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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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At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Synthesis Methods for Robust Passification and Control

The research effort under this cooperative agreement has been essentially the continuation of the work from previous grants. The ongoing work has primarily focused on developing passivity-based control techniques for Linear Time-Invariant (LTI) systems. During this period, there has been a significant progress made in the area of passivity-based control of LTI systems and some preliminary results have also been obtained for nonlinear systems, as well. The prior work has addressed optimal control design for inherently passive as well as non- passive linear systems. For exploiting the robustness characteristics of passivity-based controllers the passification methodology was developed for LTI systems that are not inherently passive. Various methods of passification were first proposed in and further developed. The robustness of passification was addressed for multi-input multi-output (MIMO) systems for certain classes of uncertainties using frequency-domain methods. For MIMO systems, a state-space approach using Linear Matrix Inequality (LMI)-based formulation was presented, for passification of non-passive LTI systems. An LMI-based robust passification technique was presented for systems with redundant actuators and sensors. The redundancy in actuators and sensors was used effectively for robust passification using the LMI formulation. The passification was designed to be robust to an interval-type uncertainties in system parameters. The passification techniques were used to design a robust controller for Benchmark Active Control Technology wing under parametric uncertainties. The results on passive nonlinear systems, however, are very limited to date. Our recent work in this area was presented, wherein some stability results were obtained for passive nonlinear systems that are affine in control.

Kelkar, Atul G.↗
DOE OSTI2024

Design and Manufacturing of High Performance, Reduced Charge Heat Exchangers

Air-to-fluid heat exchangers (HXs) play a critical role as the main heat transfer component in Heating, Ventilation, Air-Conditioning, and Refrigeration (HVAC&R) systems. However, their airside thermal resistance significantly inhibits their overall performance. Furthermore, these HXs must be continually more compact to meet the latest refrigerant charge limits to reduce emissions. Recent literature suggests that traditional HX geometries (e.g., round or flat tubes with fins), have reached their limits, and more sophisticated shape- and topology-optimized designs are required to achieve the next jump in performance. This research sheds light on the next generation of air-to-refrigerant HXs and aims to address several practical issues to commercialization such as novelty challenges (improved performance for significant charge reduction; modeling expertise & time investment), manufacturing challenges (non-round tube manufacturing; tube-header integration; product qualification, e.g., burst pressure testing, extreme operational environment, etc.), and operational challenges (flow maldistribution, fouling & wetting, noise & vibration). For example, a >20% improvement on one (or more) HX-level performance metrics (e.g., envelope volume, airside pressure drop, face area, capacity, refrigerant charge, weight, cost, etc.) must be achieved before a HX design is considered for commercialization. We present a new, comprehensive and experimentally validated air-to-refrigerant HX optimization framework with simultaneous thermal-hydraulic performance and mechanical strength considerations for novel, non-round, shape- and topology-optimized tubes capable of optimizing single and two-phase HX designs for any refrigerant choice and performance requirement with significant engineering time savings compared to conventional design practices. The framework was exercised for a wide range of applications and refrigerants, resulting in HXs which achieved greater than 20% improved performance, 20% reductions in size, and 25% reductions in refrigerant charge. To enable non-round tube bundle use in next generation HVAC&R equipment, novel manufacturing techniques were investigated, including the development of conventional manufacturing methods for small diameter, non-round tubes and novel tube-header integration strategies. In total, ten HX prototypes were manufactured, nine using conventional methods directly attributed to this project and one using advanced additive manufacturing methods. The five-year manufacturing feasibility of the proposed HXs was found to have a good outlook. The non-round tube HX simulated performance was validated through comprehensive experimental testing, including nine in-house component-level tests, one independent component-level test at an industry partner laboratory, and in-house system-level tests of using a commercially-available, residential packaged A/C unit which was retrofitted with a non-round tube prototype HX. It was found that HX designs proposed by the new framework can successfully predict experimental thermal-hydraulic performance within ±10-20% the first time with no manual design changes, eliminating the need for time-consuming and expensive prototyping efforts. This work will accelerate design and time to market for next generation HXs while simultaneously facilitating industry transition to new refrigerants at lower charge.

