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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Optimal sizing study of hybrid wind/PV/diesel power generation unit

In this paper, a methodology of sizing optimization of a stand-alone hybrid wind/PV/diesel energy system is presented. This approach makes use of a deterministic algorithm to suggest, among a list of commercially available system devices, the optimal number and type of units ensuring that the total cost of the system is minimized while guaranteeing the availability of the energy. The collection of 6 months of data of wind speed, solar radiation and ambient temperature recorded for every hour of the day were used. The mathematical modeling of the main elements of the hybrid wind/PV/diesel system is exposed showing the more relevant sizing variables. A deterministic algorithm is used to minimize the total cost of the system while guaranteeing the satisfaction of the load demand. A comparison between the total cost of the hybrid wind/PV/diesel energy system with batteries and the hybrid wind/PV/diesel energy system without batteries is presented. The reached results demonstrate the practical utility of the used sizing methodology and show the influence of the battery storage on the total cost of the hybrid system. (author)

14 SOLAR ENERGY↗
DOE OSTI2021

Approximating Nash Equilibrium in Day-ahead Electricity Market Bidding with Multi-agent Deep Reinforcement Learning

In this paper, a day-ahead electricity market bidding problem with multiple strategic generation company (GEN-CO) bidders is studied. The problem is formulated as a Markov game model, where GENCO bidders interact with each other todevelop their optimal day-ahead bidding strategies. Considering unobservable information in the problem, a model-free and data-driven approach, known as multi-agent deep deterministic policy gradient (MADDPG), is applied for approximating the Nash equilibrium (NE) in the above Markov game. The MADDPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks. The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested caseand a congested case. Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient (DDPG) demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains. In addition, the comparison with a conventional model-based method shows that the MADDPG algorithm has higher computational efficiency, which is feasible for real-world applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Quasi-Stochastic Approximation and Off-Policy Reinforcement Learning: Preprint

The Robbins-Monro stochastic approximation algorithm is a foundation of many algorithmic frameworks for reinforcement learning and often an efficient approach to solving (or approximating the solution to) complex optimal control problems. In many cases, however, practitioners are unable to apply these techniques because of an inherent high variance. This paper aims to provide a general foundation for 'quasi-stochastic approximation,' in which all of the processes under consideration are deterministic, much like quasi-Monte Carlo for variance reduction in simulation. The variance reduction can be substantial, subject to tuning of pertinent parameters in the algorithm. This paper introduces a new coupling argument to establish the optimal rate of convergence provided the gain is sufficiently large. These results are established for linear models and tested also in nonideal settings. A major application of these general results is a new class of reinforcement learning algorithms for deterministic state space models. In this setting, the main contribution is a class of algorithms for approximating the value function for a given policy, using a different policy designed to introduce exploration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Chernobyl, the HBO miniseries: Fact and fiction (Part III)

With episode four of the series, we moved even further from reality than in prior episodes. First, we need background. Exposure to ionizing radiations causes two types of medical effects: deterministic and stochastic. Deterministic effects are predictable, dose-dependent, and occur in everyone exposed to the same dose. For example, everyone exposed to an acute whole-body dose of 5 gray (5,000 millisieverts) will have a marked immediate decrease in blood granulocytes.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

High-Performance Mesostructured Organic Hybrid Pseudocapacitor Electrodes

The electrodes of a hybrid electrochemical capacitor which utilize the quinone (Q)-hydroquinone (QH 2 ) couple, a prototypical organic redox system known to provide fast and reversible proton-coupled electron-transfer reactions, are deterministically mesostructured via a colloidal templating strategy to provide good ion and electron transport pathways, enabling a high rate performance. Specifically, a conducting polymer, polypyrrole (PPy), is functionalized with a pseudocapacitive material, a Q/QH 2 -containing catechol derivative, by noncovalent interactions. The mesostructure of this hybrid material is formed into an ordered 3D porous structure by a polystyrene colloidal crystal template-assisted electrosynthesis. The catechol derivative is sufficiently bound to the PPy through noncovalent interactions to provide a volumetric capacitance as high as ≈130 F cm –3 and a capacitance retention of ≈75% over 10 000 charging/discharging cycles. Furthermore, when compared with a randomly structured electrode, the deterministically structured electrode exhibits an improved rate performance due to the mesostructure facilitated electron and ion transport.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Unraveling the Main Chain and Side Chain Effects on Thin Film Morphology and Charge Transport in Quinoidal Conjugated Polymers

