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At least 1,567 records · Page 87

Multicarrier Energy Systems: Shaping Our Energy Future

Multicarrier energy systems (MCESs) are characterized by strong coordination in operation and planning across multiple energy vectors and/or sectors to deliver reliable, cost-effective energy services to end users/customers with minimal impact on the environment. They have efficiency and flexibility benefits and are deployed in large and small scales on the supply and demand sides and at the network level but are more complex to control and manage. Here, MCESs are reviewed in the context of future low carbon energy systems based on electrification and very high variable renewable energy penetrations. Fully exploiting these systems requires some cost reductions, more sophisticated operations enabled by standardized communications and control capabilities detailed planning paradigms, and addressing their corresponding economic challenges. All these point toward the direction of analysis, markets, and technology research and development coupled with better policy and regulatory frameworks. One futuristic vision of a very low carbon energy system is proposed that illustrates potential pathways to an MCES-dominated energy future.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Interactions Between Climate Policy and Technology-influenced Travel Behavior: Mitigating Induced Demand from CACC

Advances in vehicle technology have influenced the development of automated vehicle systems, where vehicles that do not require human intervention are already deployed in the roadway networks. While these advances are proved to increase roadway safety and highway capacity, more research is needed to understand the long-term and regional-level impacts on mobility, land use, energy consumption, and emissions. This study proposes a multi-model approach to analyze the effect of vehicle automation and deep decarbonization policies over a period from 2020 to 2040 in Austin, Texas. We use the Global Change Analysis Model (GCAM) to develop internally the scenarios that are then passed to the SMART Mobility modeling workflow, a large-scale simulation framework combining the POLARIS activity-based travel demand model and mesoscopic traffic simulator with the Autonomie vehicle energy consumption model and the UrbanSim land use simulator. Results suggest that the introduction of vehicles with advanced automation could increase fuel consumption when no decarbonization policies are implemented. Also, advances in vehicle technology research and development could lead to a decline in energy use in the long-term. Energy pricing and vehicle electrification incentives could help reduce the impact of vehicle automation. Finally, our analysis indicates the relevance of introducing land use processes in longterm vehicle automation studies.

land use↗

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17-18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Apollo 16 preliminary science report

Ever since Galileo's telescope made the rugged lunar surface more clearly visible (in 1610), men have strived to learn more about the origin and history of the Earth's big natural satellite, and never has so much progress been made as in the last few years. The fifth manned lunar landing was in a highlands area, quite different from the sites visited previously, and the discoveries there now seem certain to result in significant improvements in the hypotheses of lunar scientists. Much of the Moon's surface is similar to the Descartes Highlands that the Apollo 16 astronauts examined. From this highly productive mission, more photographs were obtained than on any previous Apollo flight, a greater amount of time was spent outside the lunar module, a greater weight of scientific equipment landed on the Moon, and a record weight of scientific samples was brought back to laboratories on Earth. The network of automatic scientific stations at work on the Moon was extended into a new area and has since detected a moonquake caused by the largest meteoroid impact that has yet been recorded. Additional experiments on the surface and in flight also were successfully performed on this mission for the enlightenment of students of natural phenomena. The Apollo 16 astronauts observed, and scientists studying material they collected have subsequently deduced, that this landing site differed surprisingly from earlier expectations. Future generations consequently may benefit from better concepts of the operation of the solar system and events throughout the physical universe than have hitherto been possible. This volume is but one of a series of NASA Special Publications being issued promptly to document potentially significant discoveries in the course of the Apollo Program, thereby possibly increasing their usefulness to scientists grappling with problems that have long perplexed mankind.

NASA Editorial Review Board↗

Effects of clinorotation and microgravity on sweet clover columella cells treated with cytochalasin D

The cytoskeleton of columella cells is believed to be involved in maintaining the developmental polarity of cells observed as a reproducible positioning of cellular organelles. It is also implicated in the transduction of gravitropic signals. Roots of sweet clover (Melilotus alba L.) seedlings were treated with a microfilament disrupter, cytochalasin D, on a slowly rotating horizontal clinostat (2 rpm). Electron micrographs of treated columella cells revealed several ultrastructural effects including repositioning of the nucleus and the amyloplasts and the formation of endoplasmic reticulum (ER) whorls. However, experiments performed during fast clinorotation (55 rpm) showed an accumulation (but no whorling) of a disorganized ER network at the proximal and distal pole and a random distribution of the amyloplasts. Therefore, formation of whorls depends upon the speed of clinorotation, and the overall impact of cytochalasin D suggests the necessity of microfilaments in organelle positioning. Interestingly, a similar drug treatment performed in microgravity aboard the US Space Shuttle Endeavour (STS-54, January 1993) caused a displacement of ER membranes and amyloplasts away from the distal plasma membrane. In the present study, we discuss the role of microfilaments in maintaining columella cell polarity and the utility of clinostats to simulate microgravity.

