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At least 1,621 records · Page 90

All sources NASA NTRS DOE OSTI

DOE OSTI2019

Analysis of the Sludge Batch 8 (Macrobatch 10) DWPF Pour Stream Glass Sample

To meet the objectives of the Defense Waste Processing Facility (DWPF) Glass Product Control Program, DWPF and Saltstone Facility Engineering (D&S-FE) requested that Savannah River National Laboratory (SRNL) analyze a radioactive glass pour stream (PS) sample collected during the processing of Sludge Batch 8 (SB8), also known as Macrobatch 10 (MB10). The analyses requested by D&S-FE (listed as tasks one through five in the Technical Task Request (TTR) included determination of the chemical and radionuclide composition, performance of the Product Consistency Test (PCT), and measurement of glass density and REDOX state. The tasks associated with accomplishing these analyses were controlled under a Task Technical and Quality Assurance Plan (TTQAP) and Analytical Study Plan. This report provides results from the requested analyses and satisfies deliverable number five of the TTR.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Understanding and mitigating process drift in aerosol jet printing

Aerosol jet printing offers a versatile, high-resolution prototyping capability for flexible and hybrid electronics. Despite its rapid growth in recent years, persistent problems such as process drift hinder the adoption of this technology in production environments. Here we explore underlying causes of process drift during aerosol jet printing and introduce an engineered solution to improve deposition stability. In this work it is shown that the ink level within the cartridge is a critical factor in determining atomization efficiency, such that the reduction in ink volume resulting from printing itself can induce significant and systematic process drift. By integrating a custom 3D-printed cartridge with an ink recirculation system, ink composition and level within the cartridge are better maintained. This strategy allows extended duration printing with improved stability, as evidenced by 30 h of printing over 5 production runs. This provides an important tool for extending the duration and improving reliability for aerosol jet printing, a key factor for integration in practical manufacturing operations.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Steady-State Calibration of a Diesel Engine in CFD Using a GPU-Based Chemistry Solver

The prospect of analysis-driven pre-calibration of a modern diesel engine is extremely valuable in order to significantly reduce hardware investments and accelerate engine designs compliant with stricter EPA fuel economy regulations. Advanced modeling tools, such as CFD, are often used with the goal of streamlining significant portions of the calibration process. The success of the methodology largely relies on the accuracy of analytical predictions, especially engine-out emissions. However, the effectiveness of CFD simulation tools for in-cylinder engine combustion is often compromised by the complexity, accuracy, and computational overhead of detailed chemical kinetics necessary for combustion calculations. The standard approach has been to use skeletal kinetic mechanisms (~50 species) which consume acceptable computational time but with degraded accuracy.In this work, a comprehensive demonstration and validation of the analytical pre-calibration process is presented for a passenger car diesel engine using CFD simulations with CONVERGE™ and a GPU-based chemical kinetics solver (Zero-RK, developed at Lawrence Livermore National Laboratory) on high performance computing resources to enable the use of detailed kinetic mechanisms. Diesel engine combustion computations have been conducted over 600 operating points spanning in-vehicle speed-load map, using massively parallel ensemble simulation sets on the Titan supercomputer located at the Oak Ridge Leadership Computing Facility. The results with different mesh resolutions have been analyzed to compare differences in combustion and emissions (NO x , Carbon Monoxide CO, Unburned Hydrocarbons UHC, and Smoke) with actual engine measurements. The results show improved agreement in combustion and NO x predictions with a large n-heptane mechanism consisting of 144 species and 900 reactions with refined mesh resolution; however; agreement in CO, UHC and Smoke remain a challenge.

