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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Membrane-mediated fibrillation and toxicity of the tau hexapeptide PHF6

The aggregation of the tau protein into neurofibrillary tangles is believed to correlate with cognitive decline in several neurodegenerative disorders, including Alzheimer's disease. Recent studies suggest that tau's interactions with the cell membrane could serve as a toxicity pathway and also enhance fibrillation into paired helical filaments (PHFs). Conformational changes associated with tau-membrane interactions are poorly understood, and their characterization could improve our understanding of tau pathogenicity. In this study, we investigated the molecular level structural changes associated with the interaction of the tau hexapeptide PHF6 with model lipid membranes and characterized the effects of these interactions on membrane stability and peptide fibrillation. We used two PHF6 forms, the aggregation-prone PHF6 with N-terminal acetylation (Ac-PHF6) and the non-aggregation prone PHF6 with a standard N terminus (NH$^+_3$-PHF6). We found that both PHF6 peptides are neurotoxic and exhibit similar membrane-mediated changes, consisting of: 1) favorable interactions with anionic membranes, 2) membrane destabilization through lipid extraction, and 3) membrane-mediated fibrillation. The rate at which these changes occurred was the main difference between the two peptides. NH$^+_3$-PHF6 displayed slow membrane-mediated fibrillation after 6 days of incubation, whereas Ac-PHF6 adopted a β-sheet conformation at the surface of the membrane within hours. Ac-PHF6 interactions with the membrane were also accompanied by membrane invagination and rapid membrane destabilization. Overall, our results reveal that membrane interactions could play a critical role in tau toxicity and fibrillation, and highlight that unraveling these interactions is important for significantly advancing the development of therapeutic strategies to manage tau-associated neurodegenerative diseases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Online Data Handling and Storage at the CMS Experiment

During the LHC Long Shutdown 1, the CMS Data Acquisition (DAQ) system underwent a partial redesign to replace obsolete network equipment, use more homogeneous switching technologies, and support new detector back-end electronics. The software and hardware infrastructure to provide input, execute the High Level Trigger (HLT) algorithms and deal with output data transport and storage has also been redesigned to be completely file- based. All the metadata needed for bookkeeping are stored in files as well, in the form of small documents using the JSON encoding. The Storage and Transfer System (STS) is responsible for aggregating these files produced by the HLT, storing them temporarily and transferring them to the T0 facility at CERN for subsequent offline processing. The STS merger service aggregates the output files from the HLT from ~62 sources produced with an aggregate rate of ~2GB/s. An estimated bandwidth of 7GB/s in concurrent read/write mode is needed. Furthermore, the STS has to be able to store several days of continuous running, so an estimated of 250TB of total usable disk space is required. In this article we present the various technological and implementation choices of the three components of the STS: the distributed file system, the merger service and the transfer system.

Andre, J. M.↗
DOE OSTI2018

PIP4Kγ as a potential target for Huntington's disease

Phosphoinositide lipids provide spatial and temporal regulation of cell signaling and membrane traffic. The PI5P‐4‐kinase family is one of the less studied phosphoinositide lipid kinases. These enzymes catalyze the conversion of phosphatidylinositol 5‐phosphate to phosphatidylinositol 4,5‐bis phosphate. A recent study by Vicinanza et al., a suggested that depletion of the PI5P‐4‐kinase, PIP4Kγ promotes a non‐canonical autophagy pathway. Moreover, this depletion reduced the presence of huntingtin‐exon1‐polyglutamine aggregates. Expression of exon1 of huntingtin with an extended polyglutamine tract is a model for huntingtin protein aggregates. To further test the hypothesis that PIP4Kγ is a potential target to reduce huntingtin aggregates, we tested the depletion of PIP4Kγ in cell culture models that express full length mutant huntingtin (mHtt). We determined that depletion of PIP4Kγ, but not PIP4Kα or PIP4Kβ, lowers the levels of mHtt in patient fibroblasts and in immortalized Hdh Q111 mouse striatal neurons. We show that PIP4Kγ‐dependent depletion of mHtt requires lysosomal function because the depletion is blocked by the lysosomal inhibitor, bafilomycin A1. Moreover, we show that depletion of PIP4Kγ increases autophagic flux, as measured by an increase in LC3‐II levels by western blot analysis and an increase in LC3‐II puncta by microscopy. Our studies suggest that PIP4kγ should be further pursued as a target for Huntington's disease. Support or Funding Information This work was supported by AHA postdoctoral fellowship 14POST20480137 (Sai Srinivas Panapakkam Giridharan), NIH grants R01GM050403 and NS064015 (Lois S Weisman) and by HSS funding 1R03MH084839 (Juan Marugan)

