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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

A Dynamic Waste Isolation Pilot Plant Performance Assessment Tool - 12490

The Waste Isolation Pilot Plant (WIPP) Performance Assessment (PA) methodology comprises a toolbox used to demonstrate regulatory compliance of the repository after facility closure. The PA framework rests upon an extensive suite of computational codes. In some cases, significant alteration of code inputs is a tedious and difficult task. Due to the nature of the application for which they are used, PA codes used in support of WIPP regulatory compliance demonstration must satisfy stringent quality assurance requirements. Consequently, many of the coding practices used during original code development are still implemented today. A more efficient workflow configuration has the potential to alleviate difficulties associated with extensive code input modifications. Here, this potential is assessed via an implementation of a more flexible scientific workflow system for a subset of the codes used in WIPP PA. The scientific workflow approach taken here for a dynamic PA system enables users from disparate backgrounds to dramatically shorten the time between hypothesis and analysis by decreasing the amount of a priori knowledge, from a range of disciplines, needed to execute the code. Having smaller iteration times allows for more ideas to be tested and explored, which leads to safer and more optimized systems. Note that these high-level, dynamic tools are intended only for initial scoping studies on the personal computer of a researcher. Full, regulatory compliance calculations may occur only within a qualified computing environment. However, the WIPP PA tools here may guide future research and indicate regions of the analysis space that are worth further study. This next generation of PA software provides the ability to perform scoping investigations of repository performance quickly and easily, and has an accessible and useful interface to a variety of users, such as fuel cycle systems designers, domain experts such as repository modelers, and policy makers. The purview of this project allows for many opportunities for future work. Foremost among these is the desire to implement the full BRAGFLO suite within the workflow. This will entail porting or wrapping Genmesh, Matset, LHS, and ICSet within Python. Moreover, unifying the two GUIs into a single driver application would be a natural next step. Once the BRAGFLO suite is completed, other portions of WIPP PA could be implemented with corresponding and inter-operable work-flows. Likely first candidates for this are those codes that are similarly computationally intensive, such as the one used to generate complementary cumulative distribution functions used to demonstrate regulatory compliance (code CCDFGF). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Energy-Efficient and Comfortable Buildings through Multivariate Integrated Control (ECoMIC)

