arXiv subjects

M. Lazar

Publications and source records attributed to M. Lazar.

At least 19 recordsLinked to original sources

arXiv2026

On the modeling of oblique firehose instabilities in regularized Kappa plasmas using ALPS

In-situ measurements in space plasmas indicate that the velocity distributions of charged particles are not in thermal equilibrium, deviating from a standard Maxwellian mainly due to anisotropies and suprathermal populations which enhance high-energy tails. Although the Standard \k{appa}-Distribution (SKD) is well established in modeling these non-equilibrium distributions, its application is often viewed controversially due to certain unphysical implications, in particular divergent velocity moments. To address these issues, the Regularized \k{appa}-Distribution (RKD) was introduced. Such advanced, in general anisotropic RKDs are invoked here for the first time to investigate oblique firehose instabilities, including those induced by the temperature anisotropy of electrons and protons, the dominant species in space plasmas. In weakly collisional plasmas, both of these instabilities are expected to play significant roles in the self-regulation of the macroscopic properties of space plasmas (e.g. the expanding solar wind) reported by observations. Is not yet possible to resort to a general dispersion tensor related to RKD plasmas (whose derivation is still a challenge), instead the Arbitrary Linear Plasma Solver (ALPS) is exploited here. The unstable solutions obtained for the already established Maxwellian & SKDs successfully validate the capability of ALPS. For RKDs, the instabilities confirm the stimulating effect of suprathermal populations, with lower \k{appa} values generally enhancing the firehose growth rates. For the oblique electron firehose instability, RKDs substantially modify the competition between periodic & aperiodic branches only at intermediate angles & can sustain significantly increased growth rates of especially aperiodic modes at highly oblique propagation beyond both the Maxwellian unstable regime & the one that is accessible with SKDs.

physics.plasm-ph↗

arXiv2026

Hybrid simulations of the proton beam instabilities in the young solar wind. The formation of hammerhead-like distributions

Parker Solar Probe (PSP) observations in the young solar wind reveal new properties of both plasma particle velocity distributions (VDs) and associated electromagnetic (EM) wave fluctuations. The quasilinear (QL) kinetic theory of plasma wave instabilities has recently shown that new hammerhead (HH) proton distributions can be generated by the relaxation of proton beams through the instabilities of right-handed (RH) polarized waves. Such RH waves have indeed been reported in association with HH distributions. In this paper, new results from hybrid simulations of proton-beam-plasma systems with properties typical of those observed to excite EM-RH wave instabilities are presented. From the long-term evolution of these systems, it is found that beam relaxation is driven by instabilities and growing wave fluctuations, leading to HH-type features in the velocity distributions. The production of these features, as well as their prominence, depends on the magnetic power of the waves generated by the instabilities and, therefore, implicitly on the available free energy, quantified by the plasma beta parameter and the relative beam drift. The simulation results capture the self-consistent evolution of the instabilities and their nonlinear development. Linear theory, together with simulations, helps identify the nature of the unstable modes and the plasma conditions under which they arise. The good agreement with quasi-linear (QL) theory further indicates that it can serve as a computationally efficient complementary framework for interpreting the associated wave-particle interactions.

physics.plasm-ph↗

arXiv2026

The impact of sensor placement on graph-neural-network-based leakage detection

Sensor placement for leakage detection in water distribution networks is an important and practical challenge for water utilities. Recent work has shown that graph neural networks can estimate and predict pressures and detect leaks, but their performance strongly depends on the available sensor measurements and configurations. In this paper, we investigate how sensor placement influences the performance of GNN-based leakage detection. We propose a novel PageRank-Centrality-based sensor placement method and demonstrate that it substantially impacts reconstruction, prediction, and leakage detection on the EPANET Net1.

cs.LG↗

arXiv2025

Scalable Sensor Placement for Cyclic Networks with Observability Guarantees: Application to Water Distribution Networks

