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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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DOE OSTI2017

The need for accurate redshifts in supernova cosmology

Recent papers have shown that a small systematic redshift shift (Δ z ∼ 10{sup −5}) in measurements of type Ia supernovae can cause a significant bias (∼1%) in the recovery of cosmological parameters. Such a redshift shift could be caused, for example, by a gravitational redshift due to the density of our local environment. The sensitivity of supernova data to redshift shifts means supernovae make excellent probes of inhomogeneities. We therefore invert the analysis, and try to diagnose the nature of our local gravitational environment by fitting for Δ z as an extra free parameter alongside the usual cosmological parameters. Using the Joint Light-curve SN Ia dataset we find the best fit includes a systematic redshift shift of Δ z = (2.6{sup +2.7}{sub −2.8}) × 10{sup −4}. This is a larger shift than would be expected due to gravitational redshifts in a standard Λ-Cold Dark Matter universe (though still consistent with zero), and would correspond to a monopole Doppler shift of about 100 km s{sup −1} moving away from the Milky-Way. However, since most supernova measurements are made to a redshift precision of no better than 10{sup −3}, it is possible that a systematic error smaller than the statistical error remains in the data and is responsible for the shift; or that it is an insignificant statistical fluctuation. We find that when Δ z is included as a free parameter while fitting to the JLA SN Ia data, the constraints on the matter density shifts to Ω {sub m} = 0.313{sup +0.042}{sub −0.040}, bringing it into better agreement with the CMB cosmological parameter constraints from Planck. A positive Δ z ∼ 2.6×10{sup −4} would also cause us to overestimate the supernova measurement of Hubble's constant by Δ H {sub 0} ∼ 1 kms{sup −1}Mpc{sup −1}. However this overestimation should diminish as one increases the low-redshift cutoff, and this is not seen in the most recent data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Hybrid Cyber-attack Detection in Photovoltaic Farms

Here, to address the cyber-physical security in PV farms, a hybrid cyber-attack detection is proposed in this manuscript. To secure PV farms, the proposed method integrates model-based and data-driven methods by fusing the detection score at the device and system levels. First, a model-based cyber-attack detection method is developed for each PV inverter. A residual between the estimation of the Kalman filter and measurement is calculated. By leveraging the calculated residual from all inverters, a squared Mahalanobis distance is developed for device detection score generation. At the system level, a convolutional neural network (CNN) is proposed to detect cyber-attack using the waveform data at the point of common coupling (PCC) in PV farms. To improve the CNN detection accuracy, a set of well-designed features are extracted from the raw waveform data. Finally, a weighted detection score fusion method is proposed to combine device and system detection scores by using their complementary strength. The feasibility and robustness of the proposed method are validated by testing cases and a comparative experiment.

14 SOLAR ENERGY↗

DOE OSTI2019

Estimation of Behind-the-Meter Solar Generation by Integrating Physical with Statistical Models

Accurate estimation of solar photovoltaic (PV) generation is crucial for distribution grid control and optimization. Unfortunately, most of the residential solar PV installations are behind-the-meter. Thus, utilities only have access to the net load readings. This paper presents an unsupervised framework for estimating solar PV generation by disaggregating the net load readings. The proposed framework synergistically combines a physical PV system performance model with a statistical model for load estimation. Specifically, our algorithm iteratively estimates solar PV generation with a physical model and electric load with the Hidden Markov model regression. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is validated against net load and solar PV generation data gathered from residential customers located in Austin, Texas. The validation results show that our method reduces mean squared error by 44% compared to the state-of-the-art disaggregation algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

On the band spectrum in p-type HgTe/CdHgTe heterostructures and its transformation under temperature variation

The magnetoabsorption and interband photoconductivity spectra of HgTe/CdHgTe quantum wells exhibiting p-type conductivity are studied at different temperatures. It is shown that, for a sample with a normal band structure, the long-wavelength edge of the spectra shifts to higher energies with temperature increase, indicating an increase of the band gap in the quantum well. For a sample with an inverted band structure, it is for the first time found that the long-wavelength cut-off shifts to lower energies due to the topological phase transition from the inverted band structure to the normal structure with temperature increase. The experimental data are in agreement with the results of theoretical band-structure calculations based on the Kane model.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Physiological fidelity or model parsimony? The relative performance of reverse-toxicokinetic modeling approaches

