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At least 271 records · Page 15

Nonlinear System Identification for Aeroelastic Systems with Application to Experimental Data

Representation and identification of a nonlinear aeroelastic pitch-plunge system as a model of the Nonlinear AutoRegressive, Moving Average eXogenous (NARMAX) class is considered. A nonlinear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (1) the outputs of the NARMAX model closely match those generated using continuous-time methods, and (2) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.

Kukreja, Sunil L.↗

Fusion-Enabled Pluto Orbiter and Lander

Direct Fusion Drive (DFD) is a unique fusion engine concept based on the Princeton Field-Reversed Configuration (PFRC) fusion reactor conceived by Dr. Sam Cohen of the Princeton Plasma Physics Laboratory. DFD would enable the Pluto orbiter and lander context mission and more broadly enable true "rapid transit" to outer-planet and near interstellar space. The truly game-changing levels of thrust and power in a modestly sized package could integrate with our current launch infrastructure while radically expanding the science capability of these missions. Our Phase I was our first funded work on the DFD, with previous work at PSS occurring only under internal R&D. We established the feasibility of our Pluto mission trajectories using straight-line and planar models, including a departure spiral from Earth and insertion at Pluto. We developed our first thrust and specific impulse model using the results of the UEDGE multi-fluid code. Our specific power model was improved. During this Phase II effort, we continued our efforts to increase the fidelity of the designs for the RF, magnet, and shielding subsystems. Dedicated thrust augmentation experiments were run on the PFRC experiment, using a supersonic gas puffing valve. For the first time, we analyzed the design of a closed-loop operation mode and estimated the hardware that would be required for a dual-mode engine. In an exciting new development, we have invented a new thermophotovoltaic thermal conversion method that has the potential to have efficiencies of a Brayton or Stirling system. Our report presents details of these analyses. Our roadmap to bringing DFD to flight predicts that with sufficient support, a first flight unit could be built by 2040. We anticipate that three machine generations are required before this point: a ~1 T PFRC-3 machine hitting new plasma temperature and density levels, a ~5 T PFRC-4 machine with first demonstration of D-3He fusion, and a flight. (The current experiment, PFRC-2, is limited to about 0.1 T). In order to achieve a flight in 2035-2040, the TRL of the supporting systems must be increased in parallel, including low mass radiators, cryogenic propellant storage, and large (>100 kW) thermal conversion systems. Fortunately, many of these systems are dual-use and are required for other technologies including fission systems, so DFD would contribute to and benefit from those programs. This NIAC support and the results of our work have led to multiple follow-on contracts. We won two NASA STTRs focused on DFD subsystems, one on the RF system and one on the superconducting magnets, and our magnet STTR is now midway through a Phase II. We will be receiving a superconducting test magnet for experiments at PPPL this summer. In addition, we won an ARPA-E OPEN grant; this is the first time that OPEN has included fusion technologies, and we are part of a cohort of three alternative fusion companies now supported directly by DOE. We are very optimistic that if we are able to meet our experimental milestones in the next 12-18 months, we will be competitive for additional DOE grants to build PFRC-3.

Propulsion↗

Impact of cyber attacks on distributed compressive sensing based state estimation in power distribution grids

Modern power distribution grids suffer from multiple vulnerabilities due to the tight integration between the physical system and the cyber infrastructure. Sophisticated and malicious cyber attacks continue to adversely impact the grid operation leading to performance degradation, service interruption, and grid failure. State estimation plays an essential role in grid monitoring and advancing cyber-attack situational awareness. In this regard, this paper first proposes a distributed compressive sensing (CS) state estimation approach for an unobservable distribution grid. Further, the proposed distributed CS approach divides the distribution grid into sub-areas to perform local state estimation. Then an alternating direction method of multipliers (ADMM) based iterative information exchange among neighboring areas is employed to complete the estimation process. In this estimation process, the impact of loss of measurement data, false data injection (FDI), replay, and neighborhood cyber-attacks is analyzed. Extensive simulations are performed on the IEEE 37-bus and IEEE 123-bus standard networks to demonstrate the algorithm’s robustness to the aforementioned cyber-attacks. A quantitative analysis of computational complexity and simulation time of the distributed CS based approach is also presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Diagnostics, Prognostics, and Optimization for Lithium-Ion Battery Systems

