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DOE OSTI2016

Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations

We extend the reduced basis (RB) accelerated Bayesian inversion methods for affine-parametric, linear operator equations which are considered in [16,17] to non-affine, nonlinear parametric operator equations. We generalize the analysis of sparsity of parametric forward solution maps in [20] and of Bayesian inversion in [48,49] to the fully discrete setting, including Petrov–Galerkin high-fidelity (“HiFi”) discretization of the forward maps. We develop adaptive, stochastic collocation based reduction methods for the efficient computation of reduced bases on the parametric solution manifold. The nonaffinity and nonlinearity with respect to (w.r.t.) the distributed, uncertain parameters and the unknown solution is collocated; specifically, by the so-called Empirical Interpolation Method (EIM). For the corresponding Bayesian inversion problems, computational efficiency is enhanced in two ways: first, expectations w.r.t. the posterior are computed by adaptive quadratures with dimension-independent convergence rates proposed in [49]; the present work generalizes [49] to account for the impact of the PG discretization in the forward maps on the convergence rates of the Quantities of Interest (QoI for short). Second, we propose to perform the Bayesian estimation only w.r.t. a parsimonious, RB approximation of the posterior density. Based on the approximation results in [49], the infinite-dimensional parametric, deterministic forward map and operator admit N-term RB and EIM approximations which converge at rates which depend only on the sparsity of the parametric forward map. In several numerical experiments, the proposed algorithms exhibit dimension-independent convergence rates which equal, at least, the currently known rate estimates for N-term approximation. We propose to accelerate Bayesian estimation by first offline construction of reduced basis surrogates of the Bayesian posterior density. The parsimonious surrogates can then be employed for online data assimilation and for Bayesian estimation. They also open a perspective for optimal experimental design.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Cyber-Informed Engineering Principles: What’s in it for me?

CIE is an emerging method to integrate cybersecurity considerations into the conception, design, development, and operation of any physical system that has digital connectivity, monitoring, or control. CIE complements—but does not replace—the application of cybersecurity standards or practices currently in place within an organization. Rather, it expands cybersecurity decisions into the engineering space, not by asking engineers to become cyber experts, but by calling on engineers to apply engineering tools and make engineering decisions that improve cybersecurity outcomes. CIE examines the engineering consequences that a sophisticated cyber attacker could achieve, and drives engineering changes that may provide deterministic mitigations to limit or eliminate those consequences. Engineers and technicians that design critical energy infrastructure installations can integrate the 12 principles of CIE into each phase of the engineering lifecycle, from concept to retirement. These principles are aimed at system or design engineers, operators, and technicians, rather than software engineers or operational cybersecurity practitioners, because the engineers who design, build, operate, and maintain the physical infrastructure are best positioned to leverage a system’s engineering design to diminish the severity of cyber attacks or digital technology failures. This approach creates new opportunities for engineering teams—and not just cybersecurity teams—to secure the system using the physics and mechanics of engineering controls—not just digital monitoring and controls.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2018

An Ensemble Optimization Framework for Coupled Design of Hydropower Contracts and Real-Time Reservoir Operating Rules

Often, revenues from hydropower generation depend on the operator's ability to provide firm power in the presence of uncertain inflows. The primary options available for optimizing revenue are negotiation of a firm power contract before operations begin and adjustment of the reservoir release during operations. Contract and release strategy optimization are closely coupled and most appropriately analyzed with stochastic real-time control methods. Here we use an ensemble-based approach to stochastic optimization that provides a convenient way to construct nonparametric revenue probability distributions to explore the implications of uncertainty. The firm power contract is a simplified bilateral fixed price agreement that partially insulates operator and buyer from price fluctuations. The release control laws and firm energy target are jointly optimized to maximize the operator's expected revenue. Revenue probability distributions and related spill performance statistics indicate that predictive operating strategies such as stochastic dynamic programming and model predictive control can give significantly better performance than standard deterministic operating rules. The performance obtained from batch optimization with perfect inflow information establishes a convenient upper bound on potential revenue and provides a baseline for assessing the significance of differences between real-time operating strategies. Sensitivity analysis indicates that the benefits of predictive operational strategies are greatest for reservoirs with medium nondimensional residence times and less important for reservoirs with large residence times. Overall, probabilistic analysis of the coupled hydropower contract-operations problem provides a realistic way to assess revenue and risk for reservoirs that must provide firm power when inflows are uncertain.