Optimal sensor placement is essential for state estimation and effective network monitoring. As known in the literature, this problem becomes particularly challenging in large-scale undirected or bidirected cyclic networks with parametric uncertainties, such as water distribution networks (WDNs), where pipe resistance and demand patterns are often unknown. Motivated by the challenges of cycles, parametric uncertainties, and scalability, this paper proposes a sensor placement algorithm that guarantees structural observability for cyclic and acyclic networks with parametric uncertainties. By leveraging a graph-based strategy, the proposed method efficiently addresses the computational complexities of large-scale networks. To demonstrate the algorithm's effectiveness, we apply it to several EPANET benchmark WDNs. Most notably, the developed algorithm solves the sensor placement problem with guaranteed structured observability for the L-town WDN with 1694 nodes and 124 cycles in under 0.1 seconds.

eess.SY↗

arXiv2025

SuperADMM: Solving Quadratic Programs Faster with Dynamic Weighting ADMM

In this paper we develop an accelerated Alternating Direction Method of Multipliers (ADMM) algorithm for solving quadratic programs called superADMM. Unlike standard ADMM QP solvers, superADMM uses a novel dynamic weighting method that penalizes each constraint individually and performs weight updates at every ADMM iteration. We provide a numerical stability analysis, methods for parameter selection and infeasibility detection. The algorithm is implemented in c with efficient linear algebra packages to provide a short execution time and allows calling superADMM from popular languages such as MATLAB and Python. A comparison of superADMM with state-of-the-art ADMM solvers and widely used commercial solvers showcases the efficiency and accuracy of the developed solver.

math.OC↗

arXiv2025

Extended scenarios for solar radio emissions with downshifted electron beam plasma excitations

First-principle studies of radiative processes aimed at explaining the origin of type II and type III solar radio bursts raise questions on the implications of downshifted electron beam plasma excitations with frequency (slightly) below the plasma frequency ($\omega\lesssim\omega_{pe}$) in the generation of radio emissions. Unlike the beam-induced Langmuir waves ($\omega \gtrsim \omega_{pe}$) in the standard radio emission plasma model, the primary wave excitations of cooler and/or denser beams have predominantly downshifted frequencies. Broadbands of such downshifted excitations are also confirmed by in situ observations in association with terrestrial foreshock and electron beams (in contrast to narrowband Langmuir waves), but their involvement in radiative processes has not been examined so far. We revisit three radiative scenarios specific to downshifted primary excitations, and the results demonstrate their direct or indirect involvement in plasma radio emission. Downshifted excitations of an electron beam primarily play an indirect role, contributing to the relaxation to a plateau-on-tail still able to induce Langmuir beam waves that satisfy conditions for nonlinear wave-wave interactions leading to free radio waves. At longer time scales, the primary excitations can become predominantly downshifted, and then directly couple with the secondary (backscattered) Langmuir waves to generate the second harmonic of radio emissions. Two counterbeams are more efficient and lead to faster radiative mechanisms, involving counterpropagating downshifted excitations, which couple to each other and generate intense, broadband and isotropic radio spectra of downshifted second harmonics. Such a long-lasting (second) radio harmonic can thus be invoked to distinguish regimes with downshifted ($\omega \gtrsim \omega_{pe}$) primary excitations.

astro-ph.SR↗

arXiv2024

Exploiting structural observability and graph colorability for optimal sensor placement in water distribution networks

Water distribution networks (WDNs) are critical systems for our society and detecting leakages is important for minimizing losses and water waste. This makes optimal sensor placement for leakage detection very relevant. Existing sensor placement methods rely on simulation-based scenarios, often lacking structure and generalizability, or depend on the knowledge of specific parameters of the WDN as well as on initial sensor data for linearization and demand estimation. Motivated by this, this paper investigates the observability of an entire WDN, based on structural observability theory. This allows us to establish the conditions for the observability of the WDN model, independently of parameter uncertainties. Additionally, a sensor placement algorithm is proposed that leverages such observability conditions and graph theory and accounts for the industrial and material costs. To demonstrate the effectiveness of our approach, we apply it to a hydraulic-transient WDN model.

eess.SY↗

arXiv2024

Numerical simulations of temperature anisotropy instabilities stimulated by suprathermal protons