Health management of lithium-ion battery systems presents a host of challenges due to their complex physics, large numbers of components, and a wide variety of degradation behaviors across different battery types. Dr. Paul Gasper will present on research from the Electrochemical Energy Storage Group on Lithium-ion battery diagnostics, prognostics, and optimization. Diagnostics research, including state-estimation via machine-learning from electrochemical impedance spectroscopy and DC pulses as well as continuous state-estimation via Kalman filters, will highlight the ongoing challenges for accurately measuring the state of batteries without performing time-consuming characterization tests. NLR's industry-recognized battery prognostics work, which predicts real-world battery degradation by identifying degradation rate models from accelerated aging data using statistical modeling and machine-learning, will be used to demonstrate the critical impact of battery controls, thermal management, and operating strategy on durability and lifetime. Finally, the use of prognostic models for financial or lifetime optimization will be discussed.

25 ENERGY STORAGE↗

Design and implementation of a real-time underwater acoustic telemetry system for fish behavior study and environmental sensing

Underwater acoustic telemetry is an important tool for studying the behavior of aquatic animals and assessing the environmental impact of man-made structures such as water power turbines. However, current design of the acoustic telemetry system lacks the ability to transmit collected data continuously in real-time and requires manual retrieval of the recorded measurements, leading to a long delay of data retrieval, high operational cost associated with manpower as well as safety risk for the operators. In addition, there is no efficient way to monitor the status of the acoustic telemetry system, including acoustic transmitters and receivers. For example, if multiple receivers in an array fail, the detection efficiency could be significantly reduced. Herein, we developed a cloud-based, real-time underwater acoustic telemetry system that can be used to estimate behavior or survival of tagged aquatic animals and monitor environmental parameters in near-real-time (< 2 hours). The system incorporates microcontrollers and integrated circuits to create real-time data communication and processing units with a cloud-based service that processes the transmitted data stream to derive behavior and survival information of the tagged animals.

Yang, Yang↗

The Seismic Experiment for Interior Structure (SEIS): Experiment Data Distribution

The six sensors of SEIS (The Seismic Experiment for Interior Structure) [- one of three primary instruments on NASA's Mars Lander Insight] cover a broad range of the seismic bandwidth, from 0.01 hertz to 50 hertz, with possible extension to longer periods. Data are transmitted in the form of three continuous VBB (Very Broad-Band) components at 2 samples per second (sps), an estimation of the short period (SP) energy content from the SP at 1 sps, and a continuous compound VBB/SP vertical axis at 10 sps. The continuous streams are augmented by requested event data with sample rates from 20 to 100 sps. SEIS data products are downlinked from the spacecraft in raw CCSDS (Consultative Committee for Space Data Systems) packets and converted to both the Standard for the Exchange of Earthquake Data (SEED) format files and ASCII tables (GeoCSV) for analysis and archiving. Metadata are available in dataless SEED and StionXML. Time series data (waveforms) are available in miniseed and GeoCSV. Data are distributed according to FDSN (Federation of Digital Seismograph Networks - http://www.fdsn.org) formats and interfaces. Wind, pressure and temperature data from the Auxiliary Payload Sensor Suite (APSS) will also be available in SEED format, and can be used for decorrelation and diagnostic purposes on SEIS.

