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Quantifying and generalizing hydrologic responses to dam regulation using a statistical modeling approach
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The Role of the Consequence Management Home Team in the Fukushima Daiichi Response
The Consequence Management Home Team (CMHT) is a U.S. Department of Energy/National Nuclear Security Administration asset and played an important role in the U.S. response effort to the Fukushima Daiichi accident, ranging from the early days of the response to a continued involvement in supporting late phase efforts. Each stage of their work had distinct characteristics in terms of management of incoming data streams and creation of products. The CMHT assisted a variety of response organizations with modeling; radiological operations planning; field monitoring techniques; and the analysis, interpretation, and distribution of radiological data. In the Fukushima Daiichi response, the CMHT grew to include a broader range of support than was historically planned. Through their work, the social and economic impacts of a nuclear or radiological incident were minimized. The CMHT was an integral component of the response in Japan and acted as the central point from which all of the data and products flowed.
Solving Unit Commitment Problems with Demand Responsive Loads
This work focuses on using variations of the Frank-Wolfe (FW) algorithm for solving unit commitment problems with high volumes of demand responsive loads on the power grid. We present a formulation of the unit commitment problem with demand responsive loads. We then show through reformulation and relaxations of the problem that variations of the Frank-Wolfe algorithm can be used to determine the time series decisions for the demand responsive loads. We show through computational experiments on the IEEE Reliability Test System that the timeseries of demand responsive load decisions obtained through our approach are near optimal and describe how large-scale parallel implementations of our approach can be highly computationally efficient.
Application of the Most Likely Extreme Response Method for Wave Energy Converters: Preprint
Extreme loads are often a key cost driver for wave energy converters (WECs). As an alternative to exhaustive Monte Carlo or long-term simulations, the most likely extreme response (MLER) method allows mid- and high-fidelity simulations to be used more efficiently in evaluating WEC response to events at the edges of the design envelope, and is therefore applicable to system design analysis. The study discussed in this paper applies the MLER method to investigate the maximum heave, pitch, and surge force of a point absorber WEC. Most likely extreme waves were obtained from a set of wave statistics data based on spectral analysis and the response amplitude operators (RAOs) of the floating body; the RAOs were computed from a simple radiation-and-diffraction-theory-based numerical model. A weakly nonlinear numerical method and a computational fluid dynamics (CFD) method were then applied to compute the short-term response to the MLER wave. Effects of nonlinear wave and floating body interaction on the WEC under the anticipated 100-year waves were examined by comparing the results from the linearly superimposed RAOs, the weakly nonlinear model, and CFD simulations. Overall, the MLER method was successfully applied. In particular, when coupled to a high-fidelity CFD analysis, the nonlinear fluid dynamics can be readily captured.
Efficient Algorithms for Estimating the Absorption Spectrum within Linear Response TDDFT
We present a special symmetric Lanczos algorithm and a kernel polynomial method (KPM) for approximating the absorption spectrum of molecules within the linear response time-dependent density functional theory (TDDFT) framework in the product form. In contrast to existing algorithms, the new algorithms are based on reformulating the original non-Hermitian eigenvalue problem as a product eigenvalue problem and the observation that the product eigenvalue problem is self-adjoint with respect to an appropriately chosen inner product. This allows a simple symmetric Lanczos algorithm to be used to compute the desired absorption spectrum. The use of a symmetric Lanczos algorithm only requires half of the memory compared with the nonsymmetric variant of the Lanczos algorithm. The symmetric Lanczos algorithm is also numerically more stable than the nonsymmetric version. The KPM algorithm is also presented as a low-memory alternative to the Lanczos approach, but the algorithm may require more matrix-vector multiplications in practice. We discuss the pros and cons of these methods in terms of their accuracy as well as their computational and storage cost. Applications to a set of small and medium-sized molecules are also presented.
