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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.

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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Time delay and noise explaining the behaviour of the cell growth in fermentation process

This paper proposes to investigate the interplay between time delay and external noise in explaining the behaviour of the microbial growth in batch fermentation process. Time delay and noise are modelled jointly via stochastic delay differential equations (SDDEs). The typical behaviour of cell concentration in batch fermentation process under this model is investigated. Milstein scheme is applied for solving this model numerically. Simulation results illustrate the effects of time delay and external noise in explaining the lag and stationary phases, respectively for the cell growth of fermentation process.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch considering Wind Power Penetration

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To estimate the uncertainty in this stochastic economic dispatch problem for forecasting purposes, the conventional Monte-Carlo method is prohibitively time-consuming for practical applications. To overcome this problem, here we propose a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Facing high-dimensional real-world data representing the correlated uncertainties from wind generation, a manifold-learning-based Isomap algorithm is proposed to efficiently represent the low-dimensional hidden probabilistic structure of the data. In this low-dimensional latent space, with Latin hypercube sampling as the computer experimental design, a Gaussian-process emulator is used, for the first time, to serve as a nonparametric, surrogate model for the original complicated stochastic economic dispatch model. This reduced-order representative allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus test system reveal the impressive performance of the proposed method.

17 WIND ENERGY↗
DOE OSTI2021

Energy sector portfolio analysis with uncertainty

Governments are dealing with the challenge of how to efficiently invest in research and development portfolios related to energy technologies. Research and development investment decisions in the energy space are especially difficult due to numerous risks and uncertainties, and due to the complexity of energy's interactions with the broad economy. Historically, much of the U.S. Department of Energy's in-depth research and development analyses focused on assessing the impact of a research and development activity in isolation from other available opportunities and did not substantially consider risk and uncertainty. Endeavoring to combine integrated energy-economy modeling with uncertainty analysis and technology-specific research and development activities, the U.S. Department of Energy commissioned the development of the Stochastic Energy Deployment System to support and improve public energy research and development decision-making. The Stochastic Energy Deployment System draws from expert-elicited probability distributions for research and development-driven improvements in technology cost and performance, and it uses Monte Carlo simulations to evaluate the likelihood of outcomes within a system dynamics energy-economy model. The framework estimates the uncertain benefits and costs of various research and development portfolios and provides insight into the probability of meeting national technology goals, while accounting for interactions with the larger economy and for interactions among research and development investments spanning many energy sectors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

IMPROVED COMPUTATIONAL NEUTRONICS METHODS AND VALIDATION PROTOCOLS FOR THE ADVANCED TEST REACTOR

The Idaho National Laboratory (INL) is in the process of modernizing the various reactor physics modeling and simulation tools used to support operation and safety assurance of the Advanced Test Reactor (ATR). Key accomplishments so far have encompassed both computational as well as experimental work. A new suite of stochastic and deterministic transport theory based reactor physics codes and their supporting nuclear data libraries (HELIOS, KENO6/SCALE, NEWT/SCALE, ATTILA, and an extended implementation of MCNP5) has been installed at the INL. Corresponding models of the ATR and ATRC are now operational with all five codes, demonstrating the basic feasibility of the new code packages for their intended purpose. Of particular importance, a set of as-run core depletion HELIOS calculations for all ATR cycles since August 2009 was successfully completed during 2011. This demonstration supported a decision late in the year to proceed with the phased incorporation of the HELIOS methodology into the ATR fuel cycle management process beginning in 2012. On the experimental side of the project, new hardware was fabricated, measurement protocols were finalized, and the first four of six planned physics code validation experiments based on neutron activation spectrometry were conducted at the ATRC facility. Data analysis for the first three experiments, focused on characterization of the neutron spectrum in one of the ATR flux traps, has been completed. The six experiments will ultimately form the basis for a flexible, easily-repeatable ATR physics code validation protocol that is consistent with applicable ASTM standards.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2010

Signature of a universal statistical description for drift-wave plasma turbulence