Three series of low-bandgap polymers based on a novel quinoidal para -azaquinodimethane ( p -AQM) unit are devised and synthesized, enabling an in-depth study of the impact of structural factors such as polymer main chain, branching point of the side chain, and the length of the branch chains on the thin film morphologies and charge transport properties. Morphological studies reveal that the polymers composed of larger repeating units exhibit a stronger tendency to form edge-on lamella. In contrast, altering the side chain structures of polymers with the same main chain configuration indicates that the branching point position has a more deterministic impact than the branch chain length on the interchain interactions and the crystallite orientation. These results demonstrate a compound odd-even effect of the branching point on the chain packing and morphology, which correlates well with the corresponding field effect transistor performances. The polymer with the branching point at the fourth carbon displays the highest charge carrier mobility over 1.0 cm 2 V-Three series of low-bandgap polymers based on a novel quinoidal para -azaquinodimethane ( p -AQM) unit are devised and synthesized, enabling an in-depth study of the impact of structural factors such as polymer main chain, branching point of the side chain, and the length of the branch chains on the thin film morphologies and charge transport properties. Morphological studies reveal that the polymers composed of larger repeating units exhibit a stronger tendency to form edge-on lamella. On the other hand, altering the side chain structures of polymers with the same main chain configuration indicates that the branching point position has a more deterministic impact than the branch chain length on the interchain interactions and the crystallite orientation. These results reflect a compound odd-even effect of the branching point on the chain packing and morphology, which correlates well with the corresponding field effect transistor performances. The polymer with the branching point at the fourth carbon displays the highest charge carrier mobility over 1.0 cm 2 V -1 s -1 , concurrent with a bimodal texture. This study provides a comprehensive description of the correlations between polymer structures, thin film morphology, and device performances, providing a clear path to desirable bimodal thin film texture for charge transport.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Mutual Ferromagnetic-Ferroelectric Coupling in Multiferroic Copper-Doped ZnO

A mutual ferromagnetic and ferroelectric coupling (multiferroic behavior) in Cu-doped ZnO is demonstrated via deterministic control of Cu doping and defect engineering. The coexistence of multivalence Cu ions and oxygen vacancies is important to multiferroic behaviors in ZnO:Cu. The samples show clear ferroelectric and ferromagnetic domain patterns. These domain structures may be written reversibly via electric and magnetic bias.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Conversion of Classical Light Emission from a Nanoparticle‐Strained WSe 2 Monolayer into Quantum Light Emission via Electron Beam Irradiation

Abstract Solid‐state single photon emitters (SPEs) within atomically thin transition metal dichalcogenides (TMDs) have recently attracted interest as scalable quantum light sources for quantum photonic technologies. Among TMDs, WSe 2 monolayers (MLs) are promising for the deterministic fabrication and engineering of SPEs using local strain fields. The ability to reliably produce isolatable SPEs in WSe 2 is currently impeded by the presence of numerous spectrally overlapping states that occur at strained locations. Here nanoparticle (NP) arrays with precisely defined positions and sizes are employed to deterministically create strain fields in WSe 2 MLs, thus enabling the systematic investigation and control of SPE formation. Using this platform, electron beam irradiation at NP‐strained locations transforms spectrally overlapped sub‐bandgap emission states into isolatable, anti‐bunched quantum emitters. The dependence of the emission spectra of WSe 2 MLs as a function of strain magnitude and exposure time to electron beam irradiation is quantified and provides insight into the mechanism for SPE production. Excitons selectively funnel through strongly coupled sub‐bandgap states introduced by electron beam irradiation, which suppresses spectrally overlapping emission pathways and leads to measurable anti‐bunched behavior. The findings provide a strategy to generate isolatable SPEs in 2D materials with a well‐defined energy range.

