SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The string length calculator simplifies the choice of string length for photovoltaic power plants. It uses open source tools to model the system open-circuit voltage over time based on established approaches used in the field by principal engineers and suggests a string length.
Over the course of the five years of the Scientific Discovery through Advanced Computation (SciDAC-3) program, computational scientists working on behalf of the Department of Energy's Office of Science (DOE SC) exploited a new generation of petascale computing resources to make previously inaccessible discoveries in a broad range of disciplines including physics, chemistry, and materials science. The computing systems underpinning this work increased in performance potential from tens to hundreds of PFlop/s, but in the process evolving significantly from architectures in 2011, when SciDAC-3 began. Although Moore's law continues unabated, the end of Dennard scaling has necessitated a fundamental shift in computer architecture focused on power efficiency. To that end, processors are increasingly varied as they strive to satisfy performance, productivity, reliability, and energy efficiency in the face of divergent computational requirements. The diversity among these machines presents a number of challenges to merely porting today's scientific applications, much less achieving good performance. Therefore, at the beginning of SciDAC-3, USC and its collaborators anticipated that both vastly increased scale (e.g., more chips, more cores per chip, wider SIMD) and heterogeneity would exacerbate performance optimization challenges while simultaneously promoting the issues of energy and heterogeneity will exacerbate performance optimization challenges while simultaneously promoting the issues of energy consumption and resilience to the forefront. To ensure that DOE's computational scientists could successfully exploit emerging high performance computing (HPC) systems, DOE SC’s Advanced Scientific Computing Research Program (ASCR) funded the SciDAC-3 Institute for Sustained Performance, Energy, and Resilience (SUPER). Led by USC, this was a broadly based project with expertise in compilers and system tools, performance engineering, energy management, and resilience. SUPER followed the successful model developed in the previous SciDAC-2 Performance Engineering Research Institute (PERI) of leveraging the research investments DOE and others have made and integrating the results to create new capabilities beyond the reach of any one group. To accomplish this, SUPER was organized into multi-institution teams conducting the research activities in performance portability, energy efficiency, exascale resilience, and overall optimization. SUPER researchers also collaborated with other SciDAC-3 institutes, 13 of the 19 Scientific Computation Application Partnerships (SAP), as well as the broader DOE computational sciences community. These collaborations both focused our research on the real challenges facing petascale scientific computing as well as ensured the broad and immediate impact of the results of our research.
This report summarizes recent and past related Ceramic HEPA Filter activity for this NNSA NSR&D project and on-going plans for FY18 and FY19 work as presently planned. Prior work has evaluated concepts for a ceramic HEPA filter including ceramic support materials, filter media including electrospun filter media, and metal housing designs to accommodate the conservative temperature performance target of up to 500°C. This has been supported by modelling efforts discussed with nuclear facility system engineers and their management. The temperature and time-at-temperature are based on conservative, bounding temperatures from fire analysis presented as a draft EFCOG best practice paper for nuclear facility safety analysis. The overall goal is to improve safety of DOE nuclear facilities during fire scenarios by developing improved filtration technology including increasing performance while reducing pressure drop. Ceramic filtration technology can provide robust, passive mitigation against radiological releases. The goal of the ceramic HEPA filter is to provide a robust HEPA filter that provides increased temperature performance in an equivalent form factor (size and shape) that is similar to existing conventional HEPA filters used in facilities. Once qualified for use in facilities, the benefit of a ceramic HEPA filters could simplify and reduce safety and support system costs, as well as considerably lower life-cycle costs. Installation, monitoring, and maintenance of active fire suppression systems, as well as other related systems, are a considerable on-going cost to nuclear facilities. Reducing the level of dependence on Safety Class and Safety Significant fire alarm and fire suppression systems could allow significant cost savings. At present we are continuing research and development efforts towards the goal of a ceramic filter design, including improved ceramic filter media, that could reduce total operation cost for facilities utilizing HEPA filters.
