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DOE OSTI2023

Neural Networks-Based Inverter Control: Modeling and Adaptive Optimization for Smart Distribution Networks

The optimal voltage control of inverter-based resources, especially under the high penetration of solar photovoltaics, is critical to the stability of the distribution power system. However, the computational complexity as well as the coordinated operation performance of the voltage control optimization in the distribution power system limits the real-time applications. To mitigate this issue, a model-free based adaptive optimal control scheme for the smart inverter is proposed to maximize the active power generation, minimize the power loss, and maintain the bus voltages in smart distribution networks. An inverter-based optimization model for coordinated operation is first established, considering the uncertainties of renewable power generation. Subsequently, by collecting the data and control strategies, the neural networks (NNs) based algorithm is proposed to efficiently predict the best possible control strategy. The main objective of this scheme is to accurately predict candidate optimal solutions with near-negligible feasibility and optimization gaps, with the advantage of avoiding complicated iteration-based numerical algorithms. Thereafter, the co-simulation among OpenDSS, MATLAB, and Python is set up to fully take advantage of the three individual software. Experiments are conducted based on different control parameter characteristics and structures of NNs. Finally, the results reveal that an average mean squared error of 0.013 and 1 ms response time are achieved, which is lower than some state-of-the-art methods.

42 ENGINEERING↗

DOE OSTI2013

BrachyView: Proof-of-principle of a novel in-body gamma camera for low dose-rate prostate brachytherapy

Purpose: The conformity of the achieved dose distribution to the treatment plan strongly correlates with the accuracy of seed implantation in a prostate brachytherapy treatment procedure. Incorrect seed placement leads to both short and long term complications, including urethral and rectal toxicity. The authors present BrachyView, a novel concept of a fast intraoperative treatment planning system, to provide real-time seed placement information based on in-body gamma camera data. BrachyView combines the high spatial resolution of a pixellated silicon detector (Medipix2) with the volumetric information acquired by a transrectal ultrasound (TRUS). The two systems will be embedded in the same probe so as to provide anatomically correct seed positions for intraoperative planning and postimplant dosimetry. Dosimetric calculations are based on the TG-43 method using the real position of the seeds. The purpose of this paper is to demonstrate the feasibility of BrachyView using the Medipix2 pixel detector and a pinhole collimator to reconstruct the real-time 3D position of low dose-rate brachytherapy seeds in a phantom. Methods: BrachyView incorporates three Medipix2 detectors coupled to a multipinhole collimator. Three-dimensionally triangulated seed positions from multiple planar images are used to determine the seed placement in a PMMA prostate phantom in real time. MATLAB codes were used to test the reconstruction method and to optimize the device geometry. Results: The results presented in this paper show a 3D position reconstruction accuracy of the seed in the range of 0.5-3 mm for a 10-60 mm seed-to-detector distance interval (Z direction), respectively. The BrachyView system also demonstrates a spatial resolution of 0.25 mm in the XY plane for sources at 10 mm distance from Medipix2 detector plane, comparable to the theoretical value calculated for an equivalent gamma camera arrangement. The authors successfully demonstrated the capability of BrachyView for real-time imaging (using a 3 s data acquisition time) of different brachytherapy seed configurations (with an activity of 0.05 U) throughout a 60 Multiplication-Sign 60 Multiplication-Sign 60 mm{sup 3} Perspex prostate phantom. Conclusions: The newly developed miniature gamma camera component of BrachyView, with its high spatial resolution and real time capability, allows accurate 3D localization of seeds in a prostate phantom. Combination of the gamma camera with TRUS in a single probe will complete the BrachyView system.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Open‐Source Anaerobic Digestion Modeling Platform, Anaerobic Digestion Model No. 1 Fast (ADM1F)

