Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Stochastic processes”

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.

At least 577 records · Page 32

Microscopic Theory of Fluctuating Hydrodynamics in Nonlinear Lattices

The theory of fluctuating hydrodynamics has been an important tool for analyzing macroscopic behavior in nonlinear lattices. However, despite its practical success, its microscopic derivation is still incomplete. In this work, we provide the microscopic derivation of fluctuating hydrodynamics, using the coarse-graining and projection technique; the equivalence of ensembles turns out to be critical. The Green-Kubo (GK)-like formula for the bare transport coefficients are presented in a numerically computable form. Our numerical simulations show that the bare transport coefficients exist for a sufficiently large but finite coarse-graining length in the infinite lattice within the framework of the GK-like formula. Finally, this demonstrates that the bare transport coefficients uniquely exist for each physical system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Fracton Hydrodynamics without Time-Reversal Symmetry

Here, we present an effective field theory for the nonlinear fluctuating hydrodynamics of a single conserved charge with or without time-reversal symmetry, based on the Martin-Siggia-Rose formalism. Applying this formalism to fluids with only charge and multipole conservation, and with broken time-reversal symmetry, we predict infinitely many new dynamical universality classes, including some with arbitrarily large upper critical dimensions. Using large scale simulations of classical Markov chains, we find numerical evidence for a breakdown of hydrodynamics in quadrupole-conserving models with broken time-reversal symmetry in one spatial dimension. Our framework can be applied to the hydrodynamics around stationary states of open systems, broadening the applicability of previously developed ideas and methods to a wide range of systems in driven and active matter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Temporal Coarse Graining for Classical Stochastic Noise in Quantum Systems

Simulations of quantum systems with Hamiltonian classical stochastic noise can be challenging when the noise exhibits temporal correlations over a multitude of time scales, such as for 1/f noise in solid-state quantum information processors. Here we present an approach for simulating Hamiltonian classical stochastic noise that performs temporal coarse-graining by effectively integrating out the high-frequency components of the noise. We focus on the case where the stochastic noise can be expressed as a sum of Ornstein-Uhlenbeck processes. Temporal coarse-graining is then achieved by conditioning the stochastic process on a coarse realization of the noise, expressing the conditioned stochastic process in terms of a sum of smooth, deterministic functions and bridge processes with boundaries fixed at zero, and performing the ensemble average over the bridge processes. For Ornstein-Uhlenbeck processes, the deterministic components capture all dependence on the coarse realization, and the stochastic bridge processes are not only independent but taken from the same distribution with correlators that can be expressed analytically, allowing the associated noise propagators to be precomputed once for all simulations. This combination of noise trajectories on a coarse time grid and ensemble averaging over bridge processes has practical advantages, such as a simple concatenation rule, that we highlight with numerical examples.

Albash, Tameem [Sandia National Lab. (SNL-NM), Alb↗

Filtering with Marked Point Process Observations via Poisson Chaos Expansion

We study a general filtering problem with marked point process observations. The motivation comes from modeling financial ultra-high frequency data. First, we rigorously derive the unnormalized filtering equation with marked point process observations under mild assumptions, especially relaxing the bounded condition of stochastic intensity. Then, we derive the Poisson chaos expansion for the unnormalized filter. Based on the chaos expansion, we establish the uniqueness of solutions of the unnormalized filtering equation. Moreover, we derive the Poisson chaos expansion for the unnormalized filter density under additional conditions. To explore the computational advantage, we further construct a new consistent recursive numerical scheme based on the truncation of the chaos density expansion for a simple case. The new algorithm divides the computations into those containing solely system coefficients and those including the observations, and assign the former off-line.

97 MATHEMATICS AND COMPUTING↗

A novel stochastic modeling method to simulate cooling loads in residential districts

District cooling systems are widely used in urban residential communities in China. Most of such systems are oversized, which leads to wasted investment, low operational efficiency and, thus, waste of energy. The accurate prediction of district cooling loads that can support the rightsizing of cooling plant equipment remains a challenge. This study develops a novel stochastic modeling method that consists of (1) six prototype house models representing most apartments in a district, (2) occupant behavior models of residential buildings reflecting their spatial and temporal diversity as well as their complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was applied to a case study using the Designer's Simulation Toolkit (DeST) to simulate the cooling loads of a residential district in Wuhan, China. The simulation results agreed well with the measured data based on five performance metrics representing the aggregated cooling consumption, the peak cooling loads, the spatial load distribution, the temporal load distribution and the load profiles. Two prevalent simulation methods were also employed to simulate the district cooling loads. Here, the results showed that oversimplified assumptions about occupant behavior could lead to significant overestimation of the peak cooling load and the total cooling loads in the district. Future work will aim to simplify the workflow and data requirements of the stochastic method for its application, and to explore its use in predicting district heating loads and in commercial or mixed-use districts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Hybrid finite element and Brownian dynamics method for charged particles