This letter provides a theoretical interpretation of numerically generated probability density functions (PDFs) of intermittent plasma transport events. Specifically, nonlinear gyrokinetic simulations of ion-temperature-gradient turbulence produce the time series of heat flux that manifestly exhibit non-Gaussian PDFs with enhanced tails. It is demonstrated that, after the removal of autocorrelations, the numerical PDFs can be matched with predictions from a fluid theoretical setup based on the instanton method. This result points to a universality in the modeling of intermittent stochastic process offering a predictive capability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

A UNIFIED MODEL OF GRAIN ALIGNMENT: RADIATIVE ALIGNMENT OF INTERSTELLAR GRAINS WITH MAGNETIC INCLUSIONS

The radiative torque (RAT) alignment of interstellar grains with ordinary paramagnetic susceptibilities has been supported by earlier studies. The alignment of such grains depends on the so-called RAT parameter q {sup max}, which is determined by the grain shape. In this paper, we elaborate on our model of RAT alignment for grains with enhanced magnetic susceptibility due to iron inclusions, such that RAT alignment is magnetically enhanced, which we term the MRAT mechanism. Such grains can be aligned with high angular momentum at the so-called high- J attractor points, achieving a high degree of alignment. Using our analytical model of RATs, we derive the critical value of the magnetic relaxation parameter δ {sub m} to produce high- J attractor points as functions of q {sup max} and the anisotropic radiation angle relative to the magnetic field ψ . We find that if about 10% of the total iron abundance present in silicate grains is forming iron clusters, this is sufficient to produce high- J attractor points for all reasonable values of q {sup max}. To calculate the degree of grain alignment, we carry out numerical simulations of MRAT alignment by including stochastic excitations from gas collisions and magnetic fluctuations. We show that large grains can achieve perfect alignment when the high- J attractor point is present, regardless of the values of q {sup max}. Our obtained results pave the way for the physical modeling of polarized thermal dust emission as well as magnetic dipole emission. We also find that millimeter-sized grains in accretion disks may be aligned with the magnetic field if they are incorporated with iron nanoparticles.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Stochastic Optimization of Complex Systems

This project focused on methodologies for the solution of stochastic optimization problems based on relaxation and penalty methods, Monte Carlo simulation, parallel processing, and inverse optimization. The main results of the project were the development of a convergent method for the solution of models that include expectation constraints as in equilibrium models, improvement of Monte Carlo convergence through the use of a new method of sample batch optimization, the development of new parallel processing methods for stochastic unit commitment models, and the development of improved methods in combination with parallel processing for incorporating automatic differentiation methods into optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Rare-event Simulation for Stochastic Korteweg-de Vries Equation

An asymptotic analysis of the tail probabilities for the dynamics of a soliton wave $U(x,t)$ under a stochastic time-dependent force is developed. The dynamics of the soliton wave $U(x,t)$ is described by the Korteweg-de Vries Equation with homogeneous Dirichlet boundary conditions under a stochastic time-dependent force, which is modeled as a time-dependent Gaussian noise with amplitude $\epsilon$. The tail probability we considered is $w(b) :=P(\sup_{t\in [0,T]} U(x,t) > b ),$ as $b\rightarrow \infty,$ for some constant $T>0$ and a fixed $x$, which can be interpreted as tail probability of the amplitude of water wave on shallow surface of a fluid or long internal wave in a density-stratified ocean. Our goal is to characterize the asymptotic behaviors of $w(b)$ and to evaluate the tail probability of the event that the soliton wave exceeds a certain threshold value under a random force term. Such rare-event calculation of $w(b)$ is very useful for fast estimation of the risk of the potential damage that could caused by the water wave in a density-stratified ocean modeled by the stochastic KdV equation. In this work, the asymptotic approximation of the probability that the soliton wave exceeds a high-level $b$ is derived. In addition, we develop a provably efficient rare-event simulation algorithm to compute $w(b)$. The efficiency of the algorithm only requires mild conditions and therefore it is applicable to a general class of Gaussian processes and many diverse applications.