manned↗

The International Space Station and the Space Debris Environment: 10 Years On

For just over a decade the International Space Station (ISS), the most heavily protected vehicle in Earth orbit, has weathered the space debris environment well. Numerous hypervelocity impact features on the surface of ISS caused by small orbital debris and meteoroids have been observed. In addition to typical impacts seen on the large solar arrays, craters have been discovered on windows, hand rails, thermal blankets, radiators, and even a visiting logistics module. None of these impacts have resulted in any degradation of the operation or mission of the ISS. Validating the rate of small particle impacts on the ISS as predicted by space debris environment models is extremely complex. First, the ISS has been an evolving structure, from its original 20 metric tons to nearly 300 metric tons (excluding logistics vehicles) ten years later. Hence, the anticipated space debris impact rate has grown with the increasing size of ISS. Secondly, a comprehensive visual or photographic examination of the complete exterior of ISS has never been accomplished. In fact, most impact features have been discovered serendipitously. Further complications include the estimation of the size of an impacting particle without knowing its mass, velocity, and angle of impact and the effect of shadowing by some ISS components. Inadvertently and deliberately, the ISS has also been the source of space debris. The U.S. Space Surveillance Network officially cataloged 65 debris from ISS from November 1998 to November 2008: from lost cameras, sockets, and tool bags to intentionally discarded equipment and an old space suit. Fortunately, the majority of these objects fall back to Earth quickly with an average orbital lifetime of less than two months and a maximum orbital lifetime of a little more than 15 months. The cumulative total number of debris object-years is almost exactly 10, the equivalent of one piece of debris remaining in orbit for 10 years. An unknown number of debris too small to be tracked and cataloged have also been generated, but normally with even shorter orbital lifetimes. Finally, eight collision avoidance maneuvers have been performed to avoid potential collisions between ISS and large, tracked space debris. The most recent such maneuver was accomplished by ESA's Automated Transfer Vehicle, the Jules Verne, just three months before the 10th anniversary of the launch of ISS's first element.

Johnson, Nicholas↗

Orbital Debris Research in the United States

The presentation includes information about growth of the satellite population, the U.S. Space Surveillance Network, tracking and catalog maintenance, Haystack and HAX radar observation, Goldstone radar, the Michigan Orbital Debris Survey Telescope (MODEST), spacecraft surface examinations and sample of space shuttle impacts. GEO/LEO observations from Kwajalein Atoll, NASA s Orbital Debris Engineering Model (ORDEM2008), a LEO-to-GEO Environment Debris Model (LEGEND), Debris Assessment Software (DAS) 2.0, the NASA/JSC BUMPER-II meteoroid/debris threat assessment code, satellite reentry risk assessment, optical size and shape determination, work on more complicated fragments, and spectral studies.

Stansbery, Gene↗

A Summary of the NASA Lightning Nitrogen Oxides Model (LNOM) and Recent Results

The NASA Marshall Space Flight Center introduced the Lightning Nitrogen Oxides Model (LNOM) a couple of years ago to combine routine state-of-the-art measurements of lightning with empirical laboratory results of lightning NOx production. The routine measurements included VHF lightning source data [such as from the North Alabama Lightning Mapping Array (LMA)], and ground flash location, peak current, and stroke multiplicity data from the National Lightning Detection Network(TradeMark) (NLDN). Following these initial runs of LNOM, the model was updated to include several non-return stroke lightning NOx production mechanisms, and provided the impact of lightning NOx on an August 2006 run of CMAQ. In this study, we review the evolution of the LNOM in greater detail and discuss the model?s latest upgrades and applications. Whereas previous applications were limited to five summer months of data for North Alabama thunderstorms, the most recent LNOM analyses cover several years. The latest statistics of ground and cloud flash NOx production are provided.