Gao, Jian↗

DOE OSTI2015

Nanophase Engineering of Organic Semiconductor-based Solar Cells

Organic photovoltaics are promising low-cost, easily-processable energy sources of the future, and are the subject of current academic and industrial interest. In order to achieve the envisioned device efficiencies to surpass commercialization target values, several challenges must be met: (1) to design and synthesize conjugated molecules with low optical bandgaps and optimized electronic energy levels, (2) optimization the morphology of donor/acceptor interpenetrating networks by controlling nanoscale phase separation and self-assembly, and (3) precise tuning of the active layer/electrode interfaces and donor/acceptor interfaces for optimized charge transfer. Here, we focus on recent advances in: (i) synthetic strategies for low-bandgap conjugated polymers and novel fullerene acceptors, (ii) processing to tune film morphologies by solvent annealing, thermal annealing, and the use of solvent additives and compatibilizers, and (iii) engineering of active layer/electrode interfaces and donor/acceptor interfaces with self-assembled monolayer dipoles.

Yang, Bin↗

DOE OSTI2024

Data-scarce surrogate modeling of shock-induced pore collapse process

Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological sciences, and geophysics. However, numerical modeling of the complex pore collapse processes can be costly. To this end, a strong need exists to develop surrogate models for generating economic predictions of pore collapse processes. Here, in this work, we study the use of a data-driven reduced-order model, namely dynamic mode decomposition, and a deep generative model, namely conditional generative adversarial networks, to resemble the numerical simulations of the pore collapse process at representative training shock pressures. Since the simulations are expensive, the training data are scarce, which makes training an accurate surrogate model challenging. To overcome the difficulties posed by the complex physics phenomena, we make several crucial treatments to the plain original form of the methods to increase the capability of approximating and predicting the dynamics. In particular, physics information is used as indicators or conditional inputs to guide the prediction. In realizing these methods, the training of each dynamic mode composition model takes only around 30 s on CPU. In contrast, training a generative adversarial network model takes 8 h on GPU. Moreover, using dynamic mode decomposition, the final-time relative error is around 0.3% in the reproductive cases. We also demonstrate the predictive power of the methods at unseen testing shock pressures, where the error ranges from 1.3 to 5% in the interpolatory cases and 8 to 9% in extrapolatory cases.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Non-Gaussian photonic state engineering with the quantum frequency processor

Non-Gaussian quantum states of light are critical resources for optical quantum information processing, but methods to generate them efficiently remain challenging to implement. Here we introduce a generic approach for non-Gaussian state production from input states populating discrete frequency bins. Based on controllable unitary operations with a quantum frequency processor, followed by photon-number-resolved detection of ancilla modes, our method combines recent developments in both frequency-based quantum information and non-Gaussian state preparation. Leveraging and refining the K-function representation of quantum states in the coherent basis, we develop a theoretical model amenable to numerical optimization and, as specific examples, design quantum frequency processor circuits for the production of Schrödinger cat states, exploring the performance tradeoffs for several combinations of ancilla modes and circuit depth. Our scheme provides a valuable framework for producing complex quantum states in frequency bins, paving the way for single-spatial-mode, fiber-optic-compatible non-Gaussian resources.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Possible role of interference, protein noise, and sink effects in nonphotochemical quenching in photosynthetic complexes

Here, we analyze theoretically a simple and consistent quantum mechanical model that reveals the possible role of quantum interference, protein noise, and sink effects in the nonphotochemical quenching (NPQ) in light-harvesting complexes (LHCs). The model consists of a network of five interconnected sites (excitonic states of light-sensitive molecules) responsible for the NPQ mechanism. The model also includes the “damaging” and the dissipative channels. The damaging channel is responsible for production of singlet oxygen and other destructive outcomes. In this model, both damaging and “dissipative” charge transfer channels are described by discrete electron energy levels attached to their sinks, that mimic the continuum part of electron energy spectrum. All five excitonic sites interact with the protein environment that is modeled using a stochastic process. Our approach allowed us to derive the exact and closed system of linear ordinary differential equations for the reduced density matrix and its first momentums. Moreover, these equations are solved numerically including for strong interactions between the light-sensitive molecules and protein environment. As an example, we apply our model to demonstrate possible contributions of quantum interference, protein noise, and sink effects in the NPQ mechanism in the CP29 minor LHC. The numerical simulations show that using proper combination of quantum interference effects, properties of noise, and sinks, one can significantly suppress the damaging channel. Finally, our findings demonstrate the possible role of interference, protein noise, and sink effects for modeling, engineering, and optimizing the performance of the NPQ processes in both natural and artificial light-harvesting complexes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Assessing the Impact of a Novel TBC Material on Heat Transfer in a Spark Ignition Engine through 3D CFD-FEA Co-Simulation Routine