Xu, Gongjun↗
DOE OSTI2019

An Easy Algorithm to Generate Colored Noise Sequences

The need for accurate and efficient simulation of the noise background arises in statistical significance tests for periodic signals buried in colored noise. This paper discusses techniques for generating colored-noise sequences which simulate processes with a given spectral density. Matlab routines based on the fast fractional difference algorithm are presented. These routines can create various stochastic models (e.g., first-order autoregressive (AR(1)), power law (PL), autoregressive fractionally integrated moving average ((1, d, 0)), and generalized Gauss Markov (GGM)) that serve as possible candidate null hypotheses to test against in various scenarios. Allan variance and power spectral density (PSD) show that our algorithms are accurate and efficient, and can be easily implemented for stationary noise models and non-stationary PLs with spectral indices up to 2. Our algorithms can also be extended to produce a non-stationary PL with a range-limited steeper PSD (spectral indices up to about 4) using a GGM approximation with a proper break frequency. The red-noise leakage effect on the periodogram is further discussed. The result shows that our GGM approximation has a potential for alleviating the red-noise leakage in the PSD estimates. The Timmer and Koenig procedure is also included for comparison.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Impact of the Earthquake Rupture on Ground-Motion Variability of the 24 August 2016 M w 6.2 Amatrice, Italy, Earthquake

Here, the devastating 24 August 2016 M w 6.2 earthquake that struck Amatrice, Italy, marked the beginning of a prolonged seismic sequence dominated by three subsequent M w ≥6.0 events in the central Apennines region. The earthquake destroyed Amatrice’s historic center, claiming the lives of 299 individuals and causing widespread damage in the neighboring villages. The severity of the ground shaking, with a recorded maximum acceleration of 850 cm/s 2 on the east–west component at the Amatrice station, was far greater than the predicted acceleration based on the Italian ground-motion model (GMM). As pointed out by several investigations, the observed ground-motion amplitude and its spatial variability during the earthquake can be linked to specific rupture characteristics, including slip distribution and rupture directivity effects revealed by the observed data (Tinti et al., 2016; Pischiutta et al., 2021). In this study, we conducted physics-based 3D numerical simulations of ground motion for the Amatrice earthquake for frequencies up to 3 Hz. We employed a series of kinematic rupture models and a well-constrained local 3D velocity model incorporating surface topography. The kinematic rupture realizations were generated using multiscale hybrid and fully stochastic models, following the technique proposed by Graves and Pitarka (2016). We focused on assessing the sensitivity of near-fault ground-motion amplitudes to earthquake rupture characteristics, in particular, the spatial slip pattern. To evaluate the quality of our simulations, we employed goodness-of-fit measurements performed in comparisons of simulated and recorded ground motions. The simulated ground motions compare well with the recorded data and predictions from GMMs for Italy, ITA18 (Lanzano et al., 2019). However, we found that the simulated interevent ground-motion variability (randomness in the source process) of peak ground velocity, σ (PGV) is higher than the constant σ (PGV) predicted by conventional GMMs. Our simulations using several rupture scenarios demonstrate that the near-fault ground-motion amplification pattern is directly related to the slip distribution pattern.

3D Ground Motions Simulations↗
DOE OSTI2018

Pore diameter dependence of catalytic activity: p -nitrobenzaldehyde conversion to an aldol product in amine-functionalized mesoporous silica

The reaction yield for conversion of p-nitrobenzaldehyde (PNB) to an aldol product in amine-functionalized mesoporous silica nanoparticles (MSN) exhibits a 20-fold enhancement for a modest increase in pore diameter, d. This enhanced catalytic activity is shown to reflect a strong increase in the “passing propensity,” P, of reactant and product species inside the pores. We find that P ≈ 0, corresponding to single-file diffusion, applies for the smallest d which still significantly exceeds the linear dimensions of PNB and the aldol product. However, in this regime of narrow pores, these elongated species must align with each other and with the pore axis in order to pass. Thus, P reflects both translational and rotational diffusion. Langevin simulation accounting for these features is used to determine P versus d. The results are also augmented by analytic theory for small and large d where simulation is inefficient. The connection with the catalytic activity and yield is achieved by the incorporation of results for P into a multi-scale modeling framework. Specifically, we apply a spatially coarse-grained (CG) stochastic model for the overall catalytic reaction-diffusion process in MSN. Pores are treated as linear arrays of cells from the ends of which species adsorb and desorb, and between which species hop and exchange, with the exchange rate reflecting P. CG model predictions including yield are assessed by Kinetic Monte Carlo simulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2010