An open‐source modeling platform, called Anaerobic Digestion Model No. 1 Fast (ADM1F), is introduced to achieve fast and numerically stable simulations of anaerobic digestion processes. ADM1F is compatible with an iPython interface to facilitate model configuration, simulation, data analysis, and visualization. Faster simulations and more stable results are accomplished by implementing an advanced open‐source library of numerical methods called Portable Extensive Toolkit for Scientific Computation (PETSc) to solve the ADM1 system of equations. Leveraging PETSc, ADM1F can consistently complete a steady‐state simulation under 0.2 s, over 99% faster than a benchmark ADM1 model implemented with MATLAB while achieving agreement of model outputs within 1% of those obtained with the benchmark model. For dynamic simulations, however, ADM1F has a computational speed advantage only when the influent characteristics update more frequently than every 4 h. The ability of ADM1F to be useful as a tool to study anaerobic digestion systems is demonstrated through two example implementations of ADM1F: (1) a two‐phase co‐digestion scenario evaluating the impact of the organic loading rate and the substrate composition on reactor performance and stability, and (2) a conventional digester scenario assessing the effectiveness of recovery strategies after disruptions that led to instability. These examples demonstrate how the high simulation speed and the convenience of the iPython interface allow ADM1F to complete complex analyses within minutes, much faster than computational strategies currently reported in the literature.

anaerobic co-digestion↗

DOE OSTI2018

A biologically-inspired approach for adaptive control of advanced energy systems

Here in this article, a novel approach is proposed for integrating a Biologically-Inspired Optimal Control Strategy (BIOsingle bondCS) with an Artificial Neural Network (ANN)-based adaptive component for advanced energy systems applications. Specifically, BIOsingle bondCS employs gradient-based optimal control solvers in a biologically-inspired manner, following the rule of pursuit for ants, to simultaneously control multiple process outputs at their desired setpoints. Also, the ANN component captures the mismatch between the controller and the plant models by using a single-hidden-layer technique with online learning capabilities to augment the baseline BIOsingle bondCS control laws. The resulting approach is a unique combination of biomimetic control and data-driven methods that provides optimal solutions for dynamic systems. The applicability of the proposed framework is illustrated via an Integrated Gasification Combined Cycle (IGCC) process with carbon capture as an advanced energy system example. In particular, a multivariable control structure associated with a subsystem of the IGCC plant simulation in DYNSIM® is addressed. The proposed control laws are derived in MATLAB®, while the plant models are built in DYNSIM®, and a previously developed MATLAB®-DYNSIM® link is employed for implementation purposes. The proposed integrated approach improves the overall performance of the process up to 85% in terms of reducing the output tracking error when compared to stand-alone BIOsingle bondCS and Proportional-Integral (PI) controller implementations, resulting in faster setpoint tracking. The proposed framework thus provides a promising alternative for advanced control of energy systems of the future.

42 ENGINEERING↗

DOE OSTI2019

2D-DIC Challenge MATLAB Analysis Code v. 1.0

This code replicates the analysis completed in the published article (see reference below) for the 2D-DIC Challenge (https://sem.org/dic-challenge/), conducted under the auspices of the Society for Experimental Mechanics (SEM). The purpose of the DIC Challenge is to provide a set of vetted and standard images, which all users and developers of Digital Image Correlation (DIC) software can use -DIC Challenge results from Sample 14 and Sample 15 of the DIC-Challenge images, and computes various statistics on the data, such as the noise, bias, and spatial resolution. P.L. Reu, E. Toussaint, E. Jones, H.A. Bruck, M. Iadicola, R. Balcaen, D.Z. Turner, T. Siebert, P. Lava, M. Simonsen (2018) Exp. Mech. 58(7):1067-1099, https://doi.org/10.1007/s11340-017-0349-0.

Jones, Elizabeth↗

DOE OSTI2016

SU-C-207B-05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

Purpose: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. Methods: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using an anonymized patient’s (male, 20 yr, 72 kg) chest-abdomen-pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra-observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images. Results: The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients. Conclusion: The TWS Random Forest auto-segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT protocol parameters with an average DSC of 0.86 ± 0.04 (range: 0.80–0.99).