Pardo, Constanza↗

Estimation of Deposition Behavior of Supersaturated Silicic Acid around Geological Disposal Facility - 19137

For safety evaluation about the geological disposal system, the leakage of radionuclides with groundwater flow is estimated by considering the groundwater flow through the manmade and natural barriers. In such an estimate, we should also consider the influence of cement materials used for the construction of the disposal facility system. Cement material including high alkalinity alters groundwater condition up to 13 in pH and dissolves numerous silicate materials, which release silicic acid to groundwater. Since this groundwater mixes with natural groundwater downstream, supersaturated silicic acid will be produced with the decrease of pH to 8 around the repository system. Thus, in this study, the behavior of supersaturated silicic acid in a flow system was examined by using the column packed with amorphous silica particles under the condition of constant hydraulic head. The packed length is 20 mm or 40 mm, and the supersaturated silicic acid solution was continuously injected into the column. Here, the supersaturated silicic acid was adjusted by mixing with distilled water of pH 8 just before the solution was injected into the column. In the results, the supersaturated silicic acid quickly deposited in the packed column, and colloidal silicic acid was not detected at the outlet of the column. The deposition amount of supersaturated silicic acid in the column decreased with time, and became larger in the longer column. Besides, the flow rate decreased with the deposition of supersaturated silicic acid. Thus, this study monitored the decrease rate of permeability with the supply amount of silicic acid. In the results, the change rate did not depend on packed length. This suggested that even with a short packed length, by considering the amount of precipitation of supersaturated silicic acid, it is possible to estimate the change in permeability similarly to the case of a long packed length. Such a decrease effect in permeability may contribute to retarding the migration of radionuclides around the repository. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Development of a Consistent GEOsat Cloud Property Dataset for the CERES Climate Data Record

Cloud properties are critical for understanding the Earth’s radiation budget and cloud feedbacks. At NASA Langley Research Center, the Satellite ClOud and Radiative Property retrieval System (SatCORPS) provides real-time and historical analyses of clouds derived from Geostationary satellite (GEOsat) data for weather and climate applications. For the Clouds and the Earth’s Radiant Energy System (CERES) program, the global constellation of GEOsats has been analyzed since 2000 to help characterize and account for the diurnal cycle of clouds and their radiative impacts in the CERES climate data record. Obtaining consistent cloud properties over the GEOsat data record during the CERES era is a major objective but a significant challenge considering the diversity of imaging capabilities deployed during that time. The GEOsat data analysis approach for the current CERES Edition-4 (Ed4) data products was focused on accuracy and consistency with MODIS by employing as much spectral information as possible from each satellite. However, the inconsistent use of spectral information across GEOsats led to marked discontinuities in the spatial and temporal record of cloud properties that had to be accounted for post facto in downstream CERES processing. This paper reports progress in developing a new GEOsat analysis system for the next CERES edition (Ed5) that has potential to improve cross-platform consistency and continuity. In this approach, the spectral channel complement is limited to just 3-channels during daytime, ~0.65 µm (VIS), ~3.9 µm (NIR), and ~10.8 µm (IR), common to nearly all of the satellites in the record. At night, a 2-channel approach is taken with the NIR and IR, and ~6.7 µm bands that includes a machine learning approach for optically thick cloud properties. A tradeoff is the potential for reduced accuracy particularly using data from the more advanced satellites that have more spectral channels (e.g. SEVIRI, AHI and ABI) that are known to help improve thin cirrus detection, cloud-aerosol discrimination and estimates in other difficult conditions that challenge cloud remote sensing. The new continuity approach is applied to one month of global GEOSat data for each year of the CERES record since 2000 and compared with the Ed4 GEO and MODIS cloud property time series in order to evaluate the level of improved consistency in the GEOsat record and to assess the accuracy impacts. Cloud fraction will also be assessed with CALIPSO data. Outstanding issues and challenges will be discussed. The results are expected to guide future work needed to develop a more robust GEOsat cloud data record for CERES.