The new in situ measurements of the Solar Orbiter mission contribute to the knowledge of the suprathermal populations in the solar wind, especially of ions and protons whose characterization, although still in the early phase, seems to suggest a major involvement in the interaction with plasma wave fluctuations. Recent studies point to the stimulating effect of suprathermal populations on temperature anisotropy instabilities in the case of electrons already being demonstrated in theory and numerical simulations. Here, we investigate anisotropic protons, addressing the electromagnetic ion-cyclotron (EMIC) and the proton firehose (PFH) instabilities. Suprathermal populations enhance the high-energy tails of the Kappa velocity (or energy) distributions measured in situ, enabling characterization by contrasting to the quasi-thermal population in the low-energy (bi-)Maxwellian core. We use hybrid simulations to investigate the two instabilities (with ions or protons as particles and electrons as fluid) for various configurations relevant to the solar wind and terrestrial magnetosphere. The new simulation results confirm the linear theory and its predictions. In the presence of suprathermal protons, the wave fluctuations reach increased energy density levels for both instabilities and cause faster and/or deeper relaxation of temperature anisotropy. The magnitude of suprathermal effects also depends on each instability's specific (initial) parametric regimes. These results further strengthen the belief that wave-particle interactions govern space plasmas. These provide valuable clues for understanding their dynamics, particularly the involvement of suprathermal particles behind the quasi-stationary non-equilibrium states reported by in situ observations.

astro-ph.SR↗

arXiv2024

Decoding the formation of hammerhead ion populations observed by Parker Solar Probe

In situ observations by the Parker Solar Probe (PSP) have revealed new properties of the proton velocity distributions, including hammerhead features that suggest non-isotropic broadening of the beams. The present work proposes a very plausible explanation for the formation of these populations through the action of a proton firehose-like instability triggered by the proton beam. The quasi-linear (QL) theory proposed here shows that the resulting right-hand (RH) waves have two consequences on the protons: (i) reduce the relative drift between the beam and the core, but above all, (ii) induce a strong perpendicular temperature anisotropy, specific to the observed hammerhead ion strahl. Moreover, the long-run QL results suggest that these hammerhead distributions are rather transitory states, still subject to relaxation mechanisms, of which instabilities like the one discussed here are very likely involved.

astro-ph.SR↗

arXiv2023

Parallel Shooting Sequential Quadratic Programming for Nonlinear MPC Problems

In this paper, we propose a parallel shooting algorithm for solving nonlinear model predictive control problems using sequential quadratic programming. This algorithm is built on a two-phase approach where we first test and assess sequential convergence over many initial trajectories in parallel. However, if none converge, the algorithm starts varying the Newton step size in parallel instead. Through this parallel shooting approach, it is expected that the number of iterations to converge to an optimal solution can be decreased. Furthermore, the algorithm can be further expanded and accelerated by implementing it on GPUs. We illustrate the effectiveness of the proposed Parallel Shooting Sequential Quadratic Programming (PS-SQP) method in some benchmark examples for nonlinear model predictive control. The developed PS-SQP parallel solver converges faster on average and especially when significant nonlinear behaviour is excited in the NMPC horizon.

eess.SY↗

arXiv2023

Generalized Data-driven Predictive Control

Data-driven predictive control (DPC) is becoming an attractive alternative to model predictive control as it requires less system knowledge for implementation and reliable data is increasingly available in smart engineering systems. Two main approaches exist within DPC, which mostly differ in the construction of the predictor: estimated prediction matrices (unbiased for large data) or Hankel data matrices as predictor (allows for optimizing the bias/variance trade-off). In this paper we develop a novel, generalized DPC (GDPC) algorithm that constructs the predicted input sequence as the sum of a known input sequence and an optimized input sequence. The predicted output corresponding to the known input sequence is computed using an unbiased, least squares predictor, while the optimized predicted output is computed using a Hankel matrix based predictor. By combining these two types of predictors, GDPC can achieve high performance for noisy data even when using a small Hankel matrix, which is computationally more efficient. Simulation results for a benchmark example from the literature show that GDPC with a minimal size Hankel matrix can match the performance of data-enabled predictive control with a larger Hankel matrix in the presence of noisy data.