This project aims to develop an integrated control solution for enhanced energy efficiency and user comfort in commercial buildings. The developed technology is a zone-based control framework that minimizes energy usage while maintaining occupants’ visual and thermal comfort through control of electric lights, motorized venetian blinds and thermostats. The control framework is designed following a modular, scalable and flexible architecture to facilitate easy integration with exiting building management systems. The control framework contains two key algorithms: 1) the lighting load balancing algorithm and 2) the thermostat control algorithm. The lighting load balancing algorithm adopts a model-based closed-loop control approach to determine the optimal electric light and venetian blind settings. It is formulated into an optimization problem with minimizing lighting-related energy consumptions as the objective and delivering adequate task light and preventing daylight glare as the constraints. The thermostat control algorithm is based on a well-established thermal comfort model and formulated as a root-finding problem to dynamically determine the optimal thermostat setpoint for both energy savings and improved thermal comfort. To address building-wide scalability, a system architecture was developed for the zone-based control technology. Three levels of services are defined in the architecture: external services, facility level services and zone level services. The zone-level service includes the control algorithms described above as well as the corresponding interfaces, profiles, sensors and actuators to realize the zone controller. The facility level services connect to the zones through a backbone network, handle supervisory level information and controls, and thus facilitate building-wide scalability. The external services provide communication capability to entities outside of the building for grid interaction and remote access. Various aspects of the developed control technology were evaluated and verified through both simulations and testbed implementations. Simulations coupling a DOE medium office reference building in EnergyPlus building simulation software and a prototype controller in Matlab were performed. During summer time in a mixed-humid climate zone, the simulations revealed reductions of 27% and 42% in electric lighting load and cooling load, respectively, when compared to an advanced base case with daylight dimming and blinds automatically tilted to block direct sun. Two single-room testbeds were established. The testbed at Philips Lighting business building (Rosemont, IL) was designed for quantifying energy performance of integrated controls. This particular implementation achieved 40% and 79% savings on lighting and HVAC energy, respectively, compared to a relatively simple base case operated on predefined schedules. While the resulting energy savings was very encouraging, it should be noted that there may be several caveats associated with it. 1) The test was run during late spring and early summer, and the savings numbers might not be directly used to extrapolate the annual energy savings. 2) Due to the needs for separate control and metering of the small-scale demonstrator within a large building, the HVAC system, hence the corresponding savings, did not represent a typical energy code-compliant design. 3) The light level in the control case was regulated at a particular setpoint, which was lower than then the full-on light level in the base case, and the savings resulted from tuning down the light level to the setpoint was not attributable to the contribution of the developed technology. The testbed at the Lawrence Berkeley National Laboratory (Berkeley, CA) specifically focused on glare control integration, and has demonstrated the feasibility and capability of the glare detection and prevention technique. While the short one-month test in this testbed provided a functional indication of the developed technology, and it would require at least a full solstice-to-solstice cycle to ruinously quantify the performance, which was not possible within the project timeframe. There are certain limitations inherited from the operational assumptions, which could potentially affect the effectiveness and applicability of the developed control technologies. The system takes a typical ceiling-mounting approach for the photosensor locations, and therefore, the control performance relies on proper commissioning or the built-in intelligence of the photosensor for pertinent task light level estimations. For spaces where daylight penetration diminishes significantly deeper into the zone, certain modification to the control algorithms is required to accommodate multiple lighting control subzones and the corresponding sensors for providing a more uniform light level across the entire zone. Integrated control of visual and thermal comfort requires the lighting control zone and thermal control zone to coincide with each other. In other words, the area illuminated by a lighting circuit needs to be the same area served by the thermostat. Thus, the original zoning will potentially constrain the applicability of this technology in retrofitting projects. This project demonstrated the technical feasibility of a zone-based integrated control technology. From the simulation results and testbed implementations, up to 60% lighting energy savings in daylit areas relative to a “no-controls” case can easily be achieved. A 20% reduction of whole building energy consumption is also attainable. In the aspect of occupant comfort, the testbed demonstrated the ability to maintain specified light level on the workplane while promptly mitigate daylight glare 90% of the time. The control system also managed to maintain the thermal environment at a comfortable level 90% of the time. The aspect of system scalability was guaranteed by the system architecture design, based on which the testbeds were instantiated. Analysis on the aspect of economic benefit has yielded an about 6-year payback time for a medium-sized building, including the installation of all hardware and software, such as motorized blinds and LED luminaires. The payback time can be significantly reduced if part of the hardware is already in place for retrofitting projects. It needs to be noted that since the payback analysis was partly based on the testbed performance results, it is constrained by the caveats associated with the testbed implementations. The main uncertainty lies in the contribution from the space conditioning energy savings as it was non-trivial to realistically configure a room-size HVAC system for directly extrapolating whole-building HVAC energy savings. It is recommended to further evaluate the developed technology at a larger scale, where the lighting and HVAC energy consumption can be realistically measured at the building level, to more rigorously quantify the performance potentials.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Characteristic Performance Evaluation of a new SAGe Well Detector for Small and Large Sample Geometries

Environmental scientific research requires a detector that has sensitivity low enough to reveal the presence of any contaminant in the sample at a reasonable counting time. Canberra developed the germanium detector geometry called Small Anode Germanium (SAGe) Well detector, which is now available commercially. The SAGe Well detector is a new type of low capacitance germanium well detector manufactured using small anode technology capable of advancing many environmental scientific research applications. The performance of this detector has been evaluated for a range of sample sizes and geometries counted inside the well, and on the end cap of the detector. The detector has energy resolution performance similar to semi-planar detectors, and offers significant improvement over the existing coaxial and Well detectors. Energy resolution performance of 750 eV Full Width at Half Maximum (FWHM) at 122 keV γ-ray energy and resolution of 2.0 - 2.3 keV FWHM at 1332 keV γ-ray energy are guaranteed for detector volumes up to 425 cm{sup 3}. The SAGe Well detector offers an optional 28 mm well diameter with the same energy resolution as the standard 16 mm well. Such outstanding resolution performance will benefit environmental applications in revealing the detailed radionuclide content of samples, particularly at low energy, and will enhance the detection sensitivity resulting in reduced counting time. The detector is compatible with electric coolers without any sacrifice in performance and supports the Canberra Mathematical efficiency calibration method (In situ Object Calibration Software or ISOCS, and Laboratory Source-less Calibration Software or LABSOCS). In addition, the SAGe Well detector supports true coincidence summing available in the ISOCS/LABSOCS framework. The improved resolution performance greatly enhances detection sensitivity of this new detector for a range of sample sizes and geometries counted inside the well. This results in lower minimum detectable concentrations compared to Traditional Well detectors. The SAGe Well detectors are compatible with Marinelli beakers and compete very well with semi-planar and coaxial detectors for large samples in many applications. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Materials genome innovation for computational software (magics) center

