SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
In this paper, we study a class of stochastic nonlinear wave equation with memory driven by Lévy noise. We first show the existence and uniqueness of global mild solutions using a suitable energy function. Second, under some additional assumptions we prove the exponential stability of the solutions.
Pulsed plasmas are promising candidates to go beyond limitations of continuous waves' plasma. However, their interaction with surfaces remains poorly understood. The authors investigated the silicon etching mechanism in inductively coupled plasma (ICP) Cl{sub 2} operated either in an ICP-pulsed mode or in a bias-pulsed mode (in which only the bias power is pulsed). The authors observed systematically the development of an important surface roughness at a low duty cycle. By using plasma diagnostics, they show that the roughness is correlated to an anomalously large (Cl atoms flux)/(energetic ion flux) ratio in the pulsed mode. The rational is that the Cl atom flux is not modulated on the timescale of the plasma pulses although the ion fluxes and energy are modulated. As a result, a very strong surface chlorination occurs during the OFF period when the surface is not exposed to energetic ions. Therefore, each energetic ion in the ON period will bombard a heavily chlorinated silicon surface, leading to anomalously high etching yield. In the ICP pulsed mode (in which the ion energy is high), the authors report yields as high as 40, which mean that each individual ion impacts will generate a “crater” of about 2 nm depth at the surface. Since the ion flux is very small in the pulsed ICP mode, this process is stochastic and is responsible for the roughness initiation. The roughness expansion can then be attributed partly to the ion channeling effect and is probably enhanced by the formation of a SiClx reactive layer with nonhomogeneous thickness over the topography of the surface. This phenomenon could be a serious limitation of pulsed plasma processes.
We study the propagation of ultra-short short solitons in a cubic nonlinear medium modeled by nonlinear Maxwell's equations with stochastic variations of media. We consider three cases: variations of (a) the dispersion, (b) the phase velocity, (c) the nonlinear coefficient. Using a modified multi-scale expansion for stochastic systems, we derive new stochastic generalizations of the short pulse equation that approximate the solutions of stochastic nonlinear Maxwell's equations. Numerical simulations show that soliton solutions of the short pulse equation propagate stably in stochastic nonlinear Maxwell's equations and that the generalized stochastic short pulse equations approximate the solutions to the stochastic Maxwell's equations over the distances under consideration. This holds for both a pathwise comparison of the stochastic equations as well as for a comparison of the resulting probability densities.
We analyze electron flux maps based on RHESSI hard X-ray imaging spectroscopy data for a number of extended coronal-loop flare events. For each event, we determine the variation of the characteristic loop length L with electron energy E, and we fit this observed behavior with models that incorporate an extended acceleration region and an exterior 'propagation' region, and which may include collisional modification of the accelerated electron spectrum inside the acceleration region. The models are characterized by two parameters: the plasma density n in, and the longitudinal extent L{sub 0} of, the acceleration region. Determination of the best-fit values of these parameters permits inference of the volume that encompasses the acceleration region and of the total number of particles within it. It is then straightforward to compute values for the emission filling factor and for the specific acceleration rate (electrons s{sup -1} per ambient electron above a chosen reference energy). For the 24 events studied, the range of inferred filling factors is consistent with a value of unity. The inferred mean value of the specific acceleration rate above E{sub 0} = 20 keV is {approx}10{sup -2} s{sup -1}, with a 1{sigma} spread of about a half-order-of-magnitude above and below this value. We compare these values with the predictions of several models, including acceleration by large-scale, weak (sub-Dreicer) fields, by strong (super-Dreicer) electric fields in a reconnecting current sheet, and by stochastic acceleration processes.