Physiologically-based toxicokinetic (PBTK) models are often developed to facilitate in vitro to in vivo extrapolation (IVIVE) using a top-down, compartmental approach, favoring architectural simplicity over physiological fidelity despite the lack of general guidelines relating model design to dynamical predictions. Here we explore the impact of design choice (high vs. low fidelity) on chemical distribution throughout an animal’s organ system. Results We contrast transient dynamics and steady states of three previously proposed PBTK models of varying complexity in response to chemical exposure. The steady states for each model were determined analytically to predict exposure conditions from tissue measurements. Steady state whole-body concentrations differ between models, despite identical environmental conditions, which originates from varying levels of physiological fidelity captured by the models. These differences affect the relative predictive accuracy of the inverted models used in exposure reconstruction to link effects-based exposure data with whole-organism response thresholds obtained from in vitro assay measurements. Conclusions Our results demonstrate how disregarding physiological fideltiy in favor of simpler models affects the internal dynamics and steady state estimates for chemical accumulation within tissues, which, in turn, poses significant challenges for the exposure reconstruction efforts that underlie many IVIVE methods. Developing standardized systems-level models for ecological organisms would not only ensure predictive consistency among future modeling studies, but also ensure pragmatic extrapolation of in vivo effects from in vitro data or modeling exposure-response relationships.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1976

Aerosol size distributions - Remote determination from air-borne measurements of the solar aureole

Aerosol-size distributions were determined remotely using an airborne scanning photometer. This instrument consists of a sunshade, optics, and a large-dynamic-range photodetector-amplifier system that scans about the vertical, recording both the direct attenuated solar beam and the diffuse sky radiance in the almucantar of the sun at an effective wavelength of 0.54 micron. Inversion of the measured radiances determined aerosol radii from approximately 0.2 to 8.0 microns. Data were taken during flights over western Lake Superior and southwestern Wisconsin. Inverted aerosol-size distributions are compared with results obtained by other methods.

Twitty, J. T.↗

NASA NTRS2000

Inversion of the chordate body axis: are there alternatives?

One major morphological difference between chordates and annelids or arthropods is the opposite orientation of the nerve cord and heart. A long-standing proposal is that the chordate axis evolved by inverting the body of an ancestor with the annelid/arthropod orientation. However, the data can also be explained by a common ancestor with diffuse dorsoventral organization, followed by oppositely directed condensation of the nerve cord and relocation of the heart in the two lines.

Review, Tutorial↗

NASA NTRS2013

Integrated High-Speed Torque Control System for a Robotic Joint

A control system for achieving high-speed torque for a joint of a robot includes a printed circuit board assembly (PCBA) having a collocated joint processor and high-speed communication bus. The PCBA may also include a power inverter module (PIM) and local sensor conditioning electronics (SCE) for processing sensor data from one or more motor position sensors. Torque control of a motor of the joint is provided via the PCBA as a high-speed torque loop. Each joint processor may be embedded within or collocated with the robotic joint being controlled. Collocation of the joint processor, PIM, and high-speed bus may increase noise immunity of the control system, and the localized processing of sensor data from the joint motor at the joint level may minimize bus cabling to and from each control node. The joint processor may include a field programmable gate array (FPGA).

Davis, Donald R.↗

DOE OSTI2018

Constraints on neutrino oscillation parameters with the NOvA experiment

NOvA is a long-baseline neutrino oscillation experiment, which consists of two finely-segmented liquid-scintillator detectorsoperating 14 mrad off-axis from the NuMI muon neutrino beam. With an 810 km baseline, the combined measurements of muon neutrino disappearance and electron neutrino appearance allow the determination of neutrino oscillation unknowns, namely the mass hierarchy, the octant of the largest neutrino mixing angle, and the CP violating phase.In this dissertation, I present the joint analysis of $\nu_\mu\to\nu_\mu$ and $\nu_mu\to\nu_e$ oscillation data with an exposure of$8.85\times10^{20}$ protons on target on the 14 kton detector. It includes the estimation of neutrino energy distributions in the fardetector using near detector data constraints, details of the implementation of the simultaneous fit of \numu and \nue samples, andthe effect of systematic uncertainties on the measurement of oscillation parameters. I also discuss NOvA's projected sensitivity to determine the mass hierarchy and discover CP violation in future analyses with increased exposure and the addition ofantineutrino datasets.With 66 electron neutrino and 126 muon neutrino candidates, the bestfit to the data corresponds to the normal mass hierarchy, $\Delta m^2_{32}=2.44\times 10^{-3} eV^2/c^4$, $\sin^2\theta_{23}=0.56$,and $\delta_{CP} = 1.21\pi$, with the allowed regions $\Delta m^2_{32} \in[2.37,2.52]\times 10^{-3} eV^2/c^4$, $\sin^2\theta_{23} \in [0.43,0.51] \cup[0.52,0.60]$ and $\delta_{CP} \in [0,0.12\pi] \cup [0.91\pi,2\pi]$ at the68.3\% C.L. Our data disfavor maximal mixing by 0.8$\sigma$, $\delta_{CP}= \pi/2$ in the inverted hierarchy at higher than 3$\sigma$, and theentire inverted mass hierarchy at the 95\% confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2015