Functional layered material (LM) architectures will dominate nanomaterials science in this century. We have developed theory, modeling, simulation, and software and data tools that enhance understanding and AI guide synthesis, enable characterization of complex structures, and improve capabilities in the predictive design and growth of LMs. Research at the Center has focused on: Computational synthesis and characterization: AI guided synthesis and experimental synthesis of stacked LMs with tailored properties via optimized chemical vapor deposition (CVD) growth and liquid-phase exfoliation; study defects, edges, grain boundaries, wrinkling of atomic layers and their effects on chemical, mechanical, electrical, and optical properties. Far-from-equilibrium processes: Joint experimental and simulation based probe of electronic processes with NAQMD and ultrafast X-ray free-electron laser (XFEL) and ultrafast electron diffraction (UED) facilities at Stanford. Experimentally validate NAQMD by ultrafast electron diffraction and X-ray spectroscopy studies of structural and excited state dynamics, shape fluctuations, and phonon dynamics. Scalable software: Simulation engines for desktop-to-exascale platforms using low-overhead, linear-scaling QMD algorithms; divide-conquer-recombine NAQMD with electronic excitations; extended-Lagrangian reactive molecular dynamics (RMD), machine learning (ML) based neural-network quantum molecular dynamics (NNQMD), and super-state accelerated molecular dynamics (AMD) and kinetic Monte Carlo codes; thermal and electrical transport software; and design 3D architectures of LMs with desired functionality using scalable software. Distribution of software and data, and training: Software and simulation-experimental data generated within the Center are distributed to the materials science community via Berkeley Materials Project (MP) framework. We have also organized three workshops for software distribution and training at USC (Nov. 2017, Mar. 2018) and Gaithersburg, MD (Nov. 2018) to train researchers, with the last one in focused on underrepresented groups, in collaboration with Howard University which is one of the largest HBCUs. The Center supported a total of 46 personnel and 6 undergraduate students. These include 14 faculty, 11 postdoctoral research associates, 20 graduate research assistants, and mentored 6 undergraduate students. This resulted in the publications of 63 research papers that include 46 publications on Reactive and Quantum Dynamics Simulations, 13 publications on Machine Learning for Quantum Materials, and 4 publications on Quantum Computing.

2D Materials↗
DOE OSTI2019

Informative Path Planning for Mapping Radiation

This project involves making measurements of contaminated areas using portable radiation detectors and small robots. Tasks involve operating detectors and small robots, combining data acquisition for the two systems, and automating the systems. The end result is a radiation map of contamination areas. Localization of radioactive sources and mapping of contamination distribution is essential when responding to emergencies, or when dealing with cleanup efforts. Radiation maps are used for situational awareness and for establishing a safe perimeter. However, it may be hazardous or impossible for humans to safely enter the site and survey the area. Informative Path Planning (IPP) is used to design a path for a robotic sensor platform to gather the most information about the radiation distribution while operating under the set of constraints given by the dynamics of the robot and other requirements such as minimizing the overall mission time. The key challenge for IPP is the tight coupling in multiple layers of decisions: Selection of locations to visit; Allocation of sensors to those locations; Processing of information gathered. Given a robot equipped with a basic gamma detector and Lidar sensor, how can we efficiently explore an unknown environment in such a way that we can satisfy the robot's constraints while simultaneously maximizing the accuracy of the map and minimizing the total runtime? This work presents a systematic procedure for IPP and environmental mapping that: Selects the best locations at which to sample; Finds the best path for robots; Estimates the measurement value at unknown locations using a Gaussian Process regression framework. Given a set of points where radiation measurements were taken, we are utilizing a Gaussian Process Regression with a Matern kernel to reconstruct the shape of the underlying ground truth. Mapping is done via the Robot Operating System's OpenSlam Gmapping package, which creates a 2D occupancy grid map. Simulated trials were run with a radiation source placed at random locations, and allowing the robot to select and move towards 20 samples at separate locations, as well as sampling along the path while moving. The simulated sensor model assumed an efficiency of 0.3, collection time of 1 second. Various weighting schemes were tested and compared. Quantitative evaluations often favored exploitation-heavy weighting schemes where μ was emphasized, with the Root Mean Squared Error (RMSE) quickly tending towards 2, whereas qualitative comparisons would favor exploration-biased schemes with greater weight on σ. The difference comes from operators often favoring knowing the general location of a source, rather than the exact properties of a source. Weighting of the path-length π often reduced the quality of the regression as less exploration occurred, however this may be deemed a worthwhile trade-off under certain circumstances, such as with sensitive robots. Successfully developed a methodology for informative path-planning for a robot to efficiently gather information and create a map of radiation. In the future, we would like to focus on: Hardware trials with integration of the robot platform and the IPP software; More refined sensor and environmental modeling; Multiple robot platforms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