The Small Ethylene Response Factor ERF96 is Involved in the Regulation of the Abscisic Acid Response in Arabidopsis
We report that ethylene regulates many aspects of plant growth and development including seed germination, leaf senescence, and fruit ripening, and of plant responses to environmental stimuli including both biotic and abiotic stresses. Ethylene response factors (ERFs) are plant-specific transcription factors and are a subfamily of the AP2 (APETALA2)/ERF transcription factor family. The function of many members in this large gene family remains largely unknown. ERF96, a member of the Group IX ERF family transcription factors, has recently been shown to be a transcriptional activator that is involved in plant defense response in Arabidopsis. Here we provide evidence that ERF96 is a positive regulator of abscisic acid (ABA) responses. Bioinformatics analysis indicated that there are a total four small ERFs in Arabidopsis including ERF95, ERF96, ERF97, and ERF98, and that ERF96 forms a cluster with ERF95 and ERF97. By using quantitative RT-PCR, we found that ERF96 is expressed in all tissues and organs examined except roots, with relatively high expression in flowers and seeds. Results from the protoplast transfection assay indicated that the EDLL motif-containing C-terminal domain is responsible for ERF96’s transcriptional activity. Although loss-of-function mutant of ERF96 was morphologically similar to wild type plants, transgenic plants overexpressing ERF96 had smaller rosette size and were delayed in flowering time. In ABA sensitivity assays, we found that ERF96 overexpression plants were hypersensitive to ABA in terms of ABA inhibition of seed germination, early seedling development and root elongation. Consistent with these observations, elevated transcript levels of some ABA-responsive genes including RD29A, ABI5, ABF3, ABF4, P5CS, and COR15A were observed in the transgenic plants in the presence of ABA. However, in the absence of ABA treatment, the transcript levels of these ABA-responsive genes remained largely unchanged. Our experiments also showed that water loss in ERF96 overexpression plants was slower than that in Col wild type plants. Stomatal closure assays indicated that ERF96 overexpression plants had reduced stomatal aperture in the presence of ABA. In conclusion, taken together, our results suggest that ERF96 positively regulates ABA responses in Arabidopsis.
Mass spectrometry-based proteomics for system-level characterization of biological responses to engineered nanomaterials
The widespread applications of engineered nanomaterials or nanotechnology make the characterization of biological responses to nanomaterials as an important area of research. The applications of omics-based approaches such as mass spectrometry-based proteomics have revealed new insights into the cellular responses upon exposure to nanomaterials, including how nanomaterials interact and alter cellular pathways. For instance, a redox-dependent response is often associated with exposure of nanomaterials, which leads to the generation of reactive oxygen species. In this review, we discuss the general quantitative proteomics approaches, with an emphasis on redox proteomics and their applications for characterizing the biological responses to nanomaterials.
Diet-induced obesity reprograms the inflammatory response of the murine lung to inhaled endotoxin
The co-occurrence of environmental factors is common in complex human diseases and, as such, understanding the molecular responses involved is essential to determine risk and susceptibility to disease. We have investigated the key biological pathways that define susceptibility for pulmonary infection during obesity in diet-induced obese (DIO) and regular weight (RW) C57BL/6 mice exposed to inhaled lipopolysaccharide (LPS). LPS induced a strong inflammatory response in all mice as indicated by elevated cell counts of macrophages and neutrophils and levels of proinflammatory cytokines (MDC, MIP-1γ, IL-12, RANTES) in the bronchoalveolar lavage fluid. Additionally, DIO mice exhibited 50% greater macrophage cell counts, but decreased levels of the cytokines, IL-6, TARC, TNF-α, and VEGF relative to RW mice. Microarray analysis of lung tissue showed over half of the LPS-induced expression in DIO mice consisted of genes unique for obese mice, suggesting that obesity reprograms how the lung responds to subsequent insult. In particular, we found that obese animals exposed to LPS have gene signatures showing increased inflammatory and oxidative stress response and decreased antioxidant capacity compared with RW. Because signaling pathways for these responses can be common to various sources of environmentally induced lung damage, we further identified biomarkers that are indicative of specific toxicant exposure by comparing gene signatures after LPS exposure to those from a parallel study with cigarette smoke. These data show obesity may increase sensitivity to further insult and that co-occurrence of environmental stressors result in complex biosignatures that are not predicted from analysis of individual exposures. - Highlights: ► Obesity modulates inflammatory markers in BAL fluid after LPS exposure. ► Obese animals have a unique transcriptional signature in lung after LPS exposure. ► Obesity elevates inflammatory stress and reduces antioxidant capacity in the lung. ► Toxicant-specific biomarkers predict exposure independent of systemic inflammation.
Nanoscale mapping of electromechanical response in ionic conductive ceramics with piezoelectric inclusions
Electromechanical (EM) response in ion conductive ceramics with piezoelectric inclusions was spatially explored using strain-based atomic force microscopy. Since the sample is composed of two dominant phases of ionic and piezoelectric phases, it allows us to explore two different EM responses of electrically induced ionic response and piezoresponse over the same surface. Furthermore, EM response of the ionic phase, i.e., electrochemical strain, was quantitatively investigated from the comparison with that of the piezoelectric phase, i.e., piezoresponse. These results could provide additional information on the EM properties, including the electrochemical strain at nanoscale.
