Numerical simulation of small bubble-big bubble-liquid three-phase flows

Numerical simulations of the small bubble-big bubble-liquid three phase heterogeneous flow\ud in a square cross-sectioned bubble column were carried out with the commercial CFD\ud package CFX-4.4 to explore the effect of superficial velocity and inlet dispersed phase\ud fractions on the flow patterns. The approach of Krishna et al. (2000) was adopted in the\ud Euler-Euler framework to numerically simulate the gas-liquid heterogeneous flow in bubble\ud columns. On basis of an earlier study (Zhang et al. 2005), the extended multiphase k - ε\ud turbulence model (Pfleger and Becker, 2001) was chosen to model the turbulent viscosity in\ud the liquid phase and implicitly account for the bubble-induced turbulence. The obtained\ud results suggest that, first of all, the extended multiphase k - ε turbulence model of Pfleger and\ud Becker (2001) is capable of capturing the dynamics of the heterogeneous flow. With\ud increasing superficial velocity, the dynamics of the flow, as well as the total gas hold-up\ud increases. It is observed that with increasing inlet phase fraction of the big bubbles, the total\ud gas holdup decreases while the dynamic nature of the flow increases, which indicates that the\ud small bubble phase mainly determines the total gas holdup while the big bubble phase\ud predominantly agitates the liquid

Deep reinforcement learning-based prosumer aggregation bidding strategy in a hierarchical local electricity market

This paper investigates the application of deep reinforcement learning (DRL) algorithm for the decision-support of a prosumer aggregation in a hierarchical local electricity market (LEM) comprising a peer-to-peer (P2P) market and a corrective market. The agent first submits bids/asks to the P2P market where prosumer aggregations are able to trade electricity directly with each other. After that, the agent participates in the corrective market, where the market operator formulates the corrective market as an AC optimal power flow (OPF) problem to ensure the system is operated within its operational limits. A DRL algorithm, namely Twin Delayed Deep Deterministic Policy Gradient (TD3), is used to find the strategic bidding strategy. The algorithm is tested on a real medium-voltage distribution grid to evaluate the effectiveness of the strategic bidding method. The result of the case study demonstrates that the agent can derive trading strategies to obtain high profits based on the TD3 algorithm

Establishing a Hierarchical Local Market Structure Using Multi-cut Benders Decomposition

Local electricity markets (LEMs) such as peer-to-peer (P2P) and community-based markets allow prosumers and consumers to exchange electricity products and services locally. In order to coordinate electricity trading and flexibility services, this paper proposes a hierarchical prosumer-centric market framework with a hybrid LEM and a local flexibility market (LFM). Multi-cut Benders decomposition (MCBD) is employed to decompose the integrated hybrid LEM into a centralized P2P market and multiple community-based markets. The aggregators coordinate energy sources and demands of households in low voltage (LV) distribution networks (DN) as virtual power plants (VPPs) and engage in trading through a P2P market over the medium voltage (MV) DN. In addition, a modified MCBD (M-MCBD) approach is proposed to accelerate the convergence process. The LFM is operated by the distribution system operator (DSO) and is formulated as a mixed-integer nonlinear programming (MINLP) problem which is further relaxed to a mixed-integer second-order cone programming (MI-SOCP) problem. The case study demonstrates that aggregators were able to collaborate on trading within the hybrid LEM to minimize the costs incurred by prosumers within the network. Furthermore, the proposed M-MCBD method improves the scalability of the MCBD by enhancing its convergence speed and accuracy, as demonstrated by testing on problems of varying scales

Designing Efficient Local Flexibility Markets in the Presence of Reinforcement-Learning Agents

Local Flexibility Markets (LFMs) are considered a promising framework towards resolving voltage and congestion issues of power distribution systems in an economically efficient manner. However, the need for location-specific flexibility services renders LFMs naturally imperfectly competitive and market efficiency is severely challenged by strategic participants that exploit their locally monopolistic power. Previous works have been considering either non-strategic participants, or strategic participants with perfect information (e.g. about the network characteristics etc) that can readily compute their payoffmaximizing bidding strategy. In this paper, we take on the problem of designing an efficient LFM in the more realistic case where market participants do not possess this information and, instead, learn to improve their bidding policies through experience. To that end, we develop a multi-agent reinforcement learning algorithm to model the participants' learning-to-bid process. In this framework, we first present two popular LFM pricing schemes (pay-as-bid and distribution locational marginal pricing) and expose that learning agents can discover ways to exploit them, resulting in severe dispatch inefficiency. We then present a gametheoretic pricing scheme that theoretically incentivizes truthful bidding and empirically demonstrate that this property improves the efficiency of the resulting dispatch also in the presence of learning agents. In particular, the proposed scheme is able to outperform the popular distribution locational marginal pricing (DLMP) scheme, in terms of efficiency, by a factor of 15 − 23%

Direct approach to the problem of strong local minima in Calculus of Variations

The paper introduces a general strategy for identifying strong local minimizers of variational functionals. It is based on the idea that any variation of the integral functional can be evaluated directly in terms of the appropriate parameterized measures. We demonstrate our approach on a problem of W^{1,infinity} weak-* local minima--a slight weakening of the classical notion of strong local minima. We obtain the first quasiconvexity-based set of sufficient conditions for W^{1,infinity} weak-* local minima.Comment: 26 pages, no figure

