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All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Ultra-fast Boriding for Improved Energy Efficiency and Reduced Emissions in Materials Processing Industries (Final Report)

High-temperature treatment processes (such as nitriding, carburizing, and boronizing) are well-established and used widely by numerous industrial sectors to achieve superior mechanical and tribological properties on all kinds of engineering components made out of ferrous materials. Unfortunately, these traditional processes are very slow and energy intensive, and they produce large amounts of CO 2 emissions and liquid/solid wastes to deal with during and after each process run. From economic and environmental points of views, it is highly desirable to develop and implement novel surface treatment technologies that are quick, cheap, clean, and energy efficient. The main objective of this project was to develop an industrial-scale ultra-fast boriding process that realizes all of these desirable attributes. The other objectives of the project were to demonstrate the superior performance, properties, and durability of borided surfaces under relevant test conditions and to transfer optimized technology to industry for large-scale practice. Major research activities included process design, modeling, product optimization, and scale-up. This project has achieved all of its stated objectives, and the industrial-scale boriding process has been successfully transferred to industry. Specifically, all of the original tasks have been completed, and the milestones have been met by scaling up from a small proof-of-concept system (which could only handle about 0.25 kg melt) to a commercial-scale boriding unit that can handle up to 4,000 kg melt. Mechanical and tribological testing of borided test samples and industrial parts has also been carried out, and their superior performance over a broad range of test conditions has been validated. With the large-scale, ultra-fast boriding process, we could achieve thick, hard, and uniform boride layers on all kinds of steel components, even after only 5 min of treatment time. We could also attain excellent uniformity in thickness over the entire exposed surfaces — even in the very sharp corners or interior sections of odd-shaped workpieces. Boriding of numerous nonferrous materials was also demonstrated, and thick boride layers (up to 300 µm in thickness) were achieved on their outer surfaces. With the use of a large-scale boriding unit at Argonne National Laboratory, our research team has successfully treated large numbers of industrial components in a batch-to-batch operation without creating any in-house gaseous emissions or producing solid and/or liquid wastes. With the use of this large unit, our team achieved surface hardness values of 15 to 45 GPa, depending on the type of metal or alloy being treated. The thick boride layers attained on steel components consisted of FeB, Fe 2 B, and Fe 3 B phases, and their hardness values ranged from 16 to 22 GPa. Layers consisting of Fe 2 B phase alone are also possible with this process, and in the case of nonferrous materials. Overall, because of the much superior property and performance improvements in treated products, much lower energy consumption, and nearly zero environmental waste and emissions, the demonstrated boriding technology may find broad applications in many cross-cutting industries. Mechanical wear, abrasion, erosion, corrosion, and scuffing are often life-limiting factors in mechanical systems that are used in manufacturing, transportation, aerospace, agricultural, earth-moving, and mining equipment; but the machine components treated by ultra-fast boriding can effectively prevent such degradations. In this project, the ultra-fast boriding was proven to be most ideal for the treatment of iron and many other ferrous alloys, like stainless steel. We have shown through our experimental studies that our method can also be used for the treatment of many nonferrous metals and alloys. In the case of superalloys and Ni 3 Al intermetallic materials, more than a 300-µm thick boride layer can be achieved with our boriding furnace within 15 to 30 min (which results in five times the hardness of the base materials). Pre-carburized steels and nonferrous alloys can also be treated by the new boriding technique. Overall, very thick (more than a quarter millimeter) and hard surface layers produced by ultra-fast boriding are expected to have a positive impact on the performance and durability of all types of machine components that are subject to corrosive, erosive, abrasive, and adhesive wear. Since the focus of our project was primarily on the large-scale boriding of ferrous materials, in this report, we will broadly cover the results from various ferrous materials that were used in our experiments. Another transformational feature of ultra-fast boriding is that it does not create any greenhouse gas emissions or solid and/or liquid wastes to deal with during or after the treatment; hence, it is an ultra-green technology (no need for expensive waste disposal and/or treatment facilities). It may eliminate the use of natural gas and other carbon-based fuels as the primary energy source in the heat treatment industry (which is the most common practice at present), thus, minimizing the CO 2 , NO x , CO, and SO x emissions that are generated in large quantities by current heat treatment practices. The main ingredient used during boriding is borax, which is a natural mineral. Our demonstrated and scaled-up technology can now be used in a wide range of manufacturing and transportation applications. It can deliver thick and uniform boride layers of more than 200 µm in steels, if needed, over the entire exposed surfaces — even in the very sharp corners or interior sections of workpieces (which are typical of gears, tools, pipes, and other components). Beneath the distinct boride layers is an equally-thick boron diffusion layer or zone, which is comparable to the diffusion zones achieved by carburizing and nitriding processes. In the cases of traditional boriding, nitriding, and/or carburizing methods, achieving such layer thickness and case depths would have taken several hours, and these surfaces would not have achieved the same levels of hardness and other desirable properties as they do with ultra-fast boriding. Some of the key stakeholders of ultra-fast boriding technology include companies that are active in heat-treatment, thermal-diffusion, and hard-coating processes; metal-forming, machining, and other manufacturing processes; and technologies related to transportation (including original equipment manufacturers for autos), mining and oil exploration, agricultural and heavy equipment. Bodycote Thermal Processing was the cost-sharing industrial partner and licensee in this project. They also provided test components and technical and commercial information pertaining to industrial components that are most suitable for ultra-fast boriding. Bodycote also coordinated some of the field tests and metallurgical examinations. Overall, ultra-fast boriding is a transformational technology that can complement, if not replace, many of the energy- and carbon-intensive surface treatment processes that are in use today, such as conventional boriding, carburizing, nitriding, carbo-nitriding, and physical and chemical vapor deposition. In our project, the developed technology was scaled-up and proven to be an ultra-green, ultra-efficient process that can drastically reduce costs, increase productivity, and improve the performance and reliability of machine components used in cross-cutting industrial applications.