Thermal barrier coatings (TBCs) have been of interest since the 1970s for application in internal combustion (IC) engines. Thin TBCs exhibit a temperature swing phenomenon wherein wall temperatures dynamically respond to the transient working-gas temperature throughout the engine cycle, thus reducing the temperature difference driving the heat transfer. Determining these varying wall temperatures is necessary to evaluate and study the effect of coatings on wall heat transfer. This study focuses on developing a 3D computational fluid dynamics (CFD)-finite element analysis (FEA) coupled simulation, or co-simulation, routine to determine the wall temperatures of a piston coated with a thin TBC layer subject to spark ignition combustion heat flux. A CONVERGE 3D-CFD model was used to simulate the combustion process in a single-cylinder, light-duty experimental spark ignition (SI) engine. Transient piston heat transfer analysis was conducted using ABAQUS, a FEA package, under the simulated combustion heat flux load. The effect of the temperature swing phenomenon due to this TBC layer was observed in a CFD simulation by implementing the FEA results as the piston thermal boundary conditions. The boundary conditions were passed between the CFD and FEA tools until a quasi-steady state solution was achieved. Furthermore, a reduction in wall heat transfer was observed due to a reduced temperature difference between the wall and the working gas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Identification of Natural Oscillation Modes for Purposes of Seismic Assessment and Monitoring of HPP Dams

The paper puts forward a method for processing data from detailed seismic assessments of HPP dams (dynamic tests). A detailed assessment (hundreds of observation points in dam galleries) is performed with consideration of operating dam equipment and the microseismic noise. It is shown that dynamic oscillation characteristics (natural oscillation frequencies and modes in the main dam axes, the velocities of propagation of elastic waves with given polarization, and so on.) can be determined with sufficient accuracy by using complex transfer functions and pulse characteristics. Monitoring data is processed using data from a detailed assessment, taking account of identified natural oscillation modes and determined ranges of natural frequencies. The spectra of characteristic frequencies thus obtained are used to choose substitution models and estimate the elastic characteristics of the “dam – rock bed” construction system, viz., the modulus of elasticity (the Young modulus), the Poisson ratio, the dam section stiffness with respect to shear, tension and compression and the elastic characteristics of the rock foundation.

42 ENGINEERING↗

DOE OSTI2015

D and D decision model and mobile application for selection of fixative, strippable coating, and decontamination gel products - 15696

Deactivation and decommissioning (D and D) is the process of taking an active/excess/abandoned facility to a final disposition end state. D and D presents unique hazards that must be addressed from a safety, programmatic, environmental, and technological standpoint. This definition is especially applicable to Savannah River Site's 235-F facility. The facility was constructed in the 1950's as part of the weapons complex materials production and fabrication. The most significant quantities of residual contamination remaining in 235-F building is in the form of Plutonium-238. A Decision Model was created using MatLab to work hand-in-hand with a product list containing commercially available D and D products. This model was created to further enhance and accelerate the decontamination process. It is envisioned that D and D engineers will be able to use this Decision Model in the field via a mobile device. It allows the user to select four inputs to match their situation. The model searches the database and returns the products that fit the criteria selected by the user. This Decision Model will be made available in a web-based application and as a mobile application to ensure end users can access it when needed. The MatLab code recognizes what position in the text box is selected and then this input is converted to the corresponding string. The string is then compared with the information in the product list in the specified column in order to reduce processing time. Then the products that contain the string inputted are displayed on the table and are separated by category. The web-based version of the model will receive three specified criteria and sub-criteria from the user and update the table with products that fit each criteria. A web-based version of this Decision Model is under way and will be set up on the D and D KM-IT platform. There will be two versions of this mobile application: for devices with internet connection and for those that do not have internet connection. This will ensure end users have access to the Decision application at any time or place during the D and D process. A mobile version of the model will also be developed in the near future.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Virtual Engineering Software Framework for Integrated Biomass Conversion Modeling