13 HYDRO ENERGY↗

DOE OSTI2019

Stochastic Molecular Dynamics

I can not pass up the opportunity to honor Graeme Bird, a true gentleman and a real scholar, if there ever was one. When we first met at a rarefied gas dynamics conference, it was instant mutual admiration. Furthermore, we met rarely thereafter at subsequent rarefied gas meetings since I attended them infrequently as a physicist interested in hydrodynamics, where often even a rarefied gas without deterministic collisions can serve as a simple fluid model but Graeme attended them regularly. In that rst meeting I became aware of DSMC, read Graeme paper and found to my astonishment that the only reference in that paper was to my molecular dynamics work2. Indeed DSMC is computationally faster competitor to molecular dynamics and it is this feature that I would like to emphasize in this brief note. It is for this reason that I entitled this contribution \stochastic molecular dynamics". I once told Graeme that that would have been a more descriptive name to his method than DSMC. DSMC is also a good demonstration that an algorithm developed for an entirely different purposes can also be very useful for other applications.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2025

A Novel LDPP-MADDPG Approach for Distributed Power Allocation in mmWave Cellular Networks

This paper considers the problem of distributed beam scheduling and power allocation problem in millimeter- Wave (mmWave) cellular networks, in which multiple Base Stations (BSs) operate as individual operators over a shared spectrum. We propose a novel learning-aided approach that integrates the Lyapunov Drift-Plus-Penalty (LDPP) framework and Multi-agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithms. This offers a powerful approach to learning stable and constraint-aware policies, reaping the joint benefit of both LDPP and MADDPG, in complex multiagent environments. The major challenge for this approach is to integrate these two approaches in a meaningful and effective manner. The key idea to solve this problem is to introduce a novel feature of local observation that incorporates potential negative value of the reward function due to the stochastic constraints introduced by the LDPP framework. Empirical results demonstrate that our proposed scheme outperforms the baseline methods under various conditions.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2016

A generalized multi-resolution expansion for uncertainty propagation with application to cardiovascular modeling

Computational models are used in a variety of fields to improve our understanding of complex physical phenomena. Recently, the realism of model predictions has been greatly enhanced by transitioning from deterministic to stochastic frameworks, where the effects of the intrinsic variability in parameters, loads, constitutive properties, model geometry and other quantities can be more naturally included. A general stochastic system may be characterized by a large number of arbitrarily distributed and correlated random inputs, and a limited support response with sharp gradients or event discontinuities. This motivates continued research into novel adaptive algorithms for uncertainty propagation, particularly those handling high dimensional, arbitrarily distributed random inputs and non-smooth stochastic responses. Here, we generalize a previously proposed multi-resolution approach to uncertainty propagation to develop a method that improves computational efficiency, can handle arbitrarily distributed random inputs and non-smooth stochastic responses, and naturally facilitates adaptivity, i.e., the expansion coefficients encode information on solution refinement. Our approach relies on partitioning the stochastic space into elements that are subdivided along a single dimension, or, in other words, progressive refinements exhibiting a binary tree representation. We also show how these binary refinements are particularly effective in avoiding the exponential increase in the multi-resolution basis cardinality and significantly reduce the regression complexity for moderate to high dimensional random inputs. The performance of the approach is demonstrated through previously proposed uncertainty propagation benchmarks and stochastic multi-scale finite element simulations in cardiovascular flow.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

A high-fidelity approach towards simulation of pool boiling

A novel numerical approach is developed to simulate the multiscale problem of pool-boiling phase change. The particular focus is to develop a simulation technique that is capable of predicting the heat transfer and hydrodynamic characteristics of nucleate boiling and the transition to critical heat flux on surfaces of arbitrary shape and roughness distribution addressing a critical need to design enhanced boiling heat transfer surfaces. The macro-scale of the phase change and bubble dynamics is addressed through employing off-the-shelf Computational Fluid Dynamics (CFD) methods for interface tracking and interphase mass and energy transfer. The micro-scale of the microlayer, which forms at early stage of bubble nucleation near the wall, is resolved through asymptotic approximation of the thin-film theory which provides a closed-form solution for the distribution of the micro-layer and its influence on the evaporation process. In addition, the sub-grid surface roughness is represented stochastically through probabilistic density functions and its role in bubble nucleation and growth is then represented based on the thermodynamics of nucleation process. This combination of deterministic CFD, local approximation, and stochastic representation allows the simulation of pool boiling on any surface with known roughness and enhancement characteristics. The numerical model is validated for dynamics and hydrothermal characteristics of a single nucleated bubble on a flat surface against available literature data. In addition, the prediction of pool-boiling heat transfer coefficient is verified against experimental measurements as well as reputable correlations for various roughness distributions and different surface orientations. Finally, the model is employed to demonstrate pool-boiling phenomenon on enhanced structures with reentrance cavities and to explore the effect of enhancement feature design on thermal and hydrodynamic characteristics of these surfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Restoring Flow Regimes after Dam Construction and Operation: Evidence from Dynamic Systems Theory

