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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 19 records

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Three-Phase AC Optimal Power Flow Based Distribution Locational Marginal Price

Designing market mechanisms for electricity distribution systems has been a hot topic due to the increased presence of smart loads and distributed energy resources (DERs) in distribution systems. The distribution locational marginal pricing (DLMP) methodology is one of the real-time pricing methods to enable such market mechanisms and provide economic incentives to active market participants. Determining the DLMP is challenging due to high power losses, the voltage volatility, and the phase imbalance in distribution systems. Existing DC Optimal Power Flow (OPF) approaches are unable to model power losses and the reactive power, while single-phase AC OPF methods cannot capture the phase imbalance. To address these challenges, in this paper, a three-phase AC OPF based approach is developed to define and calculate DLMP accurately. The DLMP is modeled as the marginal cost to serve an incremental unit of demand at a specific phase at a certain bus, and is calculated using the Lagrange multipliers in the three-phase AC OPF formulation. Extensive case studies have been conducted to understand the impact of system losses and the phase imbalance on DLMPs as well as the potential benefits of flexible resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Aspects of scaling and scalability for flow-based sampling of lattice QCD

Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. However, these demonstrations have been at the scale of toy models, and it remains to be determined whether they can be applied to state-of-the-art lattice quantum chromodynamics calculations. Assessing the viability of sampling algorithms for lattice field theory at scale has traditionally been accomplished using simple cost scaling laws, but as we discuss in this work, their utility is limited for flow-based approaches. We conclude that flow-based approaches to sampling are better thought of as a broad family of algorithms with different scaling properties, and that scalability must be assessed experimentally.

Abbott, Ryan↗

DOE OSTI2024

Anomaly detection with flow-based fast calorimeter simulators

Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using caloflow as an example, we show that these models can simultaneously perform unsupervised anomaly detection with no additional training cost. As a demonstration, we consider electromagnetic showers initiated by one (background) or multiple (signal) photons. The caloflow model is designed to generate single-photon showers, but it also provides access to the shower likelihood. We use this likelihood as an anomaly score and study the showers tagged as being unlikely. As expected, the tagger struggles when the signal photons are nearly collinear but is otherwise effective. This approach is complementary to a supervised classifier trained on only specific signal models using the same low-level calorimeter inputs. While the supervised classifier is also highly effective at unseen signal models, the unsupervised method is more sensitive in certain regions, and, thus, we expect that the ultimate performance will require a combination of these approaches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Flow-based likelihoods for non-Gaussian inference

We investigate the use of data-driven likelihoods to bypass a key assumption made in many scientific analyses, which is that the true likelihood of the data is Gaussian. In particular, we suggest using the optimization targets of flow-based generative models, a class of models that can capture complex distributions by transforming a simple base distribution through layers of nonlinearities. We call these flow-based likelihoods (FBL). We analyze the accuracy and precision of the reconstructed likelihoods on mock Gaussian data, and show that simply gauging the quality of samples drawn from the trained model is not a sufficient indicator that the true likelihood has been learned. We nevertheless demonstrate that the likelihood can be reconstructed to a precision equal to that of sampling error due to a finite sample size. We then apply FBLs to mock weak lensing convergence power spectra, a cosmological observable that is significantly non-Gaussian (NG). We find that the FBL captures the NG signatures in the data extremely well, while other commonly used data-driven likelihoods, such as Gaussian mixture models and independent component analysis, fail to do so. This suggests that works that have found small posterior shifts in NG data with data-driven likelihoods such as these could be underestimating the impact of non-Gaussianity in parameter constraints. By introducing a suite of tests that can capture different levels of NG in the data, we show that the success or failure of traditional data-driven likelihoods can be tied back to the structure of the NG in the data. Here, unlike other methods, the flexibility of the FBL makes it successful at tackling different types of NG simultaneously. Because of this, and consequently their likely applicability across datasets and domains, we encourage their use for inference when sufficient mock data are available for training.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment—a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Proton-regulated alcohol oxidation for high-capacity ketone-based flow battery anolyte

Redox flow batteries have a unique architecture that potentially enables cost-effective long-duration energy storage to address the intermittency introduced by increased renewable integration for the decarbonization of the electric power sector. Targeted molecular engineering has demonstrated electrochemical reversibility in natively redox-inactive ketone molecules in aqueous electrolytes. Yet, the kinetics of fluorenone-based flow batteries continue to be limited by slow alcohol oxidation. We show how strategically designed proton regulators can accelerate alcohol oxidation and thus enhance battery kinetics. Fluorenone-based flow batteries with the organic additive ß-cyclodextrin demonstrate enhanced rate capability, high capacity, and long cycling. This study opens a new avenue to improve the kinetics of aqueous organic flow batteries by modulating the reaction pathway with a homogeneous catalyst.