TU-AB-202-03: Prediction of PET Transfer Uncertainty by DIR Error Estimating Software, AUTODIRECT

Purpose: Deformable image registration (DIR) is a powerful tool, but DIR errors can adversely affect its clinical applications. To estimate voxel-specific DIR uncertainty, a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), has been developed and validated. This work tests the ability of this software to predict uncertainty for the transfer of standard uptake values (SUV) from positron-emission tomography (PET) with DIR. Methods: Virtual phantoms are used for this study. Each phantom has a planning computed tomography (CT) image and a diagnostic PET-CT image set. A deformation was digitally applied to the diagnostic CT to create the planning CT image and establish a known deformation between the images. One lung and three rectum patient datasets were employed to create the virtual phantoms. Both of these sites have difficult deformation scenarios associated with them, which can affect DIR accuracy (lung tissue sliding and changes in rectal filling). The virtual phantoms were created to simulate these scenarios by introducing discontinuities in the deformation field at the lung rectum border. The DIR algorithm from Plastimatch software was applied to these phantoms. The SUV mapping errors from the DIR were then compared to that predicted by AUTODIRECT. Results: The SUV error distributions closely followed the AUTODIRECT predicted error distribution for the 4 test cases. The minimum and maximum PET SUVs were produced from AUTODIRECT at 95% confidence interval before applying gradient-based SUV segmentation for each of these volumes. Notably, 93.5% of the target volume warped by the true deformation was included within the AUTODIRECT-predicted maximum SUV volume after the segmentation, while 78.9% of the target volume was within the target volume warped by Plastimatch. Conclusion: The AUTODIRECT framework is able to predict PET transfer uncertainty caused by DIR, which enables an understanding of the associated target volume uncertainty.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Coupled Physics Environment (CouPE) library - Design, Implementation, and Release

Over several years, high fidelity, validated mono-­physics solvers with proven scalability on peta-­scale architectures have been developed independently. Based on a unified component-­based architecture, these existing codes can be coupled with a unified mesh-­data backplane and a flexible coupling-­strategy-­based driver suite to produce a viable tool for analysts. In this report, we present details on the design decisions and developments on CouPE, an acronym that stands for Coupled Physics Environment that orchestrates a coupled physics solver through the interfaces exposed by MOAB array-­based unstructured mesh, both of which are part of SIGMA (Scalable Interfaces for Geometry and Mesh-­Based Applications) toolkit. The SIGMA toolkit contains libraries that enable scalable geometry and unstructured mesh creation and handling in a memory and computationally efficient implementation. The CouPE version being prepared for a full open-­source release along with updated documentation will contain several useful examples that will enable users to start developing their applications natively using the native MOAB mesh and couple their models to existing physics applications to analyze and solve real world problems of interest. An integrated multi-­physics simulation capability for the design and analysis of current and future nuclear reactor models is also being investigated as part of the NEAMS RPL, to tightly couple neutron transport, thermal-­hydraulics and structural mechanics physics under the SHARP framework. This report summarizes the efforts that have been invested in CouPE to bring together several existing physics applications namely PROTEUS (neutron transport code), Nek5000 (computational fluid-dynamics code) and Diablo (structural mechanics code). The goal of the SHARP framework is to perform fully resolved coupled physics analysis of a reactor on heterogeneous geometry, in order to reduce the overall numerical uncertainty while leveraging available computational resources. The design of CouPE along with motivations that led to implementation choices are also discussed. The first release of the library will be different from the current version of the code that integrates the components in SHARP and explanation on the need for forking the source base will also be provided. Enhancements in the functionality and improved user guides will be available as part of the release. CouPE v0.1 is scheduled for an open-­source release in December 2014 along with SIGMA v1.1 components that provide support for language-agnostic mesh loading, traversal and query interfaces along with scalable solution transfer of fields between different physics codes. The coupling methodology and software interfaces of the library are presented, along with verification studies on two representative fast sodium-­cooled reactor demonstration problems to prove the usability of the CouPE library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

IDAC-Dose 2.1, an internal dosimetry program for diagnostic nuclear medicine based on the ICRP adult reference voxel phantoms