Dams provide a variety of benefits to society, including hydropower generation, flood control, recreation, and water supply for municipal, industrial, and agricultural customers. However, constructing and operating dams for these purposes can negatively impact downstream ecosystems by altering natural flow regimes and interfering with the lifecycles of aquatic and riparian species. Increased concern from environmental interests has put pressure on dam operators to quantify the flow regime changes that have resulted from dam construction and to evaluate how future modifications to dam releases may impact river systems. In this study, we present a method for analyzing the impact of dam operations by reconstructing system trajectories from hydrologic flow time series for different periods of dam operation (pre-dam, post-dam, and post-modification). We apply these methods to flow data from eight rivers in the US to determine the dimensionality of each operating period and to analyze trends based on the primary purpose of dam operation. Hydropower dams operated without flow restrictions exhibit high dimensional, stochastic behavior. However, the system dimensionality is reduced to approximately three variables when ramping restrictions are implemented, or in cases where an afterbay is present to reregulate flows. This low dimensional behavior is more similar to natural flow regimes. For dams operated primarily for flood control or water supply, the flow regimes are typically low dimensional and more deterministic.

13 HYDRO ENERGY↗

DOE OSTI2022

GANISP: A GAN-Assisted Importance Splitting Probability Estimator: Preprint

To reduce the variance of rare event probability estimator, genealogical importance splitting marches towards a rare event by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is made difficult when applied to deterministic systems as the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. It is shown that a random perturbation strategy may be effective for some systems but may also fail for others, thereby preventing variance reduction in the probability estimate. To address this limitation, it is proposed to use a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).

generative adversarial network↗

DOE OSTI2016

Application of Monte Carlo Methods to Perform Uncertainty and Sensitivity Analysis on Inverse Water-Rock Reactions with NETPATH

Methods were developed to quantify uncertainty and sensitivity for NETPATH inverse water-rock reaction models and to calculate dissolved inorganic carbon, carbon-14 groundwater travel times. The NETPATH models calculate upgradient groundwater mixing fractions that produce the downgradient target water chemistry along with amounts of mineral phases that are either precipitated or dissolved. Carbon-14 groundwater travel times are calculated based on the upgradient source-water fractions, carbonate mineral phase changes, and isotopic fractionation. Custom scripts and statistical code were developed for this study to facilitate modifying input parameters, running the NETPATH simulations, extracting relevant output, postprocessing the results, and producing graphs and summaries. The scripts read userspecified values for each constituent’s coefficient of variation, distribution, sensitivity parameter, maximum dissolution or precipitation amounts, and number of Monte Carlo simulations. Monte Carlo methods for analysis of parametric uncertainty assign a distribution to each uncertain variable, sample from those distributions, and evaluate the ensemble output. The uncertainty in input affected the variability of outputs, namely source-water mixing, phase dissolution and precipitation amounts, and carbon-14 travel time. Although NETPATH may provide models that satisfy the constraints, it is up to the geochemist to determine whether the results are geochemically reasonable. Two example water-rock reaction models from previous geochemical reports were considered in this study. Sensitivity analysis was also conducted to evaluate the change in output caused by a small change in input, one constituent at a time. Results were standardized to allow for sensitivity comparisons across all inputs, which results in a representative value for each scenario. The approach yielded insight into the uncertainty in water-rock reactions and travel times. For example, there was little variation in source-water fraction between the deterministic and Monte Carlo approaches, and therefore, little variation in travel times between approaches. Sensitivity analysis proved very useful for identifying the most important input constraints (dissolved-ion concentrations), which can reveal the variables that have the most influence on source-water fractions and carbon-14 travel times. Once these variables are determined, more focused effort can be applied to determining the proper distribution for each constraint. Second, Monte Carlo results for water-rock reaction modeling showed discrete and nonunique results. The NETPATH models provide the solutions that satisfy the constraints of upgradient and downgradient water chemistry. There can exist multiple, discrete solutions for any scenario and these discrete solutions cause grouping of results. As a result, the variability in output may not easily be represented by a single distribution or a mean and variance and care should be taken in the interpretation and reporting of results.