2D material↗
DOE OSTI2024

Hierarchical Composites Patterned via 3D Printed Cellular Fluidics

Additive manufacturing of freeform structures containing multiple materials with deterministic spatial arrangement and interactions remains a challenge for most 3D printing processes, due to complex fabrication tool requirements and limitations in printability of some material classes. Here, in this paper, a versatile method is reported to produce architected composites using the concept of cellular fluidics, in which lattices of unit cells are used as templating scaffolds to guide flowable infill materials in a programmed spatial pattern, upon which they are cured in place to produce a deterministically ordered multimaterial solid. The lattice design relies on the unit cell size, type, strut diameter, surface wetting, and distribution of cellular structures to control liquid flow and retention. Individual unit cells are tuned to achieve reliable infilling and combined into higher-order architectures to achieve multiscale composite materials with disparate mechanical properties, including those considered non-printable. Lattice design considerations for leveraging capillary phenomena and demonstrate several methods of patterning polymers in 3D-printed cellular fluidic structures are presented. The concept of tuning the compressive response of an architected composite using a flexible-elastomer as the lattice and a stiff-epoxy as the infill material is illustrated.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative Electron Beam‐Single Atom Interactions Enabled by Sub‐20‐pm Precision Targeting

The ability to probe and control matter at the picometer scale is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy offers powerful capabilities for materials analysis and modification, but sample damage, drift, and scan distortions hinder single atom analysis and deterministic manipulation. Materials analysis and modification via electron–solid interactions can be transformed by precise delivery of electrons to a specified atomic location, maintaining the beam position despite drift, and minimizing collateral dose. Here a fast, low-dose, sub-20-pm precision electron beam positioning technique is developed, “atomic lock-on,” (ALO), which offers the ability to position the beam on a specific atomic column without previously irradiating that column. This technique is used to lock onto a single selected atomic location to repeatedly measure its weak electron energy loss signal despite sample drift. Moreover, electron beam-matter interactions in single atomic events are measured with μ s time resolution. This enables observation of single-atom dynamics, such as atomic bistability, revealing partially bonded atomic configurations and recapture phenomena. This opens prospects for using electron microscopy for high-precision measurements and deterministic control of matter for quantum technologies.

2D materials↗
DOE OSTI2021

Nth-plant supply: corn stover supplies and costs in a fleet of biorefineries

Feedstock cost and cost variability is expected to increase with the number of biorefineries. To quantify this effect, this spatial-economic analysis simulates feedstock cost and cost variability of an industry based on corn stover as a function of the number of biorefineries. Results are reported for nine scenarios (a base case and sensitivity analysis of four variables – harvest efficiency, sustainability constraints, opportunity cost, and corn grain yield) under deterministic and stochastic simulations, assuming biorefineries using 658 000 Mg (725 000 tons) year- 1 of corn stover in 2019. The resulting supply curves are highly elastic (i.e. little change in cost) for the first 50 of the 121 biorefineries, with price increases in subsequent biorefineries depending on scenario. In the base-case deterministic scenario, weighted-average stover costs are $66 Mg -1 ($60 ton- 1 ), $69 Mg -1 ($62 ton -1 ), and $156 Mg -1 ($142 ton -1 ), at the first, 60th, and 121st biorefineries, respectively. The stochastic simulations, subject to observed 30-year corn yield variability, follow a similar pattern, with price distributions that vary by scenario. The base-case stochastic simulations illustrate minimal cost variability for the first 60 biorefineries, but rapid increases in cost variability in the second half of potential biorefineries, with similar patterns observed in the other scenarios. Of the four variables explored, price was most sensitive to harvest efficiency, followed by sustainability constraints, corn yield, and opportunity cost. Results suggest that, under conventional logistics, about half of the US corn stover resource is reliably available with minimum cost increase and variability. Interactive visualization is available at https://doi.org/10.11578/1828779.

09 BIOMASS FUELS↗
DOE OSTI2024

Anderson acceleration with approximate calculations: Applications to scientific computing

Here we provide rigorous theoretical bounds for Anderson acceleration (AA) that allow for approximate calculations when applied to solve linear problems. We show that, when the approximate calculations satisfy the provided error bounds, the convergence of AA is maintained while the computational time could be reduced. We also provide computable heuristic quantities, guided by the theoretical error bounds, which can be used to automate the tuning of accuracy while performing approximate calculations. For linear problems, the use of heuristics to monitor the error introduced by approximate calculations, combined with the check on monotonicity of the residual, ensures the convergence of the numerical scheme within a prescribed residual tolerance. Motivated by the theoretical studies, we propose a reduced variant of AA, which consists in projecting the least-squares used to compute the Anderson mixing onto a subspace of reduced dimension. The dimensionality of this subspace adapts dynamically at each iteration as prescribed by the computable heuristic quantities. We numerically show and assess the performance of AA with approximate calculations on: (i) linear deterministic fixed-point iterations arising from the Richardson's scheme to solve linear systems with open-source benchmark matrices with various preconditioners and (ii) non-linear deterministic fixed-point iterations arising from non-linear time-dependent Boltzmann equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Modelling the Fluctuations of Reactive Shock Waves in Heterogeneous Solid Explosives as Stochastic Processes