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

PETSc Users Manual (Rev. 3.3)

This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++ or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself; For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. PETSc also has a MATLAB interface, so portions of your code can be written in MATLAB to “try out” the PETSc solvers. The resulting code will not be scalable however because currently MATLAB is inherently not scalable; and PETSc should not be used to attempt to provide a “parallel linear solver” in an otherwise sequential code. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect any kind of reasonable performance. Do not expect to generate your matrix sequentially and then “use PETSc” to solve the linear system in parallel. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc is supported; see the web site http://www.mcs.anl.gov/petsc for information on contacting support. A http://www.mcs.anl.gov/petsc/publications may be found a list of publications and web sites that feature work involving PETSc. We welcome any reports of corrections for this document.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

PETSc Users Manual (Rev. 3.4)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

PETSc Users Manual (Rev. 3.5)

This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, C++, Python, and MATLAB (sequential). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++ or Fortran and experience using a debugger such as gdb or dbx, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself. ;For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. PETSc also has a MATLAB interface, so portions of your code can be written in MATLAB to “try out” the PETSc solvers. The resulting code will not be scalable however because currently MATLAB is inherently not scalable; and PETSc should not be used to attempt to provide a “parallel linear solver” in an otherwise sequential code. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect any kind of reasonable performance. Do not expect to generate your matrix sequentially and then “use PETSc” to solve the linear system in parallel. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc is supported; see the web site http://www.mcs.anl.gov/petsc for information on contacting support. A http://www.mcs.anl.gov/petsc/publications may be found a list of publications and web sites that feature work involving PETSc. We welcome any reports of corrections for this document.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

PtychoShelves , a versatile high-level framework for high-performance analysis of ptychographic data

Over the past decade, ptychography has been proven to be a robust tool for non-destructive high-resolution quantitative electron, X-ray and optical microscopy. It allows for quantitative reconstruction of the specimen's transmissivity, as well as recovery of the illuminating wavefront. Additionally, various algorithms have been developed to account for systematic errors and improved convergence. With fast ptychographic microscopes and more advanced algorithms, both the complexity of the reconstruction task and the data volume increase significantly. PtychoShelves is a software package which combines high-level modularity for easy and fast changes to the data-processing pipeline, and high-performance computing on CPUs and GPUs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Stereo-Optic High Definition Imaging: A New Technology to Understand Bird and Bat Avoidance of Wind Turbines

There is a critical need to develop monitoring tools to track aerofauna (birds and bats) in three dimensions around wind turbines. New monitoring systems will reduce permitting uncertainty by increasing the understanding of how birds and bats are interacting with wind turbines, which will improve the accuracy of impact predictions. Biodiversity Research Institute (BRI), The University of Maine Orono School of Computing and Information Science (UMaine SCIS), HiDef Aerial Surveying Limited (HiDef), and SunEdison, Inc. (formerly First Wind) responded to this need by using stereo-optic cameras with near-infrared (nIR) technology to investigate new methods for documenting aerofauna behavior around wind turbines. The stereo-optic camera system used two synchronized high-definition video cameras with fisheye lenses and processing software that detected moving objects, which could be identified in post-processing. The stereo- optic imaging system offered the ability to extract 3-D position information from pairs of images captured from different viewpoints. Fisheye lenses allowed for a greater field of view, but required more complex image rectification to contend with fisheye distortion. The ability to obtain 3-D positions provided crucial data on the trajectory (speed and direction) of a target, which, when the technology is fully developed, will provide data on how animals are responding to and interacting with wind turbines. This project was focused on testing the performance of the camera system, improving video review processing time, advancing the 3-D tracking technology, and moving the system from Technology Readiness Level 4 to 5. To achieve these objectives, we determined the size and distance at which aerofauna (particularly eagles) could be detected and identified, created efficient data management systems, improved the video post-processing viewer, and attempted refinement of 3-D modeling with respect to fisheye lenses. The 29-megapixel camera system successfully captured 16,173 five-minute video segments in the field. During nighttime field trials using nIR, we found that bat-sized objects could not be detected more than 60 m from the camera system. This led to a decision to focus research efforts exclusively on daytime monitoring and to redirect resources towards improving the video post- processing viewer. We redesigned the bird event post-processing viewer, which substantially decreased the review time necessary to detect and identify flying objects. During daytime field trials, we determine that eagles could be detected up to 500 m away using the fisheye wide-angle lenses, and eagle-sized targets could be identified to species within 350 m of the camera system. We used distance sampling survey methods to describe the probability of detecting and identifying eagles and other aerofauna as a function of distance from the system. The previously developed 3-D algorithm for object isolation and tracking was tested, but the image rectification (flattening) required to obtain accurate distance measurements with fish-eye lenses was determined to be insufficient for distant eagles. We used MATLAB and OpenCV to improve fisheye lens rectification towards the center of the image, but accurate measurements towards the image corners could not be achieved. We believe that changing the fisheye lens to rectilinear lens would greatly improve position estimation, but doing so would result in a decrease in viewing angle and depth of field. Finally, we generated simplified shape profiles of birds to look for similarities between unknown animals and known species. With further development, this method could provide a mechanism for filtering large numbers of shapes to reduce data storage and processing. These advancements further refined the camera system and brought this new technology closer to market. Once commercialized, the stereo-optic camera system technology could be used to: a) research how different species interact with wind turbines in order to refine collision risk models and inform mitigation solutions; and b) monitor aerofauna interactions with terrestrial and offshore wind farms replacing costly human observers and allowing for long-term monitoring in the offshore environment. The camera system will provide developers and regulators with data on the risk that wind turbines present to aerofauna, which will reduce uncertainty in the environmental permitting process.

