搜索结果：Energetic

It has been known for decades that laser fusion can be, and often is plagued by the production of energetic electrons.produced either by a instability or by the production of energetic electrons, produced either by an instability or by the energetic tail of a Maxwellian distribution. Despite more than 25 years of a variety of efforts, there is still no generally accepted way to address this issue. This work hopes to advance the progress by developing a Fokker Planck model which is simple enough to be used at every time step of a rad-hydro simulation, and accurate enough to be useful. It makes several approximations, the main one being that the percentage of these energetic electrons is small. Recent experiments confirm this so far that this is the case for plasmas subject to a laser plasma instability. Hence energetic electons interact with the background plasma, but not with each other. This work makes no attempt to solve for the entire electron distribution function. It makes a variety of tohter approximations to solve the resulting Fokker Planck equation. This rather elngthy report both summarizes the author's work with the NRL group, and presents new advances since the terminat

Energetic particle populations are ubiquitous throughout the Universe. In our solar system, the most prominent sources of energetic particles are solar flares or collisionless shocks often driven by huge eruptions of magnetised plasma called coronal mass ejections (CMEs). Remotely, low energy electrons from the Sun can be observed as solar radio bursts that are produced by accelerated electron beams undergoing beam-plasma interactions. There are still many open questions on the generation of solar energetic particles (SEP): how and where are SEPs accelerated during solar flares and CMEs and how they escape the solar atmosphere? Another important question is: what is the link between the solar radio bursts and the observed SEPs at spacecraft? SKA can provide high-resolution radio images combined with spectroscopic observations to determine the acceleration time, trajectory and escape of low energy electrons from the solar corona. The synergy between SKA and current space missions will help investigate solar activity and energetic particles across a wide range of wavelengths and particle energies. Particle data from spacecraft can be used to make a connection between radio bursts and

The performance of organic bulk heterojunction (BHJ) solar cells is highly sensitive to both nanomorphology and energetic disorder arising from microscopic molecular packing and structural defects. However, most models used to understand these devices are either one-dimensional effective medium approximations that neglect spatial and energetic disorder or three-dimensional Monte Carlo simulations that are computationally intensive. In this work, we present the results from a three-dimensional hybrid model capable of operating at both high carrier densities and incorporating the effects of energetic disorder. We first generate realistic morphologies using a phase-field approach that accounts for solvent evaporation during film formation. Using these example morphologies, we systematically study the interplay between energetic disorder and configurational disorder at carrier densities representative of real device operation. This enables us to separate and visualize the impact of the nanomorphology and energetic disorder on device performance. Our results reveal that, even when macroscopic percolation pathways remain intact, energetic disorder limits performance primarily through sup

Context: In collisionless shocks, energetic particles can carry sufficient pressure to modify the upstream plasma and the shock structure itself, a regime often invoked in theories of cosmic-ray acceleration but rarely observed in the heliosphere. Aims: We find and characterize {interplanetary} IP shocks where energetic particles dynamically dominate the upstream pressure. Methods: We analyze IP shocks observed by Solar Orbiter inside 1 au and compute the energetic particle pressure $P_{EP}$ from proton measurements above 10\,keV, comparing it with the upstream thermal $P_{Th}$ and magnetic $P_{B}$ pressures. Results: We identify four shocks for which $P_{EP} \geq P_{Th} + P_B $. These events correspond to strong and fast shocks in the high-Mach-number tail of the Solar Orbiter shock population. In several cases the $P_{EP}$ increase coincides with a decreasing upstream bulk flow speed in the shock frame, and the resulting particle-mediated foreshocks extend up to $\sim10^5$ {ion inertial lengths} $d_i$. The extent of such energetic particle dominated region depends on shock geometry. Conclusions: These observations provide evidence that accelerated particles can dynamically modify

It is found by using the gyrokinetic theory that significant radial electric fields, or zonal flows, can be generated by the radial redistribution of energetic ion pressure in a tokamak fusion device. Trapped energetic ions are more effective to generate the radial electric field than the isotropic energetic ions. This suggests that the energetic $α$ particles produced by DT fusion may induce significant radial electric field and thus help to improve the core plasma confinement in a fusion reactor.