math.OC↗

arXiv2022

The aperiodic firehose instability of counter-beaming electrons in space plasmas

Recent studies have revealed new unstable regimes of the counter-beaming electrons specific to hot and dilute plasmas from astrophysical scenarios. The (counter-)beaming electron firehose instability (BEFI) is induced for highly oblique angles of propagation relative to the magnetic field, resembling the fast growing and aperiodic mode triggered by the temperature anisotropy. It is investigated here for space plasma conditions that includes the influence of an embedding background plasma of electrons and protons. Kinetic theory is applied to prescribe the unstable regimes, and differentiate from the regimes of interplay with other instabilities. Linear theory predicts a systematic inhibition of the BEFI, by reducing the growth rates and the range of unstable wave-number with increasing the relative density of the background electrons. To obtain finite growth rates, the beam speed does not need to be high (just comparable to thermal speed), but beams must be dense enough, with a relative density at least 15-20\% of the total density. The plasma conditions favorable to this instability are reduced under the influence of background electrons. PIC simulations confirm not only that BEFI can be excited in the presence of background electrons, but also the inhibiting effect of this population. In the regimes of transition to electrostatic (ES) instabilities, BEFI is still robust enough to develop as a secondary instability, after the relaxation of beams under a quick interaction with ES fluctuations. BEFI resembles the properties of firehose heat-flux instability triggered by the electron strahl. However, BEFI is driven by a double (counter-beaming) strahl, and develops at oblique angles, which makes it effective in the regularization of the electron counter-beams observed in closed magnetic field topologies and interplanetary shocks.

physics.plasm-ph↗

arXiv2022

Model predictive eco-driving control for heavy-duty trucks using Branch and Bound optimization

Eco-driving (ED) can be used for fuel savings in existing vehicles, requiring only a few hardware modifications. For this technology to be successful in a dynamic environment, ED requires an online real-time implementable policy. In this work, a dedicated Branch and Bound (BnB) model predictive control (MPC) algorithm is proposed to solve the optimization part of an ED optimal control problem. The developed MPC solution for ED is based on the following ingredients. As a prediction model, the velocity dynamics as a function of distance is modeled by a finite number of driving modes and gear positions. Then we formulate an optimization problem that minimizes a cost function with two terms: one penalizing the fuel consumption and one penalizing the trip duration. We exploit contextual elements and use a warm-started solution to make the BnB solver run in real-time. The results are evaluated in numerical simulations on two routes in Israel and France and the long haul cycle of the Vehicle Energy consumption Calculation Tool (VECTO). In comparison with a human driver and a Pontryagin's Minimum Principle (PMP) solution, 25.8% and 12.9% fuel savings, respectively, are achieved on average.

eess.SY↗

arXiv2022

Mixing the solar wind proton and electron scales. Theory and 2D-PIC simulations of firehose instability

Firehose-like instabilities (FIs) are cited in multiple astrophysical applications. Of particular interest are the kinetic manifestations in weakly-collisional or even collisionless plasmas, where these instabilities are expected to contribute to the evolution of macroscopic parameters. Relatively recent studies have initiated a realistic description of FIs, as induced by the interplay of both species, electrons and protons, dominant in the solar wind plasma. This work complements the current knowledge with new insights from linear theory and the first disclosures from 2D PIC simulations, identifying the fastest growing modes near the instability thresholds and their long-run consequences on the anisotropic distributions. Thus, unlike previous setups, these conditions are favorable to those aperiodic branches that propagate obliquely to the uniform magnetic field, with (maximum) growth rates higher than periodic, quasi-parallel modes. Theoretical predictions are, in general, confirmed by the simulations. The aperiodic electron FI (a-EFI) remains unaffected by the proton anisotropy, and saturates rapidly at low-level fluctuations. Regarding the firehose instability at proton scales, we see a stronger competition between the periodic and aperiodic branches. For the parameters chosen in our analysis, the a-PFI is excited before than the p-PFI, with the latter reaching a significantly higher fluctuation power. However, both branches are significantly enhanced by the presence of anisotropic electrons. The interplay between EFIs and PFIs also produces a more pronounced proton isotropization.