To date, the estimated radiation-absorbed dose to organs and tissues in patients undergoing diagnostic examinations in nuclear medicine is derived via calculations based on models of the human body and the biokinetic behaviour of the radiopharmaceutical. An internal dosimetry computer program, IDAC-Dose2.1, was developed based on the International Commission on Radiological Protection (ICRP)-specific absorbed fractions and computational framework of internal dose assessment given for reference adults in ICRP Publication 133. The program uses the radionuclide decay database of ICRP Publication 107 and considers 83 different source regions irradiating 47 target tissues, defining the effective dose as presented in ICRP Publications 60 and 103. The computer program was validated against another ICRP dosimetry program, Dose and Risk Calculation (DCAL), that employs the same computational framework in evaluation of occupational and environmental intakes of radionuclides. IDAC-Dose2.1 has a sub-module for absorbed dose calculations in spherical structures of different volumes and composition; this sub-module is intended for absorbed dose estimates in radiopharmaceutical therapy. For nine specific alpha emitters, the absorbed dose contribution from their decay products is also included in the committed absorbed dose calculations. The absorbed doses and effective dose of 131 I-iodide determined by IDAC-Dose2.1 were validated against the dosimetry program DCAL, showing identical results. IDAC-Dose2.1 was used to calculate absorbed doses for intravenously administered 18 F-FDG and orally administered 99m Tc-pertechnetate and 131 I-iodide, three frequently used radiopharmaceuticals. Using the tissue weighting factors from ICRP Publication 103, the effective dose per administered activity was estimated to be 0.016 mSv/MBq for 18 F-FDG, 0.014 mSv/MBq for 99m Tc-pertechnetate, and 16 mSv/MBq for 131 I-iodide. The internal dosimetry program IDAC-Dose2.1 was developed and applied to three radiopharmaceuticals for validation against DCAL and to generate improved absorbed dose estimations for diagnostic nuclear medicine using specific absorbed fraction values of the ICRP computational voxel phantoms. The sub-module for absorbed dose calculations in spherical structures 1 mm to 9 cm in diameter and different tissue composition was included to broaden the clinical usefulness of the program. The IDAC-Dose2.1 program is free software for research and available for download at http://www.idac-dose.org.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

SU-E-E-02: An Excel-Based Study Tool for ABR-Style Exams

Purpose: As the landscape of learning and testing shifts toward a computer-based environment, a replacement for paper-based methods of studying is desirable. Using Microsoft Excel, a study tool was developed that allows the user to populate multiple-choice questions and then generate an interactive quiz session to answer them. Methods: The code for the tool was written using Microsoft Excel Visual Basic for Applications with the intent that this tool could be implemented by any institution with Excel. The base tool is a template with a setup macro, which builds out the structure based on user’s input. Once the framework is built, the user can input sets of multiple-choice questions, answer choices, and even add figures. The tool can be run in random-question or sequential-question mode for single or multiple courses of study. The interactive session allows the user to select answer choices and immediate feedback is provided. Once the user is finished studying, the tool records the day’s progress by reporting progress statistics useful for trending. Results: Six doctoral students at UTHSCSA have used this tool for the past two months to study for their qualifying exam, which is similar in format and content to the American Board of Radiology (ABR) Therapeutic Part II exam. The students collaborated to create a repository of questions, met weekly to go over these questions, and then used the tool to prepare for their exam. Conclusion: The study tool has provided an effective and efficient way for students to collaborate and be held accountable for exam preparation. The ease of use and familiarity of Excel are important factors for the tool’s use. There are software packages to create similar question banks, but this study tool has no additional cost for those that already have Excel. The study tool will be made openly available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Influence of local capillary trapping on containment system effectiveness

