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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Machine Learning-based Approach to Predict the Aggregate Flexibility of HVAC Systems

Use of renewable energy resources can play a significant role in mitigation of climate change since they reduce the dependency on fossil fuels. However, high penetration of renewable energy resources into the power grid may cause some reliability problems. Ancillary services are key to realize the potential of renewable resources without disturbing the grid reliability. In recent years, many control approaches focused on exploiting the aggregate flexible heating, ventilation, and air-conditioning (HVAC) loads of buildings. However, there are still research gaps in assessing in advance the resulting indoor temperature responses for given total power and weather profiles before implementing the controls in the buildings. Such an assessment is critical to guarantee satisfying both the grid related objective (reliability) and the building related objective (occupants' comfort). Towards addressing this research gap, this paper presents a machine-learning approach for predicting the ratings of indoor temperature responses. In this approach, a prediction model was developed using a dataset generated by a set of model-free control simulations. The developed model achieved high prediction accuracy and showed the potential of the proposed approach.

Amasyali, Kadir↗
DOE OSTI2018

Workplace Charge Management with Aggregated Building Loads

This paper describes a workplace charge management system developed to control plug-in electric vehicle charging stations based on aggregated building loads. A system to collect information from drivers was also developed for better charge management performance since the present AC charging station standard does not provide battery state of charge information. First, simulations with uncontrolled charging data were conducted to investigate several scenarios and control methods, and then one method with the most power curtailment during peak load was selected for verification tests. This paper illustrates load reduction test results for 36 charging stations and real-time campus net load data.

25 ENERGY STORAGE↗
DOE OSTI2025

Harnessing Virtual Power Plants Reliably: Enabling tools for increased observability, controllability, operation, and aggregation of distributed energy resources

Harnessing virtual power plants enhances the integration of distributed energy resources into utility grids for a sustainable energy future. Virtual power plants (VPPs) aggregate DERs to enhance resource adequacy and reduce emissions. U.S. utilities are exploring various technologies to manage DERs effectively. FERC Order 2222 allows DERs to participate in both wholesale and retail markets. Enhancing observability and controllability of behind-the-meter (BTM) DERs is essential for reliable grid operations. A hierarchical control architecture can improve coordination among residential energy resources. Field tests showed nearly 20% energy savings and 30% peak power reduction during grid events. Effective management of DERs requires enhanced situational awareness to prevent grid congestion. Integrating DER management systems (DERMS) with existing planning tools can improve operational security. Near-real-time grid models can validate optimal resource set points against resource uncertainty. Traditional uninterruptible power supplies (UPS) can be upgraded to support grid services and become part of VPPs. Upgrading UPS systems can reduce costs by 75% and unlock significant battery capacity. New battery management systems and grid-aware controllers are essential for optimizing UPS performance. Continued research and development are necessary to address challenges in integrating DERs into utility grids. Encouraging customer participation in pilot programs is vital for the evolution of VPPs. Here, the shift towards price-responsive DERs and VPPs is expected to enhance energy distribution efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Data-based Aggregate Model of Refrigerators for Electric Power Grid Services

Electrical power system stability under high penetration of intermittent renewable energy generation, such as wind and solar energy, has become a high concern. It is unpredictable and hard to control due to little inertial behavior characteristic to these generation resources. To help mitigate possibly undesirable frequency excursions, demand-side frequency control presents an emerging solution. By observing the frequency deviation, devices could switch operating modes for a short time to rebalance generation. To analyze device level control algorithms, aggregate models for their behavior are needed. Refrigerators are household devices that could help with load modulation. This paper uses real measured data to analyze the refrigerator behavior, learn about its operating mode and power consumption ranges. A Markov chain process is proposed to model the transition of a refrigerator population from one operating mode to another, such that the overall load is modulated according to the control input. The Markov-chain model can be used to design control so that the overall load is modulated according to the control input.

Markov chain model, demand response, refrigerator,↗
DOE OSTI2020

Distributed Solution Approach for a Stackelberg Pricing Game of Aggregated Demand Response

Demand-side management is a fundamental up-to-date strategy that transforms the traditional power grid to a modern smart grid where the flexible pricing mechanisms play a critical role in its successful implementation. In this paper, the pricing-demand response between a distribution system operator (DSO) and load aggregators (LAs) is modeled as a Stackelberg game, where the DSO is the price maker that adjusts its strategy based on observed responses from LAs. With the concerns of computational cost and privacy protection, two distributed solution approaches, particle swarm optimization and pattern search algorithm, are conducted and compared with the classical centralized backward induction approach. Numerical results on a small case study demonstrate the effectiveness of the proposed distributed solution approaches in leveraging flexible demand response potential.