25 ENERGY STORAGE↗

DOE OSTI2018

Small organic molecule based flow battery

The invention provides an electrochemical cell based on a new chemistry for a flow battery for large scale, e.g., gridscale, electrical energy storage. Electrical energy is stored chemically at an electrochemical electrode by the protonation of small organic molecules called quinones to hydroquinones. The proton is provided by a complementary electrochemical reaction at the other electrode. These reactions are reversed to deliver electrical energy. A flow battery based on this concept can operate as a closed system. The flow battery architecture has scaling advantages over solid electrode batteries for large scale energy storage.

25 ENERGY STORAGE↗

DOE OSTI2015

Room temperature, hybrid sodium-based flow batteries with multi-electron transfer redox reactions

We introduce a new concept of hybrid Na-based flow batteries (HNFBs) with a molten Na alloy anode in conjunction with a flowing catholyte separated by a solid Na-ion exchange membrane for grid-scale energy storage. Such HNFBs can operate at ambient temperature, allow catholytes to have multiple electron transfer redox reactions per active ion, offer wide selection of catholyte chemistries with multiple active ions to couple with the highly negative Na alloy anode, and enable the use of both aqueous and non-aqueous catholytes. Further, the molten Na alloy anode permits the decoupled design of power and energy since a large volume of the molten Na alloy can be used with a limited ion-exchange membrane size. In this proof-of-concept study, the feasibility of multielectron transfer redox reactions per active ion and multiple active ions for catholytes has been demonstrated. Furthermore, the critical barriers to mature this new HNFBs have also been explored.

25 ENERGY STORAGE↗

DOE OSTI2018

Microplasma-based flowing atmospheric-pressure afterglow (FAPA) source for ambient desorption-ionization mass spectrometry

A new direct-current microplasma-based flowing atmospheric pressure afterglow (FAPA) source was developed for use in ambient desorption-ionization mass spectrometry. The annular-shaped microplasma is formed in helium between two concentric stainless-steel capillaries that are separated by an alumina tube. Current-voltage characterization of the source shows that this version of the FAPA operates in the normal glow-discharge regime. A glass surface placed in the path of the helium afterglow reaches temperatures of up to approximately 400 °C; the temperature varies with distance from the source and helium flow rate through the source. Solid, liquid, and vapor samples were examined by means of a timeof- flight mass spectrometer. Results suggest that ionization occurs mainly through protonation, with only a small amount of fragmentation and adduct formation. Here, the mass range of the source was shown to extend up to at least m/z 2722 for singly charged species. Limits of detection for several small organic molecules were in the sub-picomole range. Examination of competitive ionization revealed that signal suppression occurs only at high (mM) concentrations of competing substances.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Highly simplified lateral flow-based nucleic acid sample preparation and passive fluid flow control

Highly simplified lateral flow chromatographic nucleic acid sample preparation methods, devices, and integrated systems are provided for the efficient concentration of trace samples and the removal of nucleic acid amplification inhibitors. Methods for capturing and reducing inhibitors of nucleic acid amplification reactions, such as humic acid, using polyvinylpyrrolidone treated elements of the lateral flow device are also provided. Further provided are passive fluid control methods and systems for use in lateral flow assays.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Highly simplified lateral flow-based nucleic acid sample preparation and passive fluid flow control

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Single Bubble Drag Force Evaluation in Turbulent Flow based on DNS Results

Two-phase turbulent flows are widely encountered in the light water reactor engineering. The interaction between liquid phase and gas phase is bi-directional: the liquid turbulence influences the dispersion and distribution of the bubbles which in turn modulates the liquid turbulence with bubble-induced turbulence. Considering a single bubble, the effect of homogeneous turbulent flow is to modify the drag force compared to that in laminar flow. The bubble can modify the homogeneous turbulence through formation of vorticities behind the bubble. The correct description of the closure laws for the interfacial forces is of great importance in numerical simulation of bubbly flows. Various closure laws for the drag coefficient have been proposed in the literature in the last few decades. Among them, Tomiyama proposed a widely accepted correlation based on spherical bubbles in pure systems. While the development of closure model for two-phase laminar flow has reached a certain level of confidence, more studies and investigations are required for turbulent flows. In this paper, we want to numerically analyze the influence of homogeneous turbulent flow on the drag force experienced by a single bubble and compare its behavior with the laminar flow conditions based on Tomiyama's correlation. A set of parametric studies will be performed to justify the relationship between typical two-phase turbulent flow parameters and drag force. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

AC Power Flow Based DLMP Calculation and Decomposition Method to Smooth Power Fluctuation of Distributed Renewable Energy Sources

As the penetration of renewable energy sources increases, the growing renewable power variability brings ramping issues to power systems. Meanwhile, the development of distributed energy resources (DERs) makes the distribution systems to provide both energy and ancillary services. To incentivise individual resources and customers to alleviate ramping issues on the demand side, a two-stage distribution locational marginal price (DLMP) calculation and decomposition method is developed to formulate the marginal power ramping price for DERs. In the first stage of the proposed method, a distribution system operator market scheduling model based on AC optimal power flow is designed to estimate the optimal operating point of the distribution system. Subsequently, the voltage and power flow constraints are linearised in stage two to calculate DLMP. Finally, based on the Lagrange function and sensitivity factors, DLMP is decomposed to the marginal costs for active/reactive power, voltage management, power loss and power variability. Case studies demonstrate that the proposed model can effectively smooth the power fluctuation and reduce the ramping flexibility requirements of distribution systems.