Adams, Evan↗

DOE OSTI2020

Highly Available Nuclear Power for Mission-Critical Applications

Some energy consumers require power on an anytime, all-year-round basis with a high level of certainty, including defense installations, isolated communities, and some industrial processes. For these customers, interruptions in electricity or heat can mean substantial financial loss or even loss of life. In the absence of grid-scale energy storage, a high level of power availability can be accomplished only through the robustness and redundancy of power generators. The NuScale small modular reactor design is well suited to provide highly available power because of several features related to both the nuclear steam supply system and the overall plant design. In analogy to Redundant Array of Independent Disks (RAID) systems used to provide highly reliable data storage, a NuScale plant can assure sustained power generation by virtue of its Redundant Array of Integral Reactors (RAIR).This paper describes the NuScale RAIR plant features and summarizes the results of a rigorous analysis of RAIR availability as a function of power or, conversely, the RAIR plant output power as a function of power availability. The analysis utilized the Matrix Laboratory code (MATLAB) and included probability distributions for the frequency and duration of module outages due to planned and unplanned events. Further, the study evaluated the impact of implementing turbine bypass rather than module shutdown and using one or more modules to supply house loads in the case of loss of off-site power (LOOP). Availability results are presented for a 12-module RAIR plant with and without turbine bypass enabled during a LOOP and for different possible connections to the off-site power distribution grid and dedicated service loads. Results indicate that a very high level of availability can be achieved at relatively high power output levels, regardless of turbine bypass and dedicated load connection, compared to the operating fleet.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

DRACS thermal performance evaluation for FHR

Direct Reactor Auxiliary Cooling System (DRACS) is a passive decay heat removal system proposed for the Fluoride-salt-cooled High-temperature Reactor (FHR) that combines coated particle fuel and a graphite moderator with a liquid fluoride salt as the coolant. The DRACS features three coupled natural circulation/convection loops, relying completely on buoyancy as the driving force. These loops are coupled through two heat exchangers, namely, the DRACS Heat Exchanger and the Natural Draft Heat Exchanger. In addition, a fluidic diode is employed to minimize the parasitic flow into the DRACS primary loop and correspondingly the heat loss to the DRACS during normal operation of the reactor, and to keep the DRACS ready for activation, if needed, during accidents. To help with the design and thermal performance evaluation of the DRACS, a computer code using MATLAB has been developed. This code is based on a one-dimensional formulation and its principle is to solve the energy balance and integral momentum equations. By discretizing the DRACS system in the axial direction, a bulk mean temperature is assumed for each mesh cell. The temperatures of all the cells, as well as the mass flow rates in the DRACS loops, are predicted by solving the governing equations that are obtained by integrating the energy conservation equation over each cell and integrating the momentum conservation equation over each of the DRACS loops. In addition, an intermediate heat transfer loop equipped with a pump has also been modeled in the code. This enables the study of flow reversal phenomenon in the DRACS primary loop, associated with the pump trip process. Experimental data from a High-Temperature DRACS Test Facility (HTDF) are not available yet to benchmark the code. A preliminary code validation is performed by using natural circulation experimental data available in the literature, which are as closely relevant as possible. The code is subsequently applied to the HTDF that is under construction at the Ohio State University.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

