle the numerical stiffness due to chemistry, a constant-pressure chemical reactor is considered in conditional space. The objective of this work is to investigate this new method in CSE by comparing its numerical performance (accuracy of predictions and computational efficiency) with previous CSE implementations using tabulated chemistry. For the first time, the constraints for mass and mixture fraction conservation in conditional space are included in the inversion process. The investigation is performed in a Reynolds Averaged Navier-Stokes (RANS) framework using a well-documented turbulent flame with detailed measurements in conditional and physical spaces for many reacting scalars. A chemical mechanism including 16 species and 35 reactions is incorporated. CSE predictions of conditional and Favre averaged temperature and different species mass fractions are analysed. CSE with full chemistry performs as well as CSE with tabulated chemistry, if not slightly better. Numerical errors are discussed. The new CSE implementation is made possible by the proposed numerical treatment of stiffness due to chemistry and first-order Tikhonov regularisation technique with additional physical constraints. The new approach is found to be more CPU intensive than CSE with a pre-tabulation technique, but as a preliminary analysis, the computational cost of CSE with direct integration of a chemical mechanism appears to be much smaller than the run time associated with CMC for a similar set-up. Further investigation is required to refine this initial conclusion. These results open new opportunities towards CSE simulations that involve fuel blends and more complex operating conditions for example with spray and multi-stream inlets with non-uniform compositions for which accurate chemistry tabulations are difficult to generate.

Novel Criteria to Provide a Locality/Normality Degree in Molecules and Their Relevance in Physical Chemistry.

In contrast to the traditional analysis of molecules using local mode behavior, where the degree of locality is given through a function in terms of Morse potential parameters, new criteria for locality/normality (LN) suitable for application to any molecular system are proposed. The approach is based on analysis of the connection between the algebraic normal and local mode representations. It is shown that both descriptions are equivalent as long as the polyad (total number of quanta) in the local representation is not conserved. The constraint of a local polyad conservation naturally provides a criterion for assigning an LN degree in quantitative form, without an analogue in configuration space. The correlation between the different parameters reveals the physical properties of molecules. A clear connection between the LN degree (based on the fundamentals) and spectroscopic properties is also presented, suggesting a promising approach for identifying mixtures of isotopologues.

A hierarchical framework for minimising emissions in hybrid gas-renewable energy systems under forecast uncertainty

Developing and deploying renewables in existing energy systems are pivotal in Europe’s transition to net-zero emissions. In this transition, gas turbines (GTs) will be central for balancing purposes. However, a significant hurdle in minimising emissions of GTs operating in combination with intermittent renewables arises from the reliance on unreliable meteorological forecasts. Here, we propose a hierarchical framework for decoupling this operational problem into a balancing and emissions minimisation problem. Balancing is ensured with a high-level stochastic balancing filter (SBF) based on data-driven stochastic grey-box models for the uncertain intermittent renewable. The filter utilises probabilistic forecasting and less conservative chance constraints to compute safe bounds, within which a proposed low-level economic predictive controller further minimises emissions of the GTs during operations. As GTs exhibit semi-continuous operating regions, complementarity constraints are utilised to fully exploit each GT’s allowed operational range. The proposed method is validated in simulation for a gas-balanced hybrid renewable system with batteries, three GTs with varying capacities, and a wind farm. Using real historical operational wind data, our simulation shows that the proposed framework balances the energy demand and minimises emissions with up to 4.35% compared with other conventional control strategies in simulation by minimising the GT emissions directly with complementarity constraints in the low-level controller and indirectly with less conservative chance constraints in the high-level filter. The simulations show that the computational cost of the proposed framework is well within requirements for real-time applications. Thus, the proposed operational framework enables increased renewable share in hybrid energy systems with GTs and renewable energy and subsequently contributes to de-carbonising these types of isolated or grid-connected systems onshore and offshore.

