simple_RDE_model
Jupyter notebook containing a simple rotating detonation engine model to generate synthetic data for exploring data analysis techniques.
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.
Jupyter notebook containing a simple rotating detonation engine model to generate synthetic data for exploring data analysis techniques.
The goal of the DOE’s Industrial Assessment Center (IAC) program is twofold: first, to help US manufacturing competitiveness by providing assessments and recommendations for small and medium-sized enterprises (SMEs) on energy efficiency, productivity, sustainability and competitiveness – including measuring the impacts of these recommendations on reducing greenhouse gas emissions; and second, to address a growing shortage of engineering professionals with applied energy and manufacturing-related skills by training a diverse cross-section of engineering students through hands-on involvement in these assessments. IUPUI has an IAC, which was established in 2011, in the Department of Mechanical and Energy Engineering, Purdue School of Engineering and Technology, IUPUI. The IAC has won the awards twice. We have built the center that has the expertise and infrastructure for quality energy assessments to manufactures and commercial building owners and aligned our current research and teaching activities to the DOE’ training mission with programs to train the next generation of industrial energy efficiency experts, with theory and hands-on experience in conducting energy assessment for small and median manufacturing enterprises. We have made recommendations that have the potential to save about $20M annually. We also initiated research projects to meet DOE priorities, specifically in the areas of smart manufacturing and cybersecurity. On the energy assessment side, the Center has provided energy assessments to 144 qualified companies, primarily located in Indiana. On the training side, the center has trained students in various programs, such as the department’s Bachelor of Science (BS) programs in Mechanical Engineering (ME) and BS in Energy Engineering (EEN), both are ABET accredited engineering programs, as well as a graduate level Energy Management and Assessment certificate program. At the present, the center has trained 95 students, 43 or them received the DOE issued certificates. Research projects were developed within the center to advance energy efficiency related technologies, which provided excellent opportunities for our trainees. The center has also been building a professional network with utilities, Manufacturing Extension Partnership (MEP), government agencies, and manufacturing companies, to increase our client base and promote collaborations. The center received the 2019 Center of Excellence of the Year Award and three students received the “Outstanding Achievement in Energy Engineering by an IAC Student” awards in the past five years.
Internal combustion engine design is increasingly driven by computational models used to predict change in performance as a result of design changes, which previously would have been made by limited intuition or expensive and time-consuming physical testing. Improved model capabilities shorten design cycles and enable the production of cleaner and more efficient engines. This project focuses on advancing the state-of-the-art in internal combustion engine simulations. The overarching goal is to enable predictive models and reduced time to solution for simulations that impact combustion engine design.
Internal combustion engine design is increasingly driven by computational models used to predict change in performance due to change of design. The design process previously depended on limited intuition and expensive and time-consuming physical testing. Improved model capabilities shorten design cycles and enable the production of cleaner and more efficient engines. This project focuses on advancing the state of the art in internal combustion engine simulations. The overarching goal is to enable predictive models and reduced time to solution for simulations that impact combustion engine design.
Within the NIF (National Ignition Facility) sustainment project, LLNL is replacing all 1,728 blast shields with new ones. Before the new blast shield glass can be installed on NIF, it must pass through the AMOL (Amplifier Main Optics Loop). The AMOL process involves cleaning and coating the glass, assembling it into a Line Replaceable Unit (LRU), and performing testing and inspection. This work will be conducted in the OPFX cleanroom facility (Building 391, Room 1250) and requires several pieces of custom equipment. One key piece of equipment within the loop is the Handling Frame Assembly Table (see Figure 1.1). Its purpose is to assist operators in installing the blast shield glass into handling frames (1014442709) (see Figure 1.2). The table is also used to store handling frame assembly components. The primary components stored are the L-frames (1015208271), which are kept in a drawer underneath the table (see Figure 1.3). The purpose of this safety note is to ensure that the system does not pose a risk of injury to personnel during a seismic event. The system owner is OMST Engineering & Maintenance.
Gasoline knock resistance is characterized by the Research and Motor Octane Number (RON and MON), which are rated on the CFR octane rating engine at naturally aspirated conditions. However, modern automotive downsized boosted spark ignition (SI) engines generally operate at higher cylinder pressures and lower temperatures relative to the RON and MON tests. Using the naturally aspirated RON and MON ratings, the octane index (OI) characterizes the knock resistance of gasolines under boosted operation by linearly extrapolating into boosted “beyond RON” conditions via RON, MON, and a linear regression K factor. Using OI solely based on naturally aspirated RON and MON tests to extrapolate into boosted conditions can lead to significant errors in predicting boosted knock resistance between gasolines due to non-linear changes in autoignition and knocking characteristics with increasing pressure conditions. Here, a new “Supercharged Octane Number” (SON) method was developed on the CFR engine at increased intake pressures, which improved the correlation to boosted knock-limited automotive SI engine data over RON for several surrogate fuels and gasolines, including five “Co-Optima” RON 98 fuels and an E10 regular grade gasoline. Furthermore, the conventional OI was extended to a newly introduced Supercharged Octane Index (OI S ) based on SON and RON, which significantly improved the correlation to fuel knock resistance measurements from modern boosted SI engine knock-limited spark advance tests. This demonstrated the first proof of concept of a SON and OI S to better characterize a fuel’s knock resistance in modern boosted SI engines.
Description of INL's approach to Digital Engineering, Digital Twins, and Digital Threads with the DeepLynx data warehouse. This will showcase the exciting work of Digital Engineering for attendees of INL IDEAL's University Days event.