A Methodology for the Assessment of Unconventional (Continuous) Resources with an Application to the Greater Natural Buttes Gas Field, Utah

The Greater Natural Buttes tight natural gas field is an unconventional (continuous) accumulation in the Uinta Basin, Utah, that began production in the early 1950s from the Upper Cretaceous Mesaverde Group. Three years later, production was extended to the Eocene Wasatch Formation. With the exclusion of 1100 non-productive ('dry') wells, we estimate that the final recovery from the 2500 producing wells existing in 2007 will be about 1.7 trillion standard cubic feet (TSCF) (48.2 billion cubic meters (BCM)). The use of estimated ultimate recovery (EUR) per well is common in assessments of unconventional resources, and it is one of the main sources of information to forecast undiscovered resources. Each calculated recovery value has an associated drainage area that generally varies from well to well and that can be mathematically subdivided into elemental subareas of constant size and shape called cells. Recovery per 5-acre cells at Greater Natural Buttes shows spatial correlation; hence, statistical approaches that ignore this correlation when inferring EUR values for untested cells do not take full advantage of all the information contained in the data. More critically, resulting models do not match the style of spatial EUR fluctuations observed in nature. This study takes a new approach by applying spatial statistics to model geographical variation of cell EUR taking into account spatial correlation and the influence of fractures. We applied sequential indicator simulation to model non-productive cells, while spatial mapping of cell EUR was obtained by applying sequential Gaussian simulation to provide multiple versions of reality (realizations) having equal chances of being the correct model. For each realization, summation of EUR in cells not drained by the existing wells allowed preparation of a stochastic prediction of undiscovered resources, which range between 2.6 and 3.4 TSCF (73.6 and 96.3 BCM) with a mean of 2.9 TSCF (82.1 BCM) for Greater Natural Buttes. A second approach illustrates the application of multiple-point simulation to assess a hypothetical frontier area for which there is no production information but which is regarded as being similar to Greater Natural Buttes.

03 NATURAL GAS↗
DOE OSTI2016

Semiparametric modeling: Correcting low-dimensional model error in parametric models

In this paper, a semiparametric modeling approach is introduced as a paradigm for addressing model error arising from unresolved physical phenomena. Our approach compensates for model error by learning an auxiliary dynamical model for the unknown parameters. Practically, the proposed approach consists of the following steps. Given a physics-based model and a noisy data set of historical observations, a Bayesian filtering algorithm is used to extract a time-series of the parameter values. Subsequently, the diffusion forecast algorithm is applied to the retrieved time-series in order to construct the auxiliary model for the time evolving parameters. The semiparametric forecasting algorithm consists of integrating the existing physics-based model with an ensemble of parameters sampled from the probability density function of the diffusion forecast. To specify initial conditions for the diffusion forecast, a Bayesian semiparametric filtering method that extends the Kalman-based filtering framework is introduced. In difficult test examples, which introduce chaotically and stochastically evolving hidden parameters into the Lorenz-96 model, we show that our approach can effectively compensate for model error, with forecasting skill comparable to that of the perfect model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Stochastic weighted particle methods for population balance equations with coagulation, fragmentation and spatial inhomogeneity