This presentation covers the design and implementation of a software tool to systematically connect computational models of unit operations to simulate an integrated process of low-temperature conversion of biomass to fuel. This virtual engineering (VE) software was designed with the overarching goal of connecting unit models written in various programming languages and requiring different computational resources within a single, flexible framework. The models and features currently considered for the VE library include mechanistic models for pretreatment, enzymatic hydrolysis, and aerobic bioreaction; high-fidelity computational fluid dynamics (CFD) simulations for enzymatic hydrolysis and aerobic bioreaction; and the capability to perform techno-economic analyses (TEA) using Aspen Plus, a commercial software package. The CFD models require access to high-performance computing (HPC) resources, so in addition to handling multiple programming languages and interfaces, the VE software must also be capable of interacting with an HPC scheduler to submit, run, and post-process jobs. Using the Python programming language, a new VE software package has been developed that contains functionality to manage the input-output communication between various unit models, schedule simulations to run on NREL's HPC and analyze those results, and interface with existing TEA software workflows. A Jupyter-notebook GUI was also created to solicit user input and provide documentation. In cases where multiple models for a particular unit-operation exist, selection between models is accomplished through a simple checkbox, with the appropriate inputs and outputs being parsed and converted seamlessly in the background. Each operation makes use of a different programming language, but the flow of information from pretreatment to enzymatic hydrolysis to bioreaction is managed with an intuitive, centralized file-communication strategy. In this talk, the programming approach and implementation details of the notebook are presented for multiple possibilities of the conversion process, including a demonstration of the ability to manage HPC resources. Additionally, an example of a sensitivity study of treatment parameters governing the overall conversion outcome is shown which highlights the ease of defining new problems using the VE Notebook workflow and leads into a discussion of ongoing work to enable outer-loop optimization studies.

biofuel↗

DOE OSTI2023

Bayesian, Multifidelity Operator Learning for Complex Engineering Systems–A Position Paper

Abstract Deep learning has significantly improved the state-of-the-art in computer vision and natural language processing, and holds great potential to design effective tools for predicting and simulating complex engineering systems. In particular, scientific machine learning seeks to apply the power of deep learning to scientific and engineering tasks, with operator learning (OL) emerging as a particularly effective tool. OL can approximate nonlinear operators arising in complex engineering systems, making it useful for simulating, designing, and controlling those systems. In this position paper, we provide a comprehensive overview of OL, including its potential applications to complex engineering domains. We cover three variations of OL approaches: deterministic OL for modeling nonautonomous systems, OL with uncertainty quantification (UQ) capabilities, and multifidelity OL. For each variation, we discuss drawbacks and potential applications to engineering, in addition to providing a detailed explanation. We also highlight how multifidelity OL approaches with UQ capabilities can be used to design, optimize, and control engineering systems. Finally, we outline some potential challenges for OL within the engineering domain.