SU-F-R-50: Radiation-Induced Changes in CT Number Histogram During Chemoradiation Therapy for Pancreatic Cancer

Purpose: In an effort of early assessment of treatment response, we investigate radiation induced changes in CT number histogram of GTV during the delivery of chemoradiation therapy (CRT) for pancreatic cancer. Methods: Diagnostic-quality CT data acquired daily during routine CT-guided CRT using a CT-on-rails for 20 pancreatic head cancer patients were analyzed. All patients were treated with a radiation dose of 50.4 in 28 fractions. On each daily CT set, the contours of the pancreatic head and the spinal cord were delineated. The Hounsfiled Units (HU) histogram in these contourswere extracted and processed using MATLAB. Eight parameters of the histogram including the mean HU over all the voxels, peak position, volume, standard deviation (SD), skewness, kurtosis, energy, and entropy were calculated for each fraction. The significances were inspected using paired two-tailed t-test and the correlations were analyzed using Spearman rank correlation tests. Results: In general, HU histogram in pancreatic head (but not in spinal cord) changed during the CRT delivery. Changes from the first to the last fraction in mean HU in pancreatic head ranged from −13.4 to 3.7 HU with an average of −4.4 HU, which was significant (P<0.001). Among other quantities, the volume decreased, the skewness increased (less skewed), and the kurtosis decreased (less sharp) during the CRT delivery. The changes of mean HU, volume, skewness, and kurtosis became significant after two weeks of treatment. Patient pathological response status is associated with the changes of SD (ΔSD), i.e., ΔSD= 1.85 (average of 7 patients) for good reponse, −0.08 (average of 6 patients) for moderate and poor response. Conclusion: Significant changes in HU histogram and the histogram-based metrics (e.g., meam HU, skewness, and kurtosis) in tumor were observed during the course of chemoradiation therapy for pancreas cancer. These changes may be potentially used for early assessment of treatment response.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Development of a water source heat pump hardware-in-the-loop (HIL) testing facility for smart building applications

Over the last decade, the global fight against climate change through electrification has led to an increase in research on building heating, ventilation, and air conditioning (HVAC) systems that utilize intelligent control algorithms to provide demand-side grid service while maintaining the thermal comfort of building occupants. As the pivotalpoint between building electricity consumption and indoor thermal comfort, high-efficiency electrical heatpumps are at the center of these emerging studies, and various grid-interactive and occupant-comfort control algorithms have been developed for them. The impact of these algorithms on the heatpump operation andperformance under different weather, building load, and grid requests calls for investigation and verification via experimental tests with actual heat pumps integrated with real-time building and grid responses. This study presents a Water-Source Heat Pump Hardware-in-The-Loop (HIL) Test Facility developed with the capability to perform such tests. The hardware configuration for this testfacility introduces a hydronic system that emulates the conditions for the heat pump water-side, and a duct system that emulates conditions for the heat pump airside. Both data acquisition and emulator control are implemented through the National Instruments (NI) LabVIEW software running on an NI PXIplatform. The HIL mechanism based on the hardware-software integration that allows the testbed to communicate with a generic simulation environment is also discussed. Currently, the test facility setup includes a single heatpump and virtual building model in EnergyPlus coupled with an occupant behavioral model in MATLAB. Preliminary test results of the current setup demonstrate the building load emulator's ability to track the simulated gone temperature with a Root Mean Square Deviation (RSME) below 0.12°C (0.216°F). An uncertainty analysis based on sensor accuracies shows that the heat pump coefficient of performance (COP) can be measured with a relative uncertainty of 10.4% in cooling and 3.7% in heating. Apartfrom the current testing on a single heat pump, the test facility also provides the flexibility to include additional heat pumps to form a heat pump cluster, as well as coupling the heat pump with active thermal storage to provide enhanced demandflexibility.