Giridharan, Sai Srinivas Panapakkam↗
DOE OSTI2021

Toward witnessing molecular exciton entanglement from spectroscopy

Entanglement is a defining feature of quantum mechanics that can be a resource in engineered and natural systems, but measuring entanglement in experiment remains elusive especially for large chemical systems. Most practical approaches require determining and measuring a suitable entanglement witness which provides some level of information about the entanglement structure of the probed state. A fundamental quantity of quantum metrology is the quantum Fisher information (QFI), which is a rigorous witness of multipartite entanglement that can be evaluated from linear response functions for certain states. Here, in this paper, we explore measuring the QFI of molecular exciton states of the first-excitation subspace from spectroscopy. In particular, we utilize the fact that the linear response of a pure state subject to a weak electric field over all possible driving frequencies encodes the variance of the collective dipole moment in the probed state, which is a valid measure for QFI. The systems that are investigated include the molecular dimer, N-site linear aggregate with nearest-neighbor coupling, and N-site circular aggregate, all modeled as a collection of interacting qubits. Our theoretical analysis shows that the variance of the collective dipole moment in the brightest dipole-allowed eigenstate is the maximum QFI. The optical response of a thermally equilibrated state in the first-excitation subspace is also a valid QFI. Theoretical predictions of the measured QFI for realistic linear dye aggregates as a function of temperature and energetic disorder due to static variations of the host matrix show that two- to three-partite entanglement is realizable. This paper lays some groundwork and inspires measurement of multipartite entanglement of molecular excitons with ultrafast pump-probe experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Impact of Uncertainty from Load-Based Reserves and Renewables on Dispatch Costs and Emissions

Aggregations of controllable loads are considered to be a fast-responding, cost-efficient, and environmental-friendly candidate for power system ancillary services. Unlike conventional service providers, the potential capacity from the aggregation is highly affected by factors like ambient conditions and load usage patterns. Previous work modeled aggregations of controllable loads (such as air conditioners) as thermal batteries, which are capable of providing reserves but with uncertain capacity. A stochastic optimal power flow problem was formulated to manage this uncertainty, as well as uncertainty in renewable generation. In this paper, we explore how the types and levels of uncertainty, generation reserve costs, and controllable load capacity affect the dispatch solution, operational costs, and CO2 emissions. We also compare the results of two methods for solving the stochastic optimization problem, namely the probabilistically robust method and analytical reformulation assuming Gaussian distributions. Case studies are conducted on a modified IEEE 9-bus system with renewables, controllable loads, and congestion. We find that different types and levels of uncertainty have significant impacts on dispatch and emissions. More controllable loads and less conservative solution methodologies lead to lower costs and emissions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Evaluating the Value of High Spatial Resolution in National Capacity Expansion Models using ReEDS

Power sector capacity expansion models (CEMs) have a broad range of spatial resolutions. This paper uses the Regional Energy Deployment System (ReEDS) model, a long-term national scale electric sector CEM, to evaluate the value of high spatial resolution for CEMs. ReEDS models the United States with 134 load balancing areas (BAs) and captures the variability in existing generation parameters, future technology costs, performance, and resource availability using very high spatial resolution data, especially for wind and solar modeled at 356 resource regions. In this paper we perform planning studies at three different spatial resolutions--native resolution (134 BAs), state-level, and NERC region level--and evaluate how results change under different levels of spatial aggregation in terms of renewable capacity deployment and location, associated transmission builds, and system costs. The results are used to ascertain the value of high geographically resolved models in terms of their impact on relative competitiveness among renewable energy resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Impacts of Lock-off Time on Virtual Battery Model from Thermostatically Controlled Loads

We test through simulation the influence of lock-off time on the aggregated flexibility of thermostatically controllable loads (TCLs), which is modeled by virtual battery (VB) models. The VB model provides a simple but effective way to quantify the aggregated flexibility of TCLs, but its efficacy is not clear when there is lock-off time for TCLs. TCLs cannot be turned on during the lock-off period, and so lock-off time imposes inevitable restrictions on not only the flexibility of individual TCLs but also the aggregated flexibility of a group of TCLs. We first test the influence of lock-off time on the efficacy of VB models with two artificial signals. One of the signal is of low frequency while the other is of high frequency. We then use real-world data to test the influence. One set of data is from a duck curve from CAISO while the other set of data is from AGC signals from PJM. The duck curve changes slowly while the AGC signals change rapidly over time. From both the artificial signals and real-word data, we find that lock-off time has a more significant impact on rapid signals than on slow signals.