Applying the starquake model to study the formation of elastic mountains on spinning neutron stars

ABSTRACT When a neutron star is spun-up or spun-down, the changing strains in its solid elastic crust can give rise to sudden fractures known as starquakes. Early interest in starquakes focused on their possible connection to pulsar glitches. While modern glitch models rely on pinned superfluid vorticity rather than crustal fracture, starquakes may nevertheless play a role in the glitch mechanism. Recently, there has been interest in the issue of starquakes resulting in non-axisymmetric shape changes, potentially linking the quake phenomenon to the building of neutron star mountains, which would then produce continuous gravitational waves. Motivated by this issue, we present a simple model that extends the energy minimization-based calculations, originally developed to model axisymmetric glitches, to also include non-axisymmetric shape changes. We show that the creation of a mountain in a quake necessarily requires a change in the axisymmetric shape too. We apply our model to the specific problem of the spin-up of an initially non-rotating star, and estimate the maximum mountain that can be built in such a process, subject only to the constraints of energy and angular momentum conservation.

Improving forest carbon sequestration through thinning strategies under soil conservation constraints: A case study in Shaanxi Province, China

Forest thinning is an effective measure to improve carbon sequestration (CS) to adapt to and mitigate global warming. However, reducing forest cover could negatively affect other ecosystem services such as soil conservation (SC), making it difficult to effectively implement thinning strategies. This problem is exacerbated by a lack of previous research. Accordingly, after assessing thinning for two typical trees (Robinia pseudoacacia L. (RP) and Quercus L. (QL)) in Shaanxi Province, we propose a forest management framework that provides detailed thinning strategies and improves CS using a process-based biogeochemical model (Biome-BGCMuSo) by considering future climate change and SC constraints. After calibration for Biome-BGCMuSo, the average root mean squared error and percentage bias for the simulated results and observed data of RP and QL decreased (39.2–54.5 % and 66.0–86.9 %) and the correlation coefficient and Nash–Sutcliffe efficiency improved (11.6–12.3 % and 114.7–153.0 %,) respectively. Comparison between current (2001–2020) and future (2081–2100) conditions predicted overall future CS increase, and future SC would display either increasing or decreasing trends in different climatic scenarios and subregions. Partial correlation analysis showed precipitation as the dominant factor positively influencing SC, while temperature, precipitation, and CO2 concentration were the dominant factors positively influencing CS. Based on the proposed framework, RP and QL should be thinned three times every 7–11 and 3–11 years during the non-growing season with intensity ranges of 2–27 % and 1–9 %, respectively. Although the average CS during 2023–2100 with thinning increased slightly (0.1–2.6 %) compared with that without thinning, a notable potential to increase CS (0.2–7.7 %) was observed for thinning during a period at the end of this century predicted to experience the most warming. The proposed framework facilitates maximizing CS and maintaining SC. As the framework is based on a process-based biogeochemical model, it could be applicable to other regions.

Towards transferable metamodels for water distribution systems with edge-based graph neural networks

Data-driven metamodels reproduce the input-output mapping of physics-based models while significantly reducing simulation times. Such techniques are widely used in the design, control, and optimization of water distribution systems. Recent research highlights the potential of metamodels based on Graph Neural Networks as they efficiently leverage graph-structured characteristics of water distribution systems. Furthermore, these metamodels possess inductive biases that facilitate generalization to unseen topologies. Transferable metamodels are particularly advantageous for problems that require an efficient evaluation of many alternative layouts or when training data is scarce. However, the transferability of metamodels based on GNNs remains limited, due to the lack of representation of physical processes that occur on edge level, i.e. pipes. To address this limitation, our work introduces Edge-Based Graph Neural Networks, which extend the set of inductive biases and represent link-level processes in more detail than traditional Graph Neural Networks. Such an architecture is theoretically related to the constraints of mass conservation at the junctions. To verify our approach, we test the suitability of the edge-based network to estimate pipe flowrates and nodal pressures emulating steady-state EPANET simulations. We first compare the effectiveness of the metamodels on several benchmark water distribution systems against Graph Neural Networks. Then, we explore transferability by evaluating the performance on unseen systems. For each configuration, we calculate model performance metrics, such as coefficient of determination and speed-up with respect to the original numerical model. Our results show that the proposed method captures the pipe-level physical processes more accurately than node-based models. When tested on unseen water networks with a similar distribution of demands, our model retains a good generalization performance with a coefficient of determination of up to 0.98 for flowrates and up to 0.95 for predicted heads. Further developments could include simultaneous derivation of pressures and flowrates.