42 ENGINEERING↗
DOE OSTI2023

Scaling Wind Power Innovation Assessment for Rapid Energy Transition with Artificial Intelligence

Planning for energy system decarbonization requires new insights into the potential of renewable technologies, deployed at unprecedented scale, to meet urgent sustainability goals. However, limited scalability of current wind energy research tools restricts characterization of innovation impacts to isolated reference sites, challenging investment and decision making under rapid growth. We demonstrate the transformative potential of artificial intelligence (AI) to inform future technology advancement and energy systems design by leveraging a state-of-the-art surrogate model to conduct a series of fleet-wide wind plant layout optimizations for greater than 6,800 projected U.S. onshore buildout locations. We show how innovative wake steering technology can address an array of barriers to large-scale deployment and integration of wind power. Specifically, wake steering reduces required plant area by an average of 18% and could preserve upwards of 13,000 km2 for future greenfield deployment, potentially easing siting challenges associated with wind energy infrastructure. Further, by enabling reduced turbine spacing and increased energy production, flexible operations of wake steering improve levelized cost of energy, particularly for large plants and in land-constrained settings. Finally, optimizations that consider dynamic energy prices can deliver increased power production and revenue capture during high-value (often low-wind) periods, further bolstering plant economics. Our computationally efficient approach offers a pathway to accelerate nationwide geographic evaluation of innovative technologies.

graph neural networks↗
DOE OSTI2025

Data-driven Community-centered Resilient Assessment and Planning Toolkit for Nexus of Energy and Water (DCRAPT-NEW)

Urban areas, including Detroit and Pittsburgh, have suffered significant dual outages of the electrical and water infrastructure in the past decade due, in part, to the increasing number of extreme weather events. With increasing temperatures and rainfall intensity, these regions need to prepare for increasing extreme events through community-based energy and water resilience analysis, planning, and enhancement. This project developed a suite of open-source, open-access, community-centered, data-driven assessment and distributed energy resource (DER) and planning tools for energy and water resilience enhancement in urban areas. Through establishing a multi-level community awareness and engagement mechanism and a comprehensive collection of power outage and flooding data, an innovative group of community energy and water resilience assessment and planning tools have been developed for a wide range of users with differing and variable sets of data available to them. The developed tools include (1) DOE EAGLE-I data-driven, deep-learning assisted resilience assessment and DER planning tools at the county level with socioeconomic factors incorporated; (2) Utility annual power outage data-driven tools for long term resilience assessment and DER planning and 15-min power outage data-driven tools for short term resilience assessment and planning; (3) Detailed engineering tools for energy and water systems resilience assessment and planning when the system topology and component fragility curves are available; (4) Alternative Resiliency Metric Calculation that extracts and separates outage and restoration processes; and (5) Co-optimization tools that evaluate the resilience of the power and sewage system and allow users to conduct joint planning with energy and wastewater systems. The developed tools provide planners, decision-makers, and stakeholders with powerful capabilities to systematically evaluate system/community resilience and optimal and actionable guidance for enhancing resilience while prioritizing DER investments. The tools have been used and validated in Detroit and Pittsburgh and can be used in other areas of the nation. In addition, this project will (1) advance the knowledge and applications of machine-learning methods in analyzing and fusing different layers of information and generating meaningful data points such as generating rare weather events; (2) significantly improve the energy and water resilience of the identified communities in Detroit and Pittsburgh and prepare for more frequent and severe weather conditions; (3) help communities assess extreme weather event impacts and address short-term and long-term resilience-related issues The developed tools have been made public via GitHub and demonstrated to community stakeholders and utility companies via the two annual workshops and numerous community engagement meetings. The project outcomes are also disseminated through publications in various journals and conference proceedings, and presentations at top conferences.