Koshak, William↗

Assimilation of Aerosol Observations in the NASA GEOS Model

In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASA's Global Modeling Assimilation Office (GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. The prognostic model is based on the GEOS model radiatively coupled to GOCART aerosol module and includes assimilation of bias-corrected Aerosol Optical Depth (AOD) at 550 nm from various space-based remote sensing platforms. In line with the transition of the GEOS meteorological data assimilation system to a hybrid Ensemble-Variational formulation, we are updating the aerosol component of our assimilation system to a variational ensemble type of scheme. In this talk we will examine the impact of replacing the current analysis splitting scheme with this new approach. Starting with the assimilation of satellite based single-channel retrievals; we will discuss the impact of this aerosol data assimilation technique on the 3D aerosol distributions by means of innovation statistics and verification against independent datasets such as the Aerosol Robotic Network (AERONET) and surface PM2.5. We will also present preliminary results related to the introduction of new aerosol data types in GEOS, including multi-spectral AOD retrievals.

AERONET↗

Observations Suggest That North African Dust Absorbs Less Solar Radiation Than Models Estimate

Desert dust accounts for a large fraction of shortwave radiation absorbed by aerosols, which adds to the climate warming produced by greenhouse gases. However, it remains uncertain exactly how much shortwave radiation dust absorbs. Here, we leverage in-situ measurements of dust single-scattering albedo to constrain absorption at mid-visible wavelength by North African dust, which accounts for approximately half of the global dust. We find that climate and chemical transport models overestimate North African dust absorption aerosol optical depth (AAOD) by up to a factor of two. This occurs primarily because models overestimate the dust imaginary refractive index, the effect of which is partially masked by an underestimation of large dust particles. Similar factors might contribute to an overestimation of AAOD retrieved by the Aerosol Robotic Network, which is commonly used to evaluate climate and chemical transport models. The overestimation of dust absorption by models could lead to substantial biases in simulated dust impacts on the Earth system, including warm biases in dust radiative effects.

Atmospheric science↗

Data-driven estimation of energy consumption for electric bus under real-world driving conditions

Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. Here, a K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.

Artificial neural network↗

Defect Production and Microstructural Feature Impact for Radiation Damage in Additively Manufactured 316 Stainless Steel

This milestone presents multi-scale modeling research results for additively manufactured 316 stainless steel. A combination of phase field, cluster dynamics, molecular dynamics, and density functional theory with machine learning is used, allowing for predictions of radiation-driven microstructural evolution in additively manufactured 316 stainless steel over a range of temperatures, damage rates, neutron spectra, and microstructures, and supporting the development of combined ion and neutron irradiation for material qualification. Informed by ion irradiation and neutron irradiation results across the Advanced Materials and Manufacturing Technologies program, we investigate the unique aspects of radiation-driven microstructure evolution in additively manufactured 316 stainless steel. In particular, we focus on understanding the impact of carbon concentration (varying, for example, between the 316L and 316H standards) on void formation; radiation-induced segregation at dislocation cells and grain boundaries; and the evolution of dislocation loops and network dislocation populations. We find that the unique characteristics of the additively manufactured microstructures must be accounted for in understanding the evolution of dislocation populations under thermal and irradiation conditions, such as the variation in sink strengths arising due to the variation in dislocation density. We also find that the radiation-induced segregation of Cr and Ni to grain boundaries and cell walls differs due to the differences in their defect sink biases. We also find that increasing the Ni content can slow vacancy diffusion, which may provide a mechanism for the observed reduction in transient swelling rate for austenitic Fe-Cr-Ni alloys with increasing Ni content. In addition, ion irradiations have shown that increasing carbon content in 316 SS results in a larger population of smaller voids, suggesting reduced vacancy diffusion. Our results show that the carbon content of additively manufactured 316 SS has a significant impact on the migration rate of defect clusters. The presence of carbon atoms results in carbon-vacancy trapping, significantly reducing the diffusion rate of vacancies. Carbon atoms may also be trapped near the surface of a void, which may reduce void growth by trapping vacancies that diffuse toward the void.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Probabilistic Voltage Sensitivity Analysis to Quantify Impact of High PV Penetration on Unbalanced Distribution System