CERES CDR↗

Phobos Ephemeris Improvement from Recent Spacecraft Observations

Despite 127 years of observations, there is still room for improvement in the ephemeris of the Martian satellite Phobos. Early in this history, Earth-based astrometric observations of Phobos and Deimos were used to estimate the mass and oblateness of Mars. As more data accumulated, it became clear that a secular acceleration in the longitude of Phobos was occurring, and this was attributed to tidal dissipation within Mars, yielding rough estimates of the tidal quality factor, or Q. At the epoch of the earliest spacecraft observations of Phobos, from Mariner 9 and the Viking Orbiters and Landers, the gravitational field of Mars, and resulting forces on Phobos, were still not particularly well known. Thus observations of natural and artificial satellite motions continued to contribute, each in their own way, to knowledge of the mass distribution within Mars. Improvements in tracking system accuracy, and the placement of satellites, like the 1996 Mars Global Surveyor and 2001 Mars Odyssey, in circular polar orbits has lead to dramatic improvements to knowledge of the Mars gravity field. The direct gravitational influence on Phobos is no longer expected to be a limiting factor in predicting its orbital motion. Despite that progress, a variety of observations of Phobos from recent orbiters and landers suggest that the best satellite ephemeris still has along-track orbit errors which are accumulating at a rate of 1.75 kilometers per year, with Phobos gaining on the predicted positions. These recent observations alone do not span sufficient time to separately resolve the positional error into changes in mean motion and changes in secular acceleration. However, combining them with earlier observations will allow improvements in both the mean motion and its first derivative. This latter parameter is particularly interesting, as it relates to tidal dissipation, and thus uniquely constrains the internal structure of Mars. The current best estimate of the secular acceleration rate of Mars implies a surprisingly low value for the tidal dissipation factor Q = (94 plus or minus 1). For the Earth, most tidal dissipation occurs within the oceans. The terrestrial mantle tidal Q is 280. An issue of interest is then: why, where, and how does Mars dissipate tidal energy so effectively.

Bills, B. G.↗

Research applications of primary biodiversity databases in the digital age

Our world is in the midst of unprecedented change-climate shifts and sustained, widespread habitat degradation have led to dramatic declines in biodiversity rivaling historical extinction events. At the same time, new approaches to publishing and integrating previously disconnected data resources promise to help provide the evidence needed for more efficient and effective conservation and management. Stakeholders have invested considerable resources to contribute to online databases of species occurrences. However, estimates suggest that only 10% of biocollections are available in digital form. The biocollections community must therefore continue to promote digitization efforts, which in part requires demonstrating compelling applications of the data. Our overarching goal is therefore to determine trends in use of mobilized species occurrence data since 2010, as online systems have grown and now provide over one billion records. To do this, we characterized 501 papers that use openly accessible biodiversity databases. Our standardized tagging protocol was based on key topics of interest, including: database(s) used, taxa addressed, general uses of data, other data types linked to species occurrence data, and data quality issues addressed. We found that the most common uses of online biodiversity databases have been to estimate species distribution and richness, to outline data compilation and publication, and to assist in developing species checklists or describing new species. Only 69% of papers in our dataset addressed one or more aspects of data quality, which is low considering common errors and biases known to exist in opportunistic datasets. Globally, we find that biodiversity databases are still in the initial stages of data compilation. Novel and integrative applications are restricted to certain taxonomic groups and regions with higher numbers of quality records. Continued data digitization, publication, enhancement, and quality control efforts are necessary to make biodiversity science more efficient and relevant in our fast-changing environment.

59 BASIC BIOLOGICAL SCIENCES↗

An Update on Monitoring Stellar Orbits in the Galactic Center

Using 25 years of data from uninterrupted monitoring of stellar orbits in the Galactic Center, we present an update of the main results from this unique data set: a measurement of mass and distance to Sgr A*. Our progress is not only due to the eight-year increase in time base, but also to the improved definition of the coordinate system. The star S2 continues to yield the best constraints on the mass of and distance to Sgr A*; the statistical errors of 0.13×10{sup 6} M{sub ⊙} and 0.12 kpc have halved compared to the previous study. The S2 orbit fit is robust and does not need any prior information. Using coordinate system priors, the star S1 also yields tight constraints on mass and distance. For a combined orbit fit, we use 17 stars, which yields our current best estimates for mass and distance: M=4.28±0.10|{sub stat.}±0.21|{sub sys}×10{sup 6} M{sub ⊙} and R{sub 0}=8.32±0.07|{sub stat.}±0.14|{sub sys} kpc. These numbers are in agreement with the recent determination of R {sub 0} from the statistical cluster parallax. The positions of the mass, of the near-infrared flares from Sgr A*, and of the radio source Sgr A* agree to within 1 mas. In total, we have determined orbits for 40 stars so far, a sample which consists of 32 stars with randomly oriented orbits and a thermal eccentricity distribution, plus eight stars that we can explicitly show are members of the clockwise disk of young stars, and which have lower-eccentricity orbits.