This is a brief summary of recent and past Ceramic HEPA Filter research and development activities under an NNSA NSR&D funded project and describes on-going plans for FY19 work as presently planned. Prior work has evaluated concepts for a ceramic HEPA filter including ceramic support materials, filter media including electrospun filter media, and metal housing designs to accommodate the conservative temperature performance target of up to 500°C. This has been supported by modelling efforts discussed with nuclear facility system engineers and their management. The temperature and time-at-temperature are based on conservative, bounding temperatures from fire analysis presented as an EFCOG best practice paper for nuclear facility safety analysis. The overall goal is to improve safety of DOE nuclear facilities during fire scenarios by developing improved filtration technology including increasing temperature performance while maintaining acceptable filter pressure drop. Ceramic filtration technology can provide robust, passive mitigation against radiological releases. The goal of the ceramic HEPA filter is to provide a HEPA filter that provides increased temperature performance in an equivalent form factor (size and shape) that is similar to existing conventional HEPA filters used in facilities. Once development is complete and qualified for use in facilities, ceramic HEPA filters could simplify and reduce safety and support system costs, as well as lower life-cycle costs. Installation, monitoring, and maintenance of active fire suppression systems, as well as other related systems, are a considerable on-going cost to nuclear facilities. Reducing the level of dependence on Safety Class and Safety Significant fire alarm and fire suppression systems could allow significant cost savings, while maintaining safety. At present we are continuing research and development efforts towards the goal of a ceramic filter design, including improved ceramic filter media, that could reduce total operation cost for facilities utilizing HEPA filters.
The Web-Based Transportation Routing Analysis Geographic Information System (WebTRAGIS) model is a geographic information system tool for modeling transportation routing. WebTRAGIS is deployed as a browser-based application interface, and the routing engine is located on a server at Oak Ridge National Laboratory (ORNL). WebTRAGIS offers users numerous options for route calculation using uniquely value-added network databases for highway, rail, and waterway infrastructures in the continental United States. The model also provides reporting information on population densities for all transportation segments using the LandScan USA population distribution data model.
The need to scale next-generation industrial engineering problems to the largest computational platforms presents unique challenges. Such problems may have complex coupled multi-physics models, as well as computationally and communication intensive algorithms not often seen in standard academic problems, while also needing high-demand I/O for analysis purposes. In such cases even codes with good existing scaling properties may need significant changes, addressed in a cross-cutting way to solve such problems at extreme scales. These challenges relate to system components that were not previously problematic on less complex problems and/or at smaller computational scales. This paper illustrates these challenges for Uintah, a highly scalable asynchronous many-task runtime system being applied to the modeling of a 1000 MWe ultra-supercritical coal boiler. In order to model this formidable, production engineering problem, not only was an integrated approach needed that built upon existing scalable components in areas such as complex stencil calculations and linear algebra, but the significant challenges of high demand I/O, complex task graphs, and infrastructure inefficiencies also had to be addressed. This integrated approach allowed this problem to run on 256K CPU cores on Mira, with good weak scaling to 512K CPU cores and similar strong scaling, except for radiation. The need for strong scaling of a new ray tracing-based radiation model required substantial Uintah infrastructure improvements before 119K CPU cores and 7.5K GPUs on Titan could be used, with scaling out to 256K CPU cores and 16K GPUs. The resulting code demonstrates not only excellent overall scalability but a significant improvement in overall performance for the full-scale, production boiler problem.
Natural gas is considered an attractive alternative fossil fuel as it produces 25% less carbon dioxide and emits fewer amounts of toxic pollutants compared to gasoline and diesel. A pre-chamber type ignition device in a natural gas engine is capable of extending the lean operating limit, and hence improving thermal efficiency, by providing spatially distributed ignition with multiple turbulent flame-jets and accelerating the combustion rate. However, the process of ignition in the main chamber triggered by turbulent jets emanating from a pre-chamber is very sensitive to the pre-chamber geometry and fueling strategy, and any effort to optimize these parameters requires fundamental understanding of the underlying physical and thermodynamic phenomena, which can be studied by a multi-dimensional numerical simulation approach. In this study, three turbulent combustion models widely adopted in the multi-dimensional engine modeling community were preliminary evaluated to simulate the combustion process in a natural gas engine with a pre-chamber spark-ignition system. One multi-zone well-stirred reactor model, and two flamelet-based combustion models, G-equation and Extended Coherent Flamelet Model, were employed for this assessment. Under a fuel-lean condition, the three combustion models were used to match experimental data and analyze the differences in terms of in-cylinder pressure and heat release rate.
This project developed and validated a novel, multi-scale, mechanism-based model to quantitatively predict creep-fatigue crack growth and failure for Ni-based Alloy 617 at 800°C. Alloy 617 is a target material for intermediate heat exchangers in Generation IV very high temperature reactor designs, and it is envisioned that this model will aid in the design of safe, long lasting nuclear power plants. The technical effectiveness of the model was shown by demonstrating that experimentally observed crack growth rates can be predicted under both steady state and overload crack growth conditions. Feasibility was considered by incorporating our model into a commercially available finite element method code, ABAQUS, that is commonly used by design engineers. While the focus of the project was specifically on an alloy targeted for Generation IV nuclear reactors, the benefits to the public are expected to be wide reaching. Indeed, creep-fatigue failure is a design consideration for a wide range of high temperature mechanical systems that rely on Ni-based alloys, including industrial gas power turbines, advanced ultra-super critical steam turbines, and aerospace turbine engines. It is envisioned that this new model can be adapted to a wide range of engineering applications.
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models.
Despite the abundance of research applications of system modeling in grid-interactive efficient buildings (GEBs), the transfer of those applications to real-world practices is still in its early stages. This is partially due to the lack of a summary on how system modeling should be established for a given application of GEBs. Here, we fill this gap by providing an extensive survey of the literature on system modeling for different GEBs that have been produced in the past decade. This survey involves over 300 relevant journal articles from the building community, the power system community, and the society of control. Specifically, we first identified key requirements of system modeling for GEBs based on various types of applications discussed in those publications. We then summarized the system modeling applications from those publications, in terms of their assumptions, modeling approach, and simulation. After that, we analyzed different assumptions made for various applications, the extent to which those modeling approaches satisfied the key requirements, and how those approaches can be scaled up for large-scale applications, which are quite common in GEBs. In addition, we gave insights on how to use system modeling for different applications in GEBs. At the end, we provided recommended directions for future studies to fully unleash the potentials of system modeling to support GEBs.
Model-based, distribution powerflow analysis is a foundational component of system planning and grid modernization efforts, but data security is an impediment to collaboration among utility engineers, researchers, developers, community members, and other stakeholders. Pacific Northwest National Laboratory (PNNL) and the National Renewable Energy Laboratory (NREL) are partnering to develop the new Distribution Grid Atlas - a publicly available catalog of realistic, geographically relevant, representative distribution feeder models without sensitive geographic information, customer data, or disclosure of utility models. We are looking for utilities to share data for the Distribution Grid Atlas.
There is a growing need in the AEC industry for the digitalization of model-based data exchange in BIM workflows. However, users continue to face difficulties exchanging data between BIM-based computer-aided design (CAD) and computer-aided engineering (CAE) software, even with the open, non-proprietary data exchange format called Industry Foundation Classes (IFC). Proposed solutions in academic research focus primarily on building systems, with comparatively little attention to piping models. Therefore, this paper introduces an interoperability tool for enabling piping model data exchange between architectural and structural analysis domains. The tool is tested using a piping model created in Autodesk Revit and shows marked improvement for IFC-based CAD-to-CAE interoperability over existing practice.
Bacterial microcompartments (BMCs) are protein-encapsulated compartments found across at least 23 bacterial phyla. BMCs contain a variety of metabolic processes that share the commonality of toxic or volatile intermediates, oxygen-sensitive enzymes and cofactors, or increased substrate concentration for magnified reaction rates. These compartmentalized reactions have been computationally modeled to explore the encapsulated dynamics, ask evolutionary-based questions, and develop a more systematic understanding required for the engineering of novel BMCs. Many crucial aspects of these systems remain unknown or unmeasured, such as substrate permeabilities across the protein shell, feasibility of pH gradients, and transport rates of associated substrates into the cell. This review explores existing BMC models, dominated in the literature by cyanobacterial carboxysomes, and highlights potentially important areas for exploration.
Numerical simulation of latent heat thermal energy storage (LHTES) systems plays a fundamental role in studying the physical process and guiding the engineering design. Discretization of the PDEs describing the nonlinear solidification/melting process of phase change materials (PCMs) leads to a large-scale complex dynamics system, where the system behavior depends on a set of parameters. In a design setting, repeated model evaluations are required over the set of parameters results in significant computational burden. In this paper, an explicit analytic solution was built for the propagation of the solidification front in a cylindrical coordinate. The analytic solution approach is further employed to develop a low computational reduced model (RM) as a module for a shell-and-tube based LHTES heat exchanger. The levelized Cost of Energy (LCOE) is used as a design metric and the RM model is used to apply system-level constraints in the nonlinear programming formulation that facilitates efficient global optimal design of the PCM properties, flow conditions and tube geometries. The use of LCOE as the design metric prevents over design of the heat transfer rate and also establishes a fair ground for evaluation of different thermal storage technologies and their integrated applications with other systems. Optimal results showed that a higher effectiveness results in a higher LCOE; the velocity of the HTF and the length of the channel are highly correlated with each other; both larger PCM latent energy and conductivity result in lower LCOE.
“Charge engineering” of semiconductor nanocrystals (NCs) through so-called electronic impurity doping is a long-standing challenge in colloidal chemistry and holds promise for ground-breaking advancements in many optoelectronic, photonic, and spin-based nanotechnologies. To date, our knowledge is limited to a few paradigmatic studies on a small number of model compounds and doping conditions, with important electronic dopants still unexplored in nanoscale systems. Equally importantly, fine-tuning of charge engineered NCs is hampered by the statistical limitations of traditional approaches. The resulting intrinsic doping inhomogeneity restricts fundamental studies to statistically averaged behaviors and complicates the realization of advanced device concepts based on their advantageous functionalities. We aim to address these issues by realizing the first example of II–VI NCs electronically doped with an exact number of heterovalent gold atoms, a known p-type acceptor impurity in bulk chalcogenides. Single-dopant accuracy across entire NC ensembles is obtained through a novel non-injection synthesis employing ligand-exchanged gold clusters as “quantized” dopant sources to seed the nucleation of CdSe NCs in organic media. Structural, spectroscopic, and magneto-optical investigations trace a comprehensive picture of the physical processes resulting from the exact doping level of the NCs. Gold atoms, doped here for the first time into II–VI NCs, are found to incorporate as nonmagnetic Au + species activating intense size-tunable intragap photoluminescence and artificially offsetting the hole occupancy of valence band states. Fundamentally, the transient conversion of Au + to paramagnetic Au 2+ (5d 9 configuration) under optical excitation results in strong photoinduced magnetism and diluted magnetic semiconductor behavior revealing the contribution of individual paramagnetic impurities to the macroscopic magnetism of the NCs. Altogether, our results demonstrate a new chemical approach toward NCs with physical functionalities tailored to the single impurity level and offer a versatile platform for future investigations and device exploitation of individual and collective impurity processes in quantum confined structures.
Environments of biological organisms are frequently nutrient and energy limited. This necessitates that systems exert tight regulation of trade-offs among key biological processes. Quantitative knowledge of these tradeoffs is essential for successful manipulation of biosystems (e.g., metabolic engineering projects). Using genome-scale constraint-based models of metabolism (GSMs), Multi-Objective Flux Analysis (MOFA) allows analyses of trade-offs among a system’s key biological tasks. The leading software for conducting a variety of constraint-based analyses using GSMs is the COBRA Toolbox for MATLAB. Results: We have developed a new add-on tool for COBRA that performs MOFA for a number of objectives only limited by computation power.