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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A physics-based model of cladding wastage layer formation rate

In this report, lower length scale simulations to inform an engineering-scale model of fuel-cladding chemical interaction (FCCI) conducted under the auspices of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in FY22 are described. An overall strategy for the implementation of the BISON model is described, and the past and current lower length scale work on FCCI formation is put into the context of this overall strategy. Density functional theory and kinetic Monte Carlo simulations are used to determine the diffusion coefficient of neodymium in iron. Density functional theory calculations are also used to parameterize a parabolic approximation for the dependence of free energy on composition for the intermetallic compound Fe 17 Nd 2 . A phase-field model of the Fe-Nd system was developed that includes the random solid solution body-centered cubic phase and the intermetallic Fe 17 Nd 2 . The model was used to simulate growth of the intermetallic layer in a diffusion couple and the results were compared to experiment. The model’s prediction of the parabolic growth constant is within 40 % of the experimental value. The model is also used to simulate the formation of a grain boundary attack layer that is observed in experiment. The simulations show enhanced Nd concentration along the grain boundaries, but do not clearly show formation of a Fe 17 Nd 2 layer

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Power system inertia estimation: Review of methods and the impacts of converter-interfaced generations

Understanding and quantifying the inertia of power systems with the integration of converter-interfaced generation (CIG) plays an essential role in the safe transition to a future low-inertia scenario. Here we provide a comprehensive summary of inertia definitions for both synchronous generators and CIGs as well as their corresponding estimation methods. In particular, the estimation methods are categorized as model-based and measurement-based approaches considering both small and large disturbances. The advantages and disadvantages of different methods are carefully discussed. This paper also offers for the first time a framework to quantify the virtual inertia of CIGs at the component and aggregation levels, an open problem in the literature. Finally, future directions for inertia estimation are identified and discussed. This significantly benefits the design of appropriate control and protection schemes in achieving a more reliable, secure, and resilient power system.

42 ENGINEERING↗
DOE OSTI2017

Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependence

Increased coupling between critical infrastructure networks, such as power and communication systems, has important implications for the reliability and security of these systems. To understand the effects of power-communication coupling, several researchers have studied models of interdependent networks and reported that increased coupling can increase vulnerability. However, these conclusions come largely from models that have substantially different mechanisms of cascading failure, relative to those found in actual power and communication networks, and that do not capture the benefits of connecting systems with complementary capabilities. In order to understand the importance of these details, this paper compares network vulnerability in simple topological models and in models that more accurately capture the dynamics of cascading in power systems. First, we compare a simple model of topological contagion to a model of cascading in power systems and find that the power grid model shows a higher level of vulnerability, relative to the contagion model. Second, we compare a percolation model of topological cascading in coupled networks to three different models of power networks coupled to communication systems. Again, the more accurate models suggest very different conclusions than the percolation model. In all but the most extreme case, the physics-based power grid models indicate that increased power-communication coupling decreases vulnerability. This is opposite from what one would conclude from the percolation model, in which zero coupling is optimal. Only in an extreme case, in which communication failures immediately cause grid failures, did we find that increased coupling can be harmful. Together, these results suggest design strategies for reducing the risk of cascades in interdependent infrastructure systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Magnetically Coupled Broadband Dual Magnetic Mass/Spring Vibrational Energy Harvesting Design

Self-powering sensors and networks are a reality. The ability to extract ambient energy from the surroundings to power electronic devices has a profound impact on the realization of smart adaptable sensor networks. In this study, a magnetically coupled dual spring and magnet design has been investigated to improve the efficiency and performance bandwidth of vibration energy harvesting (VEH) sensors. Using numerical models based on traditional systems of coupled ordinary differential equations (ODE), an optimized design was developed and compared to experimental measurements. Numerical and empirical results show good agreement. Results show improvement in the bandwidth over an equivalent linear system and corresponding improvement in output power conversion efficiency. The increased bandwidth allows improved conversion sensitivity and enhanced power harvesting capabilities. This operational bandwidth coincides with the expected input spectrum for in situ applications.