Immobilization of CO 2 injected into deep subsurface storage reservoirs is a critical component of risk assessment for geologic CO 2 storage (GCS). Local capillary trapping (LCT) is a recently established mode of immobilization that arises when CO 2 migrates due to buoyancy through heterogeneous storage reservoirs. This project sought to assess the amount and extent of LCT expected in storage formations under a range of injection conditions, and to confirm the persistence of LCT if the seal overlying the reservoir were to lose its integrity. Numerical simulation using commercial reservoir simulation software was conducted to assess the influence of injection. Laboratory experiments, modeling and numerical simulation were conducted to assess the effect of compromised seal integrity. Bench-scale (0.6 m by 0.6 m by 0.03 m) experiments with surrogate fluids provided the first empirical confirmation of the key concepts underlying LCT: accumulation of buoyant nonwetting phase at above residual saturations beneath capillary barriers in a variety of structures, which remains immobile under normal capillary pressure gradients. Immobilization of above-residual saturations is a critical distinction between LCT and the more familiar “residual saturation trapping.” To estimate the possible extent of LCT in a storage reservoir an algorithm was developed to identify all potential local traps, given the spatial distribution of capillary entry pressure in the reservoir. The algorithm assumes that the driving force for CO 2 migration can be represented as a single value of “critical capillary entry pressure” P c,entry crit , such that cells with capillary entry pressure greater/less than P c,entry crit act as barriers/potential traps during CO 2 migration. At intermediate values of P c,entry crit , the barrier regions become more laterally extensive in the reservoir, approaching a percolation threshold while non-barrier regions remain numerous. The maximum possible extent of LCT thus occurs at P c,entry crit near this threshold. Testing predictions of this simple algorithm against full-physics simulations of buoyancy-driven CO 2 migration support the concept of critical capillary entry pressure. However, further research is needed to determine whether a single value of critical capillary entry pressure always applies and how that value can be determined a priori. Simulations of injection into high-resolution (cells 0.3 m on a side) 2D and 3D heterogeneous domains show two characteristic behaviors. At small gravity numbers (vertical flow velocity much less than horizontal flow velocity) the CO 2 fills local traps as well as regions that would act as local barriers if CO 2 were moving only due to buoyancy. When injection ceases, the CO 2 migrates vertically to establish large saturations within local traps and residual saturation elsewhere. At large gravity numbers, the CO 2 invades a smaller portion of the perforated interval. Within this smaller swept zone the local barriers are not invaded, but local traps are filled to large saturation during injection and remain during post-injection gravity-driven migration. The small gravity number behavior is expected in the region within 100 m of a vertical injection well at anticipated rates of injection for commercial GCS. Simulations of leakage scenarios (through-going region of large permeability imposed in overlying seal) indicate that LCT persists (i.e. CO 2 remains held in a large fraction of the local iv traps) and the persistence is independent of injection rate during storage. Simulations of leakage for the limiting case of CO 2 migrating vertically from an areally extensive emplacement in the lower portion of a reservoir showed similar strong persistence of LCT. This research has two broad implications for GCS. The first is that LCT can retain a significant fraction of the CO 2 stored in a reservoir – above and beyond the residual saturation -- if the overlying seal were to fail. Thus frameworks for risk assessment should be extended to account for LCT. The second implication is that compared to pressure driven flow in reservoirs, CO 2 migration and trapping behave in a qualitatively different manner in heterogeneous reservoirs when buoyancy is the dominant driving force for flow. Thus simulations of GCS that neglect capillary heterogeneity will fail to capture important features of the CO 2 plume. While commercial reservoir simulation software can account for fine scale capillary heterogeneity, it has not been designed to work efficiently with such domains, and no simulators can handle fine-scale resolution throughout the reservoir. A possible way to upscale the migration and trapping is to apply an “effective residual saturation” to coarse-scale grids. While the extent of overall immobilization can be correlated in this way, all coarser grids failed to capture the distance traveled by the migrating CO 2 for large gravity number. Thus it remains unclear how best to account for LCT in the routine simulation work-flow that will be needed for large-scale GCS. Alternatives meriting investigation include streamline methods, reduced-physics proxies (e.g. particle tracking), and biased invasion percolation algorithms, which are based on precisely the capillary heterogeneity essential for LCT.

58 GEOSCIENCES↗
DOE OSTI2017

Analysis of Factors that Influence Infiltration Rates using the HELP Model

The Hydrologic Evaluation of Landfill Performance (HELP) model is used by Savannah River National Laboratory (SRNL) in conjunction with PORFLOW groundwater flow simulation software to make longterm predictions of the fate and transport of radionuclides in the environment at radiological waste sites. The work summarized in this report supports preparation of the planned 2018 Performance Assessment for the E-Area Low-Level Waste Facility (LLWF) at the Savannah River Site (SRS). More specifically, this project focused on conducting a sensitivity analysis of infiltration (i.e., the rate at which water travels vertically in soil) through the proposed E-Area LLWF closure cap. A sensitivity analysis was completed using HELP v3.95D to identify the cap design and material property parameters that most impact infiltration rates through the proposed closure cap for a 10,000-year simulation period. The results of the sensitivity analysis indicate that saturated hydraulic conductivity (Ksat) for select cap layers, precipitation rate, surface vegetation type, and geomembrane layer defect density are dominant factors limiting infiltration rate. Interestingly, calculated infiltration rates were substantially influenced by changes in the saturated hydraulic conductivity of the Upper Foundation and Lateral Drainage layers. For example, an order-of-magnitude decrease in Ksat for the Upper Foundation layer lowered the maximum infiltration rate from a base-case 11 inches per year to only two inches per year. Conversely, an order-of-magnitude increase in Ksat led to an increase in infiltration rate from 11 to 15 inches per year. This work and its results provide a framework for quantifying uncertainty in the radionuclide transport and dose models for the planned 2018 E-Area Performance Assessment. Future work will focus on the development of a nonlinear regression model for infiltration rate using Minitab 17® to facilitate execution of probabilistic simulations in the GoldSim® overall system model for the E-Area LLWF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Mind the Gap: Building Simulation in the Architectural Design Studio