Although current phenomenological burn models are useful for describing the average or bulk reactive flow behaviour of heterogeneous explosives, one fundamental weakness inherent to these models is the loss of detailed microstructural information at the scale of the calculation. In order to include the effects of the microstructure, and in particular the underlying material heterogeneities that influence the build-up to detonation, a new paradigm is put forth for modelling sub-grid, reaction-induced fluctuations (i.e. “hot spots”) at the continuum level. This modelling approach assumes that the reaction rate is stochastic, rather than deterministic, and it uses Langevin-type equations with a mathematical framework built upon Itô calculus and Lambourn's CIM model for shocked heterogeneous explosives. This approach follows directly from our previous letter, Ref. [1], and is inspired by the probability density function (pdf) methods used in turbulent reactive flows. Here, the stochastic burn model is derived, implemented, and exercised far beyond what has been shown in previous work. New hydrocode simulation results demonstrate the role of stochastic fluctuations during shock initiation; these fluctuations are approximated by collections of discrete particles, that evolve with drift (i.e. deterministic) and diffusion (i.e. stochastic) coefficients. Furthermore, the particle values are propagated and averaged to calculate the heat release, yield strength, and material impedance in each computational cell. Hydrocode simulation results further show how the fluctuating hot spot energy may or may not be transmitted to the wave front, and result in a detonation wave-like structure. The fundamental stochastic nature of this model permits simulations to have varying outcomes with the same initial conditions; this allows for go/no-go estimation (e.g., marginal or failed detonations), which might possibly be calibrated using the statistical distributions from real materials. Mesoscale calculations of shocked heterogeneous explosives also show that these fluctuations are physically justified (i.e., Ref. [2]), and it is hypothesized that the pdf functions provide a link between the meso(grain) and continuum scales for practical engineering calculations. Hence, our approach is a paradigm shift for building efficient, reduced order continuum burn models, that represent the sub-grid stochastic behaviour of shocked heterogeneous solid explosives.

42 ENGINEERING↗
DOE OSTI2020

Machine Learning Methods for Connection RTT and Loss Rate Estimation Using MPI Measurements Under Random Losses

Scientific computations are expected to be increasingly distributed across wide-area networks, and Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. Application-level measurements of MPI operations reflect the connection Round-Trip Time (RTT) and loss rate, and machine learning methods have been previously developed to estimate them under deterministic periodic losses. In this paper, we consider more complex, random losses with unform, Poisson and Gaussian distributions. We study five disparate machine leaning methods, with linear and non-linear, and smooth and non-smooth properties, to estimate RTT and loss rate over 10Gbps connections with 0-366ms RTT. The diversity and complexity of these estimators combined with the randomness of losses and TCP’s non-linear response together rule out the selection of a single best among them; instead, we fuse them to retain their design diversity. Overall, the results show that accurate estimates can be generated at low loss rates but become inaccurate at loss rates 10% and higher, thereby illustrating both their strengths and limitations.

Generalization Bounds↗
DOE OSTI2015

Inconsistent Investment and Consumption Problems

In a traditional Black–Scholes market we develop a verification theorem for a general class of investment and consumption problems where the standard dynamic programming principle does not hold. The theorem is an extension of the standard Hamilton–Jacobi–Bellman equation in the form of a system of non-linear differential equations. We derive the optimal investment and consumption strategy for a mean-variance investor without pre-commitment endowed with labor income. In the case of constant risk aversion it turns out that the optimal amount of money to invest in stocks is independent of wealth. The optimal consumption strategy is given as a deterministic bang-bang strategy. In order to have a more realistic model we allow the risk aversion to be time and state dependent. Of special interest is the case were the risk aversion is inversely proportional to present wealth plus the financial value of future labor income net of consumption. Using the verification theorem we give a detailed analysis of this problem. It turns out that the optimal amount of money to invest in stocks is given by a linear function of wealth plus the financial value of future labor income net of consumption. The optimal consumption strategy is again given as a deterministic bang-bang strategy. We also calculate, for a general time and state dependent risk aversion function, the optimal investment and consumption strategy for a mean-standard deviation investor without pre-commitment. In that case, it turns out that it is optimal to take no risk at all.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Radiation Protection Methods for the Interventionalist’s Hands: Use of an Extension Tube