42 ENGINEERING↗
DOE OSTI2024

Radiation-Induced Noise Resilience of Neuromorphic Architectures

Neuromorphic event-based networks use asynchronous time-dependent information to extract features from input data that can allow for edge-based distributed applications such as object recognition. The noise resilience properties of such networks, especially in the context of space applications, are yet to be explored. In this paper, we use the hierarchy of time surfaces (HOTS) algorithm, which is one of the neuromorphic algorithms, to understand the least and most resilient modules in a neuromorphic network. The HOTS algorithm relies on the computing of time surfaces that maps the temporal delays between neighboring pixels into normalized features that involve many computations that are also found in other neuromorphic networks such as exponential decays, distance computations, etcetera. We implemented HOTS on a Digilent PYNQ board with a Xilinx Zynq 7020 system on a chip, and we subjected the boards running the HOTS network inference to neutron radiation at the Los Alamos Neutron Science Center. Furthermore, we used simulation models from our previous similar experiments on the event-based sensor to create a neutron induced noise model to quantify the effect of this noise on the overall performance of the network. This experiment provides the preliminary measurements of the reliability of the HOTS algorithm and proposes methods to create a more reliable HOTS architecture in future spacecraft missions.

Engineering↗
DOE OSTI2017

Methodology for Path Planning with Dynamic Data-Driven Flight Capability Estimation

This paper presents methodology to enable path planning for an unmanned aerial vehicle that uses dynamic data-driven flight capability estimation. The main contribution of the work is a general mathematical approach that leverages offline high-fidelity physics-based modeling together with onboard sensor measurements to achieve dynamic path planning. The mathematical framework, expressed as a constrained partially observable Markov decision process, accounts for vehicle capability constraints and is robust to modeling error and disturbances in both the vehicle process and measurement models. Vehicle capability constraints are incorporated using probabilistic support vector machine surrogates of high-fidelity physics-based models that adequately capture the richness of the vehicle dynamics. Sensor measurements are treated in a general manner and can include combinations of multiple modalities such as that through global positioning system signals, inertial mass unit outputs, and structural strain data of the airframe. Results are presented for a simulated three-dimensional environment and point-mass airplane model. The vehicle can dynamically adjust its trajectory according to the observations it receives about its current state of health, thereby retaining a high probability of survival and mission success.

42 ENGINEERING↗
DOE OSTI2025

High fidelity multiphysics tightly coupled model for a lead cooled fast reactor concept and application to statistical calculation of hot channel factors

A tightly coupled multiphysics code system is established using the MOOSE framework for hot channel factor (HCF) evaluation on a Lead Fast Reactor (LFR) concept. The coupled system is driven by the Griffin multiphysics coupling capability under which the MOOSE Heat Transfer module and NekRS computational fluid dynamics solver are coupled for conjugate heat transfer using the Cardinal application. The coupled capability is demonstrated on an LFR assembly model based on materials and geometry of a prototypical lead-cooled fast reactor design by Westinghouse Electric Company, LLC. Moreover, the work integrates the Multiphysics Object Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM) to perform calculations for statistical analysis of HCF. Furthermore, the coupling strategy and workflow demonstrated in this paper is not only useful for predicting accurate hot channel factors for different kinds of advanced reactors but also for other engineering applications such as control rod worth assessment, generation of high-fidelity database for Artificial intelligence (AI)/machine learning (ML) training, design optimization and multi-resolution modeling.

Cardinal↗
DOE OSTI2020

Dynamic Model Reduction for Large-Scale Power Systems Using Wide-Area Measurements

To perform faster than the real-time dynamic simulation of large-scale power systems, it is necessary to reduce the simulated system size by using equivalents for surrounding areas of the study area, and existing dynamic model reduction approach could provide the needed structure of the reduced area. However, further parameter optimization is required to achieve the desired accuracy. In this paper, a particle swarm optimization (PSO) based approach is used to solve the above problem. Parameters for the individual dynamic elements in the reduced system are calibrated repeatedly until the wide-area measurements of the reduced model and the original model are very similar to each other with satisfactory accuracy. Results indicate that after optimization, the dynamic response of the reduced model matches better with that of the original one than using existing methods. Under both the generator-trip event and the bus-fault event, the reduced model has a higher frequency match and less power mismatch.