Wang, Peng↗
DOE OSTI2022

Consensus-Based Approach for Active Power Control and Reserve Estimation in Distributed PV Systems

With the increased adoption of distributed energy resources (DER), they are expected to contribute to power system reliability services and enhance power system stability. This paper presents a distributed consensus control approach for the real-time active power reserve estimation and power management in distributed PV (photovoltaic) systems. The proposed method estimates the cumulative active power reserve from numerous PV generators using only the sparse communication network for a DER aggregator to provide active power regulation services. The real-time active power reserve estimation using the proposed approach can be used by DER aggregators to manage curtailment in distributed PV systems to maintain stipulated reserves and further to provide power system frequency regulation. Using the proposed approach, the DER aggregator only requires measurements at the feeder substation and it does not require information from individual DER units, thereby improving system resiliency. The proposed distributed consensus control method is validated on IEEE 123 testbed using PSCAD simulations.

active power reserve↗
DOE OSTI2024

Robust VAR Capability Curve of DER with Uncertain Renewable Generation

Active distribution system with high penetration of inverter based distributed energy resources(DER), can be utilized for var-related ancillary services at the transmission side interface. In order to utilize the DER flexibility, transmission system operator must be presented the aggregated DER flexibility of distribution system. However, the uncertainty in renewable generation, questions the credibility of aggregated capability curve in practice. In this paper, we incorporate the uncertainty into aggregation process to develop capability curve while preserving the real physics (unbalance and lossy nature) of distribution system. The Resulting capability curve with the associated probability can be harnessed by the TSO for decision making for both planning and operation.

Kar, Aditya Shankar↗
DOE OSTI2021

Simulation of PV Variability as a Function of PV Generation and Plant Size

The deployment of photovoltaic (PV) systems continues to show significant expansion; however, this growth has brought added attention to issues around the variability of the solar resource. Both spatial and temporal variability exist. Temporal scales can range from the sub-second to multiyear, whereas spatial scales can range from a few meters to tens of kilometers. There are multiple methods described in the literature to quantify PV variability at various spatial and temporal scales. This study focuses on short-term temporal variability and uses similar approaches with the addition of PV plant size a parameter to quantify variability. The method employed here incorporates the normalization of clear-and cloudy-sky conditions and PV plant size to quantify nominal variability metrics. The distribution and fluctuations of these metrics provide relevant information that is useful for system operations. The National Solar Radiation Database (NSRDB) is used to simulate PV variability as a function of PV generation and plant size. Hypothetical but realistic system information at 33 locations is used to model PV generation by feeding NSRDB solar irradiance data to the National Renewable Energy Laboratory’s System Advisor Model (SAM). Over the selected region, it is found that the aggregated ramp rates for the 1-minute data are associated with standard deviations ranging from 0.002–0.055 on a daily basis; however, hourly intervals induce higher aggregated ramp rates than the other timescales. Even though minute-to-minute variations are significant for the 1-minute time-scale, the standard deviation aggregated into a daily metric is smaller because of the cancellation of values.

irradiance↗
DOE OSTI2024

HAPPA: A Modular Platform for HPC Application Resilience Analysis with LLMs Embedded

High-performance computing (HPC) systems are increasingly vulnerable to soft errors, which pose significant challenges in maintaining computational accuracy and reliability. Predicting the resilience of HPC applications to these errors is crucial for robust code protection and detailed resilience analysis. In this study, we present HAppA, a modular platform designed for HPC Application Resilience Analysis. Embedding Large Language Models (LLMs), HAppA addresses understanding the context information of long code sequences typical in HPC applications. HAppA implements a novel code representation module that chunks the code into fixed-size segments and aggregates the embeddings of these segments. Three aggregation methods have been explored: MeanPooling, MaxPooling, and LSTM-based techniques. We built a DAtaset for REsilience analysis using Fault Injection (FI), named DARE. Using our DARE dataset, HAppA is trained for regression prediction tasks. Our evaluation results demonstrate the predictive accuracy of HAppA compared to other models, particularly noting that the LSTM-based aggregation method -- HAppA-LSTM -- achieves a mean squared error (MSE) of 0.078 for SDC prediction, surpassing the existing state-of-the-art PARIS model, which recorded an MSE of 0.1172. Additionally, HAppA with the KeyBERT model extracts a list of keywords representing the source code. A comprehensive importance analysis of these keywords further elucidates the code patterns contributing to the error rate. These findings highlight the effectiveness of HAppA in analyzing the resilience of HPC applications and establish a new benchmark for predictive accuracy in resilience.