79 ASTRONOMY AND ASTROPHYSICS↗

Tuning and Robustness Analysis for the Orion Absolute Navigation System

The Orion Multi-Purpose Crew Vehicle (MPCV) is currently under development as NASA's next-generation spacecraft for exploration missions beyond Low Earth Orbit. The MPCV is set to perform an orbital test flight, termed Exploration Flight Test 1 (EFT-1), some time in late 2014. The navigation system for the Orion spacecraft is being designed in a Multi-Organizational Design Environment (MODE) team including contractor and NASA personnel. The system uses an Extended Kalman Filter to process measurements and determine the state. The design of the navigation system has undergone several iterations and modifications since its inception, and continues as a work-in-progress. This paper seeks to show the efforts made to-date in tuning the filter for the EFT-1 mission and instilling appropriate robustness into the system to meet the requirements of manned space ight. Filter performance is affected by many factors: data rates, sensor measurement errors, tuning, and others. This paper focuses mainly on the error characterization and tuning portion. Traditional efforts at tuning a navigation filter have centered around the observation/measurement noise and Gaussian process noise of the Extended Kalman Filter. While the Orion MODE team must certainly address those factors, the team is also looking at residual edit thresholds and measurement underweighting as tuning tools. Tuning analysis is presented with open loop Monte-Carlo simulation results showing statistical errors bounded by the 3-sigma filter uncertainty covariance. The Orion filter design uses 24 Exponentially Correlated Random Variable (ECRV) parameters to estimate the accel/gyro misalignment and nonorthogonality. By design, the time constant and noise terms of these ECRV parameters were set to manufacturer specifications and not used as tuning parameters. They are included in the filter as a more analytically correct method of modeling uncertainties than ad-hoc tuning of the process noise. Tuning is explored for the powered-flight ascent phase, where measurements are scarce and unmodelled vehicle accelerations dominate. On orbit, there are important trade-off cases between process and measurement noise. On entry, there are considerations about trading performance accuracy for robustness. Process Noise is divided into powered flight and coasting ight and can be adjusted for each phase and mode of the Orion EFT-1 mission. Measurement noise is used for the integrated velocity measurements during pad alignment. It is also used for Global Positioning System (GPS) pseudorange and delta- range measurements during the rest of the flight. The robustness effort has been focused on maintaining filter convergence and performance in the presence of unmodeled error sources. These include unmodeled forces on the vehicle and uncorrected errors on the sensor measurements. Orion uses a single-frequency, non-keyed GPS receiver, so the effects due to signal distortion in Earth's ionosphere and troposphere are present in the raw measurements. Results are presented showing the efforts to compensate for these errors as well as characterize the residual effect for measurement noise tuning. Another robustness tool in use is tuning the residual edit thresholds. The trade-off between noise tuning and edit thresholds is explored in the context of robustness to errors in dynamics models and sensor measurements. Measurement underweighting is also presented as a method of additional robustness when processing highly accurate measurements in the presence of large filter uncertainties.

Holt, Greg N.↗

Pore-Scale Analysis of Calcium Carbonate Precipitation and Dissolution Kinetics in a Microfluidic Device