Building modelling and simulation approaches are increasingly being utilized in architectural design studios to guide and inform the design process and offer evidence-based feedback on proposed building performance. The development of intuitive and simplified simulation interfaces has greatly contributed to achieving this integration. One aspect that is often overlooked is the workflow that governs and regulates integrated design, which can have significant impacts on final design outcomes. Currently there are numerous software packages available for building performance simulations. This makes it challenging to select an appropriate tool that provides accurate results yet allows a designer to make informed architectural decisions with a designer-friendly interface. Furthermore, workflows to incorporate simulations into the design process proved to highly impact student’s project design integration. Yet, it is not clear what type of workflows are successful to achieve this goal, under what conditions, and/or for which building and site typologies. This paper addresses these issues by first reviewing three different workflows for integrating building performance simulation processes and highlighting their strengths and weaknesses. Second, a comparative case study approach was employed to test three of the most common workflows in three different integrated design architectural studios at the senior and vertical studio levels as well as in two courses that run parallel and complementary to the design studios. The workflows, processes, and the resultant student projects were further analyzed based on criteria for better integrated design and architectural excellence as outlined in the American Institute of Architects Committee on the Environment (AIA COTE) Top 10 Student’s Competition. Third, to situate the pedagogical case studies’ results within a larger context, a survey of the AIA COTE Top 10 student competition award recipients over the last five years was conducted. The results are summarized in a pedagogical framework that outlines best strategies of the type of workflows, software, design process used, methods to achieve desired interaction between design process and analytical feedback, and metrics for educators to evaluate the success of this integration and their learning outcomes in the design studio. The goal is to help bridge the gap between the building design and simulation within the design studio’s creative process for more integrated design outcomes.

Elzeyadi, Ihab↗
DOE OSTI2025

Designing a Framework for Solving Multiobjective Simulation Optimization Problems

Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. Whereas an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time-consuming to integrate these techniques into real-world production-ready solvers. This is partly because of the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly because of the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate-modeling techniques, problem formulations, design spaces, and data acquisition functions. Here, this paper carefully surveys the current state of the art in MOSO algorithms, techniques, and solvers, as well as problem types and computational environments where MOSO is commonly applied. We then present several key challenges in the design of a parallel multiobjective simulation optimization framework (ParMOO) and how they have been addressed. Finally, we provide two case studies demonstrating how customized ParMOO solvers can be quickly built and deployed to solve real-world MOSO problems.

engineering design optimization↗
DOE OSTI2025

Benchmark Tracking System for Performance Monitoring

Benchmarking is essential for high-performance software development, particularly for monitoring performance across code iterations. This project focused on enhancing the benchmarking process for Lamellar, an asynchronous runtime for High-Performance Computing (HPC) systems developed at Pacific Northwest National Laboratory. Prior to this work, benchmark results were difficult to track and compare across code versions, presenting significant challenges in identifying performance regressions and long-term trends. The primary objective was to establish a systematic, reproducible approach for measuring performance and detecting regressions following code commits. Our methodology involved three key components: standardizing benchmark outputs, implementing data versioning, and developing analysis tools. We standardized the benchmark output format to JSON Line records containing specific fields (execution time, hardware specifications, and environmental variables). To address data management challenges, we evaluated several options and eventually chose a git repository dedicated to benchmark data. We developed a suite of Python tools that processed benchmark results, enriched them with metadata, and facilitated search in the repository. The resulting system enables more efficient filtering and comparison of performance metrics across commit histories, hardware configurations, and benchmark variants through a unified query interface. Our implementation reduces computational overhead by first checking for existing results through configuration matching before initiating new benchmark runs, thereby conserving resources. The system has been validated by Lamellar developers. It organizes results by benchmark type and build configurations for efficient retrieval. Future developments include a planned Large Language Model interface for predicting benchmark performance, incorporating the criterion package for statistical analysis, which will enable automated detection of statistically significant performance changes, and integration with continuous integration pipelines. Despite these enhancements being reserved for future work, this project has successfully provided the Lamellar development team with a framework for maintaining consistent performance standards and identifying optimization opportunities across workloads and hardware environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hexagonal Distributed Embedded Energy Converters (HexDEECs)