Diffusion is often the rate-determining step in many biological processes. Currently, the two main computational methods for studying diffusion are stochastic methods, such as Brownian dynamics, and continuum methods, such as the finite element method. A previous study introduced a new hybrid diffusion method that couples the strengths of each of these two methods, but was limited by the lack of interactions among the particles; the force on each particle had to be from an external field. This study further develops the method to allow charged particles. The method is derived for a general multidimensional system and is presented using a basic test case for a one-dimensional linear system with one charged species and a radially symmetric system with three charged species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Estimation of stochastic volatility with long memory for index prices of FTSE Bursa Malaysia KLCI

In recent years, modeling in long memory properties or fractionally integrated processes in stochastic volatility has been applied in the financial time series. A time series with structural breaks can generate a strong persistence in the autocorrelation function, which is an observed behaviour of a long memory process. This paper considers the structural break of data in order to determine true long memory time series data. Unlike usual short memory models for log volatility, the fractional Ornstein-Uhlenbeck process is neither a Markovian process nor can it be easily transformed into a Markovian process. This makes the likelihood evaluation and parameter estimation for the long memory stochastic volatility (LMSV) model challenging tasks. The drift and volatility parameters of the fractional Ornstein-Unlenbeck model are estimated separately using the least square estimator (lse) and quadratic generalized variations (qgv) method respectively. Finally, the empirical distribution of unobserved volatility is estimated using the particle filtering with sequential important sampling-resampling (SIR) method. The mean square error (MSE) between the estimated and empirical volatility indicates that the performance of the model towards the index prices of FTSE Bursa Malaysia KLCI is fairly well.

97 MATHEMATICS AND COMPUTING↗

Simple proof of the robustness of Gaussian entanglement in bosonic noisy channels

The extremality of Gaussian states is exploited to show that Gaussian states are the most robust, among all possible bipartite continuous-variable states at fixed energy, against disentanglement due to noisy evolutions in Markovian Gaussian channels involving dissipation and thermal hopping. This proves a conjecture raised recently [M. Allegra, P. Giorda, and M. G. A. Paris, Phys. Rev. Lett. 105, 100503 (2010)], providing a rigorous validation of the conclusions of that work. The problem of identifying continuous-variable states with maximum resilience to entanglement damping in more general bosonic open-system dynamical evolutions, possibly including correlated noise and non-Markovian effects, remains open.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Continuous-variable quantum key distribution in non-Markovian channels

We address continuous-variable quantum key distribution (QKD) in non-Markovian lossy channels and show how the non-Markovian features may be exploited to enhance security and/or to detect the presence and the position of an eavesdropper along the transmission line. In particular, we suggest a coherent-state QKD protocol which is secure against Gaussian individual attacks based on optimal 1{yields}2 asymmetric cloning machines for arbitrarily low values of the overall transmission line. The scheme relies on specific non-Markovian properties, and cannot be implemented in ordinary Markovian channels characterized by uniform losses. Our results give a clear indication of the potential impact of non-Markovian effects in QKD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Norm-based measurement of quantum correlation

In this paper we derived a necessary and sufficient condition for classical correlated states and proposed a norm-based measurement Q of quantum correlation. Using the max norm of operators, we gave the expression of the quantum correlation measurement Q and investigated the dynamics of Q in Markovian and non-Markovian cases, respectively. Q decays exponentially and vanishes only asymptotically in the Markovian case and causes periodical death and rebirth in the non-Markovian case. In the pure state, the quantum correlation Q is always larger than the entanglement, which was different from other known measurements. In addition, we showed that locally broadcastable and broadcastable are equivalent and reproved the density of quantum correlated states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Anatomical noise in contrast-enhanced digital mammography. Part I. Single-energy imaging