Global land carbon sink response to temperature and precipitation varies with ENSO phase
Climate variability associated with the El Niño-Southern Oscillation (ENSO) and its consequent impacts on land carbon sink interannual variability have been used as a basis for investigating carbon cycle responses to climate variability more broadly, and to inform the sensitivity of the tropical carbon budget to climate change. Past studies have presented opposing views about whether temperature or precipitation is the primary factor driving the response of the land carbon sink to ENSO. We show that the dominant driver varies with ENSO phase. And whereas tropical temperature explains sink dynamics following El Niño conditions (r TG,P = 0.59, p < 0.01), the post La Niña sink is driven largely by tropical precipitation (r PG,T= -0.46, p = 0.04). This finding points to an ENSO-phase-dependent interplay between water availability and temperature in controlling the carbon uptake response to climate variations in tropical ecosystems. Furthermore, we find that none of a suite of ten contemporary terrestrial biosphere models captures these ENSO-phase-dependent responses, highlighting a key uncertainty in modeling climate impacts on the future of the global land carbon sink.
Electromagnetic and neutral-weak response functions of 4 He and 12 C
A major goal of nuclear theory is to understand the strong interaction in nuclei as it manifests itself in terms of two- and many-body forces among the nuclear constituents, the protons and neutrons, and the interactions of these constituents with external electroweak probes via one- and many-body currents. The objective of the present work is to calculate the quasielastic electroweak response functions in light nuclei within the realistic dynamical framework outlined above. These response functions determine the inclusive cross section as function of the lepton momentum and energy transfers. Their ab initio calculation is a very challenging quantum many-body problem, since it requires summation over the entire excitation spectrum of the nucleus and inclusion in the electroweak currents of one- and many-body terms. Green's functions Monte Carlo methods allow one to circumvent both difficulties by computing the response in imaginary time (the so-called Euclidean response) and hence summing implicitly over the bound and continuum states of the nucleus, and by implementing specific algorithms designed to deal with the complicated spin-isospin structure of nuclear many-body operators. Theoretical predictions for 4 He and 12 C, confirmed by experiment in the electromagnetic case, show that two-body currents generate excess transverse strength from threshold to the quasielastic to the dip region and beyond. In conclusion, these results challenge the conventional picture of quasielastic inclusive scattering as being largely dominated by single-nucleon knockout processes.
Regression-Based Analytics for Response Dynamics of SDN Solutions and Components
We consider Software-Defined Network (SDN) solutions that dynamically setup and teardown connections, and also their response dynamics of connectivity and TCP throughput. There is a wide variety of choices for controllers, switches, andsoftware modules, and objective analytics of measurements are needed to compare the response dynamics of solutions based on their combinations. Throughput and ping measurements during connection changes contain significant statistical variations due to the complexities of controllers, northbound scripts, and network devices. We propose analytical methods to estimate response regression functions, which provide a rigorous and objective comparison of different SDN solutions and their components, namely, controllers, switches, and OpenFlow versions. We apply this approach in two scenarios: (a) switching of long-haul connections emulated in hardware by ANUE devices over a testbed consisting of HP and Cisco switches, and (b) multi-site science infrastructure emulated using VMs and Mininet. The results provide useful practical insights including: (i) the dpctl method responds seconds faster than the OpenDayLight (ODL) method on average under OpenFlow 1.0, but performs similar to ODL under OpenFlow 1.3, and (ii) the ping response of dpctl is about 0.6 sec faster than ODL for the second scenario.
Nonlocal, Pattern-Aware Response and Feedback Framework for Regional Climate Response
We devise a pattern-aware feedback framework for representing the forced climate response using a suite of Green’s function experiments with solar radiation perturbations. By considering the column energy balance, a comprehensive linear response function (CLRF) for important climate variables and feedback quantities such as moist static energy, sea surface temperature, albedo, cloud optical depth, and lapse rate is learned from Green’s function data. The learned CLRF delineates the effects of the energy diffusion in both the ocean and atmosphere and the pattern-aware feedbacks from the aforementioned radiatively active processes. The CLRF can then be decomposed into forcing–response mode pairs, which are in turn used to construct a reduced-order model describing the dominant dynamics of climate responses. These mode pairs capture nonlocal effects and teleconnections in the climate and thus make the reduced-order model apt for capturing regional features of climate response. A key observation is that the CLRF captures the polar-amplified response as the most excitable mode of the climate system, and this mode is explainable in the data-learned pattern-aware feedback framework. The reduced-order model can be used for predicting the response for a given forcing and for reconstructing the forcing from a given response; we demonstrate these capabilities for multiple independent forcing scenarios.