Community assembly describes how different ecological processes shape microbial community composition and structure. How environmental factors impact community assembly remains elusive. Here we sampled microbial communities and >200 biogeochemical variables in groundwater at the Oak Ridge Field Research Center, a former nuclear waste disposal site, and developed a theoretical framework to conceptualize the relationships between community assembly processes and environmental stresses. We found that stochastic assembly processes were critical (>60% on average) in shaping community structure, but their relative importance decreased as stress increased. Dispersal limitation and ‘drift’ related to random birth and death had negative correlations with stresses, whereas the selection processes leading to dissimilar communities increased with stresses, primarily related to pH, cobalt and molybdenum. Assembly mechanisms also varied greatly among different phylogenetic groups. As a result, our findings highlight the importance of microbial dispersal limitation and environmental heterogeneity in ecosystem restoration and management.
This paper investigates the controllability of discrete-time networks of coupled chaotic maps through stochastic pinning. In this control scheme, the network dynamics are steered towards a desired trajectory through a feedback control input that is applied stochastically to the network nodes. The network controllability is studied by analyzing the local mean square stability of the error dynamics with respect to the desired trajectory. Through the analysis of the spectral properties of salient matrices, a toolbox of conditions for controllability are obtained, in terms of the dynamics of the individual maps, algebraic properties of the network, and the probability distribution of the pinning control. We demonstrate the use of these conditions in the design of a stochastic pinning control strategy for networks of Chirikov standard maps. To elucidate the applicability of the approach, we consider different network topologies and compare five different stochastic pinning strategies through extensive numerical simulations.
The dynamic analysis of a continuous-time multi-agent swarm model with nonlinear profiles is investigated in this paper. It is shown that, under mild conditions, all agents in a swarm can reach cohesion within a finite time, where the upper bounds of the cohesion are derived in terms of the parameters of the swarm model. The results are then generalized by considering stochastic noise and switching between nonlinear profiles. Furthermore, swarm models with limited sensing range inducing changing communication topologies and unbounded repulsive interactions between agents are studied by switching system and nonsmooth analysis. Here, the sensing range of each agent is limited and the possibility of collision among nearby agents is high. Finally, simulation results are presented to demonstrate the validity of the theoretical analysis.
The Fermi Large Area Telescope has recently discovered two giant gamma-ray bubbles that extend north and south of the Galactic center with diameters and heights of the order of H {approx} 10 kpc. We suggest that the periodic star capture processes by the Galactic supermassive black hole Sgr A*, with a capture rate of {tau}{sup -1}{sub cap} {approx} 3 Multiplication-Sign 10{sup -5} yr{sup -1} and an energy release of W {approx} 3 Multiplication-Sign 10{sup 52} erg per capture, can result in hot plasma injecting into the Galactic halo at a wind velocity of u {approx} 10{sup 8} cm s{sup -1}. The periodic injection of hot plasma can produce a series of shocks. Energetic protons in the bubble are re-accelerated when they interact with these shocks. We show that for energy larger than E > 10{sup 15} eV, the acceleration process can be better described by the stochastic second-order Fermi acceleration. We propose that hadronic cosmic rays (CRs) within the 'knee' of the observed CR spectrum are produced by Galactic supernova remnants distributed in the Galactic disk. Re-acceleration of these particles in the Fermi Bubble produces CRs beyond the knee. With a mean CR diffusion coefficient in this energy range in the bubble D{sub B} {approx} 3 Multiplication-Sign 10{sup 30} cm{sup 2} s{sup -1}, we can reproduce the spectral index of the spectrum beyond the knee and within it. The conversion efficiency from shock energy of the bubble into CR energy is about 10%. This model provides a natural explanation of the observed CR flux, spectral indices, and matching of spectra at the knee.