The discovery of new energetic materials remains a pressing challenge hindered by limited availability of high-quality data. To address this, we have developed generative molecular language models that have been pretrained on extensive chemical data and then fine-tuned with curated energetic materials datasets. This transfer-learning strategy extends the chemical language model capabilities beyond the pharmacological space in which they have been predominantly developed, offering a framework applicable to other data-spare discovery problems. Furthermore, we discuss the benefits of fragment-based molecular encodings for chemical language models, in particular in constructing synthetically accessible structures. Together, these advances provide a foundation for accelerating the design of next-generation energetic materials with demanding performance requirements.

We analyze qubit-qubit entanglement from an energetic perspective and reveal an energetic trade-off between quantum coherence and entanglement. We decompose each qubit internal energy into a coherent and an incoherent component. The qubits' coherent energies are maximal if the qubit-qubit state is pure and separable. They decrease as qubit-qubit entanglement builds up under locally-energy-preserving processes. This yields a ``coherent energy deficit'' that we show is proportional to a well-known measure of entanglement, the square concurrence. In general, a qubit-qubit state can always be represented as a mixture of pure states. Then, the coherent energy deficit splits into a quantum component, corresponding to the average square concurrence of the pure states, and a classical one reflecting the mixedness of the joint state. Minimizing the quantum deficit over the possible pure state decompositions yields the square concurrence of the mixture. Our findings bring out new figures of merit to optimize and secure entanglement generation and distribution under energetic constraints.

Energetic particles, in the form of stellar energetic particles and cosmic rays, can lead to disequilibrium chemical effects in exoplanetary atmospheres. In Earth-like atmospheres, energetic particles can drive the formation of prebiotic molecules, the building blocks of life. Here instead, I study the transport of energetic particles through a hydrogen-dominated exoplanet atmosphere and calculate the resulting ionisation rate of molecular hydrogen using a Monte Carlo energetic particle transport model. I focus on a GJ436 b-like atmosphere at orbital distances between 0.01-0.2 au which includes the orbital distance of the exoplanet GJ436 b (0.028 au). I found that stellar energetic particles lead to high ionisation rates in a GJ436 b-like atmosphere between 0.01-0.2 au. These results motivate the use of chemical models of gas giant atmospheres including energetic particle ionisation to ultimately produce synthetic James Webb Space Telescope (JWST) and Ariel transmission spectra in the future.

Understanding the energetic efficiency of quantum computers is essential for assessing their scalability and for determining whether quantum technologies can outperform classical computation beyond runtime alone. In this work, we analyze the energy required to solve the Boson Sampling problem, a paradigmatic task for quantum advantage, using a realistic photonic quantum computing architecture. Using the Metric-Noise-Resource methodology, we establish a quantitative connection between experimental control parameters, dominant noise processes, and energetic resources through a performance metric tailored to Boson Sampling. We estimate the energy cost per sample and identify operating regimes that optimize energetic efficiency. By comparing the energy consumption of quantum and state-of-the-art classical implementations, we demonstrate the existence of a quantum energetic advantage -- defined as a lower energy cost per sample compared to the best-known classical implementation -- that emerges before the onset of computational advantage, even in regimes where classical algorithms remain faster. Finally, we propose an experimentally feasible Boson Sampling architecture, including a comp

Evidence shows that biological organisms tend to be more energetically efficient per unit size. These scaling patterns observed in biological organisms have also been observed in the energetic requirements of cities. However, at lower levels of organization where energetic interventions can be more manageable, such as buildings, this analysis has remained more elusive due to the difficulties in collecting fine-grained data. Here, we use the maintenance energy usage in buildings at the Massachusetts Institute of Technology (MIT) from 2009 to 2024 to analyze energetic trends at the scale of individual buildings and their sensitivity to strong external perturbations. We find that, similar to the baseline metabolism of biological organisms, large buildings are on average $24\%$ more energetically efficient per unit size than smaller buildings. Because it has become debatable how to better measure the efficiency of buildings, this scaling pattern naturally establishes a baseline efficiency for buildings, where deviations from the mean would imply a more or less efficient building than the baseline according to volume. This relative efficiency progressively increased to $34\%$ until 2020