Computer Science↗

DOE OSTI2022

Restoration Hydro: A Watershed Approach to Standard Modular New Hydropower

The objectives of FOA DE- FOA-0001836- “Standard Modular Hydropower” included designing a standardized, modular, and environmentally compatible hydropower schematic for implementation in greenfield sites that generate up to 10 MW of capacity. Utilizing funds competitively awarded under DOE’s Water Power Technologies Office, the Natel Energy team developed a concept for modular new stream reach (NSR) hydropower that incorporates multi species upstream and downstream fish passage, improved river channel connectivity, and recreational modules. The in-stream design of the collective modules minimized site specific design and maximized the opportunities for modularity. Financial data was also presented using actual costs from regional suppliers, with figures provided in 2022 dollars. While the project team did not address potential permitting process improvements, the site selection criteria did consider established barriers to hydropower development such as tribal and preserved lands, interconnection proximity, and endangered species to exclude or deprioritize. The project’s design schematic met the objectives of the FOA, and presented a unique solution that targets alluvial pockets as natural features for sustainable development. Natel’s concept also incorporated the company's fish-safe Restoration Hydro Turbine for safe downstream passage, while featuring a rock arch that integrates fish passage, water, recreation, and grade control modules (including sediment). Alignment with the Department of Energy Office of Energy Efficiency and Renewable Energy (EERE) “Innovative Design Concepts for Standard Modular Hydropower and Pumped-Storage Hydropower” Program: According to the Hydropower Vision (DOE, 2016), approximately 16 GW of hydropower growth is possible with the development of technology solutions that balance efficiency, economics, and environmental sustainability. The desired outcome of the SMH program is transformational innovation specifically in the site identification, conceptual, and detailed design phases of technology development lifecycles (DOE, 2018). In developing the SMH design schematic, the team aimed to address the opportunities outlined in the Vision through an inverted design philosophy; rather than singularly prioritizing efficiency and power production, the team focused on integrating hydropower with restoration of degraded streams to optimal ecosystem function and provision of exceptional recreation value as design criteria. To achieve this, Restoration Hydro incorporates the principles of nature-based engineering (WWAP, 2018) and biomimicry (Biomimicry NL.) to strategically deploy complementary combinations of permanent, semi-permanent, and ephemeral low-head structures - such as natural and engineered log jams - that harness geomorphological and hydrological processes at the landscape-scale. Primary applications of Restoration Hydro include: 1) restoration of degraded watersheds’ natural ecological function and enhancement of hydrological connectivity; and 2) creation of associated co-benefits to hydro production, including increased groundwater recharge, improved sediment transport and management, improved water security and water quality. Restoration Hydro projects build upon proven watershed restoration engineering techniques by integrating hydropower turbines into low-head structures using innovative and evolving civil works concepts that facilitate fish and sediment passage, and in some cases create additional revenue-generating recreational opportunities. Powering low-head structures creates a directly monetizable layer of economic value in the form of flexible, reliable, renewable energy on top of the already high-value water, environmental and recreational benefits of watershed and river restoration. The approach aims to create a virtuous, self-reinforcing cycle whereby Restoration Hydro projects support the scaling of ecosystem restoration activities, creating a water-energy-carbon multiplier effect that, through the principles of adaptive change management: 1) improves the resilience of landscapes and downstream population centers for changing hydrological cycles; 2) creates a reliable energy resource that facilitates the integration of intermittent renewable power sources into grids; and 3) supports climate change mitigation through grid decarbonization and enhanced ecosystem carbon capture and retention.

13 HYDRO ENERGY↗

DOE OSTI2018

Anatomy of a diffracting detonation in a circular arc of explosive

Using high-resolution numerical simulation, study diffraction of a detonation as it traverses a 270° finite-thickness condensed-phase explosive arc. This geometry admits a steady solution in a frame rotating with angular speed ω 0 , which thereby facilitates a detailed analysis of how the loss of energy from the detonation reaction zone due to the diffraction process slows the propagation of the detonation. There exists a region of subsonic flow, between the detonation shock and the curve of sonic flow (labelled the DDZ), which is responsible for setting ω 0 . Although the DDZ spans the entire thickness for thin arcs, it is localized to a region near the inside surface as the arc is thickened. Furthermore the explosive energy release near this inside surface plays a disproportionate role in the diffraction process.