AC optimal power flow↗

DOE OSTI2025

Discrete global grid system-based flow routing datasets in the Amazon and Yukon basins

Abstract. Discrete global grid systems (DGGS) are emerging spatial data structures widely used to organize geospatial datasets across scales. While DGGS have found applications in various scientific disciplines, including atmospheric science and ecology, their integration into physically based hydrological models and Earth system models (ESMs) has been hindered by the lack of flow routing datasets based on DGGS. In response to this gap, this study pioneers the development of new flow routing datasets using icosahedral Snyder equal-area (ISEA) DGGS and a novel mesh-independent flow direction model. We present flow routing datasets for two large basins, the tropical Amazon River basin and the Arctic Yukon River basin. These datasets (1) facilitate the adoption of DGGS for hydrological models and (2) provide flow routing inputs for evaluation of DGGS-based flow routing in the Amazon and Yukon river basins. The data are available at https://doi.org/10.5281/zenodo.8377765 (Liao, 2023).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Quinone and hydroquinone based flow battery

The invention provides an electrochemical cell based on a new chemistry for a flow battery for large scale, e.g., gridscale, electrical energy storage. Electrical energy is stored chemically in quinone molecules having multiple oxidation states, e.g., three or more. During charging of the battery, the quinone molecules at one electrode are oxidized by emitting electrons and protons, and the quinone molecules at the other electrode are reduced by accepting electrons and protons. These reactions are reversed to deliver electrical energy. The invention also provides additional high and low potential quinones that are useful in rechargeable batteries.

Huskinson, Brian↗

DOE OSTI2020

Probabilistic Power Flow Based on a Gaussian Process Emulator

In this letter, a novel Gaussian process emulator is proposed, for the first time, to conduct the probabilistic power-flow calculation. Based on Bayesian inference, a Gaussian process emulator is trained and served as a nonparametric, reduced-order model of the nonlinear power-flow model. This emulator has allowed us to evaluate the time-consuming power-flow solver at the sampled values with a negligible computational cost. The simulations reveal the excellent performance of this method.

42 ENGINEERING↗

DOE OSTI2017

Evaluating the effects of real power losses in optimal power flow based storage integration

This study proposes a DC optimal power flow (DCOPF) with losses formulation (the `-DCOPF+S problem) and uses it to investigate the role of real power losses in OPF based grid-scale storage integration. We derive the `- DCOPF+S problem by augmenting a standard DCOPF with storage (DCOPF+S) problem to include quadratic real power loss approximations. This procedure leads to a multi-period nonconvex quadratically constrained quadratic program, which we prove can be solved to optimality using either a semidefinite or second order cone relaxation. Our approach has some important benefits over existing models. It is more computationally tractable than ACOPF with storage (ACOPF+S) formulations and the provably exact convex relaxations guarantee that an optimal solution can be attained for a feasible problem. Adding loss approximations to a DCOPF+S model leads to a more accurate representation of locational marginal prices, which have been shown to be critical to determining optimal storage dispatch and siting in prior ACOPF+S based studies. Case studies demonstrate the improved accuracy of the `-DCOPF+S model over a DCOPF+S model and the computational advantages over an ACOPF+S formulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization

We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The objective is to determine power schedules for controllable devices in a power network to balance operational cost and conditional value-at-risk (CVaR) of device and network constraint violations. These decisions include scheduled power output adjustments and reserve policies, which specify planned reactions to forecast errors in order to accommodate fluctuating renewable energy sources. Instead of assuming the uncertainties across the networks follow prescribed probability distributions, we assume the distributions are only observable through a finite training dataset. By utilizing the Wasserstein metric to quantify differences between the empirical data-based distribution and the real data-generating distribution, we formulate a distributionally robust optimization OPF problem to search for power schedules and reserve policies that are robust to sampling errors inherent in the dataset. A multi-stage closed-loop control strategy based on model predictive control (MPC) is also discussed. A simpIe numerical example illustrates inherent tradeoffs between operational cost and risk of constraint violation, and we show how our proposed method offers a data-driven framework to balance these objectives.

24 POWER TRANSMISSION AND DISTRIBUTION↗