Purpose: The final state of the tumor at the end of a radiotherapy course is dependent on the doses given in each fraction during the treatment course. This study investigates the feasibility of using dynamic adaptive radiotherapy (DART) in treating lung cancers assuming CBCT is available to observe midtreatment tumor states. DART adapts treatment plans using a dynamic programming technique to consider the expected changes of the tumor in the optimization process. Methods: DART is constructed using a stochastic control formalism framework. It minimizes the total expected number of tumor cells at the end of a treatment course, which is equivalent to maximizing tumor control probability, subject to the uncertainty inherent in the tumor response. This formulation allows for nonstationary dose distributions as well as nonstationary fractional doses as needed to achieve a series of optimal plans that are conformal to the tumor over time, i.e., spatiotemporally optimal plans. Sixteen phantom cases with various sizes and locations of tumors and organs-at-risk (OAR) were generated using in-house software. Each case was planned with DART and conventional IMRT prescribing 60 Gy in 30 fractions. The observations of the change in the tumor volume over a treatment course were simulated using a two-level cell population model. Monte Carlo simulations of the treatment course for each case were run to account for uncertainty in the tumor response. The same OAR dose constraints were applied for both methods. The frequency of replanning was varied between 1, 2, 5 (weekly), and 29 times (daily). The final average tumor dose and OAR doses have been compared to quantify the potential dosimetric benefits of DART. Results: The average tumor max, min, mean, and D95 doses using DART relative to these using conventional IMRT were 124.0%–125.2%, 102.1%–114.7%, 113.7%–123.4%, and 102.0%–115.9% (range dependent on the frequency of replanning). The average relative maximum doses for the cord and esophagus, mean doses for the heart and lungs, and D05 for the unspecified tissue resulting 84%–102.4%, 99.8%–106.9%, 66.9%–85.6%, 58.2%–78.8%, and 85.2%–94.0%, respectively. Conclusions: It is feasible to apply DART to the treatment of NSCLC using CBCT to observe the midtreatment tumor state. Potential increases in the tumor dose and reductions in the OAR dose, particularly for parallel OARs with mean or dose–volume constraints, could be achieved using DART compared to nonadaptive IMRT.
Transport and reaction in zeolites and other porous materials, such as mesoporous silica particles, has been a focus of interest in recent years. This is in part due to the possibility of anomalous transport effects (e.g. single-file diffusion) and its impact in the reaction yield in catalytic processes. Computational simulations are often used to study these complex nonequilibrium systems. Computer simulations using Molecular Dynamics (MD) techniques are prohibitive, so instead coarse grained one-dimensional models with the aid of Kinetic Monte Carlo (KMC) simulations are used. Both techniques can be computationally expensive, both time and resource wise. These coarse-grained systems can be exactly described by a set of coupled stochastic master equations, that describe the reaction-diffusion kinetics of the system. The equations can be written exactly, however, coupling between the equations and terms within the equations make it impossible to solve them exactly; approximations must be made. One of the most common methods to obtain approximate solutions is to use Mean Field (MF) theory. MF treatments yield reasonable results at high ratios of reaction rate k to hop rate h of the particles, but fail completely at low k=h due to the over-estimation of fluxes of particles within the pore. We develop a method to estimate fluxes and intrapore diffusivity in simple one- dimensional reaction-diffusion models at high and low k=h, where the pores are coupled to an equilibrated three-dimensional fluid. We thus successfully describe analytically these simple reaction-diffusion one-dimensional systems. Extensions to models considering behavior with long range steric interactions and wider pores require determination of multiple boundary conditions. We give a prescription to estimate the required parameters for these simulations. For one dimensional systems, if single-file diffusion is relaxed, additional parameters to describe particle exchange have to be introduced. We use Langevin Molecular Dynamics (MD) simulations to assess these parameters.