In this work, we have characterized the calcium carbonate (CaCO 3 ) precipitates over time caused by reaction-driven precipitation and dissolution in a micromodel. Reactive solutions were continuously injected through two separate inlets, resulting in transverse-mixing induced precipitation during the precipitation phase. Subsequently, a dissolution phase was conducted by injecting clean water (pH = 4). The evolution of precipitates was imaged in two and three dimensions (2-, 3-D) at selected times using optical and confocal microscopy. With estimated reactive surface area, effective precipitation and dissolution rates can be quantitatively compared to results in the previous works. Our comparison indicates that we can evaluate the spatial and temporal variations of effective reactive areas more mechanistically in the microfluidic system only with the knowledge of local hydrodynamics, polymorphs, and comprehensive image analysis. Here, our analysis clearly highlights the feedback mechanisms between reactions and hydrodynamics. Pore-scale modeling results during the dissolution phase were used to account for experimental observations of dissolved CaCO 3 plumes with dissolution of the unstable phase of CaCO 3 . Mineral precipitation and dissolution induce complex dynamic pore structures, thereby impacting pore-scale fluid dynamics. Pore-scale analysis of the evolution of precipitates can reveal the significance of chemical and pore structural controls on reaction and fluid migration.

58 GEOSCIENCES↗

DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution

Downward shortwave radiation (SW) and photosynthetically active radiation (PAR) play crucial roles in Earth system dynamics. Spaceborne remote sensing techniques provide a unique means for mapping accurate spatiotemporally continuous SW–PAR, globally. However, any individual polar-orbiting or geostationary satellite cannot satisfy the desired high temporal resolution (sub-daily) and global coverage simultaneously, while integrating and fusing multisource data from complementary satellites/sensors is challenging because of co-registration, intercalibration, near real-time data delivery and the effects of discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR), launched in February 2015, offers an unprecedented possibility to bridge the gap between high temporal resolution and global coverage and characterize the diurnal cycles of SW–PAR globally. In this study, we adopted a suite of well-validated data-driven machine-learning models to generate the first global land products of SW–PAR, from June 2015 to June 2019, based on DSCOVR/EPIC data. The derived products have high temporal resolution (hourly) and medium spatial resolution (0.1°×0.1°), and they include estimates of the direct and diffuse components of SW–PAR. We used independently widely distributed ground station data from the Baseline Surface Radiation Network (BSRN), the Surface Radiation Budget Network (SURFRAD), NOAA's Global Monitoring Division and the U.S. Department of Energy's Atmospheric System Research (ASR) program to evaluate the performance of our products, and we further analyzed and compared the spatiotemporal characteristics of the derived products with the benchmarking Clouds and the Earth's Radiant Energy System Synoptic (CERES) data. We found both the hourly and daily products to be consistent with ground-based observations (e.g., hourly and daily total SWs have low biases of -3.96 and -0.71 W m -2 and root-mean-square errors (RMSEs) of 103.50 and 35.40 W m -2 , respectively). The developed products capture the complex spatiotemporal patterns well and accurately track substantial diurnal, monthly, and seasonal variations in SW–PAR when compared to CERES data. They provide a reliable and valuable alternative for solar photovoltaic applications worldwide and can be used to improve our understanding of the diurnal and seasonal variabilities of the terrestrial water, carbon and energy fluxes at various spatial scales.

54 ENVIRONMENTAL SCIENCES↗

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗

Surface accuracy measurement system deployable reflector antennas

Conceptual optical sensor configurations for measuring the surface deformations of large, deployable space antennas are described. These antennas include precision deployable reflectors up to 30 meters diameter and 1000 GHz frequency and mesh deployable reflectors up to 100 meters diameter and 30 GHz frequency. For each representative antenna configuration, the surface deformation sensor provides continuous, real-time measurements at a sufficient number of sample points to be compatible with active surface control. Moreover, the sensor system does not interfere with the mechanical or microwave characteristics of either the antenna surface or the feed. For the applications considered, the sensor system consists of a central receiver ring containing six to ten long focal length, angle measuring instruments, each viewing a dedicated set of bright point targets at the antenna. The targets, either light emitting diodes or illuminated retroreflectors, are modulated to eliminate errors from spurious backgrounds. Very preliminary performance estimates indicate that the sensor system, using commerical grade components, can produce a 20th to a 30th wavelength accuracy (3 sigma).