Challenges and opportunities for fractured rock imaging using 3D cross-borehole electrical resistivity

There is an increasing need to characterize discrete fractures away from boreholes to better define fracture distributions and monitor solute transport. We performed a 3D evaluation of static and time-lapse cross-borehole electrical resistivity tomography (ERT) data sets from a limestone quarry in which flow and transport are controlled by a bedding-plane feature. Ten boreholes were discretized using an unstructured tetrahedral mesh, and 2D panel measurements were inverted for a 3D distribution of conductivity. We evaluated the benefits of 3D versus 2.5D inversion of ERT data in fractured rock while including the use of borehole regularization disconnects (BRDs) and borehole conductivity constraints. High-conductivity halos (inversion artifacts) surrounding boreholes were removed in static images when BRDs and borehole conductivity constraints were implemented. Furthermore, applying these constraints focused transient changes in conductivity resulting from solute transport on the bedding plane, providing a more physically reasonable model for conductivity changes associated with solute transport at this fractured rock site. Assuming bedding-plane continuity between fractures identified in borehole televiewer data, we discretized a planar region between six boreholes and applied a fracture regularization disconnect (FRD). Although the FRD appropriately focused conductivity changes on the bedding plane, the conductivity distribution within the discretized fracture was nonunique and dependent on the starting homogeneous model conductivity. Synthetic studies performed to better explain field observations showed that inaccurate electrode locations in boreholes resulted in low-conductivity halos surrounding borehole locations. These synthetic studies also showed that the recovery of the true conductivity within an FRD depended on the conductivity contrast between the host rock and fractures. Our findings revealed that the potential exists to improve imaging of fractured rock through 3D inversion and accurate modeling of boreholes. However, deregularization of localized features can result in significant electrical conductivity artifacts, especially when representing features with a high degree of spatial uncertainty.

resistivity, fractured rock, inversion, imaging, g↗

DOE OSTI2016

Analysis of PV Advanced Inverter Functions and Setpoints under Time Series Simulation.

Utilities are increasingly concerned about the potential negative impacts distributed PV may have on the operational integrity of their distribution feeders. Some have proposed novel methods for controlling a PV system's grid - tie inverter to mitigate poten tial PV - induced problems. This report investigates the effectiveness of several of these PV advanced inverter controls on improving distribution feeder operational metrics. The controls are simulated on a large PV system interconnected at several locations within two realistic distribution feeder models. Due to the time - domain nature of the advanced inverter controls, quasi - static time series simulations are performed under one week of representative variable irradiance and load data for each feeder. A para metric study is performed on each control type to determine how well certain measurable network metrics improve as a function of the control parameters. This methodology is used to determine appropriate advanced inverter settings for each location on the f eeder and overall for any interconnection location on the feeder.

14 SOLAR ENERGY↗

DOE OSTI2026

Precision Measurement of Neutrino Oscillation Parameters with 10 Years of Data from the NOvA Experiment

This Letter reports measurements of muon-neutrino disappearance and electron-neutrino appearance and the corresponding antineutrino processes between the two NOvA detectors in the NuMI neutrino beam. These measurements use a dataset with double the neutrino mode beam exposure that was previously analyzed, along with improved simulation and analysis techniques. A joint fit to these samples in the three-flavor paradigm results in the most precise single-experiment constraint on the atmospheric neutrino mass-splitting, $Δm^2_{32}= 2.431^{+0.036}_{-0.034} (-2.479^{+0.036}_{-0.036}) \times 10^{-3}$~eV$^2$ if the mass ordering is Normal (Inverted). In both orderings, a region close to maximal mixing with $\sin^2θ_{23}=0.55_{+0.06}^{-0.02}$ is preferred. The NOvA data show a mild preference for the Normal mass ordering with a Bayes factor of 2.4 (corresponding to 70% of the posterior probability), indicating that the Normal ordering is 2.4 times more probable than the Inverted ordering. When incorporating a 2D $Δm^2_{32}\textrm{--}\sin^2 2θ_{13}$ constraint based on Daya Bay data, this preference strengthens to a Bayes factor of 6.6 (87\%).