Using the cosmological smoothed particle hydrodynamical code GADGET-3, we make a realistic assessment of the technique of using constant cumulative number density as a tracer of galaxy evolution at high redshift. We find that over a redshift range of 3 ≤ z ≤ 7 one can on average track the growth of the stellar mass of a population of galaxies selected from the same cumulative number density bin to within ∼0.20 dex. Over the stellar mass range that we probe (10{sup 10.4}⩽M{sub s}/M{sub ⊙}⩽10{sup 10.8} at z = 3 and 10{sup 8.5}⩽M{sub s}/M{sub ⊙}⩽10{sup 9.6} at z = 7), one can reduce this bias by selecting galaxies based on an evolving cumulative number density. We find that this cumulative number density evolution exhibits a trend toward higher values which can be quantified by simple linear formulations of −0.10Δz for descendants and 0.12Δz for progenitors. Utilizing such an evolving cumulative number density increases the accuracy of descendant/progenitor tracking by a factor of ∼2. This result is in excellent agreement, within 0.10 dex, with abundance matching results over the same redshift range. However, we find that our more physically realistic cosmological hydrodynamic simulations produce a much larger scatter in descendant/progenitor stellar masses than previous studies, particularly when tracking progenitors. This large scatter makes the application of either the constant cumulative number density or evolving cumulative number density technique limited to average stellar masses of populations only, as the diverse mass assembly histories caused by stochastic physical processes such as gas accretion or mergers lead to an even larger scatter in other physical properties such as metallicity and star formation rate.
A spatially localized energetic extreme ultra-violet (EUV) burst is imaged at the presumed position of fast magnetic reconnection in a plasma jet produced by a coaxial helicity injection source; this EUV burst indicates strong localized electron heating. A circularly polarized high frequency magnetic field perturbation is simultaneously observed at some distance from the reconnection region indicating that the reconnection emits whistler waves and that Hall dynamics likely governs the reconnection. Spectroscopic measurement also shows simultaneous fast ion heating. The electron heating is consistent with Ohmic dissipation, while the ion heating is consistent with ion trajectories becoming stochastic.
This paper summarizes research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance. As part of this research, we have developed an online asset management tool to help reduce life-cycle maintenance and repair costs. Using the latest advancements in condition monitoring, supply chain analytics, and deep reinforcement learning, we have created a predictive maintenance tool that can optimize the maintenance and spare-part management of a repairable nuclear system. To demonstrate these methods, preliminary studies were conducted on a simple, representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Through Monte Carlo simulations, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies. Not only does the decision maker reduce the average life-cycle costs, it also minimizes the chance of high cost scenarios, lowering the variance of the expected cost distributions, and reducing overall financial risk. Furthermore, this work also studies the ability of the decision maker to handle various levels of noise from observation uncertainty. By introducing uncertainty into the decision-making process, we have quantified the robustness and resiliency of the decision maker, as well as identified necessary levels of observability to demonstrate cost effectiveness.
We report a numerical analysis of the stochastic acceleration of electrons, stipulated by a random change in the phase of the force acting on the electron. The main source of randomness is the random spatial distribution of electromagnetic fields in the focal region of multimode laser radiation. A typical frequency of the random phase change corresponding to the maximum impact of the effect under consideration lies in the range of (0.25–0.5)ν (ν is the radiation frequency of a neodymium laser). A wave packet model convenient for calculations taking into account the radiative transitions of the neodymium ion is proposed. The dependence of the average energy of relativistic electrons on the flux density in the range of 10{sup 15}–10{sup 18} W cm{sup -2} is calculated. The time dependence of the average electron energy during the laser pulse in the form of approximating formulas is constructed. The typical time for the development of stochastic heating of electrons is determined. It is found that the stochastic acceleration process weakly depends on the laser pulse duration, when the latter exceeds several hundred periods of the electromagnetic wave. (interaction of laser radiation with matter. laser plasma)
Disturbances have been recognized as a key factor shaping terrestrial ecosystem states and dynamics. A general model that quantitatively describes the relationship between carbon storage and disturbance regime is critical for better understanding large scale terrestrial ecosystem carbon dynamics. We developed a model (REGIME) to quantify ecosystem carbon storage capacities (E[x]) under varying disturbance regimes with an analytical solution E[x] = U {center_dot} {tau}{sub E} {center_dot} {lambda}{lambda} + s {tau} 1, where U is ecosystem carbon influx, {tau}{sub E} is ecosystem carbon residence time, and {tau}{sub 1} is the residence time of the carbon pool affected by disturbances (biomass pool in this study). The disturbance regime is characterized by the mean disturbance interval ({lambda}) and the mean disturbance severity (s). It is a Michaelis-Menten-type equation illustrating the saturation of carbon content with mean disturbance interval. This model analytically integrates the deterministic ecosystem carbon processes with stochastic disturbance events to reveal a general pattern of terrestrial carbon dynamics at large scales. The model allows us to get a sense of the sensitivity of ecosystems to future environmental changes just by a few calculations. According to the REGIME model, for example, approximately 1.8 Pg C will be lost in the high-latitude regions of North America (>45{sup o} N) if fire disturbance intensity increases around 5.7 time the current intensity to the end of the twenty-first century, which will require around 12% increases in net primary productivity (NPP) to maintain stable carbon stocks. If the residence time decreased 10% at the same time additional 12.5% increases in NPP are required to keep current C stocks. The REGIME model also lays the foundation for analytically modeling the interactions between deterministic biogeochemical processes and stochastic disturbance events.