Training data distribution significantly impacts the estimation of tissue microstructure with machine learning

Purpose Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting. Methods We fit a two- and three-compartment biophysical model to diffusion measurements from in-vivo human brain, as well as simulated diffusion data, using both traditional model fitting and supervised ML. For supervised ML, we train several artificial neural networks, as well as random forest regressors, on different distributions of ground truth parameters. We compare the accuracy and precision of parameter estimates obtained from the different estimation approaches using synthetic test data. Results When the distribution of parameter combinations in the training set matches those observed in healthy human data sets, we observe high precision, but inaccurate estimates for atypical parameter combinations. In contrast, when training data is sampled uniformly from the entire plausible parameter space, estimates tend to be more accurate for atypical parameter combinations but may have lower precision for typical parameter combinations. Conclusion This work highlights that estimation of model parameters using supervised ML depends strongly on the training-set distribution. We show that high precision obtained using ML may mask strong bias, and visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates

Long range forces and limits on unparticle interactions

Couplings between standard model particles and unparticles from a nontrivial scale invariant sector can lead to long range forces. If the forces couple to quantities such as baryon or lepton (electron) number, stringent limits result from tests of the gravitational inverse square law. These limits are much stronger than from collider phenomenology and astrophysics.Comment: 7 pages, revtex; v2 minor changes and added reference

Unparticles-Higgs Interplay

We show that scalar unparticles coupled to the Standard Model Higgs at the renormalizable level can have a dramatic impact in the breaking of the electroweak symmetry already at tree level. In particular one can get the proper electroweak scale without the need of a Higgs mass term in the Lagrangian. By studying the mixed unparticle-Higgs propagator and spectral function we also show how unparticles can shift the Higgs mass away from its Standard Model value, \lambda v^2, and influence other Higgs boson properties. Conversely, we study in some detail how electroweak symmetry breaking affects the unparticle sector by breaking its conformal symmetry and generating a mass gap. We also show that, for Higgs masses above that gap, unparticles can increase quite significantly the Higgs width.Comment: 14 pages, 7 figures, typos correcte

Contributions from SUSY-FCNC couplings to the interpretation of the HyperCP events for the decay \Sigma^+ \to p \mu^+ \mu^-

The observation of three events for the decay $\Sigma^+ \to p \mu^+ \mu^-$ with a dimuon invariant mass of $214.3\pm0.5$MeV by the HyperCP collaboration imply that a new particle X may be needed to explain the observed dimuon invariant mass distribution. We show that there are regions in the SUSY-FCNC parameter space where the $A^0_1$ in the NMSSM can be used to explain the HyperCP events without contradicting all the existing constraints from the measurements of the kaon decays, and the constraints from the $K^0-\bar{K}^0$ mixing are automatically satisfied once the constraints from kaon decays are satisfied.Comment: 18 pages, 7 figure

Different Types of Corona Discharges Associated With High-Altitude Positive Narrow Bipolar Events Nearby Cloud Top

Single- and multi-pulse blue corona discharges are frequently observed in thunderstorm clouds. Although we know they often correlate with Narrow Bipolar Events (NBEs) in Very Low Frequency/Low Frequency radio signals, their physics is not well understood. Here, we report a detailed analysis of different types of blue corona discharges observed by the Atmosphere-Space Interactions Monitor during an overpass of a thundercloud cell nearby Malaysia. Both single- and multi-pulse blue corona discharges were associated with positive NBEs at the top of the cloud, reaching about 18 km altitude. We find that the primary pulses of multi-pulse discharges have weaker current moments than the single-pulse discharges, suggesting that the multi-pulse discharges either have shorter vertical channels or have weaker currents than the single-pulse discharges. The subsequent pulse trains of the multi-pulse discharges delayed some milliseconds are likely from horizontally oriented electrical discharges, but some NBEs, correlated with both single-and multi-pulse discharges, include small-amplitude oscillations within a few microseconds inside their waveforms, which are unresolved in the optical observation and yet to be understood. Furthermore, by jointly analyzing the optical and radio observations, we estimate the photon free mean path at the cloud top to be ∼6 m. © 2023. The Authors.This work was supported by the European Research Council (ERC) under the European Union H2020 programme/ERC Grant agreement 681257. It also received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant agreement SAINT 722337. Additionally, this work was supported by the Spanish Ministry of Science and Innovation, MINECO, under project PID2019-109269RB-C43 and FEDER program. D.L. would like to acknowledge the Independent Research Fund Denmark (Danmarks Frie Forskningsfond) under Grant agreement 1026-00420B. D.L., A.L., F.J.G.V. and F.J.P.I. would like to acknowledge financial support from the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award for the Instituto de Astrofisica de Andalucia (SEV-2017-0709). G.L. is supported by the Chinese Meridian Project, and the International Partnership Program of Chinese Academy of Sciences (No.183311KYSB20200003). ASIM is a mission of the European Space Agency (ESA) and is funded by ESA and by national grants of Denmark, Norway and Spain. The ASIM Science Data Centre is supported by ESA PRODEX contracts C 4000115884 (DTU) and 4000123438 (Bergen).Peer reviewe