42 ENGINEERING↗
DOE OSTI2017

Simulation Data Management - Requirements and Design Specification

Simulation Data Management (SDM), the ability to securely organize, archive, and share analysis models and the artifacts used to create them, is a fundamental requirement for modern engineering analysis based on computational simulation. We have worked separately to provide secure, network SDM services to engineers and scientists at our respective laboratories for over a decade. We propose to leverage our experience and lessons learned to help develop and deploy a next-generation SDM service as part of a multi-laboratory team. This service will be portable across multiple sites and platforms, and will be accessible via a range of command-line tools and well-documented APIs. In this document, we’ll review our high-level and low-level requirements for such a system, review one existing system, and briefly discuss our proposed implementation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Machine Reading at Scale: A Search Engine for Scientific and Academic Research

The Internet, much like our universe, is ever-expanding. Information, in the most varied formats, is continuously added to the point of information overload. Consequently, the ability to navigate this ocean of data is crucial in our day-to-day lives, with familiar tools such as search engines carving a path through this unknown. In the research world, articles on a myriad of topics with distinct complexity levels are published daily, requiring specialized tools to facilitate the access and assessment of the information within. Recent endeavors in artificial intelligence, and in natural language processing in particular, can be seen as potential solutions for breaking information overload and provide enhanced search mechanisms by means of advanced algorithms. As the advent of transformer-based language models contributed to a more comprehensive analysis of both text-encoded intents and true document semantic meaning, there is simultaneously a need for additional computational resources. Information retrieval methods can act as low-complexity, yet reliable, filters to feed heavier algorithms, thus reducing computational requirements substantially. In this work, a new search engine is proposed, addressing machine reading at scale in the context of scientific and academic research. It combines state-of-the-art algorithms for information retrieval and reading comprehension tasks to extract meaningful answers from a corpus of scientific documents. The solution is then tested on two current and relevant topics, cybersecurity and energy, proving that the system is able to perform under distinct knowledge domains while achieving competent performance.

Sousa, Norberto (ORCID:0000000329194817)↗
DOE OSTI2010

Accelerating Design of Batteries Using Computer-Aided Engineering Tools (Presentation)

Computer-aided engineering (CAE) is a proven pathway, especially in the automotive industry, to improve performance by resolving the relevant physics in complex systems, shortening the product development design cycle, thus reducing cost, and providing an efficient way to evaluate parameters for robust designs. Academic models include the relevant physics details, but neglect engineeringcomplexities. Industry models include the relevant macroscopic geometry and system conditions, but simplify the fundamental physics too much. Most of the CAE battery tools for in-house use are custom model codes and require expert users. There is a need to make these battery modeling and design tools more accessible to end users such as battery developers, pack integrators, and vehicle makers.Developing integrated and physics-based CAE battery tools can reduce the design, build, test, break, re-design, re-build, and re-test cycle and help lower costs. NREL has been involved in developing various models to predict the thermal and electrochemical performance of large-format cells and has used in commercial three-dimensional finite-element analysis and computational fluid dynamics tostudy battery pack thermal issues. These NREL cell and pack design tools can be integrated to help support the automotive industry and to accelerate battery design.

25 ENERGY STORAGE↗
DOE OSTI2011

Accelerating Battery Design Using Computer-Aided Engineering Tools: Preprint

Computer-aided engineering (CAE) is a proven pathway, especially in the automotive industry, to improve performance by resolving the relevant physics in complex systems, shortening the product development design cycle, thus reducing cost, and providing an efficient way to evaluate parameters for robust designs. Academic models include the relevant physics details, but neglect engineeringcomplexities. Industry models include the relevant macroscopic geometry and system conditions, but simplify the fundamental physics too much. Most of the CAE battery tools for in-house use are custom model codes and require expert users. There is a need to make these battery modeling and design tools more accessible to end users such as battery developers, pack integrators, and vehicle makers.Developing integrated and physics-based CAE battery tools can reduce the design, build, test, break, re-design, re-build, and re-test cycle and help lower costs. NREL has been involved in developing various models to predict the thermal and electrochemical performance of large-format cells and has used in commercial three-dimensional finite-element analysis and computational fluid dynamics tostudy battery pack thermal issues. These NREL cell and pack design tools can be integrated to help support the automotive industry and to accelerate battery design.