Neiswander, R. S.↗

2018 Wind Technologies Market Report

The U.S. Department of Energy (DOE)’s Wind Technologies Market Report provides an annual overview of trends in the U.S. wind power market. Highlights of this year’s report include: ~Wind power capacity additions continued at a robust pace in 2018: $11 billion was invested in new wind power plants in 2018. In 2018, wind energy contributed 6.5% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and more than 30% in three of those states—Kansas, Iowa, and Oklahoma. 232 gigawatts of wind are seeking transmission interconnection, 2% of which represents proposed wind plants paired with storage. ~Bigger turbines are enhancing wind project performance: Increased blade lengths have dramatically increased wind project capacity factors, one measure of project performance, and taller towers are on the horizon. The average 2018 capacity factor among projects built from 2014 through 2017 was 42%, compared to an average of 31% among projects built from 2004 to 2011 and 24% among projects built from 1998 to 2001. Low wind turbine pricing continues to push down installed project costs: Wind turbine prices have fallen to $700–$900/kW. The average installed cost of wind projects in 2018 was $1,470/kW, down 40 percent since the peak in 2009 and 2010. ~Wind energy prices are at historical lows: After topping out at 7¢/kWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped to below 2¢/kWh—though this nationwide average is dominated by projects that hail from the lowest-priced Interior region of the country. These prices, which are possible in part due to federal tax support, compare favorably to the projected future fuel costs of gas-fired generation. Solar PPA prices have declined precipitously, pressuring wind’s competitive position. ~The grid-system value of wind has declined over the last decade, but rebounded over the last two years. The value of wind in wholesale power markets is impacted by the location of wind plants, their hourly output profiles, and how those characteristics correlate with real-time electricity prices and capacity markets. The market value of wind in 2018 was the lowest in the Southwest Power Pool and Texas (average of $17/MWh and $18/MWh, respectively) whereas the highest-value market was New England (at $41/MWh). Wind energy prices are generally competitive with these value estimates. ~The domestic supply chain for wind equipment is diverse: For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (75-90%), and blades and hubs (50-70%), but is much lower (<20%) for most components internal to the nacelle. Wind sector employment reached a new high of more than 114,000 full-time workers at the end of 2018. ~Continued strong growth in wind capacity is anticipated in the near term: With federal tax incentives still available, various forecasts for the domestic market show expected wind power capacity additions of 9,000-12,000 MW in 2019 and 11,000-15,000 in 2020, with market contraction anticipated beginning in 2021 as those tax incentives are phased out.

17 WIND ENERGY↗

Optimizing district energy systems by integrating Borehole Thermal Energy Storage Using a Mixed-Integer Linear Programming g-function framework with a Multi-Timescale Rolling Horizon method

Shallow geothermal has gained increasing attention in recent years; however, a reliable framework for its accurate incorporation into large-scale energy system optimization remains lacking. This study proposes a Mixed-Integer Linear Programming (MILP) framework combined with the g-function approach to integrate Borehole Thermal Energy Storage (BTES) technology into energy system optimization. Validation against a Modelica-based reservoir network simulation demonstrates that the proposed framework effectively captures the ground thermal response under varying energy loads and accurately estimates the borefield energy supply. To enhance scalability, a Rolling Horizon with Multi-Timescale (RH-MTS) method is further introduced, reducing computational time by 73 % for the 1-year optimization model with only minor loss of optimality. The framework is demonstrated through the case study of the UC Berkeley campus. Results indicate that BTES is a cost-effective and low-carbon solution: two borefields comprising 382 boreholes can meet 8.0 % and 6.6 % of the total campus heating and cooling demand, respectively, at an average energy rate of 0.70–0.77 USD/kWh and carbon intensity of 0.54 kg-CO2/kWh. Short-term analysis reveals a 35%–65% decline in BTES energy flow after 3–6 months of continuous heating/cooling operation, while long-term simulation shows that annual energy production of BTES can vary by up to 12.0 % after four years before stabilizing. Overall, this study develops a novel optimization framework that couples physics-based g-function method with MILP optimization framework, thereby advancing methodological development for shallow-geothermal integration and providing actionable guidance for BTES deployment in district-energy systems.

Yang, Jiahui↗