The question of the energetic efficiency of quantum computers has gained increasing attention recently. A precise understanding of the resources required to operate a quantum computer with a targeted computational performance and how the energy requirements can impact the scalability is still missing. In this work, one implementation of the quantum Fourier transform algorithm in a trapped-ion setup was studied. The main focus was to obtain a theoretical characterization of the energetic costs of quantum computation, based on actual experimental measurements performed on a similar trapped-ion setup.The energetic cost of the computation was estimated by analyzing the components of the setup and all the steps involved, from the cooling and preparation of the ions to the execution of the algorithm and readout of the result. In the Noisy Intermediate-Scale Quantum regime, a potential scaling of the energetic costs was argued and used to find a possible threshold for an energetic quantum advantage against state-of-the-art classical supercomputers. Remarkably, this threshold appears to be lower than the one for which computational time advantage is expected.

The breaking of surface gravity waves is a key process contributing to air-sea fluxes and turbulent ocean mixing. The highly nonlinear nature of wave breaking, combined with the challenges of observing this process in a laboratory or field setting, leaves our understanding of the energetic processes underpinning wave breaking incomplete. Progress towards refining this understanding was made in a recent study (D. G. Boettger et. al., An energetic signature for breaking inception in surface gravity waves, Journal of Fluid Mechanics 959, A33 (2023)), which identified an energetic signature in the wave kinetic energy evolution that preceded breaking onset and correlated with the strength of the breaking event. In this study, we examine the influence of wind forcing on this energetic signature. We develop a numerical wave tank that simulates wind flowing over mechanically generated waves and construct an ensemble of cases with varying wave steepness and wind forcing speed. The wind is shown to modulate the wave geometry and elevate kinetic energy at crest tip by up to 35 %. Despite these influences, the energetic inception signature was found to robustly indicate breaking inception in a

An adjoint formulation of energetic particle confinement in axisymmetric tokamak geometry is derived and evaluated using a physics-informed neural network (PINN). The PINN estimates the mean escape time of energetic ions by solving an inhomogeneous adjoint of the drift kinetic equation with a Lorentz collision operator, yielding predictions of fast ion loss in tokamak geometry due to direct ion orbit loss and collisional transport. To our knowledge, this is the first time a PINN has been used to solve the drift kinetic equation in tokamak geometry, a challenging problem due to the large time scale separation between the rapid transit time of energetic ions and their slow collisional time scale. It is shown that a careful and intentional design of a PINN is able to learn the mean escape time across the majority of the plasma volume, suggesting a path toward constructing a rapid surrogate for use within a broader optimization framework.

Bounding energetic growth of gyrokinetic instabilities is a complementary approach to linear instability analyses involving normal eigenmodes. Previous work has focused on upper bounds which are valid linearly and nonlinearly. However, if an upper bound on linear instability growth is desired, these nonlinearly valid bounds may be a poor predictor of the growth of the most unstable eigenmode. This is most evident for the simplest of instabilities: the ion-temperature-gradient (ITG) mode in slab geometry. In this work, we derive energetic upper bounds specifically for linear instability growth, focusing on the slab ITG. We show that there is no fundamental limitation on how tightly linear growth can be bounded by an energetic norm, with the tightest possible bound being given by a special energy comprised of projection coefficients of the linear eigenmode basis. Additionally, we consider `constrained optimal modes' that maximise energy growth subject to constraints that are also obeyed by the linear eigenmodes. This yields computationally efficient upper bounds that closely resemble the linear growth rate, capturing effects connected to the real frequency of instabilities, which hav

Magnetic reconnection regions in space and astrophysics are known as active particle acceleration sites. There is ample evidence showing that energetic particles can take a substantial amount of converted energy during magnetic reconnection. However, there has been a lack of studies understanding the backreaction of energetic particles at magnetohydrodynamical scales in magnetic reconnection. To address this, we have developed a new computational method to explore the feedback by non-thermal energetic particles. This approach considers the backreaction from these energetic particles by incorporating their pressure into Magnetohydrodynamics (MHD) equations. The pressure of the energetic particles is evaluated from their distribution evolved through Parker's transport equation, solved using stochastic differential equations (SDE), so we coin the name MHD-SDE. Applying this method to low-beta magnetic reconnection simulations, we find that reconnection is capable of accelerating a large fraction of energetic particles that contain a substantial amount of energy. When the feedback from these particles is included, their pressure suppresses the compression structures generated by magnet