25 ENERGY STORAGE↗
DOE OSTI2021

Hybrid electric buses fuel consumption prediction based on real-world driving data

Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. Here, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Quantifying Power Distribution System Resiliency Using Code-Based Metric

It is essential to improve the resiliency of power distribution systems (PDS) with the increase in extreme weather events, number of malicious threats and consumers’ need for higher reliability. Here, this paper provides a formal approach to evaluate the operational resiliency of PDS, and quantify the resiliency of a system using a code-based metric. A combination of steady state and dynamic simulation tools is used to determine the resiliency metric. Dynamic simulation tools help with analyzing impact of short-term events, which might affect operational resiliency in long term. A dynamic optimization algorithm for changing operating criteria to increase the sustainability of the most critical loads has been proposed. The proposed theoretical approach is validated using a simple power distribution system model and simulation results demonstrate the ability to quantify the resiliency using the proposed code-based metric. The time-dependent quantification of resiliency has been demonstrated on a test system of two connected CERTS microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Catalyst Ammonia Storage Measurements Using Radio Frequency Sensing1

Motivated by increasingly strict nitrogen oxides (NOx) limits, engine manufacturers have adopted selective catalytic reduction (SCR) technology to reduce engine-out NOx. In the SCR process, NOx react with ammonia (NH3) to form nitrogen and water vapor. The reaction is influenced by several variables, including stored ammonia on the catalyst, exhaust gas composition, and catalyst temperature. Currently, measurements from NOx and/or NH3 sensors upstream and downstream of the SCR are used with predictive models to estimate ammonia storage levels on the catalyst and control urea dosing. This study investigated a radio frequency (RF)-based method to directly monitor the ammonia storage state of the SCR. This approach utilizes the catalyst as a cavity resonator, in which an RF antenna excites electromagnetic waves within the cavity to monitor changes in the catalyst state. Ammonia storage causes changes in the dielectric properties of the catalyst, which directly impacts the RF signal. Changes in the RF signal relative to stored ammonia (NH3) were evaluated over a wide range of frequencies, temperatures, and exhaust conditions. The RF response to NH3 storage, desorption, and oxidation on the SCR was well correlated with changes in the catalyst state. Calibrated RF measurements demonstrate the ability to monitor the adsorption state of the SCR to within 10% of the sensor full scale. The results indicate direct measurement of SCR ammonia storage levels, and resulting catalyst feedback control, via RF sensing to have significant potential for optimizing the SCR system to improve NOx conversion and decrease urea consumption.

Engineering↗
DOE OSTI2015

Viscosity Meaurement Technique for Metal Fuels

Metallic fuels have exceptional transient behavior, excellent thermal conductivity, and a more straightforward reprocessing path, which does not separate out pure plutonium from the process stream. Fabrication of fuel containing minor actinides and rare earth (RE) elements for irradiation tests, for instance, U-20Pu-3Am-2Np-1.0RE-15Zr samples at the Idaho National Laboratory, is generally done by melt casting in an inert atmosphere. For the design of a casting system and further scale up development, computational modeling of the casting process is needed to provide information on melt flow and solidification for process optimization. Therefore, there is a need for melt viscosity data, the most important melt property that controls the melt flow. The goal of the project was to develop a measurement technique that uses fully sealed melt sample with no Americium vapor loss to determine the viscosity of metallic melts and at temperatures relevant to the casting process. The specific objectives of the project were to: develop mathematical models to establish the principle of the measurement method, design and build a viscosity measurement prototype system based on the established principle, and calibrate the system and quantify the uncertainty range. The result of the project indicates that the oscillation cup technique is applicable for melt viscosity measurement. Detailed mathematical models of innovative sample ampoule designs were developed to not only determine melt viscosity, but also melt density under certain designs. Measurement uncertainties were analyzed and quantified. The result of this project can be used as the initial step toward the eventual goal of establishing a viscosity measurement system for radioactive melts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Environmental Protection Coating System for Refractory Metal Alloys (EPCS for RMAs)