13 HYDRO ENERGY↗
NASA NTRS2011

A Concept for Flexible Operations and Optimized Traffic into Metroplex Regions

A "Flexible Flight Operations" concept for airport metroplexes was studied. A flexible flight is one whose destination airport is not assigned until a threshold is reached near the arrival area at which time the runway which reduces overall delay is assigned. The concept seeks to increase throughput by exploiting flexibility. The quantification of best-case benefits from the concept was pursued to establish whether concept research is warranted. Findings indicate that indeed the concept has potential for significant reductions in delay (and cost due to delay) in the N90 (NY/NJ) and SCT (Southern California) metroplexes. Delay reductions of nearly 26% are possible in N90 when 30% of the commercial airline flights are flexible (smartly selected by their low probability of connecting passengers); nearly 40% delay reduction is found when 50% of the flights are flexible. In the SCT metroplex, delay reductions estimates are greater. Greater reductions result at SCT since it is less constrained currently than N90, providing "more room" to take advantage of flexibility. Using the flexible operations concept for on-demand/air taxi and General Aviation flights were found to be beneficial at NY/NJ, indicating the flexible operations concepts may be useful to wide variety of users..

DeLaurentis, Daniel↗
NASA NTRS1981

Metallization for large-area solar cells

In large area, low cost solar cells of any type, the contact and grid structure metallization is an important factor which has an effect on the efficiency of the solar cell and its reliability. The present investigation is concerned with aspects of solar cell efficiency. An optimized metallization design leads to minimum total power loss, which is related to a simultaneous minimization of ohmic voltage drops and of shading of the front surface of the cell by the overlaid metal. The requirements regarding the design for a low-loss metallization pattern for the front surface of large area solar cells are represented by a set of design rules listed in a table. The total shading and voltage drop on such cells can be held to about 5%. However, not every metallization process is suited for meeting the requirements of the low-cost design. The low losses can be achieved only by use of several bus lines containing a bulk conductor, such as a wire.

Wolf, M.↗
DOE OSTI2019

Thermal Properties of Carbon-Containing Iron Ore Composite Prepared by Vapor Deposition of Tar for Limonite

The optimal conditions for preparing composites, in which tar-derived carbonaceous materials completely fill the pores in Indonesian limonite(IL), were investigated using the vapor deposition (VD) method of tar. Characterization of the composites and their reactivity (reduction/combustion/gasification) during heat treatment was examined. The specific surface area and pore volume of the as-received-IL and dehydrated-IL decreased after the VD treatment for different combinations of tar pyrolysis temperature (TPT) and VD temperature (VDT). It was also noted that the carbon content and crushing strength of the samples increased after the VD treatment, and the optimal values for the TPT-VDT are 700 °C and 350 °C. The C content and the crushing strengths of the composites prepared at TPT-VDT = 700 °C to 350 °C increased with an increase of the VD time to 50 pct carbon content and 10 daN at 240 minutes, respectively. The strength was similar to that of commercial metallurgical coke used in blast furnaces. The reduction of the composite prepared at TPT-VDT = 700 °C to 350 °C for 240 minutes started above 400 °C and was almost completed at 1000 °C. This reduction rate was higher than that of commercial cold-bonded pellets or a coke/IL mixture. The combustion and gasification rates of the composites prepared under different conditions depended on the preparation condition of composites, and the rates were higher than those of commercial metallurgical coke used in blast furnaces.