Purpose: The use of an intravenously injected iodinated contrast agent could help increase the sensitivity of digital mammography by adding information on tumor angiogenesis. Two approaches have been made for clinical implementation of contrast-enhanced digital mammography (CEDM), namely, single-energy (SE) and dual-energy (DE) imaging. In each technique, pairs of mammograms are acquired, which are then subtracted with the intent to cancel the appearance of healthy breast tissue to permit sensitive detection and specific characterization of lesions. Patterns of contrast agent uptake in the healthy parenchyma, and uncanceled signal from background tissue create a 'clutter' that can mask or mimic an enhancing lesion. This type of 'anatomical noise' is often the limiting factor in lesion detection tasks, and thus, noise quantification may be useful for cascaded systems analysis of CEDM and for phantom development. In this work, the authors characterize the anatomical noise in CEDM clinical images and the authors evaluate the influence of the x-ray energy used for acquisition, the presence of iodine in the breast, and the timing of imaging postcontrast administration on anatomical noise. The results are presented in a two-part report, with SE CEDM described here, and DE CEDM in Part II. Methods: A power law is used to model anatomical noise in CEDM images. The exponent, {beta}, which describes the anatomical structure, and the constant {alpha}, which represents the magnitude of the noise, are determined from Wiener spectra (WS) measurements on images. A total of 42 SE CEDM cases from two previous clinical pilot studies are assessed. The parameters {alpha} and {beta} are measured both from unprocessed images and from subtracted images. Results: Consistent results were found between the two SE CEDM pilot studies, where a significant decrease in {beta} from a value of approximately 3.1 in the unprocessed images to between about 1.1 and 1.8 in the subtracted images was observed. Increasing the x-ray energy from that used in conventional DM to those of typical SE CEDM spectra with mean energies above 33 keV significantly decreased {alpha} by about a factor of 19, in agreement with theory. Compared to precontrast images, in the unprocessed postcontrast images at 30 s postinjection, {alpha} was larger by about 7.4 Multiplication-Sign 10{sup -7} mm{sup 2} and {beta} was decreased by 0.2. While {alpha} did not vary significantly with the time after contrast administration, {beta} from the unprocessed image WS increased linearly, and {beta} from subtracted image WS increased with an initial quadratic relationship that plateaued by about 5 min postinjection. Conclusions: The presence of an iodinated contrast agent in the breast produced small, but significant changes in the power law parameters of unprocessed CEDM images compared to the precontrast images. Image subtraction in SE CEDM significantly reduced anatomical noise compared to conventional DM, with a reduction in both {alpha} and {beta} by about a factor of 2. The data presented here, and in Part II of this work, will be useful for modeling of CEDM backgrounds, for systems characterization and for lesion detectability experiments using models that account for anatomical noise.

60 APPLIED LIFE SCIENCES↗

Game-Theoretic Modeling of Vegetation Composition, Structure, and Dynamics: Physical Constraints, Fundamental Processes, and Emergent Properties

Vegetation structural and compositional dynamics emerge from plant physiological and demographic processes, individual-based competition, vegetation-soil feedbacks, and environmental variations and disturbance events. Predicting long-term changes in vegetation requires scaling plant individual behavior to large scale ecosystem processes. In this presentation, we summarize our studies in the modeling of vegetation demographic processes, competitively dominant plant traits, plant hydraulic processes, and stochastic disturbance effects on ecosystems, and illustrate the roles of the underlying ecological processes and eco-evolutionary optimization in vegetation modeling. With the case studies of evolutionarily stable strategy of allocation, leaf traits, and plant hydraulic processes, we show how the ecosystem processes and vegetation dynamics are determined by the individual-based plant competition and variations of soil and climate conditions. The predictions of ecosystem carbon dynamics can be greatly different with those from the traditional “single-tree” models. We also discuss the tradeoffs of plant traits and evolutionarily optimal strategies in the modeling of terrestrial ecosystem dynamics in an Earth system model.

vegetation models↗

Research Needs and Impacts in Predictive Simulation for Internal Combustion Engines (PreSICE)