phase dissolution and precipitation amounts↗

DOE OSTI2020

A signal compensation based cascaded PI control for an industrial heat exchange system

Since the characteristics of the industrial heat exchange system (HES) are drastically affected by variations of the steam pressure, ambient temperature and water quality, it is difficult for the classical cascade PI control system to meet the desirable control objectives. Therefore, in this paper, a deterministic linear model with unmodeled dynamics is used to describe the dynamic characteristics of the HES. It should be noted that the unmodeled dynamics at the previously sampled instant can be calculated using actual measurements and that the tracking error, which includes effects of the rate of changes of unmodeled dynamics on the controlled model, can be accurately determined. An additional compensating signal is obtained with the feedforward control together with a one-step optimal control law. Consequently, the cascade PI control strategy with the signal compensation is established. Furthermore, the simulation and industrial application show that the proposed method can effectively improve the performance of the cascade PI control system, especially when the unmodeled dynamics is subjected to unknown variations.

42 ENGINEERING↗

DOE OSTI2025

Laser-Modulation-Driven X-Ray Pulse Shaping in Regenerative Amplifier Free-Electron Lasers

In this Letter, we present a robust method for generating custom-shaped, coherent hard x-ray pulses in regenerative amplifier free-electron lasers (RAFELs) using laser-induced energy modulation of the electron beam. A temporally shaped optical modulation imprints an optical-wavelength energy pattern on the beam, which interacts with the recirculated seed in the x-ray cavity to generate spectral sidebands. These sidebands lie outside the Bragg reflection bandwidth and are extracted as out-coupled radiation, while the seed is retained for continued amplification, enabling a novel mechanism for both output coupling and pulse shaping. Furthermore, this approach allows deterministic control over the spectral and temporal properties of the emitted x-rays, including multicolor generation, twin-pulse structures, pulse duration tuning, and complex temporal waveforms. Importantly, it does not require modification of the RAFEL cavity or optics and is fully compatible with high-repetition-rate operation, offering a practical pathway to deliver coherent, shaped, and on-demand x-ray pulses for advanced time-resolved and nonlinear x-ray science.

Free-electron lasers↗

DOE OSTI2016

Apparent Monte Carlo Source Convergence Problem with BWR Fuel Depleted with Partial Control Blade Insertion

Oak Ridge National Laboratory (ORNL) and the US Nuclear Regulatory Commission (NRC) have initiated a multiyear project to investigate application of burnup credit (BUC) to boiling-water reactor (BWR) fuel in storage and transportation casks and spent fuel pools (SFPs). Ideally, a single combination of all operating parameters including moderator density and axial burnup profiles, control blade exposure, and others can be identified to allow a simple, bounding approach to BWR BUC. One aspect of this investigation involves examining the effect of control blade insertion during operation on discharged assembly reactivity. In the course of these calculations, an apparent source convergence problem was discovered for fuel that has experienced partial blade insertion for the entire lifetime of the assembly. The source convergence problem appears not to impact k{sub eff}, but it manifests in predictions of the axial fission density profile. This phenomenon is of interest in identifying areas of concern with BWR BUC since it has not been studied as thoroughly as BUC for pressurized-water (PWR) reactor fuel. There may also be interest in this configuration for further study of a realistic source Monte Carlo convergence problem as opposed to some earlier manufactured source convergence problems through careful selection of initial conditions. This summary presents the depletion conditions used to generate the problem, a description of the models, a background based on well-behaved calculations, the identification of the problem, and attempts made to resolve the problem. Only a brief description of the depletion conditions and models is possible within the limited scope of this summary. Finally, conclusions are presented. This summary identifies a source convergence problem in the process of investigating BUC for BWR spent fuel. The depletion scenario which created the depleted fuel and burnable absorber compositions included a control blade inserted in the bottom 14 nodes throughout the entire depletion. This insertion is slightly over half the length of the assembly, and it appears to create two regions of nearly equal reactivity. As expected, one region is near the top of the assembly, and the other region is near the middle of the assembly. This node includes significant {sup 239}Pu generation as a result of rodded operation, but it is also the top node of the full lattice zone. While the calculated k{sub eff} values appear to be statistically equivalent, the axial neutron production profile varies significantly among independent simulations of the same model. A range of standard approaches has been used to attempt to resolve the source convergence problem and determine the true axial neutron production profile. This profile is of interest in cask criticality safety analysis because it influences the interaction among assemblies loaded in the cask. The forthcoming SCALE 6.2 release will include source convergence testing based on Shannon entropy, which may assist in identifying these types of problems in the future. It should be noted that this source convergence problem results from the depletion conditions and the final distribution of fissile and absorber material within the discharged fuel assembly. The results, while entirely generated within the SCALE code system, are representative of any Monte Carlo program attempting to calculate k{sub eff} for such a system. The most important conclusion of this work is that the BUC criticality safety community must be aware of the potential for this type of source convergence problem. Analysts must approach such partially rodded scenarios with caution and critically examine the results. Fortunately, it is extremely unlikely that BWR assemblies would experience the bladed operation necessary to create this particular source convergence problem. These results may also have some bearing on the analysis of PWR BUC for new plant designs such as AP1000 that incorporate significant rodded operations. In a broader context, the source convergence problem identified here could have been magnified if it had occurred in a Monte Carlo depletion simulation. It may be possible to identify this type of problem in a depletion calculation by comparing the apparently converged fluxes between steps to ensure that no oscillations or other serious shifts are occurring. The depletion calculations performed in this work were performed with a two-dimensional deterministic code and are therefore not subject to this problem. These results may also raise questions about the validity of 2D depletion models with extruded geometry in the 3D k{sub eff} calculation. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Adaptive Generation of Training Data for ML Reduced Model Creation