36 MATERIALS SCIENCE↗
NASA NTRS1998

Automated Subpixel Snow Parameter Mapping with AVIRIS Data

We describe an automated algorithm (MEMSCAG) for mapping subpixel snow covered area (SCA) and snow grain size with AVIRIS data. The algorithm is based on the multiple endmember approach to spectral mixture analysis in which the spectral endmembers and the number of endmembers can vary on a pixel-by-pixel basis. This approach accounts for surface cover heterogeneity within a scene. The mixture analysis runs on endmembers from a spectral library of snow, vegetation, rock, soil, and lake ice spectra. Snow endmembers of varying grain size were produced with a radiative transfer model. All non-snow endmembers were collected with a portable field spectrometer. Mapping is performed through sequential 2-endmember, 3-endmember, and 4- endmember mixture model runs, each subject to constraints on RMS, residuals, fractions and priority. Grain size is determined by the grain size of the snow endmember used in the optimal mixture model. We apply MEMSCAG to AVIRIS data collected over Mammoth Mountain, CA and the northern site of the BOREAS in Manitoba, Canada. MEMSCAG produces appropriate snow covered area estimates in all regions. A preliminary comparison of grain size estimates from MEMSCAG with field measurements demonstrates high accuracy.

Painter, Thomas H.↗
NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗
NASA NTRS1990

PLA realizations for VLSI state machines

A major problem associated with state assignment procedures for VLSI controllers is obtaining an assignment that produces minimal or near minimal logic. The key item in Programmable Logic Array (PLA) area minimization is the number of unique product terms required by the design equations. This paper presents a state assignment algorithm for minimizing the number of product terms required to implement a finite state machine using a PLA. Partition algebra with predecessor state information is used to derive a near optimal state assignment. A maximum bound on the number of product terms required can be obtained by inspecting the predecessor state information. The state assignment algorithm presented is much simpler than existing procedures and leads to the same number of product terms or less. An area-efficient PLA structure implemented in a 1.0 micron CMOS process is presented along with a summary of the performance for a controller implemented using this design procedure.

Gopalakrishnan, S.↗
DOE OSTI2021

Graphitic Aza-Fused π-Conjugated Networks: Construction, Engineering, and Task-Specific Applications

2D π-conjugated networks linked by aza-fused units represent a pivotal category of graphitic materials with stacked nanosheet architectures. Extensive efforts have been directed at their fabrication and application since the discovery of covalent triazine frameworks (CTFs). Besides the triazine cores, tricycloquinazoline and hexaazatriphenylene linkages are further introduced to tailor the structures and properties. Diverse related materials have been developed rapidly, and a thorough outlook is necessitated to unveil the structure–property–application relationships across multiple subcategories, which is pivotal to guide the design and fabrication toward enhanced task-specific performance. Herein, the structure types and development of related materials including CTFs, covalent quinazoline networks, and hexaazatriphenylene networks, are introduced. Advanced synthetic strategies coupled with characterization techniques provide powerful tools to engineer the properties and tune the associated behaviors in corresponding applications. Case studies in the areas of gas adsorption, membrane-based separation, thermo-/electro-/photocatalysis, and energy storage are then addressed, focusing on the correlation between structure/property engineering and optimization of the corresponding performance, particularly the preferred features and strategies in each specific field. In the last section, the underlying challenges and opportunities in construction and application of this emerging and promising material category are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Ground sculpting to enhance energy yield of vertical bifacial solar farms

The prospect of additional energy yield and improved reliability have increased commercial interest in bifacial solar modules. Recent publications have quantified the bifacial gain for several configurations. For example, a standalone, optimally-tilted bifacial panel placed over a flat ground (with 50% albedo) is expected to produce a bifacial energy gain of 30% (per module area). In contrast, self and mutual shading in a farm with periodically spaced panels reduces the bifacial gain to 10–15% (per farm area). Bifacial gain is negligible for vertical arrays—although the configuration is of significant interest since it can prevent soiling. Here, we calculate the bifacial gain of a solar farm where vertical arrays are placed over sculpted/patterned ground. We conclude that vertical panels straddling (upward) triangle-shaped ground maximizes the energy output. Our worldwide calculation with up-triangle ground configuration and 50% albedo leads to the following conclusion. Compared to a traditional tilted monofacial design, the bifacial gain is (i) small up to 20° latitude, (ii) increases to 50% at 40° latitude, and (iii) reaches up to 100% at 60° latitude. Overall, high bifacial gains are observed in many regions particularly those with moderate to low clearness index. The enhanced output, along with reduced soiling loss and lower cleaning cost of the ground sculpted vertical bifacial (GvBF) solar farm could be of significant technological interest, especially in regions such as the Middle East and North Africa (MENA) susceptible to significant soiling losses.

