Evolution and turnover in scaling systems

Scaling has been discovered in the long tails of size distributions characterizing a variety of diverse systems, many of which evolve in terms of the size of their components through competition. Such time-invariant macro distributions, however, often obscure the micro-dynamics of change, such as continual turnover in the rank order of the constituents. Here we show how a model drawn from evolutionary theory can explain this change, such that the time spent in the top ranked constituents is finite and also characterized by longtailed distributions. To show the broad applicability of this model, we compare typical model runs to real-world examples including US boys’ names, UK Number One for pop albums, journal article keywords, and city sizes

Flexible scheme to truncate the hierarchy of pure states

The hierarchy of pure states (HOPS) is a wavefunction-based method which can be used for numerically modeling open quantum systems. Formally, HOPS recovers the exact system dynamics for an infinite depth of the hierarchy. However, truncation of the hierarchy is required to numerically implement HOPS. We want to choose a 'good' truncation method, where by 'good' we mean that it is numerically feasible to check convergence of the results. For the truncation approximation used in previous applications of HOPS, convergence checks are numerically challenging. In this work we demonstrate the application of the '$n$-particle approximation' ($n$PA) to HOPS. We also introduce a new approximation, which we call the '$n$-mode approximation' ($n$MA). We then explore the convergence of these truncation approximations with respect to the number of equations required in the hierarchy. We show that truncation approximations can be used in combination to achieve convergence in two exemplary problems: absorption and energy transfer of molecular aggregates.Comment: 8 pages, 3 figure

Generic guide concepts for the European Spallation Source

The construction of the European Spallation Source (ESS) faces many challenges from the neutron beam transport point of view: The spallation source is specified as being driven by a 5 MW beam of protons, each with 2 GeV energy, and yet the requirements in instrument background suppression relative to measured signal vary between 10$^{-6}$ and 10$^{-8}$. The energetic particles, particularly above 20 MeV, which are expected to be produced in abundance in the target, have to be filtered in order to make the beamlines safe, operational and provide good quality measurements with low background. We present generic neutron guides of short and medium length instruments which are optimized for good performance at minimal cost. Direct line of sight to the source is avoided twice, with either the first point out of line of sight or both being inside the bunker (20\,m) to minimize shielding costs. These guide geometries are regarded as a baseline to define standards for instruments to be constructed at ESS. They are used to find commonalities and develop principles and solutions for common problems. Lastly, we report the impact of employing the over-illumination concept to mitigate losses from random misalignment passively, and that over-illumination should be used sparingly in key locations to be effective. For more widespread alignment issues, a more direct, active approach is likely to be needed

New battery model and state-of-health determination through subspace parameter estimation and state-observer techniques

This paper describes a novel adaptive battery model based on a remapped variant of the well-known Randles' lead-acid model. Remapping of the model is shown to allow improved modeling capabilities and accurate estimates of dynamic circuit parameters when used with subspace parameter-estimation techniques. The performance of the proposed methodology is demonstrated by application to batteries for an all-electric personal rapid transit vehicle from the Urban Light TRAnsport (ULTRA) program, which is designated for use at Heathrow Airport, U. K. The advantages of the proposed model over the Randles' circuit are demonstrated by comparisons with alternative observer/estimator techniques, such as the basic Utkin observer and the Kalman estimator. These techniques correctly identify and converge on voltages associated with the battery state-of-charge (SoC), despite erroneous initial conditions, thereby overcoming problems attributed to SoC drift (incurred by Coulomb-counting methods due to overcharging or ambient temperature fluctuations). Observation of these voltages, as well as online monitoring of the degradation of the estimated dynamic model parameters, allows battery aging (state-of-health) to also be assessed and, thereby, cell failure to be predicted. Due to the adaptive nature of the proposed algorithms, the techniques are suitable for applications over a wide range of operating environments, including large ambient temperature variations. Moreover, alternative battery topologies may also be accommodated by the automatic adjustment of the underlying state-space models used in both the parameter-estimation and observer/estimator stages

Novel battery model of an all-electric personal rapid transit vehicle to determine state-of-health through subspace parameter estimation and a Kalman Estimator

Abstract--The paper describes a real-time adaptive battery model for use in an all-electric Personal Rapid Transit vehicle. Whilst traditionally, circuit-based models for lead-acid batteries centre on the well-known Randles’ model, here the Randles’ model is mapped to an equivalent circuit, demonstrating improved modelling capabilities and more accurate estimates of circuit parameters when used in Subspace parameter estimation techniques. Combined with Kalman Estimator algorithms, these techniques are demonstrated to correctly identify and converge on voltages associated with the battery State-of-Charge, overcoming problems such as SoC drift (incurred by coulomb-counting methods due to over-charging or ambient temperature fluctuations). Online monitoring of the degradation of these estimated parameters allows battery ageing (State-of-Health) to be assessed and, in safety-critical systems, cell failure may be predicted in time to avoid inconvenience to passenger networks. Due to the adaptive nature of the proposed methodology, this system can be implemented over a wide range of operating environments, applications and battery topologies

Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles

Abstract—This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence—an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack