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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Enterprise and system of systems capability development life-cycle processes.

This report and set of appendices are a collection of memoranda originally drafted circa 2007-2009 for the purpose of describing and detailing a models-based systems engineering approach for satisfying enterprise and system-of-systems life cycle process requirements. At the time there was interest and support to move from Capability Maturity Model Integration (CMMI) Level One (ad hoc processes) to Level Three. The main thrust of the material presents a rational exposâe of a structured enterprise development life cycle that uses the scientific method as a framework, with further rigor added from adapting relevant portions of standard systems engineering processes. While the approach described invokes application of the Department of Defense Architectural Framework (DoDAF), it is suitable for use with other architectural description frameworks.

42 ENGINEERING↗
DOE OSTI2017

An assessment of CFD-based wall heat transfer models in piston engines

The lack of accurate submodels for in-cylinder heat transfer has been identified as a key shortcoming in developing truly predictive, physics-based computational fluid dynamics (CFD) models that can be used to develop combustion systems for advanced high-efficiency, low-emissions engines. Only recently have experimental methods become available that enable accurate near-wall measurements to enhance simulation capability via advancing models. Initial results show crank-angle dependent discrepancies with respect to previously used boundary-layer models of up to 100%. However, available experimental data is quite sparse (only few data points on engine walls) and limited (available measurements are those of heat flux only). Predictive submodels are needed for medium-resolution ("engineering") LES and for unsteady Reynolds-averaged simulations (URANS). Recently, some research groups have performed DNS studies on engine-relevant conditions using simple geometries. These provide very useful data for benchmarking wall heat transfer models under such conditions. Further, a number of new and more sophisticated models have also become available in the literature which account for these engine-like conditions. Some of these have been incorporated while others of a more complex nature, which include solving additional partial differential equations (PDEs) within the thin boundary layer near the wall, are underway. These models will then be tested against the available DNS/experimental data in both SI (spark-ignition) and CI (compression-ignition) engines.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Validation of IMEP on Alcator C-Mod and JET-ILW ELMy H-mode plasmas

Abstract The recently developed integrated model based on engineering parameters (IMEP) (Luda et al 2020 Nucl. Fusion 61 126048; Luda et al 2021 Nucl. Fusion 60 036023), so far validated on ASDEX Upgrade, has been tested on a database of 3 Alcator C-Mod and 55 JET-ILW ELMy (type I) H-mode stationary phases. The empirical pedestal transport model included in IMEP, consisting now of imposing a fixed value of R < ∇ T e > / T e , t o p = − 82.5 , allows an accurate prediction of the pedestal top temperature (when the pedestal top density is fixed to the experimental measurements) across these three machines with different sizes, when the pedestal is peeling–ballooning (PB) limited. Cases far from the ideal PB boundary, corresponding to high edge Spitzer resistivity, are instead strongly overpredicted by IMEP. A comparison between the predictions of Europed and IMEP for a subset of JET-ILW cases shows that IMEP can more accurately reproduce the experimental pedestal width. This allows IMEP to better capture profile effects on the pedestal stability, and therefore to correctly describe the negative effect of fueling on the pedestal pressure for PB limited cases. A strong correlation between the separatrix density and the fueling rate has been identified for a subset of JET-ILW cases, when taking into account different divertor configurations. Overall, these promising results encourage further developments of integrated models to obtain reliable predictions of pedestal and global confinement using only engineering parameters for present and future machines.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

Hydrogen Fuel Cell development in Columbia (SC)

This is an update to the final report filed after the extension of this program to May of 2011. The activities of the present program contributed to the goals and objectives of the Fuel Cell element of the Hydrogen, Fuel Cells and Infrastructure Technologies Program of the Department of Energy through five sub-projects. Three of these projects have focused on PEM cells, addressing the creation of carbon-based metal-free catalysts, the development of durable seals, and an effort to understand contaminant adsorption/reaction/transport/performance relationships at low contaminant levels in PEM cells. Two programs addressed barriers in SOFCs; an effort to create a new symmetrical and direct hydrocarbon fuel SOFC designs with greatly increased durability, efficiency, and ease of manufacturing, and an effort to create a multiphysics engineering durability model based on electrochemical impedance spectroscopy interpretations that associate the micro-details of how a fuel cell is made and their history of (individual) use with specific prognosis for long term performance, resulting in attendant reductions in design, manufacturing, and maintenance costs and increases in reliability and durability.

08 HYDROGEN↗
DOE OSTI2016

Multiscale Modeling of Accident Tolerant Fuels under the NEAMS ATF Program

The U.S. Department of Energy's Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has been developing computational analysis tools for nuclear fuel simulation. These codes include BISON and Marmot for investigation of fuel behavior at the continuum and microstructural scales, respectively. In 2014, NEAMS introduced what it calls High Impact Problems (HIPs) into its development plan. These HIPs are intended to make significant advancements in a particular area of nuclear power research in a short period of time (3 years or less). NEAMS has chosen an accident tolerant fuel (ATF) project, which emphasizes utilizing BISON and Marmot to model proposed materials, as its first HIP. Since the events that occurred at the Fukushima-Daiichi nuclear power plant in 2011, the U.S. Department of Energy's Office of Nuclear Energy accelerated research in nuclear fuel concepts with enhanced accident tolerance as part of its Fuel Cycle Research and Development (FCRD) Advanced Fuels Campaign (AFC). Accident tolerant fuels are expected to perform at least as well as conventional fuel during normal conditions and provide significantly increased response time in the event of an accident. Given the many unknowns associated with proposed ATF concepts, the research and development being undertaken by AFC in this area is a challenge, particularly considering the desire to have a lead test rod or assembly in a commercial plant by 2022. With the AFC as its primary stakeholder, the NEAMS ATF HIP is developing material models (using a multi-scale approach) for multiple candidate materials. Through a variety of analyses employing these models, the ATF HIP is providing insight and support to the AFC as it strives to understand these materials and prioritize further development. Use of computational tools such as BISON and Marmot are a vital component of the AFC's strategy since experimental data will not be available broadly and quickly enough to understand the proposed materials fully by the time the AFC must make further development decisions. This summary reviews the overall approach of the NEAMS ATF HIP. First, we give an overview of the current materials chosen for investigation. We also review the overall multi-scale approach to model development. We briefly review the work underway at multiple national laboratories to develop mechanistic engineering scale models based on lower length scale insight. Note that coordinated work is also underway at multiple universities, but those efforts are not detailed here. The NEAMS ATF HIP is assisting the AFC in assessing two accident tolerant materials, uranium silicide fuel and FeCrAl cladding. A multi-scale material model development approach is being undertaken to formulate the models necessary to investigate the materials over a range of normal and off-normal scenarios. This approach includes molecular dynamics, density functional theory, mesoscale modeling, and engineering scale modeling. Sensitivity studies are underway to understand the most critical scenarios and material model input parameters. The information generated is being given to the AFC for their use in selecting promising ATF concepts. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Ultra-High Operation Temperature SiC-matrix Solar Thermal Air Receiver (HOTSSTAR) enabled by additive manufacturing: Test Facility & Performance Evaluations

Solar Heat for Industrial Processes (SHIP) cavity receivers are capable of generating electricity or industrial process heat by absorbing thermal energy from solar radiation, focused on a small area. The concentration of solar radiation on the small area of the receiver enables the achievement of high temperatures (ranging from 400°C to 1,100°C) of a working fluid, thus making the SHIP technology thermodynamically comparable with conventional power plants. A volumetric receiver consists of a porous structure-generally made of silicon carbide or metal, which absorbs solar radiation and converts it into heat energy. Heat energy from the porous materials is then transferred to the fluid following through them. A volumetric receiver acts as a convective heat exchanger, transferring heat to the fluid through convection. Open-loop volumetric receivers work with air at atmospheric pressure and are suitable for single-cycle or multi-cycle energy plants. A Model Based Systems Engineering (MBSE) approach was used to develop a test bed at Sandia national Laboratories (SNL) capable of demonstrating an open-loop volumetric air receiver developed by General Electric Aerospace (GE Aerospace). This paper presents the development of the various MBSE methods, test bed, and testing operations for the GE air receiver, which was experimentally demonstrated to achieve 1,350°C for over 3 hours of operation and an approximate 70% receiver efficiency. By being able to achieve such high temperatures >1,000°C, this work provides the potential to support many SHIP industrial use cases.

14 SOLAR ENERGY↗
DOE OSTI2014

Quantum Statistical Testing of a Quantum Random Number Generator

The unobservable elements in a quantum technology, e.g., the quantum state, complicate system verification against promised behavior. Using model-based system engineering, we present methods for verifying the opera- tion of a prototypical quantum random number generator. We begin with the algorithmic design of the QRNG followed by the synthesis of its physical design requirements. We next discuss how quantum statistical testing can be used to verify device behavior as well as detect device bias. We conclude by highlighting how system design and verification methods must influence effort to certify future quantum technologies.

quantum communication↗
DOE OSTI2021

Summary of Work on a Software Adapter for Project Requirements and MBSE

The National Reactor Innovation Center (NRIC) has funded the development of a digital pipeline to pull a project’s requirements from IBM’s Rational Dynamic Object Oriented Requirements System (DOORS), save them to Deep Lynx, then push them into the corresponding project in Innoslate. These utilities are accessible from a web browser and allow a project’s functional and business requirements to be tied to its tree of model-based systems engineering (MBSE) data.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

MODEL BASED BIOMASS SYSTEM DESIGN OF FEEDSTOCK SUPPLY SYSTEMS FOR BIOENERGY PRODUCTION

Engineering feedstock supply systems that deliver affordable, high-quality biomass remains a challenge for the emerging bioenergy industry. Cellulosic biomass is geographically distributed and has diverse physical and chemical properties. Because of this feedstock supply systems that deliver cellulosic biomass resources to biorefineries require integration of a broad set of engineered unit operations. These unit operations include harvest and collection, storage, preprocessing, and transportation processes. Design decisions for each feedstock supply system unit operation impact the engineering design and performance of the other system elements. These interdependencies are further complicated by spatial and temporal variances such as climate conditions and biomass characteristics. This paper develops an integrated model that couples a SQL-based data management engine and systems dynamics models to design and evaluate biomass feedstock supply systems. The integrated model, called the Biomass Logistics Model (BLM), includes a suite of databases that provide 1) engineering performance data for hundreds of equipment systems, 2) spatially explicit labor cost datasets, and 3) local tax and regulation data. The BLM analytic engine is built in the systems dynamics software package PowersimTM. The BLM is designed to work with thermochemical and biochemical based biofuel conversion platforms and accommodates a range of cellulosic biomass types (i.e., herbaceous residues, short- rotation woody and herbaceous energy crops, woody residues, algae, etc.). The BLM simulates the flow of biomass through the entire supply chain, tracking changes in feedstock characteristics (i.e., moisture content, dry matter, ash content, and dry bulk density) as influenced by the various operations in the supply chain. By accounting for all of the equipment that comes into contact with biomass from the point of harvest to the throat of the conversion facility and the change in characteristics, the BLM evaluates economic performance of the engineered system, as well as determining energy consumption and green house gas performance of the design. This paper presents a BLM case study delivering corn stover to produce cellulosic ethanol. The case study utilizes the BLM to model the performance of several feedstock supply system designs. The case study also explores the impact of temporal variations in climate conditions to test the sensitivity of the engineering designs. Results from the case study show that under certain conditions corn stover can be delivered to the cellulosic ethanol biorefinery for $35/dry ton.

09 BIOMASS FUELS↗
DOE OSTI2021

Thermodynamic Modeling of Heat Engines Including Heat Transfer and Compression–Expansion Irreversibilities

In this paper, a thermodynamic model based on an endoreversible engine approach is developed to analyze the performance of heat engines operating under different thermodynamic cycles. The model considers finite heat transfer rate, variable heat source and sink temperatures, and irreversibilities associated with the expansion and compression. Expressions for the maximum power and efficiency at maximum power output are obtained as a function of hot and cold reservoir temperatures, the equivalent isentropic efficiency of compression and expansion components, and the effective conductance ratio between heat exchangers. In all cases, the Curzon–Ahlborn efficiency is retrieved at constant reservoir temperatures and neglected compression–expansion irreversibilities. The proposed model allows assessing the effect of isentropic efficiencies and heat exchanger design and operation characteristics for different thermodynamic cycles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for System Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.

42 - ENGINEERING↗
DOE OSTI2025

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world?s energy demands and build energy security.

42 - ENGINEERING↗
DOE OSTI2024

Description of FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

The urgency to deliver fusion power is growing now more than ever, with increasing pressure for both public programs and private companies to meet milestones timelines and overcome significant remaining technical challenges to ensure growth of a nascent fusion industry in time to meet rapidly growing clean energy demands. With incredible advancements in computation and years of investment in fusion model development and validation, integrated modeling is poised to fill a key role in accelerating the timeline to a fusion pilot plant (FPP). Future fusion pilot plants will operate in regimes far beyond current experience, and device design will rely on physics-based prediction and extrapolation. Many concepts will also rely on simulation to assess safety (shielding, tritium management, materials activation and lifetimes), economics and scalability before the decision to build. Importantly, integrated simulation can be used to reveal and solve the complexities of system integration that may otherwise not be apparent in physical components or models developed in isolation. New experimental test facilities that produce relevant conditions to validate and resolve key technical challenges for various subsystems (materials, blankets, fuel cycle, etc.) have been repeatedly called for by the fusion community but are not yet realized. Integrated modeling has an important role in identifying realistic load conditions (thermal, electromagnetic, plasma, neutron and photon loads, etc.) and defining the components and experiments for these test facilities in order to ensure meaningful validation that sufficiently reduces modeling uncertainties and technical risk for the full integrated reactor. The Fusion REactor Design and Assessment (FREDA) SciDAC project is building a component-based integrated modeling framework & data structure to enable self-consistent, multi-fidelity, iterative optimization workflows for the fusion reactor design process. FREDA aims to shorten the time to viable designs by providing a set of flexible workflows to support the various stages of the design process using an integrated model hierarchy, ranging from the simple analytic descriptions to the highest fidelity, theory-based plasma and engineering modeling developed by the fusion and fission communities. These tools are expected to be needed for timely support of FPP design in the milestone program and in the FIRE collaboratives. The plasma simulation backbone of FREDA is IPS-FASTRAN with newly developed coupled Core-Edge Pedestal-SOL (CESOL) workflows, which is being extended to the far-SOL region up to the plasma facing components. FREDA incorporates the FERMI engineering modeling suite and will enable self-consistent evaluation of the thermal shields, limiters, blanket, magnets, and other surrounding structures with predictions of temperatures, erosion, dpa, activation, tritium generation and transport, creep, corrosion, material degradation, etc. Parametric generation of 3D CAD enables rapid iteration of component geometry in response to plasma and loading specifications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Utilizing data-based modeling with low life cycle GHG emissions algae biofuels for engine optimization

Aquatic microalgae are a highly promising feedstock for the production of biocrude and tailored biofuels, with distinct advantages over traditional terrestrial crops, such as reduced land use and avoidance of food production competition. However, unlocking their full potential requires the development of biofuels with low life cycle greenhouse emissions biofuels, such as algae biofuels, which can significantly reduce the environmental impact of the transportation systems without requiring a complete overhaul of existing engine technology. In this study, we employ cutting-edge data-based AI modeling techniques to optimize the performance of heavy-duty engines, with a focus on transitioning towards biofuels with low life cycle greenhouse emissions biofuels. Our methodology offers significant advantages over traditional sweep testing, enabling efficient and accurate optimization of engine performance with minimal time and resources consumption. Our findings demonstrate the potential of utilizing this approach, with up to 55% NOx emissions reductions and up to 2% reduction in fuel consumption compared to the baseline optimized point. Moving forward, we plan to utilize a 30% blend of algae biofuels with diesel fuel, with the ultimate goal of achieving up to 60% lifecycle GHG emissions. Lastly, we plan to compare the results with 100% renewable biodiesel to add an additional dimension of investigating the impact of fuel chemistry on engine optimization. Overall, this study underscores the vital importance of biofuels for reducing the carbon footprint of the transportation sector and supporting a sustainable future. By harnessing the power of data-based AI modeling with low life cycle greenhouse emissions biofuels, we can accelerate the adoption of more environmentally friendly transportation systems and reduce their impact on the planet. Our findings contribute to this transition and offer insights for developing efficient and effective strategies for addressing global climate change.

09 BIOMASS FUELS↗
DOE OSTI2012

Overview of the TriBITS Lifecycle Model: Lean/Agile Software Lifecycle Model for Research-based Computational Science and Engineering Software

Software lifecycles are becoming an increasingly important issue for computational science & engineering (CSE) software. The process by which a piece of CSE software begins life as a set of research requirements and then matures into a trusted high-quality capability is both commonplace and extremely challenging. Although an implicit lifecycle is obviously being used in any effort, the challenges of this process--respecting the competing needs of research vs. production--cannot be overstated. Here we describe a proposal for a well-defined software lifecycle process based on modern Lean/Agile software engineering principles. What we propose is appropriate for many CSE software projects that are initially heavily focused on research but also are expected to eventually produce usable high-quality capabilities. The model is related to TriBITS, a build, integration and testing system, which serves as a strong foundation for this lifecycle model, and aspects of this lifecycle model are ingrained in the TriBITS system. Indeed this lifecycle process, if followed, will enable large-scale sustainable integration of many complex CSE software efforts across several institutions.

Agile↗
DOE OSTI2012

TriBITS Lifecycle Model: A Lean/Agile Software Lifecycle Model for Research-based Computational Science and Engineering and Applied Mathematical Software (V.1.0)

Software lifecycles are becoming an increasingly important issue for computational science and engineering (CSE) software. The process by which a piece of CSE software begins life as a set of research requirements and then matures into a trusted high-quality capability is both commonplace and extremely challenging. Although an implicit lifecycle is obviously being used in any effort, the challenges of this process - respecting the competing needs of research vs. production - cannot be overstated. Here we describe a proposal for a well-defined software lifecycle process based on modern Lean/Agile software engineering principles. What we propose is appropriate for many CSE software projects that are initially heavily focused on research but also are expected to eventually produce usable high-quality capabilities. The model is related to TriBITS, a build, integration and testing system, which serves as a strong foundation for this lifecycle model, and aspects of this lifecycle model are ingrained in the TriBITS system. Here, we advocate three to four phases or maturity levels that address the appropriate handling of many issues associated with the transition from research to production software. The goals of this lifecycle model are to better communicate maturity levels with customers and to help to identify and promote Software Engineering (SE) practices that will help to improve productivity and produce better software. An important collection of software in this domain is Trilinos, which is used as the motivation and the initial target for this lifecycle model. However, many other related and similar CSE (and non-CSE) software projects can also make good use of this lifecycle model, especially those that use the TriBITS system. Indeed this lifecycle process, if followed, will enable large-scale sustainable integration of many complex CSE software efforts across several institutions.

99 GENERAL AND MISCELLANEOUS↗