Erdemir, Ali↗
DOE OSTI2012

Tailored Materials for High Efficiency CIDI Engines

The overall goal of the project, Tailored Materials for High Efficiency Compression Ignition Direct Injection (CIDI) Engines, is to enable the implementation of new combustion strategies, such as homogeneous charge compression ignition (HCCI), that have the potential to significantly increase the energy efficiency of current diesel engines and decrease fuel consumption and environmental emissions. These strategies, however, are increasing the demands on conventional engine materials, either from increases in peak cylinder pressure (PCP) or from increases in the temperature of operation. The specific objective of this project is to investigate the application of a new material processing technology, friction stir processing (FSP), to improve the thermal and mechanical properties of engine components. The concept is to modify the surfaces of conventional, low-cost engine materials. The project focused primarily on FSP in aluminum materials that are compositional analogs to the typical piston and head alloys seen in small- to mid-sized CIDI engines. Investigations have been primarily of two types over the duration of this project: (1) FSP of a cast hypoeutectic Al-Si-Mg (A356/357) alloy with no introduction of any new components, and (2) FSP of Al-Cu-Ni alloys (Alloy 339) by physically stirring-in various quantities of carbon nanotubes/nanofibers or carbon fibers. Experimental work to date on aluminum systems has shown significant increases in fatigue lifetime and stress-level performance in aluminum-silicon alloys using friction processing alone, but work to demonstrate the addition of carbon nanotubes and fibers into aluminum substrates has shown mixed results due primarily to the difficulty in achieving porosity-free, homogeneous distributions of the particulate. A limited effort to understand the effects of FSP on steel materials was also undertaken during the course of this project. Processed regions were created in high-strength, low-alloyed steels up to 0.5 in. deep that showed significant grain refinement and homogeneous microstructures favorable to increased fracture toughness and fatigue performance. The final tasks of the project demonstrated that the FSP concept can be applied to a relevant part geometry by fabricating diesel piston crowns with FSP regions applied selectively to the edge of the bowl rim. This area of the piston typically suffers from conditions at high PCP that cause severe thermal fatigue issues. It is expected that, given the data from coupon testing, the durability of pistons modified by FSP will allow much higher fatigue lifetime and potentially also greater resistance to elevated stress-level effects on fatigue.

33 ADVANCED PROPULSION SYSTEMS↗
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 OSTI2011

ISOLOK VALVE ACCEPTANCE TESTING FOR DWPF SME SAMPLING PROCESS

Evaluation of the Defense Waste Processing Facility (DWPF) Chemical Process Cell (CPC) cycle time identified several opportunities to improve the CPC processing time. Of the opportunities, a focus area related to optimizing the equipment and efficiency of the sample turnaround time for DWPF Analytical Laboratory was identified. The Mechanical Systems & Custom Equipment Development (MS&CED) Section of the Savannah River National Laboratory (SRNL) evaluated the possibility of using an Isolok{reg_sign} sampling valve as an alternative to the Hydragard{reg_sign} valve for taking process samples. Previous viability testing was conducted with favorable results using the Isolok sampler and reported in SRNL-STI-2010-00749 (1). This task has the potential to improve operability, reduce maintenance time and decrease CPC cycle time. This report summarizes the results from acceptance testing which was requested in Task Technical Request (TTR) HLW-DWPF-TTR-2010-0036 (2) and which was conducted as outlined in Task Technical and Quality Assurance Plan (TTQAP) SRNL-RP-2011-00145 (3). The Isolok to be tested is the same model which was tested, qualified, and installed in the Sludge Receipt Adjustment Tank (SRAT) sample system. RW-0333P QA requirements apply to this task. This task was to qualify the Isolok sampler for use in the DWPF Slurry Mix Evaporator (SME) sampling process. The Hydragard, which is the current baseline sampling method, was used for comparison to the Isolok sampling data. The Isolok sampler is an air powered grab sampler used to 'pull' a sample volume from a process line. The operation of the sampler is shown in Figure 1. The image on the left shows the Isolok's spool extended into the process line and the image on the right shows the sampler retracted and then dispensing the liquid into the sampling container. To determine tank homogeneity, a Coliwasa sampler was used to grab samples at a high and low location within the mixing tank. Data from the two locations were compared to determine if the contents of the tank were well mixed. The Coliwasa sampler is a tube with a stopper at the bottom and is designed to obtain grab samples from specific locations within the drum contents. A position paper (4) was issued to address the prototypic flow loop issues and simulant selections. A statistically designed plan (5) was issued to address the total number of samples each sampler needed to pull, to provide the random order in which samples were pulled and to group samples for elemental analysis. The TTR required that the Isolok sampler perform as well as the Hydragard sampler during these tests to ensure the acceptability of the Isolok sampler for use in the DWPF sampling cells. Procedure No.L9.4-5015 was used to document the sample parameters and process steps. Completed procedures are located in R&D Engineering job folder 23269.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