From an operational and planning perspective, it is important to quantify the impact of increasing penetration of photovoltaics on the distribution system. Most existing impact assessment studies are scenario-based where derived results are scenario specific and not generalizable. Moreover, stochasticity in the temporal behavior of spatially distributed PVs requires a large number of scenarios that increase with the size of the network and the level of penetration. Therefore, we propose a new computationally efficient analytical framework of voltage sensitivity analysis that allows for stochastic analysis of voltage change due to random changes in PV generation. We first derive an analytical approximation for voltage change at any node of the network due to change in power at other nodes in an unbalanced distribution network. The quality of this approximation is reinforced via bounds on the approximation error. Then, we derive the probability distribution of voltage change at a certain node due to random changes in power injections/consumptions at multiple locations of the network. The accuracy of the proposed PVSA is illustrated using a modified version of the IEEE 37 bus test system. As a result, the proposed PVSA can serve as a powerful tool for proactive monitoring/control and ease the computational burden associated with perturbation based cybersecurity mechanisms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Boron Coordination in Multicomponent Glasses: Analytical Models and Machine Learning With Uncertainty

Borosilicate glasses are extensively used in a variety of applications from kitchenware to nuclear waste immobilization due to the strong network formed by the Si-O-B bond that makes it resistant to chemical corrosion and gives it a low thermal expansion. Boron, however, exists in both trigonal BO3 and tetrahedral BO4 bonds in glass systems, which impacts the chemical durability and thermal resistance of the glass, amongst other properties. Boron coordination (N4), or the ratio of the amount of BO4 to BO3 within a glass, may aid in predicting these properties but is difficult to derive without experimental data due to the complexity of impacts from varied glass compositions and processing factors. For this reason, compositional models have been developed to predict boron coordination, but the models typically include a limited number of glass components. To help fill this gap in the models, in this work, a diverse multicomponent glass dataset of 809 glasses is compiled from a literature search, and then a number of analytical and machine learning (ML) models are trained on the dataset. Previously developed modified Bernstein and modified Du Stebbins analytical models were fitted to update parameters with the new dataset. Then, partially Bayesian neural networks, Gaussian process regressor, and heteroskedastic deterministic neural networks were evaluated. The ML models examined all have different strategies to overcome the potential for overfitting as a result of a limited training dataset, and return results that account for model uncertainty, which can be valuable for understanding model reliability. For the first time, cooling rate is introduced as an input parameter for ML models, showing consistent improvements in performance and solidifying the importance of including parameters outside of composition alone for N4 prediction. The machine learning models examined here show promise in accurate predictions of boron coordination in borosilicate glasses, all achieving R2 values of 0.91.

boron coordination↗

Integration of SPS with utility system networks

The integration of Satellite Power System (SPS) power in electric utility power systems is discussed. Specifically, the nature of the power output variations from the spacecraft to the rectenna, the operational characteristics of the rectenna power, and the impacts on the electric utility system from utilizing SPS power to serve part of the system load are treated. It is concluded that if RF beam control is an acceptable method for power control, and that the site distribution of SPS rectennas do not cause a very high local penetration (40 to 50%), SPS may be integrated into electric utility system with a few negative impacts. Increased regulating duty on the conventional generation, and a potential impact on system reliability for SPS penetration in excess of about 25% appear to be two areas of concern.

Kaupang, B. M.↗

Hybrid Architectures and Their Impact on Intelligent Design

In this presentation we investigate a novel framework for the design of autonomous fuzzy intelligent systems. The system integrates the following modules into a single autonomous entity: (1) a fuzzy expert system; (2) artificial neural network; (3) genetic algorithm; and (4) case-base reasoning. We describe the integration of these units into one intelligent structure and discuss potential applications.

Kandel, Abe↗

Opinion Dynamics on Networks under Correlated Disordered External Perturbations

We study an influence network of voters subjected to correlated disordered external perturbations, and solve the dynamical equations exactly for fully connected networks. The model has a critical phase transition between disordered unimodal and ordered bimodal distribution states, characterized by an increase in the vote-share variability of the equilibrium distributions. The fluctuations (variance and correlations) in the external perturbations are shown to reduce the impact of the external influence by increasing the critical threshold needed for the bimodal distribution of opinions to appear. The external fluctuations also have the surprising effect of driving voters towards biased opinions. Furthermore, the first and second moments of the external perturbations are shown to affect the first and second moments of the vote-share distribution. This is shown analytically in the mean field limit, and confirmed numerically for fully connected networks and other network topologies. Studying the dynamic response of complex systems to disordered external perturbations could help us understand the dynamics of a wide variety of networked systems, from social networks and financial markets to amorphous magnetic spins and population genetics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