FOS: Physical sciences↗

DOE OSTI2019

Bi-Level Volt-VAR Optimization to Coordinate Smart Inverters with Voltage Control Devices

Conservation voltage reduction (CVR) uses voltage control to reduce customer power demand and help improve the operational efficiency of the electric power distribution system. With the advent of advanced distribution management systems (ADMS), the underlying data and control-rich platform can be utilized to enable a centralized Volt-VAR optimization (VVO) scheme that includes smart inverter controls along with legacy voltage control devices such as voltage regulators and capacitor banks to ensure a maximum CVR benefit. The objective of this paper to present a VVO approach that controls system’s legacy voltage control devices and coordinates their operation with smart inverter control. An optimal power flow (OPF) formulation is proposed by developing linear and nonlinear power flow approximations for a three-phase unbalanced electric power distribution system. A bi-level VVO approach is proposed where, Level-1 optimizes the control of legacy devices and smart inverters using a linear approximate three-phase power flow. In Level-2, the control parameters for smart inverters are adjusted to obtain an optimal and feasible solution by solving the approximate nonlinear OPF model. Level-1 is modeled as a Mixed Integer Linear Program (MILP) while Level-2 as a Quadratically Constrained Quadratic Program (QCQP). The proposed approach is validated using IEEE test cases and the results demonstrate the applicability of the framework in achieving the CVR objective.

Jha, Rahul Ranjan↗

DOE OSTI2023

Proactive Intrusion Detection and Mitigation System

SAND2023-05661O The proactive intrusion detection and mitigation system (PIDMS) provides grid-edge situational awareness for cybersecurity defense by capturing real-time distributed energy resource (DER) network traffic and performance data with a novel approach that improves the detection and prevention of cyber-physical attacks. The PIDMS addresses the grid-edge security gap with real-time analysis of both network traffic and photovoltaic performance data to deliver a novel, cyber-physical intrusion detection system (IDS) approach that increases the accuracy and effectiveness of detection and mitigation. This hybrid IDS analysis enables dual monitoring that increases the workload of the adversary; both cyber and physical data would have to be simultaneously spoofed to evade detection. Furthermore, monitoring and analyzing cyber data are insufficient in some cases. For example, in an insider threat aimed at disrupting inverter grid-support functions where proper credentials and authentication are achieved, only the altered PV performance would indicate abnormal behavior. All in all, the PIDMS provides novel capabilities for: • Distributed, real-time cyber-physical detection and mitigation analysis • Cybersecurity defense for grid-edge systems • Analysis framework that can provide situational awareness across the transmission, distribution, and DER systems The PIDMS sensor is designed to collect cyber-physical data, process the data using machine-learning algorithms, detect abnormal events, and deploy mitigations. With these goals, the main functional PIDMS objectives are: • Capability to collect cyber-physical data • Onboard storage of cyber-physical data • Peer-to-peer communication • Computationally efficient machine-learning algorithms • Online cyber-physical data analysis • Alerting/visualization capabilities • Mitigation deployment capability with bump-in-the-wire (BITW) implementation Each of these functional objectives enable PIDMS to perform effective cyber-physical intrusion detection and mitigation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Jones, Christian↗

DOE OSTI2016

WE-FG-207B-02: Material Reconstruction for Spectral Computed Tomography with Detector Response Function

Purpose: Different from the conventional computed tomography (CT), spectral CT based on energy-resolved photon-counting detectors is able to provide the unprecedented material composition. However, an important missing piece for accurate spectral CT is to incorporate the detector response function (DRF), which is distorted by factors such as pulse pileup and charge-sharing. In this work, we propose material reconstruction methods for spectral CT with DRF. Methods: The polyenergetic X-ray forward model takes the DRF into account for accurate material reconstruction. Two image reconstruction methods are proposed: a direct method based on the nonlinear data fidelity from DRF-based forward model; a linear-data-fidelity based method that relies on the spectral rebinning so that the corresponding DRF matrix is invertible. Then the image reconstruction problem is regularized with the isotropic TV term and solved by alternating direction method of multipliers. Results: The simulation results suggest that the proposed methods provided more accurate material compositions than the standard method without DRF. Moreover, the proposed method with linear data fidelity had improved reconstruction quality from the proposed method with nonlinear data fidelity. Conclusion: We have proposed material reconstruction methods for spectral CT with DRF, whichprovided more accurate material compositions than the standard methods without DRF. Moreover, the proposed method with linear data fidelity had improved reconstruction quality from the proposed method with nonlinear data fidelity. Jiulong Liu and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Real-Time Testbed for Studying Cyberattacks and Defense in DER-integrated Smart Inverter Systems