We investigate two-dimensional image decomposition of nearby, morphologically selected early-type galaxies (ETGs). We are motivated by recent observational evidence of significant size growth of quiescent galaxies and theoretical development advocating a two-phase formation scenario for ETGs. We find that a significant fraction of nearby ETGs show changes in isophotal shape that require multi-component models. The characteristic sizes of the inner and outer component are ∼3 and ∼15 kpc. The inner component lies on the mass–size relation of ETGs at z ∼ 0.25–0.75, while the outer component tends to be more elliptical and hints at a stochastic buildup process. We find real physical differences between single- and double-component ETGs, with double-component galaxies being younger and more metal-rich. The fraction of double-component ETGs increases with increasing σ and decreases in denser environments. We hypothesize that double-component systems were able to accrete gas and small galaxies until later times, boosting their central densities, building up their outer parts, and lowering their typical central ages. In contrast, the oldest galaxies, perhaps due to residing in richer environments, have no remaining hints of their last accretion episode.
Highlights: • We compare two remediation scenarios; one with resource recovery and one without. • Economic analysis includes relevant direct costs and revenues for the landfill owner. • High degrees of metal and/or combustible contents are important economic factors. • Landfill tax and the access to a CHP can have a large impact on the result. • Combining landfill mining and remediation may decrease the project cost. - Abstract: This article analyzes the economic potential of integrating material separation and resource recovery into a landfill remediation project, and discusses the result and the largest impact factors. The analysis is done using a direct costs/revenues approach and the stochastic uncertainties are handled using Monte Carlo simulation. Two remediation scenarios are applied to a hypothetical landfill. One scenario includes only remediation, while the second scenario adds resource recovery to the remediation project. Moreover, the second scenario is divided into two cases, case A and B. In case A, the landfill tax needs to be paid for re-deposited material and the landfill holder does not own a combined heat and power plant (CHP), which leads to disposal costs in the form of gate fees. In case B, the landfill tax is waived on the re-deposited material and the landfill holder owns its own CHP. Results show that the remediation project in the first scenario costs about €23/ton. Adding resource recovery as in case A worsens the result to −€36/ton, while for case B the result improves to −€14/ton. This shows the importance of landfill tax and the access to a CHP. Other important factors for the result are the material composition in the landfill, the efficiency of the separation technology used, and the price of the saleable material.
In this work, we study the problem of mean-variance hedging with a random horizon T∧τ, where T is a deterministic constant and τ is a jump time of the underlying asset price process. We first formulate this problem as a stochastic control problem and relate it to a system of BSDEs with a jump. We then provide a verification theorem which gives the optimal strategy for the mean-variance hedging using the solution of the previous system of BSDEs. Finally, we prove that this system of BSDEs admits a solution via a decomposition approach coming from filtration enlargement theory.