Poles, shocks, and tygers: The time-reversible Burgers equation.

We construct a formally time-reversible, one-dimensional forced Burgers equationby imposing a global constraint of energy conservation, wherein the constant viscosity is modified to a fluctuating state-dependent dissipation coefficient. The system exhibits dynamical properties which bear strong similarities with those observed for the Burgers equationand can be understood using the dynamics of the poles, shocks, and truncation effects, such as tygers. A complex interplay of these give rise to interesting statistical regimes ranging from hydrodynamic behavior to a completely thermalized warm phase. The end of the hydrodynamic regime is associated with the appearance of a shock in the solution and a continuous transition leading to a truncation-dependent state. Beyond this, the truncation effects such as tygers and the appearance of secondary discontinuity at the resonance point in the solution strongly influence the statistical properties. These disappear at the second transition, at which the global quantities exhibit a jump and attain values that are consistent with the establishment of a quasiequilibrium state characterized by energy equipartition among the Fourier modes. Our comparative analysis shows that the macroscopic statistical properties of the formally time-reversible system and the Burgers equationare equivalent in all the regimes, irrespective of the truncation effects, and this equivalence is not just limited to the hydrodynamic regime, thereby further strengthening the Gallavotti's equivalence conjecture. The properties of the system are further examined by inspecting the complex space singularities in the velocity field of the Burgers equation. Furthermore, an effective theory is proposed to describe the discontinuous transition.

Directionally‐split volume‐of‐fluid technique for front propagation under curvature flow

AbstractA directionally‐split volume‐of‐fluid (VOF) methodology for evolving interfaces under curvature‐dependent speed is devised. The interface is reconstructed geometrically and the volume fraction is advected with a technique to incorporate a topological volume conservation constraint. The proposed approach uses the idea that the role of curvature in a speed function is analogous to the role of viscosity in the corresponding hyperbolic conservation law to propagate complex interfaces where singularities may exist. The approach has the advantage of simple implementation and straightforward extension to more complex multiphase systems by formulating the interface evolution problem using energy functionals to derive an expression for the interface‐advecting velocity. The numerical details of the volume‐of‐fluid based formulation are discussed with emphasis on the importance of curvature estimation. Finally, canonical curves and surfaces traditionally investigated by the level set (LS) method are tested with the devised approach and the results are compared with existing work in LS.

The heterogeneous effects of non-hydro renewable energy and water resources on industrial development of the Yellow river and Yangtze river basins

Exploring the relationship between non-hydro renewable energy (NHRE) and water resources can help achieve high-quality development in China’s industrial sector under the constraints of water conservation. This study investigated the interrelationships and regional heterogeneity of capital, labor, water and NHRE in the industrial sectors of 158 cities in the Yellow River Basin (YRB) and Yangtze River Basin (YZRB) in China using a heterogeneous stochastic frontier model. Based on reconstructed NHRE consumption data, it is found that the NHRE output elasticity (OE) in the NHRE-rich YRB (median value: 0.1) was lower than that in the NHRE-scarce YZRB (0.24), while the water OE in the water-rich YZRB (0.07) was lower than that in the water-scarce YRB (0.09). Classification analysis of infrastructure construction revealed that the energy OE in NHRE outflow regions (0.11) was lower than in NHRE inflow regions (0.28). This study also optimized the allocation of projected NHRE additions in China by 2030 and found that the optimized strategy can generate an additional industrial output of 8.8 × 1011 Yuan and save 8.6 × 107 tons of water compared to existing strategies. The results indicated that a win-win situation of industrial growth and water conservation could be achieved by reallocation of NHRE.