This paper reviews the status of technology in design and manufacture of new wrought 21 polycrystalline Ni-base superalloys for critical engineering applications. There is a strong 22 motivation to develop new alloys that are capable of operating at higher temperatures to 23 realise improvements in thermal efficiency, which are necessary to achieve environmental 24 targets for reduced emissions of harmful green-house gases. From the aerospace sector, 25 the development of new powder metallurgy and ingot metallurgy alloys are discussed for 26 disk rotor and static applications. New compositions for powder metallurgy contain about 27 50-55% of gamma prime (γ') strengthening precipitates to ensure components operate 28 successfully at temperatures up to 788°C (1450°F). In contrast, new compositions for ingot 29 metallurgy aim to occupy a design space in temperature capability between Alloy 718 and 30 current powder alloys that are in-service, and show levels of γ' of about 30-44%. The focus 31 in developing these alloys was design for manufacturability. To complement the aerospace 32 developments, a review of work to understand the suitability of candidate alloys for multiple 33 applications in Advanced-Ultra Supercritical (AUSC) power plants has been undertaken by 34 Detrois, Jablonski and Hawk from the National Energy Technology Laboratory. In these 35 power plants, steam temperatures are required to reach 700-760°C. The common thread is 36 to develop alloys that demonstrate a combination of high temperature properties, which are 37 reliant on both the alloy composition and microstructure and can be produced readily at the 38 right price. For the AUSC applications, the emphasis is on high temperature strength, long 39 term creep life, phase stability, oxidation resistance and robust welding for fabrications. 40 Whereas for powder disk rotors in aircraft engines, the priority is enhanced resistance to 41 time dependent crack growth, phase stability and resistance to environmental damage, while 42 extending the current strength levels, which are shown by existing alloys, to higher 43 temperatures.
Not provided.
The licensing process for any nuclear installation generally requires the review and approval of numerous documents by a regulatory body. Alternate approaches are needed where similar nuclear installations are planned to be built and operated at multiple sites around the world. Such is the case for the deployment of small modular nuclear reactors (SMRs) under development by multiple enterprises in multiple countries. As a use case for this paper, the authors propose an alternative to the Documented Safety Analysis (DSA) required by the United States Department of Energy (DOE) for the licensing of its nuclear installations. The DSA is a DOE construct that requires the review and approval of numerous documents. It includes an in-depth description of hazards, safe boundaries, and hazard controls.
LANL has hazardous facilities. In short, we make important pieces of nuclear weapons.
Chemical phase equilibria and phase diagrams are well established and have served as indispensable guides for designing materials through chemical tuning. However, a general thermodynamic theory for strain phase equilibria remains elusive despite extensive studies which have revealed dramatic impacts of mechanical strain on the stability of phases and domain states in solid-state materials. Here, we establish the thermodynamic theory of strain equilibria and a general framework for efficiently constructing multidomain and multiphase diagrams of arbitrarily strained solids under incoherent conditions. As examples, we obtain temperature-strain phase diagrams of ferroelectric PbTiO 3 , strongly correlated VO 2 , and unconventional ferroelectric Hf 0.5 Zr 0.5 O 2 . We reveal the analogs of the Gibbs phase rules, multiple- and multi-critical points, common-tangent construction, and level rule in strain equilibria to those in the familiar chemical phase equilibria. Finally, our strain equilibria theory offers a powerful framework for predicting the thermodynamic stability of structural phases and domains in strained solids and for guiding the strain engineering of their functional properties.
Sediment settling velocities are commonly estimated from analytical or process-based approaches. These approaches have theoretical constraints due to the incompletely resolved settling physics. A parametric data-driven approach was recently proposed without theoretical constraints, but it is limited by its mathematical assumptions. To overcome these limitations, here we apply a machine learning algorithm to an aggregated sediment settling experimental database and develops a nonparametric data-driven model to estimate the noncohesive sediment settling velocity in water. A cross-comparison against five process-based equations and a parametric data-driven equation demonstrates the higher accuracy and better consistency of the new model in estimating sediment settling velocities under various physical regimes. The new model also shows an easily implemented self-update capability by assimilating theoretical data derived from the process-based equations. The updated model, incorporating experimental and theoretical data of sediment settling processes, further improves the accuracy and reduces the uncertainty in estimating sediment settling velocities. This study demonstrates the capability of machine learning in sediment transport study and illustrates an alternative framework for other hydraulic engineering challenges.
Significance Reinforcement learning (RL) has been applied effectively in games and robotic manipulation. We demonstrate the effectiveness of RL in experimental fluid mechanics by applying it to reduce the drag of circular cylinders in turbulent flow, a canonical fluid–structure interaction problem. Although physics agnostic, RL managed to reduce the drag by 30 % or reach another specified optimum point very quickly. Following this discovery, we used high-fidelity simulations to probe the underlying physical mechanisms so that the discovered control techniques can be generalized to other similar flow problems. More broadly, RL-guided active control can lead to efficient exploration of additional flow-control strategies in experimental fluid mechanics, potentially paving the way for accelerating scientific discovery and different designs in flow-related engineering problems.
The goals of this teaching workshop are for the learners to leave with a broad view of University curriculum, successes and limitations, and the state of industry; an awareness of grand challenges in software engineering; and an awareness of current approaches to address those challenges.
The research team formulated a workable analytical abrasion model that relates critical rotary shear process parameters (shear geometry and rotational speed) to critical material attributes of inorganic mineral species in feedstock (density, size, and aspect ratio) and substrate (hardness and elastic modulus) that enabled us to model the wear of shear cutters in a rotary shear milling system developed by Forest Concepts. We compared results of the model to experimental observations of the shape of rotary shear cutters used in a Forest Concept Crumbler® shear mill. Results showed good agreement between the predicted and measured shape of a worn cutter; thus, a quality-by-design (QbD) approach can be developed to predict component reliability based on scientific engineering principles in lieu of trial-and-error approaches.