Jiang, Hailong [Kent State University]↗
DOE OSTI2019

Individual Load Model Parameter Estimation in Distribution Systems Using Load Switching Events

There currently exists a mature literature on modeling the aggregate load of a distribution feeder by making use of measurements at its feeder-head at substation. The primary application of such feeder-aggregated load models is in sub-transmission or transmission system analysis. However, there is a growing need in practice also to model each individual load across the feeder. If available, such individual load models have applications in power distribution system analysis, e.g., to better integrate distributed energy resources or to improve power quality and reliability. Motivated by this observation, in this paper, we propose a new method for individual load modeling in power distribution systems. It works by using the measurements only at the feeder-head. It takes an innovative approach to analyzing the load switching events across the distribution feeder itself, instead of or in addition to relying on upstream voltage events that are commonly used in feeder-aggregated load modeling. By tracking the downstream load switching events, the proposed method can make a robust estimation of the ZIP load model parameters for all individual loads. The proposed method is examined on small illustrative test-feeders as well as the IEEE 33-bus test system under various operating scenarios. The adverse impact of errors in measurements and system parameters are also investigated on the performance of the developed load modeling method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Bayesian Framework for Multi-Timescale State Estimation in Low-Observable Distribution Systems

To support the smart grid paradigm, there has been a significant increase in sensor deployments and metering infrastructure in distribution systems. However, the measurements provided by these sensors and metering devices are typically sampled at different rates and could suffer from losses during the aggregation process. It is crucial to effectively reconcile the time-series measurements for a reliable state estimation. While weighted least squares has been the traditional approach for state estimation, sparsity-based approaches like matrix completion have become popular due to their superior performance in low-observability conditions. This paper proposes a Bayesian framework for both multi-timescale data aggregation and matrix completion based state estimation. Specifically, the multiscale time-series data aggregated from heterogenous sources are reconciled using a multitask Gaussian process that exploits the spatio-temporal correlations. Here, the resulting consistent timeseries alongwith the confidence bound on the imputations are fed into a Bayesian matrix completion method augmented with linearized power-flow constraints to accurately estimate the states in low-observability conditions. Results on three phase unbalanced IEEE 37 and IEEE 123 bus test systems reveal the superior performance of the proposed Bayesian framework. The computational complexity for the proposed Bayesian framework is also quantified.

42 ENGINEERING↗
DOE OSTI2023

Fairness-Aware Distributed Energy Coordination for Voltage Regulation in Power Distribution Systems

The accelerating deployment of solar photovoltaics into low-voltage distribution networks can cause reverse power flow and overvoltage problems. However, if coordinated properly, the real and reactive power flexibility of these resources enables distribution operators to manage their networks more efficiently. Existing literature is rich in droop-based control (Volt-Watt and Volt-VAr) and optimization-based distributed energy coordination for four-quadrant control of photovoltaics to prevent overvoltage issues. While optimal coordination can effectively mitigate overvoltage, it tends to treat resources at sensitive parts of the grid unfairly. Here, to address this concern, we propose a distributed optimal power flow formulation that incorporates fairness in curtailing photovoltaic generation and utilizes the reactive power capability of smart inverters. The proposed distributed formulation allows for scalable resource aggregation that can be leveraged to achieve fairness within a certain segment of the grid and/or fairness across the entire network. Fair curtailment of photovoltaic systems is demonstrated with aggregation at each of two layers in a distribution network: 1) area-level fairness and 2) feeder-level fairness. To explore the trade-off between fairness and optimal utilization, the fairness-aware control actions are compared against the performance of a centralized controller that aims to maximize the aggregate PV generation without incorporating fairness. Simulation results show that introducing area-level fairness increased curtailment by 0.0101 percentage points and feeder-level fairness increased curtailment by 0.0458 percentage points compared to a fairness-agnostic control.