This report is based on a SC/EERE Workshop to Identify Research Needs and Impacts in Predictive Simulation for Internal Combustion Engines (PreSICE), held March 3, 2011, to determine strategic focus areas that will accelerate innovation in engine design to meet national goals in transportation efficiency. The U.S. has reached a pivotal moment when pressures of energy security, climate change, and economic competitiveness converge. Oil prices remain volatile and have exceeded $100 per barrel twice in five years. At these prices, the U.S. spends $1 billion per day on imported oil to meet our energy demands. Because the transportation sector accounts for two-thirds of our petroleum use, energy security is deeply entangled with our transportation needs. At the same time, transportation produces one-quarter of the nation’s carbon dioxide output. Increasing the efficiency of internal combustion engines is a technologically proven and cost-effective approach to dramatically improving the fuel economy of the nation’s fleet of vehicles in the near- to mid-term, with the corresponding benefits of reducing our dependence on foreign oil and reducing carbon emissions. Because of their relatively low cost, high performance, and ability to utilize renewable fuels, internal combustion engines—including those in hybrid vehicles—will continue to be critical to our transportation infrastructure for decades. Achievable advances in engine technology can improve the fuel economy of automobiles by over 50% and trucks by over 30%. Achieving these goals will require the transportation sector to compress its product development cycle for cleaner, more efficient engine technologies by 50% while simultaneously exploring innovative design space. Concurrently, fuels will also be evolving, adding another layer of complexity and further highlighting the need for efficient product development cycles. Current design processes, using “build and test” prototype engineering, will not suffice. Current market penetration of new engine technologies is simply too slow—it must be dramatically accelerated. These challenges present a unique opportunity to marshal U.S. leadership in science-based simulation to develop predictive computational design tools for use by the transportation industry. The use of predictive simulation tools for enhancing combustion engine performance will shrink engine development timescales, accelerate time to market, and reduce development costs, while ensuring the timely achievement of energy security and emissions targets and enhancing U.S. industrial competitiveness. In 2007 Cummins achieved a milestone in engine design by bringing a diesel engine to market solely with computer modeling and analysis tools. The only testing was after the fact to confirm performance. Cummins achieved a reduction in development time and cost. As important, they realized a more robust design, improved fuel economy, and met all environmental and customer constraints. This important first step demonstrates the potential for computational engine design. But, the daunting complexity of engine combustion and the revolutionary increases in efficiency needed require the development of simulation codes and computation platforms far more advanced than those available today. Based on these needs, a Workshop to Identify Research Needs and Impacts in Predictive Simulation for Internal Combustion Engines (PreSICE) convened over 60 U.S. leaders in the engine combustion field from industry, academia, and national laboratories to focus on two critical areas of advanced simulation, as identified by the U.S. automotive and engine industries. First, modern engines require precise control of the injection of a broad variety of fuels that is far more subtle than achievable to date and that can be obtained only through predictive modeling and simulation. Second, the simulation, understanding, and control of these stochastic in-cylinder combustion processes lie on the critical path to realizing more efficient engines with greater power density. Fuel sprays set the initial conditions for combustion in essentially all future transportation engines; yet today designers primarily use empirical methods that limit the efficiency achievable. Three primary spray topics were identified as focus areas in the workshop: The fuel delivery system, which includes fuel manifolds and internal injector flow, The multi-phase fuel–air mixing in the combustion chamber of the engine, and The heat transfer and fluid interactions with cylinder walls. Current understanding and modeling capability of stochastic processes in engines remains limited and prevents designers from achieving significantly higher fuel economy. To improve this situation, the workshop participants identified three focus areas for stochastic processes: Improve fundamental understanding that will help to establish and characterize the physical causes of stochastic events, Develop physics-based simulation models that are accurate and sensitive enough to capture performance-limiting variability, and Quantify and manage uncertainty in model parameters and boundary conditions. Improved models and understanding in these areas will allow designers to develop engines with reduced design margins and that operate reliably in more efficient regimes. All of these areas require improved basic understanding, high-fidelity model development, and rigorous model validation. These advances will greatly reduce the uncertainties in current models and improve understanding of sprays and fuel–air mixture preparation that limit the investigation and development of advanced combustion technologies. The two strategic focus areas have distinctive characteristics but are inherently coupled. Coordinated activities in basic experiments, fundamental simulations, and engineering-level model development and validation can be used to successfully address all of the topics identified in the PreSICE workshop. The outcome will be: New and deeper understanding of the relevant fundamental physical and chemical processes in advanced combustion technologies, Implementation of this understanding into models and simulation tools appropriate for both exploration and design, and Sufficient validation with uncertainty quantification to provide confidence in the simulation results. These outcomes will provide the design tools for industry to reduce development time by up to 30% and improve engine efficiencies by 30% to 50%. The improved efficiencies applied to the national mix of transportation applications have the potential to save over 5 million barrels of oil per day, a current cost savings of $500 million per day.