As part of ARPA-E’s Ultra High Temperature Impervious Materials Advancing Turbine Efficiency (UITIMATE) program, this project aims to develop an environmental protection coating system (EPCS) for refractory metal alloys (RMAs) – which will provide radical improvement in long term protection for ultra-high temperature refractory metal components in harsh gas-turbine environments. The drive for higher fuel efficiency and higher core power of gas turbines used in electric power generation and in aircraft propulsion requires higher peak operation temperatures. The temperature capability of state-of-the-art materials cannot meet the new requirements. Refractory metal alloys have desirable capabilities for achieving the required peak temperatures, but most of them are subject to oxidation in the gas turbine environment. Thus, there is a strong need to develop novel environmental protective coatings that enable the refractory metals to operate in an ultra-high temperature engine environment. This project is composed of four major technical innovations that can provide a potentially transformational solution in providing environmental protection for refractory metal alloys over a wide range of temperature. 1) Multi-layer self-healing environmental barrier coating (EBC) that provides protection against combustion gas environment. 2) Oxidation resistant diffusion barrier (DB) on the alloy surface to enhance coating stability. 3) Synergistic integration of the multi-layer self-healing EBC with the diffusion barrier to extend coating life. 4) Guidance of physics-based modeling to accelerate coating development and optimization. Our team has successfully developed a multi-layer self-healing environmental protection coating system (EPCS) that can provide oxidation protection for commercially available molybdenum based refractory metal alloys over a wide temperature range of up to 1600 °C for fuel-efficient gas turbine applications. The team also developed a multi-layer high temperature coating design tool to accelerate coating development and optimization based on physics-based modeling. The team also engaged RTX business unite, notable Pratt and Whitney, for future commercialization of this technology. The successful completion of this Phase I effort led to the selection of performing Phase II of the program. In Phase II, the team plans to apply the EPCS in the protection of a newly discovered molybdenum-based metal alloy which has the potential of superior high temperature properties to the commercial products. Th coating will be evaluated under test conditions and environment that mimic gas turbine operation.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Periodic Material-Based Seismic Base Isolators for Small Modular Reactors

Important structures such as nuclear power plants should experience very little vibrations in order to maintain the safety of the nuclear facilities during seismic events. Protection of these structures by seismic isolators requires the isolators to provide seismic isolation irrespective of the direction of excitations and to develop a stable response without rocking. Conventional seismic isolation systems, however, can only fulfill partial requirements for the seismic protection. This study adopts the principle of periodic materials for seismic isolation. These materials exhibit unique properties of frequency band gaps, where incoming waves with frequencies falling inside the frequency band gaps are forbidden. The new isolation systems, known as periodic foundations, function both as a structural foundation to support the gravitational weight of the superstructure and also as a seismic isolator. The isolation mechanism, hypothetically, can easily fulfill all the requirements for seismic protection of the critical facilities. This study focuses on the design of one-dimensional (1D) and three-dimensional (3D) periodic foundations for seismic isolation of small modular reactor (SMR) buildings. The theoretical study was first conducted to reveal the fundamental behavior of periodic foundations. Then global sensitivity analysis was utilized to study the effect of design parameters on the frequency band gaps. In addition, simple straight-forward design equations based on the sensitivity analysis are proposed for the design of periodic foundations. Utilizing the proposed equations, prototypes of 1D and 3D periodic foundations were designed to isolate an SMR building. Scaled models were fabricated and experimentally tested to validate the hypothesis and design. The periodic foundation structural systems were tested under various input waves, including seismic waves, in the horizontal and vertical directions and the torsional mode. The shake table test results show that the periodic foundations can reduce the acceleration response of the SMR building up to 90% in the horizontal direction and the torsional mode. As much as a 40% response reduction in the vertical direction is also observed. Moreover, the periodic foundation-isolated structure exhibited stable response with negligible rocking on the structure. This study proved the capability of periodic foundations to enhance the seismic safety of critical structures.

42 ENGINEERING↗