Poudel, Shiva↗
DOE OSTI2014

A Structure-Based Distance Metric for High-Dimensional Space Exploration with Multi-Dimensional Scaling

Although the Euclidean distance does well in measuring data distances within high-dimensional clusters, it does poorly when it comes to gauging inter-cluster distances. This significantly impacts the quality of global, low-dimensional space embedding procedures such as the popular multi-dimensional scaling (MDS) where one can often observe non-intuitive layouts. We were inspired by the perceptual processes evoked in the method of parallel coordinates which enables users to visually aggregate the data by the patterns the polylines exhibit across the dimension axes. We call the path of such a polyline its structure and suggest a metric that captures this structure directly in high-dimensional space. This allows us to better gauge the distances of spatially distant data constellations and so achieve data aggregations in MDS plots that are more cognizant of existing high-dimensional structure similarities. Our MDS plots also exhibit similar visual relationships as the method of parallel coordinates which is often used alongside to visualize the high-dimensional data in raw form. We then cast our metric into a bi-scale framework which distinguishes far-distances from near-distances. The coarser scale uses the structural similarity metric to separate data aggregates obtained by prior classification or clustering, while the finer scale employs the appropriate Euclidean distance.

Clustering↗
DOE OSTI2018

Microbial interactions in the anaerobic oxidation of methane: model simulations constrained by process rates and activity patterns: Process modelling of anaerobic methane oxidation

Proposed syntrophic interactions between the archaeal and bacterial cells mediating anaerobic oxidation of methane coupled with sulfate reduction include electron transfer through (1) the exchange of H2 or small organic molecules between methane-oxidizing archaea and sulfate-reducing bacteria, (2) the delivery of disulfide from methane-oxidizing archaea to bacteria for disproportionation and (3) direct interspecies electron transfer. Each of these mechanisms was implemented in a reactive transport model. The simulated activities across different arrangements of archaeal and bacterial cells and aggregate sizes were compared to empirical data for AOM rates and intra-aggregate spatial patterns of cell-specific anabolic activity determined by FISH-nanoSIMS. Additionally, simulation results showed that rates for chemical diffusion by mechanism (1) were limited by the build-up of metabolites, while mechanisms (2) and (3) yielded cell specific rates and archaeal activity distributions that were consistent with observations from single cell resolved FISH-nanoSIMS analyses. The novel integration of both intra-aggregate and environmental data provided powerful constraints on the model results, but the similarities in model outcomes for mechanisms (2) and (3) highlight the need for additional observational data (e.g. genomic or physiological) on electron transfer and metabolic functioning of these globally important methanotrophic consortia.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Local Communication in Populations of Thermostatically Controlled Loads

Thermostatically controlled loads (TCLs) have shown great potential for demand response (DR) in electric grid operations. However, it has been commonly seen that DR events using TCLs may cause load synchronization and unwanted oscillatory effects, especially in homogeneous populations. In an attempt to mitigate the negative impacts of DR events, a decentralized method is proposed that modifies each thermostat behavior based on the activity of a small number of nearby TCLs. This feedback introduces the possibility of instability in the aggregate behavior. A stability analysis is performed on a linearized model of the aggregate system and the results of that analysis compared to simulation results. The proposed modification of thermostat behavior results in fourfold reduction in the post-DR peak while suppressing ensuing oscillations at the expense of a modest increase in compressor cycling. Further, the linearized model also provides insight into the aggregate behavior of the population.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Characterization of Syntrophic Geobacter Communities Using ToF-SIMS

The aggregation of syntrophic Geobacter metallireducens and Geobacter sulfurreducens is known to be beneficial for enhancing direct interspecies electron transfer (DIET). However, the chemical speciation of such co-cultured communities was unclear. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been used to characterize biological samples relevant to aggregates of Geobacter, including G. metallireducens and G. sulfurreducens single cultures in planktonic, biofilms formed from either the single bacteria or both, and co-cultures of these two strains in planktonic and aggregate forms. By using principle component analysis (PCA), the syntrophic Geobacter communities were significantly distinguished from their planktonic cells by a series of characteristic peaks such as amino acids, fatty acids/lipids, and quorum sensing signal molecules, indicating a different chemical composition and structure formed on their surfaces. Additionally, insights of co-culture community dynamics were obtained from a new perspective . These first SIMS results demonstrate that proteins played an important role in microbial communications.

ToF-SIMS, Geobacter, DIET, syntrophic↗