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Wind tunnel investigation of effect of wing location, power, and flap deflection on effective dihedral of a typical single-engine fighter-airplane model with tail removed
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Photovoltaic and Behind-the-Meter Battery Storage: Advanced Smart Inverter Controls and Field Demonstration
Electric utilities have little visibility of the electrical distribution system, and consequently, limited diagnostic capabilities. The distribution grid was designed for a unidirectional power flow, where energy is supplied by few large centralized power plants; however, this is set to change to meet California’s aggressive decarbonization goals. The large-scale deployment of distributed renewable generation, such as photovoltaics (PV), can have many negative effects on an unprepared grid. As illustrated in the California “duck curve,” PV generation modifies load profiles during the day, which causes steep ramping in the evening. This effect indicates the need to revise the electrical grid’s design and operation. This project sought to (a) create a centralized resource in California to test and validate distribution technology controls with industry standard PV, storage, and high-fidelity sensors, (b) support strategic and operational decisions for new grid architectures by providing simulation models, (c) promote new ways to control clusters of PV smart inverters, in accordance with California Rule 21, and (d) innovate, develop, and field test a predictive controller to maximize economic profit for the customer while supporting the grid. The controller was built using the state-of-the-art model predictive control methodology to optimally control behind-the-meter PV and battery storage. In consideration of the duck curve, the controller optimally controls the battery dispatch by charging during excess generation periods and discharging during the critical afternoon demand hours. The controller was evaluated in annual simulations and revealed the potential cost-effectiveness of behind-the-meter battery storage. The simulations showed that the annual electricity bill could be reduced by as much as 35 percent, with a payback period of the investment in battery storage in about 6 years – significantly shorter than the manufacturer’s 10-year warranty. All developed simulation models, the grid event library, and the MPC controller are open-source and available online.
Constrained reduced-order models based on proper orthogonal decomposition
A novel approach is presented to constrain reduced-order models (ROM) based on proper orthogonal decomposition (POD). The Karush–Kuhn–Tucker (KKT) conditions were applied to the traditional reduced-order model to constrain the solution to user-defined bounds. The constrained reduced-order model (C-ROM) was applied and validated against the analytical solution to the first-order wave equation. C-ROM was also applied to the analysis of fluidized beds. Lastly, it was shown that the ROM and C-ROM produced accurate results and that C-ROM was less sensitive to error propagation through time than the ROM.
Toward an In-Depth Material Model for Cermet Nuclear Thermal Rocket Fuel Elements
The development and qualification of nuclear thermal propulsion (NTP) fuel element technologies would be aided by an in-depth model of material response and failure modes at operating conditions. Integrated computational materials engineering techniques have the potential to provide such a model, as demonstrated here through three case studies focused on a tungsten-uranium mononitride cermet fuel. The first case focuses on the erosion of tungsten (W, also named wolfram), a nominal coating/cladding material, in hot hydrogen. Ab initio techniques are used to calculate erosion rates and thermal expansion at NTP operating conditions. The second focuses on the stability of uranium mononitride (UN) fuels at high temperature and in the presence of hydrogen. Phase diagram techniques reveal potential instabilities and decomposition pathways at high hydrogen concentrations. The third focuses on using microstructure information to predict high temperature mechanical response and failure of tungsten, used in refractory cermet materials. Combined finite element and discrete dislocation dynamics techniques provide mechanical properties in agreement with experimental methods. The integration of these techniques for an all-encompassing material model is discussed.
USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better Than Simple Trend Analyses?
Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R^(2) of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R^(2) results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.
Robust Solution Approach for Bilevel Demand Response Game at Distribution Level
In this paper, a bilevel electricity pricing and demand response game between a distribution system operator (DSO) and load aggregators (LAs) is considered, and a robust decision model is proposed for the DSO to deal with the uncertainties from the wholesale market prices and demand consumptions of LAs. With the max-min objective at the upper level, the robust bilevel model is converted into a single level model by the Karush-Kuhn-Tucker (KKT) conditions and prime-dual transformation. Several groups of experiments have been conducted based on different preferences on uncertainty gaps and peak load reductions to show its effectiveness. After-the-fact scenario analysis has indicated that the robust solution is more beneficial in reducing the risk of inaccurate predictions as compared to the risk neutral strategy.
BI-LEVEL OPTIMIZATION FOR ELECTRICITY TRANSACTION IN SMART COMMUNITY WITH MODULAR PUMP HYDRO STORAGE
Grid integration of the increasing distributed energy resources could be challenging in terms of new infrastructure investment, power grid stability, etc. To resolve more renewables locally and reduce the need for extensive electricity transmission, a community energy transaction market is assumed with market operator as the leader whose responsibility is to generate local energy prices and clear the energy transaction payment among the prosumers (followers). The leader and multi-followers have competitive objectives of revenue maximization and operational cost minimization. This non-cooperative leader-follower (Stackelberg) game is formulated using a bi-level optimization framework, where a novel modular pump hydro storage technology (GLIDES system) is set as an upper level market operator, and the lower level prosumers are nearby commercial buildings. The best responses of the lower level model could be derived by necessary optimality conditions, and thus the bi-level model could be transformed into single level optimization model via replacing the lower level model by its Karush-Kuhn-Tucker (KKT) necessary conditions. Several experiments have been designed to compare the local energy transaction behavior and profit distribution with the different demand response levels and different local price structures. The experimental results indicate that the lower level prosumers could benefit the most when local buying and selling prices are equal, while maximum revenue potential for the upper level agent could be reached with non-equal trading prices.