astro-ph.SR↗

arXiv2021

A new low-beta regime for unstable proton firehose modes in bi-Kappa distributed plasmas

In the solar wind plasma an excess of kinetic temperature along the background magnetic field stimulates proton firehose modes to grow if the parallel plasma beta parameter is sufficiently high, i.e., $β_{p \parallel}\gtrsim 1$. This instability can prevent the expansion-driven anisotropy from increasing indefinitely, and explain the observations. Moreover, such kinetic instabilities are expected to be even more effective in the presence of suprathermal Kappa-distributed populations, which are ubiquitous in the solar wind, are less affected by collisions than the core population, but contribute with an additional free energy. In this work we use both linear and extended quasi-linear (QL) frameworks to characterize the unstable periodic proton firehose modes (propagating parallel to the magnetic field) under the influence of suprathermal protons. Linear theory predicts a systematic stimulation of the instability, suprathermals amplifying the growth rates and decreasing the instability thresholds to lower anisotropies and lower plasma betas ($β_{p \parallel}<1$). In perfect agreement with these results, the QL approach reveals a significant enhancement of the resulting electromagnetic fluctuations up to the saturation with a stronger back reaction on protons, leading also to a faster and more efficient relaxation of the temperature anisotropy.

physics.plasm-ph↗

arXiv2021

General dispersion properties of magnetized plasmas with drifting bi-Kappa distributions. DIS-K: DIspersion Solver for Kappa plasmas

Space plasmas are known to be out of (local) thermodynamic equilibrium, as observations show direct or indirect evidences of non-thermal velocity distributions of plasma particles. Prominent are the anisotropies relative to the magnetic field, anisotropic temperatures, field-aligned beams or drifting populations, but also, the suprathermal populations enhancing the high-energy tails of the observed distributions. Drifting bi-Kappa distribution functions can provide a good representation of these features and enable for a kinetic fundamental description of the dispersion and stability of these collision-poor plasmas, where particle-particle collisions are rare but wave-particle interactions appears to play a dominant role in the dynamic. In the present paper we derive the full set of components of the dispersion tensor for magnetized plasma populations modeled by drifting bi-Kappa distributions. A new solver called DIS-K (DIspersion Solver for Kappa plasmas) is proposed to solve numerically the dispersion relations of high complexity. The solver is validated by comparing to the damped and unstable wave solutions obtained with other codes, operating in the limits of drifting Maxwellian and non-drifting Kappa models. These new theoretical tools enable more realistic characterizations, both analytical and numerical, of wave fluctuations and instabilities in complex kinetic configurations measured in-situ in space plasmas.

physics.plasm-ph↗

arXiv2021

Data-driven predictive control with estimated prediction matrices and integral action

This paper presents a data-driven approach to the design of predictive controllers. The prediction matrices utilized in standard model predictive control (MPC) algorithms are typically constructed using knowledge of a system model such as, state-space or input-output models. Instead, we directly estimate the prediction matrices relating future outputs with current and future inputs from measured data, off-line. On-line, the developed data--driven predictive controller reduces to solving a quadratic program with a similar structure and complexity as linear MPC. Additionally, we develop a new procedure for estimating prediction matrices from data for predictive controllers with integral action, corresponding to the rate-based formulation of linear MPC. The effectiveness of the developed data-driven predictive controller is illustrated on position control of a linear motor model.

eess.SY↗

arXiv2021

Design and assessment of an eco--driving PMP algorithm for optimal deceleration and gear shifting in trucks

In this paper, an eco--driving Pontryagin maximum principle (PMP) algorithm is designed for optimal deceleration and gear shifting in trucks based on switching among a finite set of driving modes. The PMP algorithm is implemented and assessed in the IPG TruckMaker traffic simulator as an eco--driving assistance system (EDAS). The developed EDAS strategy reduces fuel consumption with an optimized velocity profile and, in practice, allows contextual feedback incorporation from the driver for safety. Furthermore, the optimization over driving modes is computationally inexpensive, allowing the methodology to be used online, in real--time. Simulation results show that significant fuel savings can be achieved proportional to the number of velocity events and the difference between current velocity and final desired velocity for each event.

eess.SY↗