42 ENGINEERING↗

DOE OSTI2025

Micromechanical Surrogate Machine Learning Model for Creep Deformation Modeling

Process variability during the manufacture of gas turbine engine hot section components can significantly affect the material’s resulting microstructure. In casting, for instance, geometric variation within a component (thin sections versus thick sections, radial location) influences cooling rates and the resulting grain size. The high temperature creep response is known to be sensitive to grain size owing to a diffusional creep mechanism which occurs more readily along grain boundaries. Microstructural variation correspondingly drives mechanical behavior which propagates into component scale performance uncertainty. These factors are essential when planning inspection, maintenance, and repair strategies within a reliability framework. These benefits provide opportunities to increase overall energy efficiency through refined margins. Critically, there is an opportunity to bolster existing data-driven reliability models using physics-driven process-structure-property relations. Here we present recent work establishing a framework for evaluating the probabilistic creep performance of high-temperature materials. A novel microstructure-sensitive crystal plasticity finite element model is established that captures both grain boundary and crystallographic deformation effects. The computationally expensive physics model is calibrated using a statistical approach and this high-fidelity model is subsequently used to train a computationally efficient machine learning surrogate model. The surrogate model is essential for sampling a large ensemble of simulated structure-property pair results. The ensemble data are then mined to extract salient trends to be incorporated into a microstructure-sensitive reliability model. The proposed approach represents a novel way to capture microstructure-sensitive trends from physics-based models within a modern reliability framework.

Fernandez-Zelaia, Patxi [ORNL]↗

DOE OSTI2014

Samuel P. Massie Chair of Excellence In Environmental Disciplines: Hampton University 1994-2010 Year Report

The establishment of the DOE-EM Dr. Samuel P. Massie Chair of Excellence provides an excellent opportunity for Hampton University to be involved in key environmental issues in the 21 st Century. The main areas of focus are on: 1. Coal gasification with respect to pollution prevention and reduction. 2. Solid waste treatment through bioremediation technology and 3. Industrial wastewater treatment Synthesizing ion catalysts suitable for use in slurry bubble column reaction was carried out. Construction of an autoclave continuous stirred tank reactor has been completed. At the initial stage of the development of this program, work was conducted in the area of formic acid recovery from waste streams, which yielded useful results. We also succeeded in the removal of priority metal ions such as cadmium, chromium, copper, lead, mercury, nickel, silver, thallium, zinc, etc., from industrial and municipal wastewater by using natural wastes. The process uses tree leaves to adsorb the metal ions in the wastewater. The ultimate goal is to develop inexpensive, highly available, effective metal ion adsorbents from natural wastes as an alternative to existing commercial adsorbents, and also to explain the possible adsorption mechanism that is taking place. This technology uses natural wastes to eliminate other wastes. Obviously, there are several advantages: (1) the negative impact on environment is eliminated, (2) the complicated regeneration step is not needed, and (3) the procedure saves money and energy. Twelve different types of leaves have been tested with lead, zinc, and nickel. The study mechanism showed that the leaf tannin is an active ingredient in the adsorption of metal ions. The ion-exchange mechanism controlled the adsorption process.

42 ENGINEERING↗

DOE OSTI2017

Fitness Tracker for Weight Lifting Style Workouts

This document proposes an early, high level design for a fitness tracking system which can automatically log weight lifting style workouts. The system will provide an easy to use interface both physically through the use of several wireless wristband style motion trackers worn on the limbs, and graphically through a smartphone application. Exercise classification will be accomplished by calibration of the user’s specific motions. The system will accurately track a user’s workout, miscounting no more than one repetition in every 20, have sufficient battery life to last several hours, work with existing smartphones and have a cost similar to those of current fitness tracking devices. This document presents the mission background, current state-of-theart, stakeholders and their expectations, the proposed system’s context and concepts, implementation concepts, system requirements, first sublevel function decomposition, possible risks for the system, and a reflection on the design process.

42 ENGINEERING↗

DOE OSTI2018

AM Modeling & Simulation

A predictive simulation capability for AM is important to improve quality, decrease processing time and minimize costs. Has significant impact as LANL continues to be a leader in modeling and simulation for AM. This improved simulation capability will allow important AM specific physics questions to be answered for the first time. This approach, in combination with LANL's HPC resources will enable tractable, predictive simulation of AM and other solidification processes.

42 ENGINEERING↗