In this paper, we propose a Hardware-in-the-Loop (HIL) simulation testbed suitable for the implementation and testing of realistic cyberattacks on grid-tied smart inverter systems integrated with Distributed Energy Resources (DER) that use the Distributed Network Protocol-3 (DNP3) protocol for communications between grid components. Specifically, our testbed combines a Real-Time Digital Simulator (RTDS) NovaCor device, outfitted with GNETx2 network interface cards, a gridtied DER topology implemented via the RTDS software package RSCAD, and a custom virtual network that emulates a man in the middle attacker. The Man-in-the-Middle (MITM) attacker captures DNP3 traffic and falsifies telemetry data in DNP3 packets to trigger unwarranted commands from a DNP3 controller that exploit smart inverter grid support functions. We choose DNP3 and implement grid support functions according to the IEEE Std. 1547-2018 mandated for the interconnection and interoperability of DER power systems with associated power components. Furthermore, we develop a protocol payload agnostic attack detection framework that leverages the round-trip time (RTT) anomalies between DNP3 requests and responses and can detect the presence of attacks without having to analyze the payload’s contents, while balancing trade-offs between false alarm counts, missed detections, and time to detection. To facilitate further research, we publicly release benign and attack network traffic exchanged between various sensors, controllers, and actuators in our grid-tied inverter testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

The search for molecular outflows in local volume AGNs with Herschel-pacs

We present the results from a systematic search for galactic-scale, molecular (OH 119 μm) outflows in a sample of 52 Local Volume (d<50 Mpc) Burst Alert Telescope detected active galactic nuclei (BAT AGNs) with Herschel-PACS. We combine the results from our analysis of the BAT AGNs with the published Herschel/PACS data of 43 nearby (z<0.3) galaxy mergers, mostly ultra-luminous infrared galaxies (ULIRGs) and QSOs. The objects in our sample of BAT AGNs have, on average, ∼10--100 times lower AGN luminosities, star formation rates, and stellar masses than those of the ULIRG and QSO samples. OH 119 μm is detected in 42 of our BAT AGN targets. Evidence for molecular outflows (i.e., OH absorption profiles with median velocities more blueshifted than −50 km s{sup −1} and/or blueshifted wings with 84% velocities less than −300 km s{sup −1}) is seen in only four BAT AGNs (NGC 7479 is the most convincing case). Evidence for molecular inflows (i.e., OH absorption profiles with median velocities more redshifted than 50 km s{sup −1}) is seen in seven objects, although an inverted P-Cygni profile is detected unambiguously in only one object (Circinus). Our data show that both the starburst and AGN contribute to driving OH outflows, but the fastest OH winds require AGNs with quasar-like luminosities. We also confirm that the total absorption strength of OH 119 μm is a good proxy for dust optical depth as it correlates strongly with the 9.7 μm silicate absorption feature, a measure of obscuration originating in both the nuclear torus and host galaxy disk.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1996

Surface and Atmospheric Parameter Retrieval From AVIRIS Data: The Importance of Non-Linear Effects

AVIRIS data represent a new and important approach for the retrieval of atmospheric and surface parameters from optical remote sensing data. Not only as a test for future space systems, but also as an operational airborne remote sensing system, the development of algorithms to retrieve information from AVIRIS data is an important step to these new approaches and capabilities. Many things have been learned since AVIRIS became operational, and the successive technical improvements in the hardware and the more sophisticated calibration techniques employed have increased the quality of the data to the point of almost meeting optimum user requirements. However, the potential capabilities of imaging spectrometry over the standard multispectral techniques have still not been fully demonstrated. Reasons for this are the technical difficulties in handling the data, the critical aspect of calibration for advanced retrieval methods, and the lack of proper models with which to invert the measured AVIRIS radiances in all the spectral channels. To achieve the potential of imaging spectrometry, these issues must be addressed. In this paper, an algorithm to retrieve information about both atmospheric and surface parameters from AVIRIS data, by using model inversion techniques, is described. Emphasis is put on the derivation of the model itself as well as proper inversion techniques, robust to noise in the data and an inadequate ability of the model to describe natural variability in the data. The problem of non-linear effects is addressed, as it has been demonstrated to be a major source of error in the numerical values retrieved by more simple, linear-based approaches. Non-linear effects are especially critical for the retrieval of surface parameters where both scattering and absorption effects are coupled, as well as in the cases of significant multiple-scattering contributions. However, sophisticated modeling approaches can handle such non-linear effects, which are especially important over vegetated surfaces. All the data used in this study were acquired during the 1991 Multisensor Airborne Campaign (MAC-Europe), as part of the European Field Experiment on a Desertification-threatened Area (EFEDA), carried out in Spain in June-July 1991.

Green Robert O.↗