Chen, Yang↗
DOE OSTI2014

Aggregated Residential Load Modeling Using Dynamic Bayesian Networks

Abstract—It is already obvious that the future power grid will have to address higher demand for power and energy, and to incorporate renewable resources of different energy generation patterns. Demand response (DR) schemes could successfully be used to manage and balance power supply and demand under operating conditions of the future power grid. To achieve that, more advanced tools for DR management of operations and planning are necessary that can estimate the available capacity from DR resources. In this research, a Dynamic Bayesian Network (DBN) is derived, trained, and tested that can model aggregated load of Heating, Ventilation, and Air Conditioning (HVAC) systems. DBNs can provide flexible and powerful tools for both operations and planing, due to their unique analytical capabilities. The DBN model accuracy and flexibility of use is demonstrated by testing the model under different operational scenarios.

Bayesian↗
DOE OSTI2017

On defense strategies for system of systems using aggregated correlations

We consider a System of Systems (SoS) wherein each system Si, i = 1; 2; ... ;N, is composed of discrete cyber and physical components which can be attacked and reinforced. We characterize the disruptions using aggregate failure correlation functions given by the conditional failure probability of SoS given the failure of an individual system. We formulate the problem of ensuring the survival of SoS as a game between an attacker and a provider, each with a utility function composed of asurvival probability term and a cost term, both expressed in terms of the number of components attacked and reinforced. The survival probabilities of systems satisfy simple product-form, first-order differential conditions, which simplify the Nash Equilibrium (NE) conditions. We derive the sensitivity functions that highlight the dependence of SoS survival probability at NE on cost terms, correlation functions, and individual system survival probabilities.We apply these results to a simplified model of distributed cloud computing infrastructure.

Rao, Nageswara S.↗
DOE OSTI2020

Improving MPI Collective I/O for High Volume Non-Contiguous Requests With Intra-Node Aggregation

Two-phase I/O is a well-known strategy for implementing collective MPI-IO functions. It redistributes I/O requests among the calling processes into a form that minimizes the file access costs. As modern parallel computers continue to grow into the exascale era, the communication cost of such request redistribution can quickly overwhelm collective I/O performance. In this study, this effect has been observed from parallel jobs that run on multiple compute nodes with a high count of MPI processes on each node. Furthermore, to reduce the communication cost, we present a new design for collective I/O by adding an extra communication layer that performs request aggregation among processes within the same compute nodes. This approach can significantly reduce inter-node communication contention when redistributing the I/O requests. We evaluate the performance and compare it with the original two-phase I/O on Cray XC40 parallel computers (Theta and Cori) with Intel KNL and Haswell processors. Using I/O patterns from two large-scale production applications and an I/O benchmark, we show our proposed method effectively reduces the communication cost and hence maintains the scalability for a large number of processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Real-Time Charging Strategies for an Electric Vehicle Aggregator to Provide Ancillary Services

Real-time charging strategies, in the context of vehicle to grid (V2G) technology, are needed to enable the use of electric vehicle (EV) fleets batteries to provide ancillary services (AS). Here, we develop tools to manage charging and discharging in a fleet to track an Automatic Generation Control (AGC) signal when aggregated. We also propose a real-time controller that considers bidirectional charging efficiency and extend it to study the effect of looking ahead when implementing Model Predictive Control (MPC). Simulations show that the controller improves tracking error as compared with benchmark scheduling algorithms, as well as regulation capacity and battery cycling.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

WE-AB-303-01: FEATURED PRESENTATION: A Dual-Detector Phase-Matched Digital Tomosynthesis (DTS) Imaging Scheme Using Aggregated KV and MV Projections for Intra-Treatment Lung Tumor Tracking

Purpose: To develop a dual-detector phase-matched DTS technique for continuous and fast intra-treatment lung tumor localization. Methods: Tumor localization accuracy of limited-angle DTS imaging is affected by low inter-slice resolution. The dual-detector DTS technique aims to overcome this limitation through combining orthogonally acquired beam’s eye view MV projections and kV projections for intra-treatment DTS reconstruction and localization. To aggregate the kV and MV projections for reconstruction, the MV projections were linearly converted to synthesize corresponding kV projections. To further address the lung motion induced localization errors, this technique uses respiratory phase-matching to match the motion information between on-board DTS and reference DTS to offset the adverse effects of motion blurriness in tumor localization.A study was performed using the CIRS008A lung phantom to simulate different on-board target variation scenarios for localization. The intra-treatment kV and MV acquisition was achieved through the Varian TrueBeam Developer Mode. Four methods were compared for their localization accuracy: 1. the proposed dual-detector phase-matched DTS technique; 2. the single-detector phase-matched DTS technique; 3. the dual-detector 3D-DTS technique without phase-matching; and 4. the single-detector 3D-DTS technique without phase-matching. Results: For scan angles of 2.5°, 5°, 10°, 20° and 30°, the dual-detector phase-matched DTS technique localized the tumor with average(±standard deviations) errors of 0.4±0.3 mm, 0.5±0.3 mm, 0.6±0.2 mm, 0.9±0.4 mm and 1.0±0.3 mm, respectively. The corresponding values of single-detector phase-matched DTS technique were 4.0±2.5 mm, 2.7±1.1 mm, 1.7±1.2 mm, 2.2±0.9 mm and 1.5±0.8 mm, respectively. The values of dual-detector 3D-DTS technique were 6.2±1.7 mm, 6.3±1.2 mm, 5.3±1.3 mm, 2.0±2.2 mm and 1.5±0.5 mm, respectively. And the values of single-detector 3D-DTS technique were 9.7±8.9 mm, 9.8±8.8 mm, 10.0±9.7 mm, 3.9±2.7 mm and 2.2±1.3 mm, respectively. Conclusion: The dual-detector phase-matched DTS technique substantially improves the tumor localization accuracy, which can be applied to real-time intra-treatment lung tumor localization. The research was funded by the National Institutes of Health Grant No. R01-CA184173 and a grant from Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Facet-dependent dispersion and aggregation of aqueous hematite nanoparticles