Earthquake response of head-mounted equipment in advanced nuclear reactors
The seismic response of safety-related equipment mounted on the head of an advanced reactor, including pumps, control rod drive mechanisms, and reactor monitoring devices, will affect the design and layout of many advanced reactors. High earthquake-induced accelerations in such equipment may challenge their seismic qualification and trigger the need for additional support framing on the reactor head. Base isolation is a design solution that can drastically reduce seismic demands on equipment. This article describes a set of earthquake-simulator experiments conducted on a scale-model of a base-isolated reactor vessel including four representations of head-mounted equipment, with frequencies spanning from 4.5 to 27 Hz. Dynamic responses of the head-mounted equipment, including displacements, accelerations, and strains, were measured in the experiments for three support conditions: conventional, and seismically isolated using single concave Friction Pendulum (SFP) bearings and triple Friction Pendulum (TFP) bearings. Seismic isolation was effective at reducing equipment responses (accelerations, displacements, and strains) with respect to those in the conventionally supported vessel across a range of seismic inputs. Companion numerical studies highlight the accuracy to be expected in the calculation of different response quantities for lightly damped equipment. The importance of characterizing damping in head-mounted, safety-related equipment through physical experiments to support design and risk assessment is made clear through the numerical simulations.
Development and evaluation of a list mode neutron coincidence collar for spatial response measurements of fresh fuel assemblies
A traditional safeguards neutron coincidence counting system, the Neutron Coincidence Collar, has been modified to incorporate individual preamplifiers on each of its 18 3He tubes in active interrogation mode. When used with list mode data acquisition (LMDA) and analysis, a signal from each 3He tube can be recorded and analyzed to allow a spatial response measurement to be performed on an item based on count rate and 3He tube location within the system. The ultimate goal of this project is to demonstrate the capabilities of a list mode response matrix for the nondestructive assay of fresh nuclear fuel assemblies. To enable partial defect detection of fuel pin locations and absences within an assembly, the project aims to extend well-established correlated neutron analysis techniques on a preexisting Neutron Coincidence Collar by extracting a greater number of useful signatures from the system than are currently generated. LMDA, combined with the addition of multiple preamplifiers, facilitates this capability by increasing the number of simultaneous signals that can be measured; this allows an in-depth analysis of neutron coincidence events to determine a fissioning item’s location based on the measured doubles count rate in various channel logic coincidence combinations. All of this can be done from a single measurement pulse train in offline analysis, which is the major benefit provided by LMDA. Through various stages of development and testing, a Mirion Technologies model JCC-71 Neutron Coincidence Collar has been successfully retrofit with modern electronics designed in-house at Oak Ridge National Laboratory, matching preexisting JAB-01 electronics performance, while maintaining the original system footprint. This paper presents these various stages of development and experimental evaluation of the proof of concept system.
Experimental Determination of PV Inverter Response to Grid Phase Shift Events: Preprint
With the continued growth of renewable energy resources which interface to the electric grid via inverters, the understanding of such devices becomes ever more important to the safe and reliable operation of the bulk power system. This work investigates the specific response of a utility-scale PV inverter to grid voltage phase shift-type disturbances which sometimes occur during grid fault events. The role of the PV inverter's phase-locked-loop (PLL) is identified as important to modeling the response. Switching-level simulations of a utility-scale PV inverter with a modeled PLL show a characteristic response when phase shift disturbances require the PLL to track what appear as fast frequency changes. Additionally, in this work a full-scale laboratory testing was carried out using the Opal real time (RT) OP5142 real time simulator and a large grid simulator to create phase shift disturbances with a high degree of repeatability. A photovoltaic (PV) inverter was connected to a grid simulator, and phase shifts were instantaneously implemented on the simulated grid, the results of the currents were then obtained. The experimental results obtained were validated with simulation results obtained from MATLAB/Simulink.
Multiple response optimization for higher dimensions in factors and responses
When optimizing a product or process with multiple responses, a two-stage Pareto front approach is a useful strategy to evaluate and balance trade-offs between different estimated responses to seek optimum input locations for achieving the best outcomes. After objectively eliminating non-contenders in the first stage by looking for a Pareto front of superior solutions, graphical tools can be used to identify a final solution in the second subjective stage to compare options and match with user priorities. Until now, there have been limitations on the number of response variables and input factors that could effectively be visualized with existing graphical summaries. We present novel graphical tools that can be more easily scaled to higher dimensions, in both the input and response spaces, to facilitate informed decision making when simultaneously optimizing multiple responses. A key aspect of these graphics is that the potential solutions can be flexibly sorted to investigate specific queries, and that multiple aspects of the solutions can be simultaneously considered. As a result, recommendations are made about how to evaluate the impact of the uncertainty associated with the estimated response surfaces on decision making with higher dimensions.