Machine learning proxy models are often used to speed up or completely replace complex computational models. The greatly reduced and deterministic computational costs enable new use cases such as digital twin control systems and global optimization. The challenge of building these proxy models is generating the training data. A naive uniform sampling of the input space can result in a non-uniform sampling of the output space of a model. This can cause gaps in the training data coverage that can miss finer scale details resulting in poor accuracy. While larger and larger data sets could eventually fill in these gaps, the computational burden of full-scale simulation codes can make this prohibitive. In this paper, we present an adaptive data generation method that utilizes uncertainty estimation to identify regions where training data should be augmented. By targeting data generation to areas of need, representative data sets can be generated efficiently. The effectiveness of this method will be demonstrated on a simple one-dimensional function and a complex multidimensional physics model.

Cianciosa, Mark↗

DOE OSTI2016

Radiation Transport in Random Media With Large Fluctuations

Strongly heterogeneous media arise in several applications that include radiation shields, nuclear fuel, BWR moderators, clouds, planetary and stellar atmospheres, turbulent gases and plasmas. Neutral and charged particle transport computations in such media have relied heavily on formulating transport equations with spatially random coefficients (physical data) and developing solution methods to deal with the additional stochastic dimensions. Attempts at developing approximate closures that yield only low order statistical information (e.g., mean and variance of the flux) have proved to be highly restrictive under real physics conditions or rely on techniques that require fluctuation amplitudes to be small for robustness. Recently, stochastic spectral methods such as polynomial chaos and stochastic collocation have been developed for aleatoric uncertainty quantification and sensitivity analysis, and successfully applied in radiation transport work. Advances in UQ techniques have tended to focus on efficiently handling large numbers of uncertain variables but the rigorous stochastic basis of the approach also promotes its use in situations where stochasticity is due to spatial heterogeneity and the associated uncertainty is large. Here we apply these techniques to radiation transport in media with spatially randomly varying cross sections without restriction on fluctuation amplitudes. Specifically, we represent the cross sections as a lognormal spatial random process with specified mean, variance and covariance function and use a Karhunen-Loeve (KL) decomposition to generate cross section realizations that are strictly positive. Woodcock Monte Carlo (WMC) is then used to simulate transport using random sampling of cross sections and deterministic sampling based on a stochastic collocation technique. Numerical results for the mean and variance in the scalar flux and leakage currents are obtained for weak and strong random variations. We focus in this work on demonstrating the approach and defer application to specific problems to a future investigation. We have demonstrated that a combination of stochastic spectral representation and Woodcock Monte Carlo simulation allows efficient computation of radiation transport in strongly random media. The use of a lognormal distribution to represent the fluctuations in medium properties facilitates enforcement of strict positivity of cross section realizations, a condition that has challenged standard approaches that assume Gaussian fluctuations. The fact that the lognormal random process is a memoryless nonlinear transformation of a Gaussian process further enables efficient and accurate reconstruction of the random cross section realizations using a Karhunen-Loeve representation of the cross section. Finally, the robustness of this model enables fluctuations of arbitrarily large amplitude to be studied, using both random sampling Monte Carlo and more efficient stochastic collocation techniques. The present work is being extended to allow reconstruction of probability density functions of output variables using polynomial chaos representation, and to accommodate general cross section covariance functions. Also, techniques are being explored to more judiciously select ceiling cross sections to enhance the efficiency of Woodcock Monte Carlo for transport in random media. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2014