Nanoparticle aggregates in solution controls surface reactivity and function. Complete dispersion often requires additive sorbents to impart a net repulsive interaction between particles. Facet engineering of nanocrystals offers an alternative approach to produce monodisperse suspensions simply based on facet-specific interaction with solvent molecules. Here, we measure the dispersion/aggregation of three morphologies of hematite (α-Fe 2 O 3 ) nanoparticles in varied aqueous solutions using ex situ electron microscopy and in situ small-angle x-ray scattering. We demonstrate a unique tendency of (104) hematite nanoparticles to maintain a monodisperse state across a wide range of solution conditions not observed with (001)- and (116)-dominated particles. Density functional theory calculations reveal an inert, densely hydrogen-bonded first water layer on the (104) facet that favors interparticle dispersion. Results validate the notion that nanoparticle dispersions can be controlled through morphology for specific solvents, which may help in the development of various nanoparticle applications that rely on their interfacial area to be highly accessible in stable suspensions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

The immune-evasive proline-283 substitution in influenza nucleoprotein increases aggregation propensity without altering the native structure

Nucleoprotein (NP) is a key structural protein of influenza ribonucleoprotein complexes and is central to viral RNA packing and trafficking. NP also determines the sensitivity of influenza to myxovirus resistance protein 1 (MxA), an innate immunity factor that restricts influenza replication. A few critical MxA-resistant mutations have been identified in NP, including the highly conserved proline-283 substitution. This essential proline-283 substitution impairs influenza growth, a fitness defect that becomes particularly prominent at febrile temperature (39°C) when host chaperones are depleted. Here, we biophysically characterize proline-283 NP and serine-283 NP to test whether the fitness defect is caused by the proline-283 substitution introducing folding defects. We show that the proline-283 substitution changes the folding pathway of NP, making NP more aggregation prone during folding, but does not alter the native structure of the protein. These findings suggest that influenza has evolved to hijack host chaperones to promote the folding of otherwise biophysically incompetent viral proteins that enable innate immune system escape.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Insights into the Structure and Metabolic Function of Microbes That Shape Pelagic Iron-Rich Aggregates ( Iron Snow )

Metaproteomics combined with total nucleic acid-based methods aided in deciphering the roles of microorganisms in the formation and transformation of iron-rich macroscopic aggregates (iron snow) formed in the redoxcline of an acidic lignite mine lake. Iron snow had high total bacterial 16S rRNA gene copies, with 2 x 109 copies g (dry wt)-1 in the acidic (pH 3.5) central lake basin and 4 x 1010 copies g (dry wt)-1 in the less acidic (pH 5.5) northern lake basin. Active microbial communities in the central basin were dominated by Alphaproteobacteria (36.6%) and Actinobacteria (21.4%), and by Betaproteobacteria (36.2%) in the northern basin. Microbial Fe-cycling appeared to be the dominant metabolism in the schwertmannite-rich iron snow, because cloning and qPCR assigned up to 61% of active bacteria as Fe-cycling bacteria (FeB). Metaproteomics revealed 70 unique proteins from central basin iron snow and 283 unique proteins from 43 genera from northern basin. Protein identification provided a glimpse into in situ processes, such as primary production, motility, metabolism of acidophilic FeB, and survival strategies of neutrophilic FeB. Expression of carboxysome shell proteins and RubisCO indicated active CO2 fixation by Fe(II) oxidizers. Flagellar proteins from heterotrophs indicated their activity to reach and attach surfaces. Gas vesicle proteins related to CO2-fixing Chlorobium suggested that microbes could influence iron snow sinking. We suggest that iron snow formed by autotrophs in the redoxcline acts as a microbial parachute, since it is colonized by motile heterotrophs during sinking which start to dissolve schwertmannite.