14 SOLAR ENERGY↗
DOE OSTI2025

Impact of low-chemical storage pretreatment of loblolly pine bark on biochar from microwave pyrolysis

Forest product residues such as bark represent a low-cost, abundant feedstock for bioenergy, but their high ash and alkali and alkaline earth metal (AAEM) content limit thermochemical conversion efficiency. This study evaluates the use of low-severity chemical pretreatments during anaerobic storage to improve the performance of microwave pyrolysis for loblolly pine bark. Bark was treated with dilute sulfuric acid (0.1% and 1%, w/w) or sodium hydroxide (4%, w/w) and incubated anaerobically for one or two weeks to simulate in-pile biorefinery storage. The most effective treatment—1% H2SO4 for two weeks—reduced AAEM content by 35.7% and increased bio-oil yield by 11% compared to untreated controls, while also reducing pyrolysis gas production. In contrast, alkali treatment did not reduce AAEM levels and led to decreased bio-oil yields with increased gas formation. Although biochar yields were relatively stable across treatments, their physicochemical characteristics varied significantly. Acid-treated bark yielded biochars with higher carbon content, lower O/C and H/C ratios, greater surface area, and enhanced heating values. These improvements suggest that chemical pretreatment during storage can tailor biochar quality for specific end uses. Biochars produced under optimized conditions exhibited properties suitable for soil amendment, carbon sequestration, and solid fuel applications. This integrated approach—combining storage, mild chemical conditioning, and microwave pyrolysis—provides a viable pathway to enhance the value and sustainability of bark-derived bioenergy products.

09 - BIOMASS FUELS↗
DOE OSTI2025

Surrogate models for development of unconventional shale reservoirs by an integrated numerical approach of hydraulic fracturing, flow and geomechanics, and machine learning

We develop well-completion surrogate models by taking an integrated workflow of hydraulic fracturing, flow, geomechanics, and machine learning simulation. There are three steps in the proposed workflow. First, history-matching processes are conducted with the field data including pumping and production data for characterization. Second, full-physics simulation is performed with various parameters of the field development (e.g., cluster spacing, clusters per stage, pumping rates and times, amount of proppant, and well spacing) to generate multiple simulation results by changing the parameters of the completion design with well-known hydraulic fracturing, reservoir, geomechanics simulators to calculate fracture geometry, reservoir depressurization, induced stress changes. The workflow is demonstrated over a field in the Southern Midland Basin. Here, we take two completion scenarios: a single well case followed by a multi-well case. Finally, a Long Short-Term Memory (LSTM) machine learning algorithm is employed to create surrogate models that can replicate the full-physics simulation results. Furthermore, results show that the trained models applied in the single well and multi-well cases for a particular geological system can provide good accuracy close to those provided by full-physics simulations. Specifically, the site-specific surrogate models can predict fracture parameters (length, height, and surface area) and cumulative production accurately with computational efficiency, suggesting our proposed workflow can be used as a pragmatic tool for expediting the well completion optimization process.