33 ADVANCED PROPULSION SYSTEMS↗

Stochastic decision analysis

Small space flight project design at NASA Langley Research Center goes through a multi-phase process from preliminary analysis to flight operations. The process insures that each system achieves its technical objectives with demonstrated quality and within planned budgets and schedules. A key technical component of early phases is decision analysis, which is a structure procedure for determining the best of a number of feasible concepts based upon project objectives. Feasible system concepts are generated by the designers and analyzed for schedule, cost, risk, and technical measures. Each performance measure value is normalized between the best and worst values and a weighted average score of all measures is calculated for each concept. The concept(s) with the highest scores are retained, while others are eliminated from further analysis. This project automated and enhanced the decision analysis process. Automation of the decision analysis process was done by creating a user-friendly, menu-driven, spreadsheet macro based decision analysis software program. The program contains data entry dialog boxes, automated data and output report generation, and automated output chart generation. The enhancements to the decision analysis process permit stochastic data entry and analysis. Rather than enter single measure values, the designers enter the range and most likely value for each measure and concept. The data can be entered at the system or subsystem level. System level data can be calculated as either sum, maximum, or product functions of the subsystem data. For each concept, the probability distributions are approximated for each measure and the total score for each concept as either constant, triangular, normal, or log-normal distributions. Based on these distributions, formulas are derived for the probability that the concept meets any given constraint, the probability that the concept meets all constraints, and the probability that the concept is within a given amount of the best score. Formulas are also derived for the probability that one concept's total score is within a given amount of a second concept's total score. These probabilistic calculations provide more realistic data entry and output information for designers, enabling designers to better determine which concepts to eliminate and which concepts to retain at the decision points of each design phase.

Lacksonen, Thomas A.↗

Stochasticity in microbiology: managing unpredictability to reach the Sustainable Development Goals

Pure (single) cultures of microorganisms and mixed microbial communities (microbiomes) have been important for centuries in providing renewable energy, clean water and food products to human society and will continue to play a crucial role to pursue the Sustainable Development Goals. To use microorganisms effectively, microbial engineered processes require adequate control. Microbial communities are shaped by manageable deterministic processes, but also by stochastic processes, which can promote unforeseeable variations and adaptations. Here, we highlight the impact of stochasticity in single culture and microbiome engineering. First, we discuss the concepts and mechanisms of stochasticity in relation to microbial ecology of single cultures and microbiomes. Second, we discuss the consequences of stochasticity in relation to process performance and human health, which are reflected in key disadvantages and important opportunities. Third, we propose a suitable decision tool to deal with stochasticity in which monitoring of stochasticity and setting the boundaries of stochasticity by regulators are central aspects. Stochasticity may give rise to some risks, such as the presence of pathogens in microbiomes. We argue here that by taking the necessary precautions and through clever monitoring and interpretation, these risks can be mitigated.

59 BASIC BIOLOGICAL SCIENCES↗

Occupation Time of a Randomly Accelerated Particle on the Positive Half Axis: Results for the First Five Moments

In the random acceleration process a point particle is accelerated by Gaussian white noise with zero mean. Although several fundamental statistical properties of the motion have been analyzed in detail, the statistics of occupation times is still not well understood. We consider the occupation or residence time T{sub +} on the positive x axis of a particle which is randomly accelerated on the unbounded x axis for a time t. The first two moments of T{sub +} were recently derived by Ouandji Boutcheng et al. (J Stat Mech 053213:1–10, 2016). With an alternate approach utilizing basis functions which have proved useful in other studies of randomly accelerated motion, results for the first five moments are obtained in this paper.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Toroidal regularization of the guiding center Lagrangian

In the Lagrangian theory of guiding center motion, an effective magnetic field B* = B+ (m/e)v ∥ ∇ x b appears prominently in the equations of motion. Because the parallel component of this field can vanish, there is a range of parallel velocities where the Lagrangian guiding center equations of motion are either ill-defined or very badly behaved. Moreover, the velocity dependence of B* greatly complicates the identification of canonical variables and therefore the formulation of symplectic integrators for guiding center dynamics. Here, this letter introduces a simple coordinate transformation that alleviates both these problems simultaneously. In the new coordinates, the Liouville volume element is equal to the toroidal contravariant component of the magnetic field. Consequently, the large-velocity singularity is completely eliminated. Moreover, passing from the new coordinate system to canonical coordinates is extremely simple, even if the magnetic field is devoid of flux surfaces. We demonstrate the utility of this approach in regularizing the guiding center Lagrangian by presenting a new and stable one-step variational integrator for guiding centers moving in arbitrary time-dependent electromagnetic fields.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