acidic/ iron cycling / iron snow / metaproteomics/↗
DOE OSTI2021

Microbiome Aggregated Traits and Assembly Are More Sensitive to Soil Management than Diversity

How soil is managed, particularly for agriculture, exerts stresses upon soil microbiomes, resulting in altered community structures and functional states. Understanding how soil microbiomes respond to combined stresses is important for predicting system performance under different land use scenarios, aids in identification of the most environmentally benign managements, and provides insight into how system function can be recovered in degraded soils. We use a long-established field experiment to study the effects of combined chronic (press) disturbance of the magnitude of organic carbon inputs with acute (pulse) effects of physical disturbance by tillage and chemical disturbance due to inorganic fertilization and pesticide application. We show that because of the variety of ways it can be assessed, biodiversity—here based on microbial small subunit rRNA gene phylotypes—does not provide a consistent view of community change. In contrast, aggregated traits associated with soil microbiomes indicate general loss of function, measured as a reduction of average genome lengths, associated with chronic reduction of organic inputs in arable or bare fallow soils and altered growth strategies associated with rRNA operon copy number in prokaryotes, as well as a switch to pathogenicity in fungal communities. In addition, pulse disturbance by soil tillage is associated with an increased influence of stochastic processes upon prokaryote community assembly, but fungicide used in arable soils results in niche assembly of fungal communities compared to untilled grassland. Overall, bacteria, archaea, and fungi do not share a common response to land management change, and estimates of biodiversity do not capture important facets of community adaptation to stresses adequately.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Identification of Absorption Bands of Monomers and Aggregates in a Layer of Cyanine Dye and Determination of the Orientation of Molecules

The problem of allocating bands of individual monomers or aggregates of the absorption spectrum of a multicomponent structure arises in the study of molecular layers or organic thin films. Not counting the concentrations of the components, the optical properties of this kind of samples are determined by the tilt of the molecules relative to the substrate surface. Molecular orientation can be determined from the dichroic ratio spectra recorded with the tilt probing of samples. Using the example of the tricarbocyanine dye layers, the following two independent methods for the identification of the spectra of the components from the absorption spectrum of a layer are compared: by comparing the spectra of layers of different thicknesses and based on the dichroic ratio spectra. The effect of laser radiation on the samples, which leads to a change in the dichroic ratio, is studied. Photoinduced changes of the orientation angles of molecules are calculated, which allows one to refine the range of possible values of these angles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Photoinduced electron transfer from zinc meso -tetraphenylporphyrin to a one-dimensional perylenediimide aggregate: Probing anion delocalization effects

Organic photovoltaics incorporating non-fullerene acceptors based on perylenediimide (PDI) now rival fullerene acceptor-based devices in performance, although the mechanisms of charge generation in PDI-based devices are not yet fully understood. Fullerene-based systems are proposed to undergo electron transfer directly from the photoexcited donor into a band of delocalized acceptor states, thus increasing charge generation efficiency. Similarly, anion delocalization has been shown to enhance the rate of electron transfer from a photoexcited donor to two electronically coupled PDI acceptors. Here we investigate how additional electron acceptors may further increase the rate of electron transfer from the donor zinc meso-tetraphenylporphyrin (ZnTPP) to an aggregate of PDI acceptors (PDI[Formula: see text]. Femtosecond transient visible and mid-infrared absorption spectroscopies show that the rate of electron transfer from 1 *ZnTPP to the PDI assembly ZnTPP 2 -PDI 3 is statistically identical to that of the previously examined ZnTPP-PDI 2 . A Marcus theory analysis indicates that the parameters governing electron transfer are nearly identical for the two molecules, suggesting that the maximum electron transfer rate enhancement has been achieved in a cofacial PDI dimer because the ZnTPP directly couples to the first two PDI acceptors whereas the coupling to the third PDI is too weak.

bio-inspired↗
DOE OSTI2015

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\ models the heat diffusion traces along the entire time scale, so it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Data Integration Aggregated Model For Nuclear Deployment

The data of a nuclear utility or plant are stored in various isolated forms in different systems. These data have different structures and tools and are, therefore, used independently. The integration of data sources is often needed and is performed manually and on an as-needed basis. The integration of the data would enable creating a data warehouse for a single nuclear power plant (NPP) or multiple plants. A data model contains objects that make up a given domain along with their attributes and relationships and could be used as a standard for exchange between applications for simple integration and data aggregation. After a survey of existing models, it was determined that a new model was required to meet the scope of this problem. DIAMOND has been developed in the form of an ontology to meet these needs. An ontology is simply a data model that also acts as a taxonomy for the domain, organizing the objects in a hierarchy with the most general objects at the top.

Browning, JerenM↗