Graphical abstract: -- Highlights: •Problems concerning multi-compartment population balance equations are studied. •A class of fragmentation weight transfer functions is presented. •Three stochastic weighted algorithms are compared against the direct simulation algorithm. •The numerical errors of the stochastic solutions are assessed as a function of fragmentation rate. •The algorithms are applied to a multi-dimensional granulation model. -- Abstract: This paper introduces stochastic weighted particle algorithms for the solution of multi-compartment population balance equations. In particular, it presents a class of fragmentation weight transfer functions which are constructed such that the number of computational particles stays constant during fragmentation events. The weight transfer functions are constructed based on systems of weighted computational particles and each of it leads to a stochastic particle algorithm for the numerical treatment of population balance equations. Besides fragmentation, the algorithms also consider physical processes such as coagulation and the exchange of mass with the surroundings. The numerical properties of the algorithms are compared to the direct simulation algorithm and an existing method for the fragmentation of weighted particles. It is found that the new algorithms show better numerical performance over the two existing methods especially for systems with significant amount of large particles and high fragmentation rates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

First principles simulation of ultracold ion crystals in a Penning trap with Doppler cooling and a rotating wall potential

A direct numerical simulation of many interacting ions in a Penning trap with a rotating wall is presented. The ion dynamics is modeled classically. Both axial and planar Doppler laser cooling processes are modeled using stochastic momentum impulses based on two-level atomic scattering rates. The plasmas being modeled are ultracold two-dimensional crystals made up of hundreds of ions. We compare Doppler cooled results directly to a previous linear eigenmodes analysis. Agreement in both frequency and mode structure is obtained. Additionally, when Doppler laser cooling is applied, the laser cooled steady state plasma axial temperature agrees with the Doppler cooling limit. Numerical simulations using the approach described and benchmarked here will provide insights into the dynamics of large trapped-ion crystals, improving their performance as a platform for quantum simulation and sensing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Stochastic Error Cancellation in Analog Quantum Simulation

Analog quantum simulation is a promising path towards solving classically intractable problems in many-body physics on near-term quantum devices. However, the presence of noise limits the size of the system and the length of time that can be simulated. In our work, we consider an error model in which the actual Hamiltonian of the simulator differs from the target Hamiltonian we want to simulate by small local perturbations, which are assumed to be random and unbiased. We analyze the error accumulated in observables in this setting and show that, due to stochastic error cancellation, with high probability the error scales as the square root of the number of qubits instead of linearly. We explore the concentration phenomenon of this error as well as its implications for local observables in the thermodynamic limit. Moreover, we show that stochastic error cancellation also manifests in the fidelity between the target state at the end of time-evolution and the actual state we obtain in the presence of noise. This indicates that, to reach a certain fidelity, more noise can be tolerated than implied by the worst-case bound if the noise comes from many statistically independent sources.

Analog quantum simulation↗
DOE OSTI2015

Coupling fracture facies with in-situ permeability measurements to generate stochastic simulations of tight carbonate aquifer properties: Example from the Lower Cretaceous aquifer, Northern Provence, SE France

The relationships between fracture facies and permeability distribution have been explored using a well-constrained 3D implicit structural model of hemipelagic low porosity/low permeability carbonate from the Northern Provence region, SE France. Fracture and permeability facies were determined using an extensive dataset of 99 hydrogeological wells. Data processing was undertaken using a step-by-step approach, involving: (i) identification of the fracture facies based on well logs detailing structure and stratigraphy; (ii) determination of permeability facies from the a priori correlation between the dimension of the hydraulic radius of influence (deduced from slug test analyses) and the type of reservoir heterogeneity (fissure, fracture, fault zone, etc.); (iii) three dimensional plot of fracture and permeability facies in the geological model using a variographic analysis of data. Thirty-three sequential indicator simulations (SIS) based on geostatistic analyses were realised on both fracture and permeability facies. Finally, a connectivity algorithm was developed to compute the probability of connection between selected infiltration areas and the major aquifer springs via moderate- to high-permeability geological bodies. Key results are: (i) fault zones have a greater role in controlling permeability facies distribution than on fracture facies repartition; (ii) there is little correlation between permeability and fracture facies distributions; (iii) connectivity results highlight the compartmentalisation of aquifers in the Cadarache area, the extensions of permeable geological bodies being limited by the N130 faults.

58 GEOSCIENCES↗