FORESTR: Finding, Organizing, Representing, Explaining, Summarizing, and Thinning Random forests
Random forests have become popular models used for data driven predictions. As a result, random forests are currently used or being considered for high-consequence mission applications in national security, such as the prediction of yield from optical signals and malware detection. While random forests may provide accurate predictions, the complexity of the algorithm causes a lack of interpretability. Random forests are an ensemble of regression or decision trees. Individual regression and decision trees are interpretable, but ensembles are inherently difficult to interpret due to the compilation of many models. We aim to increase the interpretability of random forests by finding patterns in the ensemble of trees that can be used to “thin” (or remove) trees. As a starting point, in this report, we develop a new distance metric for quantifying the similarity between trees based on their topologies (i.e., shapes). We base the metric on a novel distance metric for graphs that is a proper mathematical distance, is invariant to transformations, has registration between graphs, and computes topological evolutions between graphs. We use the tree distance metric to compute tree statistics such as a “mean tree” and to identify clusters of trees. We apply the developed methodology to a toy dataset and a mission relevant product inspection dataset to demonstrate how the metric can provide insight into random forests. Furthermore, we discuss the limitations of the approach and ideas for future research into how the metric could be used as a thinning tool to develop less complex models.
Inversion of the Humboldt Glacier Basal Friction Coefficient Field in an Uncertain Ice Sheet Model.
Abstract not provided.
QBTNs - Quantum Boolean Tensor Networks
We develop algorithms and software that uses the D-Wave 2000Q quantum annealer to solve several types of Boolean tensor factorization problems. Boolean tensor factorization refers to the problem of representing a high-dimensional tensor filled with Boolean values as a product of smaller Boolean core tensors and Boolean matrices. We consider different tensor factorization models, including Boolean Tensor Train, Boolean Tucker, and Boolean Hierarchical Tucker. As an exact decomposition of a given type may not exist in the general case, the objective is to minimize the difference between the input high-dimensional tensor and the product of the lower-dimensional tensors of the proposed factorization, using a specified tensor norm. In our approach, we reduce the Boolean tensor factorization problem to a sequence of quadratic unconstrained binary optimization problems suitable for the D-Wave 2000Q quantum annealer. Although current quantum technology is still fairly restricted in the problems it can tackle, we show that complex tensor factorization problems as the ones addressed by us can be solved efficiently and accurately.
A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization: Preprint
This paper considers time-varying nonconvex optimization problems, utilized to model optimal operational trajectories of systems governed by possibly nonlinear physical or logical models. Algorithms for tracking a Karush-Kuhn-Tucker point are synthesized, based on a regularized primal-dual gradient method. In particular, the paper proposes a feedback-based primal-dual gradient algorithm, where analytical models for system state or constraints are replaced with actual measurements. When cost and constraints functions are twice continuously differentiable, conditions for the proposed algorithms to have bounded tracking error are derived, and a discussion of their practical implications is provided. Illustrative numerical simulations are presented for an application in power systems.
Global Energetics of Solar Flares. VI. Refined Energetics of Coronal Mass Ejections
In this study, we refine the coronal mass ejection (CME) model that was presented in an earlier study of the global energetics of solar flares and associated CMEs and apply it to all (860) GOES M- and X-class flare events observed during the first seven years (2010–2016) of the Solar Dynamics Observatory (SDO) mission. The model refinements include (1) the CME geometry in terms of a 3D volume undergoing self-similar adiabatic expansion, (2) the solar gravitational deceleration during the propagation of the CME, which discriminates between eruptive and confined CMEs, (3) a self-consistent relationship between the CME center-of-mass motion detected during EUV dimming and the leading-edge motion observed in white-light coronagraphs, (4) the equipartition of the CME’s kinetic and thermal energies, and (5) the Rosner–Tucker–Vaiana scaling law. The refined CME model is entirely based on EUV-dimming observations (using Atmospheric Imager Assembly (AIA)/SDO data) and complements the traditional white-light scattering model (using Large-Angle and Spectrometric Coronagraph Experiment (LASCO)/Solar and Heliospheric Observatory data), and both models are independently capable of determining fundamental CME parameters. Comparing the two methods, we find that (1) LASCO is less sensitive than AIA in detecting CMEs (in 24% of the cases), (2) CME masses below m{sub cme}≲10{sup 14} g are underestimated by LASCO, (3) AIA and LASCO masses, speeds, and energies agree closely in the statistical mean after the elimination of outliers, and (4) the CME parameters speed v, emission measure-weighted flare peak temperature T {sub e}, and length scale L are consistent with the following scaling laws: v∝T{sub e}{sup 1/2}, v∝(m{sub cme}){sup 1/4}, and m{sub cme}∝L{sup 2}.
Collaborative research in tunneling and field emission pumped surface wave local oscillators and amplifiers for infrared and submillimeter wavelengths under director's discretionary fund
Progress is reported in work towards the development of surface wave sources for the infrared and sub-millimeter portion of the spectrum to be based upon electron pumping by tunneling electrons in metal-barrier-metal or metal-barrier-semiconductor devices. Tunneling phenomena and the coupling of radiation to tunnel junctions were studied. The propagation characteristics of surface electro-magnetic modes in metal-insulator-p(++) semiconductor structures as a function of frequency were calculated. A model for the gain process based upon Tucker's formalism was developed and used to estimate what low frequency gain might be expected from such structures. The question of gain was addressed from a more fundamental viewpoint using the method of Lasher and Stern.
Flow and filtration performance of wire cloth filter media.
Mathematical models of flow and filtration performance of wire cloth filter media in series and parallel configuration
Utilizing Functional Data in an Elastic Model Calibration Framework.
Abstract not provided.
Experimental investigation of the jet-boundary constriction correction for a model spanning a closed circular tunnel
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