Convergence property of Nesterov‐accelerated adaptive moment estimation with safety helmet detection and classification in smart industry application

We propose a technique for first‐order gradient‐based optimization of stochastic objective functions called Nesterov‐accelerated adaptive moment assessment, which makes use of dynamic evaluations of lower‐order moments. The adaptive moment assessment and the Nesterov acceleration gradient are combined. Consequently, it has perks, and this technique is convenient to use, numerically economical, memory‐light, and very well‐suited for challenges with massive amounts of information and characteristics. Additionally, we investigate the algorithm's convergence characteristics and propose a conservative constraint on the convergence rate. Finally, we employ this technique for the detection and classification of safety helmets.

Constraining dark photons with self-consistent simulations of globular cluster stars

We revisit stellar constraints on dark photons. We undertake dynamical stellar evolution simulations which incorporate the resonant and off-resonant production of transverse and longitudinal dark photons. We compare our results with observables derived from measurements of globular cluster populations, obtaining new constraints based on the luminosity of the tip of the red-giant branch (RGB), the ratio of populations of RGB to horizontal branch (HB) stars (the R-parameter), and the ratio of asymptotic giant branch to HB stars (the R 2-parameter). We find that previous bounds derived from static stellar models do not capture the effects of the resonant production of light dark photons leading to overly conservative constraints, and that they over-estimate the effects of heavier dark photons on the RGB-tip luminosity. This leads to differences in the constraints of up to an order of magnitude in the kinetic mixing parameter.

Gender disparities and potentials in STEM approach in Jordan and Saudi Arabia – An analytical literature review

AbstractIn recent years, the integrated approach of STEM disciplines (Science, Technology, Engineering, and Mathematics) has been adopted in the Middle East to improve students' scientific capacities and their formative thinking. Nevertheless, this approach encounters complications in the application, including many due to gender differences. Middle Eastern women's life and education is affected by conservative constraints and social norms where gender stereotypes and culture impact shared views about specific domains. Research regarding gender has frequently emphasized gender imbalance in virtually all STEM study fields and professions. This study explores STEM education's conceptual framework in Jordan and Saudi Arabia. Likewise, it provides an overview of STEM teachers' practices and their gender perspectives in the classrooms. We investigate the implementations and gender differences in STEM education by scrutinizing relevant literature and studies in the selected countries. The conclusions indicate a shortage of teachers' knowledge in applying STEM education in classrooms and a need for more development programs that qualify teachers for STEM education applications. In addition, the results show that gender disparities are promoted by the education system and teachers who teach STEM subjects in schools because social norms and gender stereotypes influence them.

Direct-imaging Discovery of a Substellar Companion Orbiting the Accelerating Variable Star HIP 39017

We present the direct-imaging discovery of a substellar companion (a massive planet or low-mass brown dwarf) to the young, γ Doradus (γ Dor)-type variable star HIP 39017 (HD 65526). The companion’s SCExAO/CHARIS JHK (1.1–2.4 μm) spectrum and Keck/NIRC2 L′ photometry indicate that it is an L/T transition object. A comparison of the JHK+L ′ spectrum to several atmospheric model grids finds a significantly better fit to cloudy models than cloudless models. Orbit modeling with relative astrometry and precision stellar astrometry from Hipparcos and Gaia yields a semimajor axis of 23.8−6.1+8.7 au, a dynamical companion mass of 30−12+31 M J, and a mass ratio of ∼1.9%, properties most consistent with low-mass brown dwarfs. However, its mass estimated from luminosity models is a lower ∼13.8 M J due to an estimated young age (≲115 Myr); using a weighted posterior distribution informed by conservative mass constraints from luminosity evolutionary models yields a lower dynamical mass of 23.6−7.4+9.1 M J and a mass ratio of ∼1.4%. Analysis of the host star’s multifrequency γ Dor-type pulsations, astrometric monitoring of HIP 39017 b, and Gaia Data Release 4 astrometry of the star will clarify the system age and better constrain the mass and orbit of the companion. This discovery further reinforces the improved efficiency of targeted direct-imaging campaigns informed by long-baseline, precision stellar astrometry.