PurposeCumulative radiation exposure to the hands during certain interventional procedures may be high. It is important to decrease the amount of radiation to the operator due to the possibility of deterministic effects. We performed a pilot study to demonstrate a significant decrease in operator dose when using extension tubing (ET) in combination with shielding and collimation during a simulated percutaneous transhepatic cholangiogram (PTC) procedure.MethodsA whole body, anthropomorphic phantom was used to simulate the patient. A Unfors-Xi Survey detector (to measure scatter) supported by a retort stand and trolley was placed in various positions to simulate the position of hands and eyes/thyroid of an interventionalist. Radiation dose was measured simulating left and right-sided PTC punctures with and without a lead shield, and with and without ET.ResultsRegarding the radiation dose to the hands; the use of an ET reduces dose by 54 % in right-sided PTC punctures without a shield and by 91 % if used in combination with a shield. For left-sided PTC punctures, ET reduces hand dose by 75 %. The use of collimation decreases hand dose by approximately 60 %. The use of shielding reduces dose to the eyes/thyroid by 98 %.ConclusionsThe dose to the hands can be significantly reduced with the appropriate use of a shield, ET, and tight collimation. The use of a shield is paramount to reduce dose to the eyes/thyroid. It is important for interventionalists to adhere to radiation protective practice considering the potential deterministic effects during a lifelong career.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Radiation Dose and Risk Estimates of CT-Guided Percutaneous Liver Ablations and Factors Associated with Dose Reduction

PurposeTo determine the radiation dose associated with CT-guided percutaneous liver ablations and identify potential risk factors that result in higher radiation doses.Materials and MethodsBetween June 2011 and June 2015, 245 consecutive patients underwent 304 CT-guided liver ablation treatments. Patient demographics, tumor characteristics and procedural parameters were identified and analyzed. The peak skin dose and effective dose were assessed for each procedure. Excess relative risk related to radiation effects was calculated. A logistic regression model was prepared by means of stepwise logistic regression to identify variables predictive of increased radiation exposure.ResultsTumor ablations were performed with microwave (n = 220), radiofrequency (n = 74) or irreversible electroporation (IRE) (n = 10). The mean peak skin dose for ablations was 239.2 ± 136.4 mGy, and the mean effective dose was 36.6 ± 22.3 mSv. Of the patient and procedural parameters that were analyzed, increasing weight, use of intravenous contrast and/or hydrodissection during the procedure, together with treatment of multiple lesions in the same sitting were all associated with higher radiation exposure. The mean increase in the absolute risk of fatal malignancy from a single procedure was 0.18% (range 0.02–0.9%). No deterministic skin changes were identified in the patient cohort.ConclusionThe overall risk of stochastic and deterministic effects from radiation associated with CT-guided ablations is low compared with other inherent procedural complications. This study identifies several factors that are associated with higher radiation dose in percutaneous liver ablation procedures.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Attributing runoff changes to climate variability and human activities: Uncertainty analysis using four monthly water balance models

Hydrological simulations to delineate the impacts of climate variability and human activities are subjected to uncertainties related to both parameter and structure of the hydrological models. To analyze the impact of these uncertainties on the model performance and to yield more reliable simulation results, a global calibration and multimodel combination method that integrates the Shuffled Complex Evolution Metropolis (SCEM) and Bayesian Model Averaging (BMA) of four monthly water balance models was proposed. The method was applied to the Weihe River Basin (WRB), the largest tributary of the Yellow River, to determine the contribution of climate variability and human activities to runoff changes. The change point, which was used to determine the baseline period (1956-1990) and human-impacted period (1991-2009), was derived using both cumulative curve and Pettitt’s test. Results show that the combination method from SCEM provides more skillful deterministic predictions than the best calibrated individual model, resulting in the smallest uncertainty interval of runoff changes attributed to climate variability and human activities. This combination methodology provides a practical and flexible tool for attribution of runoff changes to climate variability and human activities by hydrological models.

runoff change, uncertainty assessment, monthly wat↗