The HexDEEC is a small, characteristic length approximating a centimeter, energy transducer that converts the dynamic deformations of its elastomer housing into electricity through a variable capacitance charging-discharging cycle. This device is a type of Distributed Embedded Energy Converter Technology (DEEC-Tec), a new domain for marine renewable energy research that utilizes a conglomeration of small distributed embedded energy converters (DEECs) that, in aggregate, form larger metamaterial frameworks. These resulting DEEC-Tec metamaterials can then, in turn, be used to construct flexible ocean wave energy converters called flexWECs, which can utilize a broad band of ocean wave frequencies and lack highly loaded rigid bodies. These systems also provide new avenues of wave energy harvesting such as actively transforming topologies (e.g., shape and form) and morphologies (e.g., stiffness and damping throughout its entire structure) in real time. Presented, is one specific type of DEEC: the HexDEEC, which is currently being developed by the United States National Renewable Energy Laboratory. This transducer shows promise in aiding the adoption and further development of the DEEC-Tec domain. The following presentation focuses on the promise of this technology and current work being done to analyze the performance of an individual HexDEEC design. The HexDEEC is composed of a hyperelastic hexagonal housing, nominally silicon rubber, with six electrodes on its inner faces. The upper three electrodes share the same charge while the lower three electrodes oppose the upper electrode charges. Externally, the HexDEEC has two arms extending away from the middle vertices of the hexagon. Via principles governing the relationship between electrical capacitance and electrical potential (voltage and charge), electricity is generated when the HexDEEC's arms are dynamically pulled or released under tensile loading, as doing so causes the distance between the upper and lower sets of electrodes to change - varying the energy converter's overall capacitance. Analytical and numerical modeling is being used to evaluate the mechanics and electrical energy generated by the HexDEEC. Equations to describe the capacitance and electrostatic forces acting on this unique system have been developed and implemented into the numerical modeling software STAR-CCM+, along with models to describe its hyperelastic material, such as the Mooney-Rivlin 3-parameter model. So far, an initial design has been analyzed and we plan to further optimize it to increase power production. Individual HexDEECs have been fabricated by drawing uncured liquid silicon rubber into molds via vacuum pressure. To simplify manufacturing, HexDEEC sub-components - e.g., electrodes, wires - can be placed within those molds such that they are directly embedded into the hexagonal housing during the curing process. Furthermore, DEEC-Tec metamaterials can be created by interweaving or sequentially layering multiple HexDEEC strands together. The HexDEEC based metamaterial could then generate electricity through its gross deformations. Ultimately, HexDEECs represent a specific type of energy transducer that can be leveraged, by the DEEC-Tec domain, to create metamaterials used to construct novel flexWECs.

DEEC-Tec↗
DOE OSTI2015

New risk metrics and mathematical tools for risk analysis: Current and future challenges

The current status of the food safety supply world wide, has led Food and Agriculture Organization (FAO) and World Health Organization (WHO) to establishing Risk Analysis as the single framework for building food safety control programs. A series of guidelines and reports that detail out the various steps in Risk Analysis, namely Risk Management, Risk Assessment and Risk Communication is available. The Risk Analysis approach enables integration between operational food management systems, such as Hazard Analysis Critical Control Points, public health and governmental decisions. To do that, a series of new Risk Metrics has been established as follows: i) the Appropriate Level of Protection (ALOP), which indicates the maximum numbers of illnesses in a population per annum, defined by quantitative risk assessments, and used to establish; ii) Food Safety Objective (FSO), which sets the maximum frequency and/or concentration of a hazard in a food at the time of consumption that provides or contributes to the ALOP. Given that ALOP is rather a metric of the public health tolerable burden (it addresses the total ‘failure’ that may be handled at a national level), it is difficult to be interpreted into control measures applied at the manufacturing level. Thus, a series of specific objectives and criteria for performance of individual processes and products have been established, all of them assisting in the achievement of FSO and hence, ALOP. In order to achieve FSO, tools quantifying the effect of processes and intrinsic properties of foods on survival and growth of pathogens are essential. In this context, predictive microbiology and risk assessment have offered an important assistance to Food Safety Management. Predictive modelling is the basis of exposure assessment and the development of stochastic and kinetic models, which are also available in the form of Web-based applications, e.g., COMBASE and Microbial Responses Viewer), or introduced into user-friendly softwares, (e.g., Seafood Spoilage Predictor) have evolved the use of information systems in the food safety management. Such tools are updateable with new food-pathogen specific models containing cardinal parameters and multiple dependent variables, including plate counts, concentration of metabolic products, or even expression levels of certain genes. Then, these tools may further serve as decision-support tools which may assist in product logistics, based on their scientifically-based and “momentary” expressed spoilage and safety level.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