Design and characterization of ultra-stretchable monolithic silicon fabric

Stretchable electronic systems can play instrumental role for reconfigurable macro-electronics such as distributed sensor networks for wearable and bio-integrated electronics. Typically, polymer composite based materials and its deterministic design as interconnects are used to achieve such systems. Nonetheless, non-polymeric inorganic silicon is the predominant material for 90% of electronics. Therefore, we report the design and fabrication of an all silicon based network of hexagonal islands connected through spiral springs to form an ultra-stretchable arrangement for complete compliance to highly asymmetric shapes. Several design parameters are considered and their validation is carried out through finite element analysis. The fabrication process is based on conventional microfabrication techniques and the measured stretchability is more than 1000% for single spirals and area expansions as high as 30 folds in arrays. The reported method can provide ultra-stretchable and adaptable electronic systems for distributed network of high-performance macro-electronics especially useful for wearable electronics and bio-integrated devices.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A Gridded Solar Irradiance Ensemble Prediction System Based on WRF-Solar EPS and the Analog Ensemble

The WRF-Solar Ensemble Prediction System (WRF-Solar EPS) and a calibration method, the analog ensemble (AnEn), are used to generate calibrated gridded ensemble forecasts of solar irradiance over the contiguous United States (CONUS). Global horizontal irradiance (GHI) and direct normal irradiance (DNI) retrievals, based on geostationary satellites from the National Solar Radiation Database (NSRDB) are used for both calibrating and verifying the day-ahead GHI and DNI predictions (GDIP). A 10-member ensemble of WRF-Solar EPS is run in a re-forecast mode to generate day-ahead GDIP for three years. The AnEn is used to calibrate GDIP at each grid point independently using the NSRDB as the “ground truth”. Performance evaluations of deterministic and probabilistic attributes are carried out over the whole CONUS. The results demonstrate that using the AnEn calibrated ensemble forecast from WRF-Solar EPS contributes to improving the overall quality of the GHI predictions with respect to an AnEn calibrated system based only on the deterministic run of WRF-Solar. In fact, the calibrated WRF-Solar EPS’s mean exhibits a lower bias and RMSE than the calibrated deterministic WRF-Solar. Moreover, using the ensemble mean and spread as predictors for the AnEn allows a more effective calibration than using variables only from the deterministic runs. Finally, it has been shown that the recently introduced algorithm of correction for rare events is of paramount importance to obtain the lowest values of GHI from the calibrated ensemble (WRF-Solar EPS AnEn), qualitatively consistent with those observed from the NSRDB.

14 SOLAR ENERGY↗

DOE OSTI2016

Restoring the Grand Canyon Ecosystem: Evidence from Dynamic Systems Theory

Constructing and operating dams for hydropower generation can impact downstream ecosystems by altering natural flow regimes and interfering with the lifecycles of aquatic and riparian species. Increased concern from environmental interests, coupled with federal relicensing requirements that aim to minimize environmental impacts, have put pressure on dam operators to quantify the flow regime changes that have resulted from dam construction and to evaluate how future modifications to dam releases may impact river systems. In this study, we present a method for analyzing the impact of dam operations by reconstructing system trajectories from hydrologic flow time series for different periods of dam operation (pre-dam, post-dam, and post-modification). We apply these methods to flow data from Lees Ferry, Colorado, just downstream of Glen Canyon Dam on the Colorado River, to determine how the dimensionality of the river system has changed over time and to evaluate the effectiveness of a new operating policy put into place after the 1992 Grand Canyon Protection Act was passed. While the post-dam period governed by hydropower operations is characterized by high dimensional, stochastic behavior, the pre-dam and post-modification periods are low dimensional and less complex, demonstrating the success of the new operating policy in mirroring the natural flow regime. Our analysis also suggests that applying a strictly deterministic or stochastic modeling approach to historical flow data does not appropriately capture the varying complexity of the Colorado River system during the three distinct periods of hydrologic behavior.

13 HYDRO ENERGY↗