We have developed a hybrid code GMEC: Gyro-kinetic Magnetohydrodynamics (MHD) Energetic-particle Code that can numerically simulate energetic particle-driven Alfvén eigenmodes and energetic particle transport in tokamak plasmas. In order to resolve the Alfvén eigenmodes with high toroidal numbers effectively, the field-aligned coordinates and meshes are adopted. The extended MHD equations are solved with five-points finite difference method and fourth order Runge-Kutta method. The gyrokinetic equations are solved by particle-in-cell (PIC) method for the perturbed energetic particle pressures that are coupled into the MHD equations. Up to now, a simplified version of the hybrid code has been completed with several successful verifications including linear simulations of toroidal Alfvén eigenmodes and reversed shear Alfvén eigenmodes.

A key first step to constrain the impact of energetic particles in exoplanet atmospheres is to detect the chemical signature of ionisation due to stellar energetic particles and Galactic cosmic rays. We focus on GJ$\,$436, a well-studied M dwarf with a warm Neptune-like exoplanet. We demonstrate how the maximum stellar energetic particle momentum can be estimated from the stellar X-ray luminosity. We model energetic particle transport through the atmosphere of a hypothetical exoplanet at orbital distances between $a=0.01-0.2\,$au from GJ$\,$436, including GJ$\,$436$\,$b's orbital distance (0.028$\,$au). For these distances we find that, at top-of-atmosphere, stellar energetic particles ionise molecular hydrogen at a rate of $ζ_{\rm StEP,H_2} \sim 4\times10^{-10}-2\times10^{-13}\,\mathrm{s^{-1}}$. In comparison, Galactic cosmic rays alone lead to $ζ_{\rm GCR, H_2}\sim2\times 10^{-20}-10^{-18} \,\mathrm{s^{-1}}$. At 10au we find that ionisation due to Galactic cosmic rays equals that of stellar energetic particles: $ζ_{\rm GCR,H_2} = ζ_{\rm StEP,H_2} \sim 7\times10^{-18}\,\rm{s^{-1}}$ for the top-of-atmosphere ionisation rate. At GJ$\,$436$\,$b's orbital distance, the maximum ion-pai

The negative internal energetic contribution to the elastic modulus (negative energetic elasticity) has been recently observed in polymer gels. This finding challenges the conventional notion that the elastic moduli of rubberlike materials are determined mainly by entropic elasticity. However, the microscopic origin of negative energetic elasticity has not yet been clarified. Here, we consider the $n$-step interacting self-avoiding walk on a cubic lattice as a model of a single polymer chain (a subchain of a network in a polymer gel) in a solvent. We theoretically demonstrate the emergence of negative energetic elasticity based on an exact enumeration up to $n=20$ and analytic expressions for arbitrary $n$ in special cases. Furthermore, we demonstrate that the negative energetic elasticity of this model originates from the attractive polymer--solvent interaction, which locally stiffens the chain and conversely softens the stiffness of the entire chain. This model qualitatively reproduces the temperature dependence of negative energetic elasticity observed in the polymer-gel experiments, indicating that the analysis of a single chain can explain the properties of negative energetic

As the amount and variety of energetics research increases, machine aware topic identification is necessary to streamline future research pipelines. The makeup of an automatic topic identification process consists of creating document representations and performing classification. However, the implementation of these processes on energetics research imposes new challenges. Energetics datasets contain many scientific terms that are necessary to understand the context of a document but may require more complex document representations. Secondly, the predictions from classification must be understandable and trusted by the chemists within the pipeline. In this work, we study the trade-off between prediction accuracy and interpretability by implementing three document embedding methods that vary in computational complexity. With our accuracy results, we also introduce local interpretability model-agnostic explanations (LIME) of each prediction to provide a localized understanding of each prediction and to validate classifier decisions with our team of energetics experts. This study was carried out on a novel labeled energetics dataset created and validated by our team of energetics exp