Geomechanics↗
DOE OSTI2025

Efficiently predicting pressure-composition-temperature diagrams to discover low-stability metal hydrides

Quantitatively accurate computational predictions of metal hydride thermodynamics are challenging but critical for alloy performance optimization across a multitude of technological domains, including hydrogen storage, compression, purification, and getters. Recent machine learning approaches have demonstrated great success in this area, but can potentially suffer from several shortcomings since they rely on imbalanced experimental training data and can have poor out-of-distribution (ood) test performance. Here, in this study, we circumvent such pitfalls by developing a computationally efficient, first principles-based workflow for direct prediction of metal hydride phase equilibrium, i.e., the pressure-composition-temperature (PCT) diagram. We then demonstrate its utility on predicting low stability hydrides derived from compositionally complex C14 Laves phase AB2 alloys. Specifically, we computationally predict and then experimentally validate an AB 2 alloy series (z < 0.6 for Ti 2−z Zr z CrMnFeNi) with ideal hydriding thermodynamics for a two-stage metal hydride-based compressor for pressurizing boil off from liquefied hydrogen. Importantly, this study lays the groundwork for accurate and efficient discovery/optimization of ood, low-stability hydrides for which purely data-driven approaches lack sufficient accuracy.

08 HYDROGEN↗
DOE OSTI2015

Physics-based statistical learning approach to mesoscopic model selection

In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. Here, we apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD “training” data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD “test” data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2015

DOE-EM/HBCU Projects for Environmental Management Research and Workshop Development at SCSU [Conference Papers/Proceedings]. Final Report

The Department of Energy’s Office of Environmental Management (DOE-EM) established a Memorandum of Understanding (MOU) with nine HBCUs in South Carolina and Northeast Georgia. The nine HBCUs (Allen University, Benedict College, Claflin University, Clinton Junior College, Demark Technical College, Morris College, South Carolina State University, Voorhees College and Paine College of Northeast Georgia) have formulated a consortium to address the technical and workforce development needs of the DOE’s Office of Environmental Management. These institutions have the qualifications and expertise to provide the technical cooperation and operations support in the areas of environmental remediation, nuclear regulatory and transportation safety, modeling, technology transfer and workforce development. Several of the HBCUs have received awards from the DOE to address issues related to ground water, surface water, plant communities, the development of an interdisciplinary waste management institute, and the establishment of an environmental science field station. Also, the acquisition of these grant awards have afforded the participating institutions with the ability to instruct students by providing them with the appropriate skill sets to address the future employment needs of DOE to include: Hands-on field experiences; Classroom demonstrations; and, Research and education experiences. South Carolina State University will build upon its existing relationships with DOE-EM by creating a "pipeline" for minorities in Mathematics, Science & Technology, Engineering, Research, and other related fields. Students will be specifically trained and mentored to enter the DOE workforce in these technical areas of need. The integration of technology in research and education is essential for meeting the demanding need to develop a workable mechanism to optimize the effectiveness of research and education in this nation. This is extremely critical in this age of unprecedented technological advances when information delivery, training, and continuous research to improve performance and skill enhancement hold the key to competition. This development relies on a program of applied science to identify and help implement the technologies, as well as programs that energize and mentor the next generation of scientists and engineers. To foster these goals, SCSU proposes research and educational projects that will support the integration of technology in research and education.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

GoldSim® Aquifer Model Calibration and Plume Interaction

The E-Area Low-Level Waste Facility (ELLWF) Probabilistic Aquifer Model (PAM) utilizes GoldSim ® Monte Carlo simulation software (GTG, 2017) to evaluate the transport of a tracer species as it travels from the water table below the disposal unit footprint, through the aquifer, to the Point of Assessment (POA) at the 100-meter boundary. This report documents the development and calibration of PAM as well as the implementation of plume interaction. PAM is a key component of the effort to include uncertainty quantification and sensitivity analysis (UQSA) in the next revision of the E-Area Performance Assessment (PA), considering recommendations from the 2015 PA strategic planning team outlined by Butcher and Phifer (2016). The Aquifer Model and associated optimized geometric parameters will be implemented in the future GoldSim® (GS) system model that will simulate subsurface flow and radionuclide transport from the ground surface to the 100-meter POA.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