Calfa, Caleb↗

DOE OSTI2012

Constructing realistic Szekeres models from initial and final data

The Szekeres family of inhomogeneous solutions, which are defined by six arbitrary metric functions, offers a wide range of possibilities for modelling cosmic structure. Here we present a model construction procedure for the quasispherical case using given data at initial and final times. Of the six arbitrary metric functions, the three which are common to both Szekeres and Lemaître-Tolman models are determined by the model construction procedure of Krasinski and Hellaby. For the remaining three functions, which are unique to Szekeres models, we derive exact analytic expressions in terms of more physically intuitive quantities — density profiles and dipole orientation angles. Using MATLAB, we implement the model construction procedure and simulate the time evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

Foundations of Feature Selection and Classification for Non-Gaussian Distributed Targets

This report describes the author’s research toward creating algorithms for selecting useful statistical features in pattern/target classification problems in which the features are non-Gaussian distributed. In engineering practice, it is common to either (1) Not perform any feature selection procedure, or (2) Use a feature selection algorithm that assumes the features are Gaussian distributed. This process can be far from optimal if the data are actually non-Gaussian, as they frequently are in practice. This research has the goal of mitigating that problem by creating a useful algorithm that can be used in practice. The approach is to focus on the performance indices used in common feature selection algorithms, such as Branch and Bound algorithms and the Sequential Forward Selection Algorithm. Ordinarily, the performance index used to measure the class separation in feature space involves assuming the data are Gaussian and deriving tractable performance indices that use closed form equations that represent the probability density functions of the class data. The advantage of this approach is that it produces feature selection algorithms that have low computational complexity and do not require knowledge of the actual data densities. The disadvantage is that these algorithms may not perform reasonably when the data are non-Gaussian, as they commonly are in practice. This research examines the use of information-theoretic class separability measures that can deal with the non-Gaussian case. In particular, this work shows that the Hellinger Distance (a divergence measure), has very desirable mathematical properties and can be useful for feature selection. The work in this paper employs a multivariate kernel density estimator along with the Hellinger Distance and an optimal subset selection algorithm to do feature selection. This report (1) Defines the classification and feature selection problems, (2) Describes the continuous variable Hellinger distance in one dimension and discusses its mathematical properties in comparison with other distance measures, (3) Develops the discrete variable Hellinger distance expression for one and two dimensions, and shows that the discrete Hellinger distance for multiple dimensions is a straightforward extension, and (4) Presents experimental density estimation results using a new MATLAB implementation of a multivariate kernel density estimator. The draft of this report served as a starting point for Ensign Matthew Wilder’s MSEE thesis work at the Naval Postgraduate School (NPS) in 2010 and 2011. Ensign Wilder completed his thesis and graduated from NPS in June 2011 (M. J. Wilder, “Automatic Target Recognition: Statistical Feature Selection of Non-Gaussian Distributed Target Classes,” MSEE Thesis, Naval Postgraduate School, Thesis Advisor: Grace A. Clark, Second Reader: Monique P. Fargues, June 2011). In the thesis, Ensign Wilder created implementations of the Branch and Bound and Sequential Feature Selection algorithms, extended the multivariate kernel density estimator code, and evaluated the performance of Hellinger Distance-based feature selection algorithms using both simulated data and data from a real-world benchmark data set. The new algorithm was shown to be very effective for both Gaussian and non-Gaussian data sets. The advantages gained for non-Gaussian data come at the cost of increased computational complexity. Using a laptop computer for practical problems, we generally use initial feature vectors containing no more than about ten or twelve features. Proposed future research lies in creating faster kernel density estimator algorithms that would allow for the use of higher dimensional data.

42 ENGINEERING↗

DOE OSTI2013

Annual Report: Carbon Capture Simulation Initiative (CCSI) (30 September 2013)

The Carbon Capture Simulation Initiative (CCSI) is a partnership among national laboratories, industry and academic institutions that is developing and deploying state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. The CCSI Toolset will provide end users in industry with a comprehensive, integrated suite of scientifically validated models, with uncertainty quantification (UQ), optimization, risk analysis and decision making capabilities. The CCSI Toolset incorporates commercial and open-source software currently in use by industry and is also developing new software tools as necessary to fill technology gaps identified during execution of the project. Ultimately, the CCSI Toolset will (1) enable promising concepts to be more quickly identified through rapid computational screening of devices and processes; (2) reduce the time to design and troubleshoot new devices and processes; (3) quantify the technical risk in taking technology from laboratory-scale to commercial-scale; and (4) stabilize deployment costs more quickly by replacing some of the physical operational tests with virtual power plant simulations. CCSI is led by the National Energy Technology Laboratory (NETL) and leverages the Department of Energy (DOE) national laboratories’ core strengths in modeling and simulation, bringing together the best capabilities at NETL, Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), and Pacific Northwest National Laboratory (PNNL). The CCSI’s industrial partners provide representation from the power generation industry, equipment manufacturers, technology providers and engineering and construction firms. The CCSI’s academic participants (Carnegie Mellon University, Princeton University, West Virginia University, Boston University and the University of Texas at Austin) bring unparalleled expertise in multiphase flow reactors, combustion, process synthesis and optimization, planning and scheduling, and process control techniques for energy processes. During Fiscal Year (FY) 13, CCSI announced the initial release of its first set of computational tools and models during the October 2012 meeting of its Industry Advisory Board. This initial release led to five companies licensing the CCSI Toolset under a Test and Evaluation Agreement this year. By the end of FY13, the CCSI Technical Team had completed development of an updated suite of computational tools and models. The list below summarizes the new and enhanced toolset components that were released following comprehensive testing during October 2013. 1. FOQUS. Framework for Optimization and Quantification of Uncertainty and Sensitivity. Package includes: FOQUS Graphic User Interface (GUI), simulation-based optimization engine, Turbine Client, and heat integration capabilities. There is also an updated simulation interface and new configuration GUI for connecting Aspen Plus or Aspen Custom Modeler (ACM) simulations to FOQUS and the Turbine Science Gateway. 2. A new MFIX-based Computational Fluid Dynamics (CFD) model to predict particle attrition. 3. A new dynamic reduced model (RM) builder, which generates computationally efficient RMs of the behavior of a dynamic system. 4. A completely re-written version of the algebraic surrogate model builder for optimization (ALAMO). The new version is several orders of magnitude faster than the initial release and eliminates the MATLAB dependency. 5. A new suite of high resolution filtered models for the hydrodynamics associated with horizontal cylindrical objects in a flow path. 6. The new Turbine Science Gateway (Cluster), which supports FOQUS for running multiple simulations for optimization or UQ using a local computer or cluster. 7. A new statistical tool (BSS-ANOVA-UQ) for calibration and validation of CFD models. 8. A new basic data submodel in Aspen Plus format for a representative high viscosity capture solvent, 2-MPZ system. 9. An updated RM tool for CFD (REVEAL) that can create a RM from MFIX. A new lightweight, stand-alone version will be available in late 2013. 10. An updated RM integration tool to convert the RM from REVEAL into a CAPE-OPEN or ACM model for use in a process simulator. 11. An updated suite of unified steady-state and dynamic process models for solid sorbent carbon capture included bubbling fluidized bed and moving bed reactors. 12. An updated and unified set of compressor models including steady-state design point model and dynamic model with surge detection. 13. A new framework for the synthesis and optimization of coal oxycombustion power plants using advanced optimization algorithms. This release focuses on modeling and optimization of a cryogenic air separation unit (ASU). 14. A new technical risk model in spreadsheet format. 15. An updated version of the sorbent kinetic/equilibrium model for parameter estimation for the 1st generation sorbent model. 16. An updated process synthesis superstructure model to determine optimal process configurations utilizing surrogate models from ALAMO for adsorption and regeneration in a solid sorbent process. 17. Validation models for NETL Carbon Capture Unit utilizing sorbent AX. Additional validation models will be available for sorbent 32D in 2014. 18. An updated hollow fiber membrane model and system example for carbon capture. 19. An updated reference power plant model in Thermoflex that includes additional steam extraction and reinjection points to enable heat integration module. 20. An updated financial risk model in spreadsheet format.

01 COAL, LIGNITE, AND PEAT↗